diff --git a/Yolov5-Deepsort/AIDetector_pytorch.py b/Yolov5-Deepsort/AIDetector_pytorch.py deleted file mode 100644 index f1818d8dcba71cf0f0bf75065f214bb620c28029..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/AIDetector_pytorch.py +++ /dev/null @@ -1,74 +0,0 @@ -import torch -import numpy as np -from models.experimental import attempt_load -from utils.general import non_max_suppression, scale_coords -from utils.BaseDetector import baseDet -from utils.torch_utils import select_device -from utils.datasets import letterbox -import rich - -class Detector(baseDet): - - def __init__(self): - super(Detector, self).__init__() - self.init_model() - self.build_config() - - def init_model(self): - - self.weights = 'weights/yolov5s.pt' - self.device = '0' if torch.cuda.is_available() else 'cpu' - self.device = select_device(self.device) - model = attempt_load(self.weights, map_location=self.device) - model.to(self.device).eval() - model.half() - # torch.save(model, 'test.pt') - self.m = model - self.names = model.module.names if hasattr( - model, 'module') else model.names - - def preprocess(self, img): - - img0 = img.copy() - img = letterbox(img, new_shape=self.img_size)[0] - img = img[:, :, ::-1].transpose(2, 0, 1) - img = np.ascontiguousarray(img) - img = torch.from_numpy(img).to(self.device) - img = img.half() # 半精度 - img /= 255.0 # 图像归一化 - if img.ndimension() == 3: - img = img.unsqueeze(0) - - return img0, img - - def detect(self, im): - - im0, img = self.preprocess(im) - - pred = self.m(img, augment=False)[0] - #rich.print(pred.shape) - pred = pred.float() - - - pred = non_max_suppression(pred, self.threshold, 0.4) - #rich.print((pred)) - - - pred_boxes = [] - for det in pred: - - if det is not None and len(det): - det[:, :4] = scale_coords( - img.shape[2:], det[:, :4], im0.shape).round() - - for *x, conf, cls_id in det: - lbl = self.names[int(cls_id)] - if not lbl in ['person', 'car', 'truck']: - continue - x1, y1 = int(x[0]), int(x[1]) - x2, y2 = int(x[2]), int(x[3]) - pred_boxes.append( - (x1, y1, x2, y2, lbl, conf)) - - return im, pred_boxes - diff --git a/Yolov5-Deepsort/DDM_DeepSort/.gitattributes b/Yolov5-Deepsort/DDM_DeepSort/.gitattributes deleted file mode 100644 index a6344aac8c09253b3b630fb776ae94478aa0275b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/DDM_DeepSort/.gitattributes +++ /dev/null @@ -1,35 +0,0 @@ -*.7z filter=lfs diff=lfs merge=lfs -text -*.arrow filter=lfs diff=lfs merge=lfs -text -*.bin filter=lfs diff=lfs merge=lfs -text -*.bz2 filter=lfs diff=lfs merge=lfs -text -*.ckpt filter=lfs diff=lfs merge=lfs -text -*.ftz filter=lfs diff=lfs merge=lfs -text -*.gz filter=lfs diff=lfs merge=lfs -text -*.h5 filter=lfs diff=lfs merge=lfs -text -*.joblib filter=lfs diff=lfs merge=lfs -text -*.lfs.* filter=lfs diff=lfs merge=lfs -text -*.mlmodel filter=lfs diff=lfs merge=lfs -text -*.model filter=lfs diff=lfs merge=lfs -text -*.msgpack filter=lfs diff=lfs merge=lfs -text -*.npy filter=lfs diff=lfs merge=lfs -text -*.npz filter=lfs diff=lfs merge=lfs -text -*.onnx filter=lfs diff=lfs merge=lfs -text -*.ot filter=lfs diff=lfs merge=lfs -text -*.parquet filter=lfs diff=lfs merge=lfs -text -*.pb filter=lfs diff=lfs merge=lfs -text -*.pickle filter=lfs diff=lfs merge=lfs -text -*.pkl filter=lfs diff=lfs merge=lfs -text -*.pt filter=lfs diff=lfs merge=lfs -text -*.pth filter=lfs diff=lfs merge=lfs -text -*.rar filter=lfs diff=lfs merge=lfs -text -*.safetensors filter=lfs diff=lfs merge=lfs -text -saved_model/**/* filter=lfs diff=lfs merge=lfs -text -*.tar.* filter=lfs diff=lfs merge=lfs -text -*.tar filter=lfs diff=lfs merge=lfs -text -*.tflite filter=lfs diff=lfs merge=lfs -text -*.tgz filter=lfs diff=lfs merge=lfs -text -*.wasm filter=lfs diff=lfs merge=lfs -text -*.xz filter=lfs diff=lfs merge=lfs -text -*.zip filter=lfs diff=lfs merge=lfs -text -*.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/Yolov5-Deepsort/DDM_DeepSort/README.md b/Yolov5-Deepsort/DDM_DeepSort/README.md deleted file mode 100644 index c183fca4d1c01de7a67e192c36ef9ba7a961fbd7..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/DDM_DeepSort/README.md +++ /dev/null @@ -1,13 +0,0 @@ ---- -title: DDM DeepSort -emoji: 📚 -colorFrom: yellow -colorTo: gray -sdk: gradio -sdk_version: 5.6.0 -app_file: app.py -pinned: false -short_description: 将Drift Diffusion Model 应用于 yolov5 + deepsort ---- - -Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference diff --git a/Yolov5-Deepsort/DDM_DeepSort/app.py b/Yolov5-Deepsort/DDM_DeepSort/app.py deleted file mode 100644 index cbffdf1ba490e3ae1fb244c10909cccfa7652993..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/DDM_DeepSort/app.py +++ /dev/null @@ -1,7 +0,0 @@ -import gradio as gr - -def greet(name): - return "Hello " + name + "!!" - -demo = gr.Interface(fn=greet, inputs="text", outputs="text") -demo.launch() \ No newline at end of file diff --git a/Yolov5-Deepsort/LICENSE b/Yolov5-Deepsort/LICENSE deleted file mode 100644 index 9e419e042146a2ce2e354202d4f7d8e4a3d66f31..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ -GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - 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If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. \ No newline at end of file diff --git a/Yolov5-Deepsort/README.md b/Yolov5-Deepsort/README.md deleted file mode 100644 index c63236631bd4d84ba1e5c5072ef1c2dd7e3def5b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/README.md +++ /dev/null @@ -1,139 +0,0 @@ -# 本文禁止转载! - - -本文地址：[https://blog.csdn.net/weixin_44936889/article/details/112002152](https://blog.csdn.net/weixin_44936889/article/details/112002152) - -# 项目简介： -使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中。 - -代码地址（欢迎star）： - -[https://github.com/Sharpiless/yolov5-deepsort/](https://github.com/Sharpiless/yolov5-deepsort/) - -最终效果： -![在这里插入图片描述](https://github.com/Sharpiless/Yolov5-Deepsort/blob/main/image.png) -# YOLOv5检测器： - -```python -class Detector(baseDet): - - def __init__(self): - super(Detector, self).__init__() - self.init_model() - self.build_config() - - def init_model(self): - - self.weights = 'weights/yolov5m.pt' - self.device = '0' if torch.cuda.is_available() else 'cpu' - self.device = select_device(self.device) - model = attempt_load(self.weights, map_location=self.device) - model.to(self.device).eval() - model.half() - # torch.save(model, 'test.pt') - self.m = model - self.names = model.module.names if hasattr( - model, 'module') else model.names - - def preprocess(self, img): - - img0 = img.copy() - img = letterbox(img, new_shape=self.img_size)[0] - img = img[:, :, ::-1].transpose(2, 0, 1) - img = np.ascontiguousarray(img) - img = torch.from_numpy(img).to(self.device) - img = img.half() # 半精度 - img /= 255.0 # 图像归一化 - if img.ndimension() == 3: - img = img.unsqueeze(0) - - return img0, img - - def detect(self, im): - - im0, img = self.preprocess(im) - - pred = self.m(img, augment=False)[0] - pred = pred.float() - pred = non_max_suppression(pred, self.threshold, 0.4) - - pred_boxes = [] - for det in pred: - - if det is not None and len(det): - det[:, :4] = scale_coords( - img.shape[2:], det[:, :4], im0.shape).round() - - for *x, conf, cls_id in det: - lbl = self.names[int(cls_id)] - if not lbl in ['person', 'car', 'truck']: - continue - x1, y1 = int(x[0]), int(x[1]) - x2, y2 = int(x[2]), int(x[3]) - pred_boxes.append( - (x1, y1, x2, y2, lbl, conf)) - - return im, pred_boxes - -``` - -调用 self.detect 方法返回图像和预测结果 - -# DeepSort追踪器： - -```python -deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT, - max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, - nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, - max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, - use_cuda=True) -``` - -调用 self.update 方法更新追踪结果 - -# 运行demo： - -```bash -python demo.py -``` - -# 训练自己的模型： -参考我的另一篇博客： - -[【小白CV】手把手教你用YOLOv5训练自己的数据集（从Windows环境配置到模型部署）](https://blog.csdn.net/weixin_44936889/article/details/110661862) - -训练好后放到 weights 文件夹下 - -# 调用接口： - -## 创建检测器： - -```python -from AIDetector_pytorch import Detector - -det = Detector() -``` - -## 调用检测接口： - -```python -result = det.feedCap(im) -``` - -其中 im 为 BGR 图像 - -返回的 result 是字典，result['frame'] 返回可视化后的图像 - -# 联系作者： - -> B站：[https://space.bilibili.com/470550823](https://space.bilibili.com/470550823) - -> CSDN：[https://blog.csdn.net/weixin_44936889](https://blog.csdn.net/weixin_44936889) - -> AI Studio：[https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156](https://aistudio.baidu.com/aistudio/personalcenter/thirdview/67156) - -> Github：[https://github.com/Sharpiless](https://github.com/Sharpiless) - -遵循 GNU General Public License v3.0 协议，标明目标检测部分来源：https://github.com/ultralytics/yolov5/ - - diff --git a/Yolov5-Deepsort/__pycache__/AIDetector_pytorch.cpython-37.pyc b/Yolov5-Deepsort/__pycache__/AIDetector_pytorch.cpython-37.pyc deleted file mode 100644 index f3df550cd00fdac6fc6194a125a0779564e4a21f..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/__pycache__/AIDetector_pytorch.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/__pycache__/tracker.cpython-37.pyc b/Yolov5-Deepsort/__pycache__/tracker.cpython-37.pyc deleted file mode 100644 index 30b3fa8312b1b89000fd7043669defba41cad800..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/__pycache__/tracker.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/configs/deep_sort.yaml b/Yolov5-Deepsort/deep_sort/configs/deep_sort.yaml deleted file mode 100644 index 6105f46a8d155c2dc7ba9cebb7178b4e0ed389a3..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/configs/deep_sort.yaml +++ /dev/null @@ -1,10 +0,0 @@ -DEEPSORT: - REID_CKPT: "deep_sort/deep_sort/deep/checkpoint/ckpt.t7" - MAX_DIST: 0.2 - MIN_CONFIDENCE: 0.3 - NMS_MAX_OVERLAP: 0.5 - MAX_IOU_DISTANCE: 0.7 - MAX_AGE: 70 - N_INIT: 3 - NN_BUDGET: 100 - diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/README.md b/Yolov5-Deepsort/deep_sort/deep_sort/README.md deleted file mode 100644 index e89c9b3ea08691210046fbb9184bf8e44e88f29e..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Deep Sort - -This is the implemention of deep sort with pytorch. \ No newline at end of file diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/__init__.py b/Yolov5-Deepsort/deep_sort/deep_sort/__init__.py deleted file mode 100644 index 5fe5d0fd796ec4f46dc4141f5e4f9f5092f7d321..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -from .deep_sort import DeepSort - - -__all__ = ['DeepSort', 'build_tracker'] - - -def build_tracker(cfg, use_cuda): - return DeepSort(cfg.DEEPSORT.REID_CKPT, - max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, - nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, - max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=use_cuda) - - - - - - - - - - diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/__init__.cpython-36.pyc b/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/__init__.cpython-36.pyc deleted file mode 100644 index dff7fa67ddcea8ac5a16ace2ede46d492353e1d4..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/__init__.cpython-36.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/__init__.cpython-37.pyc b/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index c92d420992773f89b21cac6794ebfcbbfa90b11e..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/__init__.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/deep_sort.cpython-36.pyc b/Yolov5-Deepsort/deep_sort/deep_sort/__pycache__/deep_sort.cpython-36.pyc deleted file mode 100644 index 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a/Yolov5-Deepsort/deep_sort/deep_sort/deep/__pycache__/model.cpython-37.pyc b/Yolov5-Deepsort/deep_sort/deep_sort/deep/__pycache__/model.cpython-37.pyc deleted file mode 100644 index a6111abffcd22ea119d6b601a0614959a5777009..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/deep_sort/deep/__pycache__/model.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/.gitkeep b/Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/.gitkeep deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/ckpt.t7 b/Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/ckpt.t7 deleted file mode 100644 index cd7ceebe86bdfaea299f31994844488295f00ca8..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/ckpt.t7 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:df75ddef42c3d1bda67bc94b093e7ce61de7f75a89f36a8f868a428462198316 -size 46034619 diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/evaluate.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep/evaluate.py deleted file mode 100644 index 31c40a46eaea0ad7b6fc50a15e39329b954561ff..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/evaluate.py +++ /dev/null @@ -1,15 +0,0 @@ -import torch - -features = torch.load("features.pth") -qf = features["qf"] -ql = features["ql"] -gf = features["gf"] -gl = features["gl"] - -scores = qf.mm(gf.t()) -res = scores.topk(5, dim=1)[1][:,0] -top1correct = gl[res].eq(ql).sum().item() - -print("Acc top1:{:.3f}".format(top1correct/ql.size(0))) - - diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/feature_extractor.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep/feature_extractor.py deleted file mode 100644 index 0443e37d94dc743e6c4467fab2dcb29f39bb6435..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/feature_extractor.py +++ /dev/null @@ -1,55 +0,0 @@ -import torch -import torchvision.transforms as transforms -import numpy as np -import cv2 -import logging - -from .model import Net - -class Extractor(object): - def __init__(self, model_path, use_cuda=True): - self.net = Net(reid=True) - self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" - state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)['net_dict'] - self.net.load_state_dict(state_dict) - logger = logging.getLogger("root.tracker") - logger.info("Loading weights from {}... Done!".format(model_path)) - self.net.to(self.device) - self.size = (64, 128) - self.norm = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), - ]) - - - - def _preprocess(self, im_crops): - """ - TODO: - 1. to float with scale from 0 to 1 - 2. resize to (64, 128) as Market1501 dataset did - 3. concatenate to a numpy array - 3. to torch Tensor - 4. normalize - """ - def _resize(im, size): - return cv2.resize(im.astype(np.float32)/255., size) - - im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze(0) for im in im_crops], dim=0).float() - return im_batch - - - def __call__(self, im_crops): - im_batch = self._preprocess(im_crops) - with torch.no_grad(): - im_batch = im_batch.to(self.device) - features = self.net(im_batch) - return features.cpu().numpy() - - -if __name__ == '__main__': - img = cv2.imread("demo.jpg")[:,:,(2,1,0)] - extr = Extractor("checkpoint/ckpt.t7") - feature = extr(img) - print(feature.shape) - diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/model.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep/model.py deleted file mode 100644 index 97e87547cd8e36b1a65e2dc1ad4cfb310f0f4c23..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/model.py +++ /dev/null @@ -1,104 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class BasicBlock(nn.Module): - def __init__(self, c_in, c_out,is_downsample=False): - super(BasicBlock,self).__init__() - self.is_downsample = is_downsample - if is_downsample: - self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False) - else: - self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(c_out) - self.relu = nn.ReLU(True) - self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(c_out) - if is_downsample: - self.downsample = nn.Sequential( - nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), - nn.BatchNorm2d(c_out) - ) - elif c_in != c_out: - self.downsample = nn.Sequential( - nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), - nn.BatchNorm2d(c_out) - ) - self.is_downsample = True - - def forward(self,x): - y = self.conv1(x) - y = self.bn1(y) - y = self.relu(y) - y = self.conv2(y) - y = self.bn2(y) - if self.is_downsample: - x = self.downsample(x) - return F.relu(x.add(y),True) - -def make_layers(c_in,c_out,repeat_times, is_downsample=False): - blocks = [] - for i in range(repeat_times): - if i ==0: - blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),] - else: - blocks += [BasicBlock(c_out,c_out),] - return nn.Sequential(*blocks) - -class Net(nn.Module): - def __init__(self, num_classes=751 ,reid=False): - super(Net,self).__init__() - # 3 128 64 - self.conv = nn.Sequential( - nn.Conv2d(3,64,3,stride=1,padding=1), - nn.BatchNorm2d(64), - nn.ReLU(inplace=True), - # nn.Conv2d(32,32,3,stride=1,padding=1), - # nn.BatchNorm2d(32), - # nn.ReLU(inplace=True), - nn.MaxPool2d(3,2,padding=1), - ) - # 32 64 32 - self.layer1 = make_layers(64,64,2,False) - # 32 64 32 - self.layer2 = make_layers(64,128,2,True) - # 64 32 16 - self.layer3 = make_layers(128,256,2,True) - # 128 16 8 - self.layer4 = make_layers(256,512,2,True) - # 256 8 4 - self.avgpool = nn.AvgPool2d((8,4),1) - # 256 1 1 - self.reid = reid - self.classifier = nn.Sequential( - nn.Linear(512, 256), - nn.BatchNorm1d(256), - nn.ReLU(inplace=True), - nn.Dropout(), - nn.Linear(256, num_classes), - ) - - def forward(self, x): - x = self.conv(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - x = self.layer4(x) - x = self.avgpool(x) - x = x.view(x.size(0),-1) - # B x 128 - if self.reid: - x = x.div(x.norm(p=2,dim=1,keepdim=True)) - return x - # classifier - x = self.classifier(x) - return x - - -if __name__ == '__main__': - net = Net() - x = torch.randn(4,3,128,64) - y = net(x) - import ipdb; ipdb.set_trace() - - diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/original_model.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep/original_model.py deleted file mode 100644 index 72453a6392b9a360c03034eefee1d6be30f8121b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/original_model.py +++ /dev/null @@ -1,106 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class BasicBlock(nn.Module): - def __init__(self, c_in, c_out,is_downsample=False): - super(BasicBlock,self).__init__() - self.is_downsample = is_downsample - if is_downsample: - self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=2, padding=1, bias=False) - else: - self.conv1 = nn.Conv2d(c_in, c_out, 3, stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(c_out) - self.relu = nn.ReLU(True) - self.conv2 = nn.Conv2d(c_out,c_out,3,stride=1,padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(c_out) - if is_downsample: - self.downsample = nn.Sequential( - nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), - nn.BatchNorm2d(c_out) - ) - elif c_in != c_out: - self.downsample = nn.Sequential( - nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), - nn.BatchNorm2d(c_out) - ) - self.is_downsample = True - - def forward(self,x): - y = self.conv1(x) - y = self.bn1(y) - y = self.relu(y) - y = self.conv2(y) - y = self.bn2(y) - if self.is_downsample: - x = self.downsample(x) - return F.relu(x.add(y),True) - -def make_layers(c_in,c_out,repeat_times, is_downsample=False): - blocks = [] - for i in range(repeat_times): - if i ==0: - blocks += [BasicBlock(c_in,c_out, is_downsample=is_downsample),] - else: - blocks += [BasicBlock(c_out,c_out),] - return nn.Sequential(*blocks) - -class Net(nn.Module): - def __init__(self, num_classes=625 ,reid=False): - super(Net,self).__init__() - # 3 128 64 - self.conv = nn.Sequential( - nn.Conv2d(3,32,3,stride=1,padding=1), - nn.BatchNorm2d(32), - nn.ELU(inplace=True), - nn.Conv2d(32,32,3,stride=1,padding=1), - nn.BatchNorm2d(32), - nn.ELU(inplace=True), - nn.MaxPool2d(3,2,padding=1), - ) - # 32 64 32 - self.layer1 = make_layers(32,32,2,False) - # 32 64 32 - self.layer2 = make_layers(32,64,2,True) - # 64 32 16 - self.layer3 = make_layers(64,128,2,True) - # 128 16 8 - self.dense = nn.Sequential( - nn.Dropout(p=0.6), - nn.Linear(128*16*8, 128), - nn.BatchNorm1d(128), - nn.ELU(inplace=True) - ) - # 256 1 1 - self.reid = reid - self.batch_norm = nn.BatchNorm1d(128) - self.classifier = nn.Sequential( - nn.Linear(128, num_classes), - ) - - def forward(self, x): - x = self.conv(x) - x = self.layer1(x) - x = self.layer2(x) - x = self.layer3(x) - - x = x.view(x.size(0),-1) - if self.reid: - x = self.dense[0](x) - x = self.dense[1](x) - x = x.div(x.norm(p=2,dim=1,keepdim=True)) - return x - x = self.dense(x) - # B x 128 - # classifier - x = self.classifier(x) - return x - - -if __name__ == '__main__': - net = Net(reid=True) - x = torch.randn(4,3,128,64) - y = net(x) - import ipdb; ipdb.set_trace() - - diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/test.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep/test.py deleted file mode 100644 index ebd590336f7b17c44738c4c15458f02f33f08017..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/test.py +++ /dev/null @@ -1,77 +0,0 @@ -import torch -import torch.backends.cudnn as cudnn -import torchvision - -import argparse -import os - -from model import Net - -parser = argparse.ArgumentParser(description="Train on market1501") -parser.add_argument("--data-dir",default='data',type=str) -parser.add_argument("--no-cuda",action="store_true") -parser.add_argument("--gpu-id",default=0,type=int) -args = parser.parse_args() - -# device -device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu" -if torch.cuda.is_available() and not args.no_cuda: - cudnn.benchmark = True - -# data loader -root = args.data_dir -query_dir = os.path.join(root,"query") -gallery_dir = os.path.join(root,"gallery") -transform = torchvision.transforms.Compose([ - torchvision.transforms.Resize((128,64)), - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -]) -queryloader = torch.utils.data.DataLoader( - torchvision.datasets.ImageFolder(query_dir, transform=transform), - batch_size=64, shuffle=False -) -galleryloader = torch.utils.data.DataLoader( - torchvision.datasets.ImageFolder(gallery_dir, transform=transform), - batch_size=64, shuffle=False -) - -# net definition -net = Net(reid=True) -assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!" -print('Loading from checkpoint/ckpt.t7') -checkpoint = torch.load("./checkpoint/ckpt.t7") -net_dict = checkpoint['net_dict'] -net.load_state_dict(net_dict, strict=False) -net.eval() -net.to(device) - -# compute features -query_features = torch.tensor([]).float() -query_labels = torch.tensor([]).long() -gallery_features = torch.tensor([]).float() -gallery_labels = torch.tensor([]).long() - -with torch.no_grad(): - for idx,(inputs,labels) in enumerate(queryloader): - inputs = inputs.to(device) - features = net(inputs).cpu() - query_features = torch.cat((query_features, features), dim=0) - query_labels = torch.cat((query_labels, labels)) - - for idx,(inputs,labels) in enumerate(galleryloader): - inputs = inputs.to(device) - features = net(inputs).cpu() - gallery_features = torch.cat((gallery_features, features), dim=0) - gallery_labels = torch.cat((gallery_labels, labels)) - -gallery_labels -= 2 - -# save features -features = { - "qf": query_features, - "ql": query_labels, - "gf": gallery_features, - "gl": gallery_labels -} -torch.save(features,"features.pth") \ No newline at end of file diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/train.jpg b/Yolov5-Deepsort/deep_sort/deep_sort/deep/train.jpg deleted file mode 100644 index 3635a614738828b880aa862bc52423848ac8e472..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/deep_sort/deep/train.jpg and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep/train.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep/train.py deleted file mode 100644 index a931763d155f27743d944ac46da10f57d35a8ddb..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep/train.py +++ /dev/null @@ -1,189 +0,0 @@ -import argparse -import os -import time - -import numpy as np -import matplotlib.pyplot as plt -import torch -import torch.backends.cudnn as cudnn -import torchvision - -from model import Net - -parser = argparse.ArgumentParser(description="Train on market1501") -parser.add_argument("--data-dir",default='data',type=str) -parser.add_argument("--no-cuda",action="store_true") -parser.add_argument("--gpu-id",default=0,type=int) -parser.add_argument("--lr",default=0.1, type=float) -parser.add_argument("--interval",'-i',default=20,type=int) -parser.add_argument('--resume', '-r',action='store_true') -args = parser.parse_args() - -# device -device = "cuda:{}".format(args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu" -if torch.cuda.is_available() and not args.no_cuda: - cudnn.benchmark = True - -# data loading -root = args.data_dir -train_dir = os.path.join(root,"train") -test_dir = os.path.join(root,"test") -transform_train = torchvision.transforms.Compose([ - torchvision.transforms.RandomCrop((128,64),padding=4), - torchvision.transforms.RandomHorizontalFlip(), - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -]) -transform_test = torchvision.transforms.Compose([ - torchvision.transforms.Resize((128,64)), - torchvision.transforms.ToTensor(), - torchvision.transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) -]) -trainloader = torch.utils.data.DataLoader( - torchvision.datasets.ImageFolder(train_dir, transform=transform_train), - batch_size=64,shuffle=True -) -testloader = torch.utils.data.DataLoader( - torchvision.datasets.ImageFolder(test_dir, transform=transform_test), - batch_size=64,shuffle=True -) -num_classes = max(len(trainloader.dataset.classes), len(testloader.dataset.classes)) - -# net definition -start_epoch = 0 -net = Net(num_classes=num_classes) -if args.resume: - assert os.path.isfile("./checkpoint/ckpt.t7"), "Error: no checkpoint file found!" - print('Loading from checkpoint/ckpt.t7') - checkpoint = torch.load("./checkpoint/ckpt.t7") - # import ipdb; ipdb.set_trace() - net_dict = checkpoint['net_dict'] - net.load_state_dict(net_dict) - best_acc = checkpoint['acc'] - start_epoch = checkpoint['epoch'] -net.to(device) - -# loss and optimizer -criterion = torch.nn.CrossEntropyLoss() -optimizer = torch.optim.SGD(net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4) -best_acc = 0. - -# train function for each epoch -def train(epoch): - print("\nEpoch : %d"%(epoch+1)) - net.train() - training_loss = 0. - train_loss = 0. - correct = 0 - total = 0 - interval = args.interval - start = time.time() - for idx, (inputs, labels) in enumerate(trainloader): - # forward - inputs,labels = inputs.to(device),labels.to(device) - outputs = net(inputs) - loss = criterion(outputs, labels) - - # backward - optimizer.zero_grad() - loss.backward() - optimizer.step() - - # accumurating - training_loss += loss.item() - train_loss += loss.item() - correct += outputs.max(dim=1)[1].eq(labels).sum().item() - total += labels.size(0) - - # print - if (idx+1)%interval == 0: - end = time.time() - print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format( - 100.*(idx+1)/len(trainloader), end-start, training_loss/interval, correct, total, 100.*correct/total - )) - training_loss = 0. - start = time.time() - - return train_loss/len(trainloader), 1.- correct/total - -def test(epoch): - global best_acc - net.eval() - test_loss = 0. - correct = 0 - total = 0 - start = time.time() - with torch.no_grad(): - for idx, (inputs, labels) in enumerate(testloader): - inputs, labels = inputs.to(device), labels.to(device) - outputs = net(inputs) - loss = criterion(outputs, labels) - - test_loss += loss.item() - correct += outputs.max(dim=1)[1].eq(labels).sum().item() - total += labels.size(0) - - print("Testing ...") - end = time.time() - print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format( - 100.*(idx+1)/len(testloader), end-start, test_loss/len(testloader), correct, total, 100.*correct/total - )) - - # saving checkpoint - acc = 100.*correct/total - if acc > best_acc: - best_acc = acc - print("Saving parameters to checkpoint/ckpt.t7") - checkpoint = { - 'net_dict':net.state_dict(), - 'acc':acc, - 'epoch':epoch, - } - if not os.path.isdir('checkpoint'): - os.mkdir('checkpoint') - torch.save(checkpoint, './checkpoint/ckpt.t7') - - return test_loss/len(testloader), 1.- correct/total - -# plot figure -x_epoch = [] -record = {'train_loss':[], 'train_err':[], 'test_loss':[], 'test_err':[]} -fig = plt.figure() -ax0 = fig.add_subplot(121, title="loss") -ax1 = fig.add_subplot(122, title="top1err") -def draw_curve(epoch, train_loss, train_err, test_loss, test_err): - global record - record['train_loss'].append(train_loss) - record['train_err'].append(train_err) - record['test_loss'].append(test_loss) - record['test_err'].append(test_err) - - x_epoch.append(epoch) - ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train') - ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val') - ax1.plot(x_epoch, record['train_err'], 'bo-', label='train') - ax1.plot(x_epoch, record['test_err'], 'ro-', label='val') - if epoch == 0: - ax0.legend() - ax1.legend() - fig.savefig("train.jpg") - -# lr decay -def lr_decay(): - global optimizer - for params in optimizer.param_groups: - params['lr'] *= 0.1 - lr = params['lr'] - print("Learning rate adjusted to {}".format(lr)) - -def main(): - for epoch in range(start_epoch, start_epoch+40): - train_loss, train_err = train(epoch) - test_loss, test_err = test(epoch) - draw_curve(epoch, train_loss, train_err, test_loss, test_err) - if (epoch+1)%20==0: - lr_decay() - - -if __name__ == '__main__': - main() diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/deep_sort.py b/Yolov5-Deepsort/deep_sort/deep_sort/deep_sort.py deleted file mode 100644 index 44f8746fd299527fd1129027e98435bf9f52f671..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/deep_sort.py +++ /dev/null @@ -1,101 +0,0 @@ -import numpy as np -import torch -import rich -from .deep.feature_extractor import Extractor -from .sort.nn_matching import NearestNeighborDistanceMetric -from .sort.preprocessing import non_max_suppression -from .sort.detection import Detection -from .sort.tracker import Tracker - - -__all__ = ['DeepSort'] - - -class DeepSort(object): - def __init__(self, model_path, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True): - self.min_confidence = min_confidence - self.nms_max_overlap = nms_max_overlap - - self.extractor = Extractor(model_path, use_cuda=use_cuda) - - max_cosine_distance = max_dist - nn_budget = 100 - metric = NearestNeighborDistanceMetric( - "cosine", max_cosine_distance, nn_budget) - self.tracker = Tracker( - metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init) - - def update(self, bbox_xywh, confidences, clss, ori_img): - self.height, self.width = ori_img.shape[:2] - # generate detections - features = self._get_features(bbox_xywh, ori_img) - bbox_tlwh = self._xywh_to_tlwh(bbox_xywh) - detections = [Detection(bbox_tlwh[i], clss[i], conf, features[i]) for i, conf in enumerate( - confidences) if conf > self.min_confidence] - # update tracker - self.tracker.predict() - self.tracker.update(detections) - - # output bbox identities - outputs = [] - for track in self.tracker.tracks: - if not track.is_confirmed() or track.time_since_update > 1: - continue - #rich.print(track) - box = track.to_tlwh() - x1, y1, x2, y2 = self._tlwh_to_xyxy(box) - outputs.append((x1, y1, x2, y2, track.cls_, track.track_id)) - return outputs - - @staticmethod - def _xywh_to_tlwh(bbox_xywh): - if isinstance(bbox_xywh, np.ndarray): - bbox_tlwh = bbox_xywh.copy() - elif isinstance(bbox_xywh, torch.Tensor): - bbox_tlwh = bbox_xywh.clone() - if bbox_tlwh.size(0): - bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2]/2. - bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3]/2. - return bbox_tlwh - - def _xywh_to_xyxy(self, bbox_xywh): - x, y, w, h = bbox_xywh - x1 = max(int(x-w/2), 0) - x2 = min(int(x+w/2), self.width-1) - y1 = max(int(y-h/2), 0) - y2 = min(int(y+h/2), self.height-1) - return x1, y1, x2, y2 - - def _tlwh_to_xyxy(self, bbox_tlwh): - """ - TODO: - Convert bbox from xtl_ytl_w_h to xc_yc_w_h - Thanks JieChen91@github.com for reporting this bug! - """ - x, y, w, h = bbox_tlwh - x1 = max(int(x), 0) - x2 = min(int(x+w), self.width-1) - y1 = max(int(y), 0) - y2 = min(int(y+h), self.height-1) - return x1, y1, x2, y2 - - def _xyxy_to_tlwh(self, bbox_xyxy): - x1, y1, x2, y2 = bbox_xyxy - - t = x1 - l = y1 - w = int(x2-x1) - h = int(y2-y1) - return t, l, w, h - - def _get_features(self, bbox_xywh, ori_img): - im_crops = [] - for box in bbox_xywh: - x1, y1, x2, y2 = self._xywh_to_xyxy(box) - im = ori_img[y1:y2, x1:x2] - im_crops.append(im) - if im_crops: - features = self.extractor(im_crops) - else: - features = np.array([]) - return features diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/__init__.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/__pycache__/__init__.cpython-36.pyc b/Yolov5-Deepsort/deep_sort/deep_sort/sort/__pycache__/__init__.cpython-36.pyc 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a/Yolov5-Deepsort/deep_sort/deep_sort/sort/__pycache__/tracker.cpython-37.pyc b/Yolov5-Deepsort/deep_sort/deep_sort/sort/__pycache__/tracker.cpython-37.pyc deleted file mode 100644 index d4b34b661f767b14aef6e01fa8a86f5a037bcdc7..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/deep_sort/sort/__pycache__/tracker.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/detection.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/detection.py deleted file mode 100644 index 0c2c1ae42addc84cfd53bcfa3cdcc8885662ec44..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/detection.py +++ /dev/null @@ -1,28 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -import numpy as np - - -class Detection(object): - - def __init__(self, tlwh, cls_, confidence, feature): - self.tlwh = np.asarray(tlwh, dtype=np.float) - self.cls_ = cls_ - self.confidence = float(confidence) - self.feature = np.asarray(feature, dtype=np.float32) - - def to_tlbr(self): - """Convert bounding box to format `(min x, min y, max x, max y)`, i.e., - `(top left, bottom right)`. - """ - ret = self.tlwh.copy() - ret[2:] += ret[:2] - return ret - - def to_xyah(self): - """Convert bounding box to format `(center x, center y, aspect ratio, - height)`, where the aspect ratio is `width / height`. - """ - ret = self.tlwh.copy() - ret[:2] += ret[2:] / 2 - ret[2] /= ret[3] - return ret diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/iou_matching.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/iou_matching.py deleted file mode 100644 index c4dd0b88391d2ac28036c7163d5d4c988d4a9c4c..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/iou_matching.py +++ /dev/null @@ -1,81 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -from __future__ import absolute_import -import numpy as np -from . import linear_assignment - - -def iou(bbox, candidates): - """Computer intersection over union. - - Parameters - ---------- - bbox : ndarray - A bounding box in format `(top left x, top left y, width, height)`. - candidates : ndarray - A matrix of candidate bounding boxes (one per row) in the same format - as `bbox`. - - Returns - ------- - ndarray - The intersection over union in [0, 1] between the `bbox` and each - candidate. A higher score means a larger fraction of the `bbox` is - occluded by the candidate. - - """ - bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:] - candidates_tl = candidates[:, :2] - candidates_br = candidates[:, :2] + candidates[:, 2:] - - tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis], - np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]] - br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis], - np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]] - wh = np.maximum(0., br - tl) - - area_intersection = wh.prod(axis=1) - area_bbox = bbox[2:].prod() - area_candidates = candidates[:, 2:].prod(axis=1) - return area_intersection / (area_bbox + area_candidates - area_intersection) - - -def iou_cost(tracks, detections, track_indices=None, - detection_indices=None): - """An intersection over union distance metric. - - Parameters - ---------- - tracks : List[deep_sort.track.Track] - A list of tracks. - detections : List[deep_sort.detection.Detection] - A list of detections. - track_indices : Optional[List[int]] - A list of indices to tracks that should be matched. Defaults to - all `tracks`. - detection_indices : Optional[List[int]] - A list of indices to detections that should be matched. Defaults - to all `detections`. - - Returns - ------- - ndarray - Returns a cost matrix of shape - len(track_indices), len(detection_indices) where entry (i, j) is - `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`. - - """ - if track_indices is None: - track_indices = np.arange(len(tracks)) - if detection_indices is None: - detection_indices = np.arange(len(detections)) - - cost_matrix = np.zeros((len(track_indices), len(detection_indices))) - for row, track_idx in enumerate(track_indices): - if tracks[track_idx].time_since_update > 1: - cost_matrix[row, :] = linear_assignment.INFTY_COST - continue - - bbox = tracks[track_idx].to_tlwh() - candidates = np.asarray([detections[i].tlwh for i in detection_indices]) - cost_matrix[row, :] = 1. - iou(bbox, candidates) - return cost_matrix diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/kalman_filter.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/kalman_filter.py deleted file mode 100644 index 787a76e6a43870a9538647b51fda6a5254ce2d43..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/kalman_filter.py +++ /dev/null @@ -1,229 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -import numpy as np -import scipy.linalg - - -""" -Table for the 0.95 quantile of the chi-square distribution with N degrees of -freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv -function and used as Mahalanobis gating threshold. -""" -chi2inv95 = { - 1: 3.8415, - 2: 5.9915, - 3: 7.8147, - 4: 9.4877, - 5: 11.070, - 6: 12.592, - 7: 14.067, - 8: 15.507, - 9: 16.919} - - -class KalmanFilter(object): - """ - A simple Kalman filter for tracking bounding boxes in image space. - - The 8-dimensional state space - - x, y, a, h, vx, vy, va, vh - - contains the bounding box center position (x, y), aspect ratio a, height h, - and their respective velocities. - - Object motion follows a constant velocity model. The bounding box location - (x, y, a, h) is taken as direct observation of the state space (linear - observation model). - - """ - - def __init__(self): - ndim, dt = 4, 1. - - # Create Kalman filter model matrices. - self._motion_mat = np.eye(2 * ndim, 2 * ndim) - for i in range(ndim): - self._motion_mat[i, ndim + i] = dt - self._update_mat = np.eye(ndim, 2 * ndim) - - # Motion and observation uncertainty are chosen relative to the current - # state estimate. These weights control the amount of uncertainty in - # the model. This is a bit hacky. - self._std_weight_position = 1. / 20 - self._std_weight_velocity = 1. / 160 - - def initiate(self, measurement): - """Create track from unassociated measurement. - - Parameters - ---------- - measurement : ndarray - Bounding box coordinates (x, y, a, h) with center position (x, y), - aspect ratio a, and height h. - - Returns - ------- - (ndarray, ndarray) - Returns the mean vector (8 dimensional) and covariance matrix (8x8 - dimensional) of the new track. Unobserved velocities are initialized - to 0 mean. - - """ - mean_pos = measurement - mean_vel = np.zeros_like(mean_pos) - mean = np.r_[mean_pos, mean_vel] - - std = [ - 2 * self._std_weight_position * measurement[3], - 2 * self._std_weight_position * measurement[3], - 1e-2, - 2 * self._std_weight_position * measurement[3], - 10 * self._std_weight_velocity * measurement[3], - 10 * self._std_weight_velocity * measurement[3], - 1e-5, - 10 * self._std_weight_velocity * measurement[3]] - covariance = np.diag(np.square(std)) - return mean, covariance - - def predict(self, mean, covariance): - """Run Kalman filter prediction step. - - Parameters - ---------- - mean : ndarray - The 8 dimensional mean vector of the object state at the previous - time step. - covariance : ndarray - The 8x8 dimensional covariance matrix of the object state at the - previous time step. - - Returns - ------- - (ndarray, ndarray) - Returns the mean vector and covariance matrix of the predicted - state. Unobserved velocities are initialized to 0 mean. - - """ - std_pos = [ - self._std_weight_position * mean[3], - self._std_weight_position * mean[3], - 1e-2, - self._std_weight_position * mean[3]] - std_vel = [ - self._std_weight_velocity * mean[3], - self._std_weight_velocity * mean[3], - 1e-5, - self._std_weight_velocity * mean[3]] - motion_cov = np.diag(np.square(np.r_[std_pos, std_vel])) - - mean = np.dot(self._motion_mat, mean) - covariance = np.linalg.multi_dot(( - self._motion_mat, covariance, self._motion_mat.T)) + motion_cov - - return mean, covariance - - def project(self, mean, covariance): - """Project state distribution to measurement space. - - Parameters - ---------- - mean : ndarray - The state's mean vector (8 dimensional array). - covariance : ndarray - The state's covariance matrix (8x8 dimensional). - - Returns - ------- - (ndarray, ndarray) - Returns the projected mean and covariance matrix of the given state - estimate. - - """ - std = [ - self._std_weight_position * mean[3], - self._std_weight_position * mean[3], - 1e-1, - self._std_weight_position * mean[3]] - innovation_cov = np.diag(np.square(std)) - - mean = np.dot(self._update_mat, mean) - covariance = np.linalg.multi_dot(( - self._update_mat, covariance, self._update_mat.T)) - return mean, covariance + innovation_cov - - def update(self, mean, covariance, measurement): - """Run Kalman filter correction step. - - Parameters - ---------- - mean : ndarray - The predicted state's mean vector (8 dimensional). - covariance : ndarray - The state's covariance matrix (8x8 dimensional). - measurement : ndarray - The 4 dimensional measurement vector (x, y, a, h), where (x, y) - is the center position, a the aspect ratio, and h the height of the - bounding box. - - Returns - ------- - (ndarray, ndarray) - Returns the measurement-corrected state distribution. - - """ - projected_mean, projected_cov = self.project(mean, covariance) - - chol_factor, lower = scipy.linalg.cho_factor( - projected_cov, lower=True, check_finite=False) - kalman_gain = scipy.linalg.cho_solve( - (chol_factor, lower), np.dot(covariance, self._update_mat.T).T, - check_finite=False).T - innovation = measurement - projected_mean - - new_mean = mean + np.dot(innovation, kalman_gain.T) - new_covariance = covariance - np.linalg.multi_dot(( - kalman_gain, projected_cov, kalman_gain.T)) - return new_mean, new_covariance - - def gating_distance(self, mean, covariance, measurements, - only_position=False): - """Compute gating distance between state distribution and measurements. - - A suitable distance threshold can be obtained from `chi2inv95`. If - `only_position` is False, the chi-square distribution has 4 degrees of - freedom, otherwise 2. - - Parameters - ---------- - mean : ndarray - Mean vector over the state distribution (8 dimensional). - covariance : ndarray - Covariance of the state distribution (8x8 dimensional). - measurements : ndarray - An Nx4 dimensional matrix of N measurements, each in - format (x, y, a, h) where (x, y) is the bounding box center - position, a the aspect ratio, and h the height. - only_position : Optional[bool] - If True, distance computation is done with respect to the bounding - box center position only. - - Returns - ------- - ndarray - Returns an array of length N, where the i-th element contains the - squared Mahalanobis distance between (mean, covariance) and - `measurements[i]`. - - """ - mean, covariance = self.project(mean, covariance) - if only_position: - mean, covariance = mean[:2], covariance[:2, :2] - measurements = measurements[:, :2] - - cholesky_factor = np.linalg.cholesky(covariance) - d = measurements - mean - z = scipy.linalg.solve_triangular( - cholesky_factor, d.T, lower=True, check_finite=False, - overwrite_b=True) - squared_maha = np.sum(z * z, axis=0) - return squared_maha diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/linear_assignment.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/linear_assignment.py deleted file mode 100644 index 2006230c960fa07b128b39187cb5df2775300544..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/linear_assignment.py +++ /dev/null @@ -1,159 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -from __future__ import absolute_import -import numpy as np -# from sklearn.utils.linear_assignment_ import linear_assignment -from scipy.optimize import linear_sum_assignment as linear_assignment -from . import kalman_filter - - -INFTY_COST = 1e+5 - - -def min_cost_matching( - distance_metric, max_distance, tracks, detections, track_indices=None, - detection_indices=None): - if track_indices is None: - track_indices = np.arange(len(tracks)) - if detection_indices is None: - detection_indices = np.arange(len(detections)) - - if len(detection_indices) == 0 or len(track_indices) == 0: - return [], track_indices, detection_indices # Nothing to match. - - cost_matrix = distance_metric( - tracks, detections, track_indices, detection_indices) - cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5 - - row_indices, col_indices = linear_assignment(cost_matrix) - - matches, unmatched_tracks, unmatched_detections = [], [], [] - for col, detection_idx in enumerate(detection_indices): - if col not in col_indices: - unmatched_detections.append(detection_idx) - for row, track_idx in enumerate(track_indices): - if row not in row_indices: - unmatched_tracks.append(track_idx) - for row, col in zip(row_indices, col_indices): - track_idx = track_indices[row] - detection_idx = detection_indices[col] - if cost_matrix[row, col] > max_distance: - unmatched_tracks.append(track_idx) - unmatched_detections.append(detection_idx) - else: - matches.append((track_idx, detection_idx)) - return matches, unmatched_tracks, unmatched_detections - - -def matching_cascade( - distance_metric, max_distance, cascade_depth, tracks, detections, - track_indices=None, detection_indices=None): - """Run matching cascade. - - Parameters - ---------- - distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray - The distance metric is given a list of tracks and detections as well as - a list of N track indices and M detection indices. The metric should - return the NxM dimensional cost matrix, where element (i, j) is the - association cost between the i-th track in the given track indices and - the j-th detection in the given detection indices. - max_distance : float - Gating threshold. Associations with cost larger than this value are - disregarded. - cascade_depth: int - The cascade depth, should be se to the maximum track age. - tracks : List[track.Track] - A list of predicted tracks at the current time step. - detections : List[detection.Detection] - A list of detections at the current time step. - track_indices : Optional[List[int]] - List of track indices that maps rows in `cost_matrix` to tracks in - `tracks` (see description above). Defaults to all tracks. - detection_indices : Optional[List[int]] - List of detection indices that maps columns in `cost_matrix` to - detections in `detections` (see description above). Defaults to all - detections. - - Returns - ------- - (List[(int, int)], List[int], List[int]) - Returns a tuple with the following three entries: - * A list of matched track and detection indices. - * A list of unmatched track indices. - * A list of unmatched detection indices. - - """ - if track_indices is None: - track_indices = list(range(len(tracks))) - if detection_indices is None: - detection_indices = list(range(len(detections))) - - unmatched_detections = detection_indices - matches = [] - for level in range(cascade_depth): - if len(unmatched_detections) == 0: # No detections left - break - - track_indices_l = [ - k for k in track_indices - if tracks[k].time_since_update == 1 + level - ] - if len(track_indices_l) == 0: # Nothing to match at this level - continue - - matches_l, _, unmatched_detections = \ - min_cost_matching( - distance_metric, max_distance, tracks, detections, - track_indices_l, unmatched_detections) - matches += matches_l - unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches)) - return matches, unmatched_tracks, unmatched_detections - - -def gate_cost_matrix( - kf, cost_matrix, tracks, detections, track_indices, detection_indices, - gated_cost=INFTY_COST, only_position=False): - """Invalidate infeasible entries in cost matrix based on the state - distributions obtained by Kalman filtering. - - Parameters - ---------- - kf : The Kalman filter. - cost_matrix : ndarray - The NxM dimensional cost matrix, where N is the number of track indices - and M is the number of detection indices, such that entry (i, j) is the - association cost between `tracks[track_indices[i]]` and - `detections[detection_indices[j]]`. - tracks : List[track.Track] - A list of predicted tracks at the current time step. - detections : List[detection.Detection] - A list of detections at the current time step. - track_indices : List[int] - List of track indices that maps rows in `cost_matrix` to tracks in - `tracks` (see description above). - detection_indices : List[int] - List of detection indices that maps columns in `cost_matrix` to - detections in `detections` (see description above). - gated_cost : Optional[float] - Entries in the cost matrix corresponding to infeasible associations are - set this value. Defaults to a very large value. - only_position : Optional[bool] - If True, only the x, y position of the state distribution is considered - during gating. Defaults to False. - - Returns - ------- - ndarray - Returns the modified cost matrix. - - """ - gating_dim = 2 if only_position else 4 - gating_threshold = kalman_filter.chi2inv95[gating_dim] - measurements = np.asarray( - [detections[i].to_xyah() for i in detection_indices]) - for row, track_idx in enumerate(track_indices): - track = tracks[track_idx] - gating_distance = kf.gating_distance( - track.mean, track.covariance, measurements, only_position) - cost_matrix[row, gating_distance > gating_threshold] = gated_cost - return cost_matrix diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/nn_matching.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/nn_matching.py deleted file mode 100644 index 2e7bfea4b87b0c256274937c8323ffc93fa5d61b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/nn_matching.py +++ /dev/null @@ -1,177 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -import numpy as np - - -def _pdist(a, b): - """Compute pair-wise squared distance between points in `a` and `b`. - - Parameters - ---------- - a : array_like - An NxM matrix of N samples of dimensionality M. - b : array_like - An LxM matrix of L samples of dimensionality M. - - Returns - ------- - ndarray - Returns a matrix of size len(a), len(b) such that eleement (i, j) - contains the squared distance between `a[i]` and `b[j]`. - - """ - a, b = np.asarray(a), np.asarray(b) - if len(a) == 0 or len(b) == 0: - return np.zeros((len(a), len(b))) - a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1) - r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :] - r2 = np.clip(r2, 0., float(np.inf)) - return r2 - - -def _cosine_distance(a, b, data_is_normalized=False): - """Compute pair-wise cosine distance between points in `a` and `b`. - - Parameters - ---------- - a : array_like - An NxM matrix of N samples of dimensionality M. - b : array_like - An LxM matrix of L samples of dimensionality M. - data_is_normalized : Optional[bool] - If True, assumes rows in a and b are unit length vectors. - Otherwise, a and b are explicitly normalized to lenght 1. - - Returns - ------- - ndarray - Returns a matrix of size len(a), len(b) such that eleement (i, j) - contains the squared distance between `a[i]` and `b[j]`. - - """ - if not data_is_normalized: - a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True) - b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True) - return 1. - np.dot(a, b.T) - - -def _nn_euclidean_distance(x, y): - """ Helper function for nearest neighbor distance metric (Euclidean). - - Parameters - ---------- - x : ndarray - A matrix of N row-vectors (sample points). - y : ndarray - A matrix of M row-vectors (query points). - - Returns - ------- - ndarray - A vector of length M that contains for each entry in `y` the - smallest Euclidean distance to a sample in `x`. - - """ - distances = _pdist(x, y) - return np.maximum(0.0, distances.min(axis=0)) - - -def _nn_cosine_distance(x, y): - """ Helper function for nearest neighbor distance metric (cosine). - - Parameters - ---------- - x : ndarray - A matrix of N row-vectors (sample points). - y : ndarray - A matrix of M row-vectors (query points). - - Returns - ------- - ndarray - A vector of length M that contains for each entry in `y` the - smallest cosine distance to a sample in `x`. - - """ - distances = _cosine_distance(x, y) - return distances.min(axis=0) - - -class NearestNeighborDistanceMetric(object): - """ - A nearest neighbor distance metric that, for each target, returns - the closest distance to any sample that has been observed so far. - - Parameters - ---------- - metric : str - Either "euclidean" or "cosine". - matching_threshold: float - The matching threshold. Samples with larger distance are considered an - invalid match. - budget : Optional[int] - If not None, fix samples per class to at most this number. Removes - the oldest samples when the budget is reached. - - Attributes - ---------- - samples : Dict[int -> List[ndarray]] - A dictionary that maps from target identities to the list of samples - that have been observed so far. - - """ - - def __init__(self, metric, matching_threshold, budget=None): - - - if metric == "euclidean": - self._metric = _nn_euclidean_distance - elif metric == "cosine": - self._metric = _nn_cosine_distance - else: - raise ValueError( - "Invalid metric; must be either 'euclidean' or 'cosine'") - self.matching_threshold = matching_threshold - self.budget = budget - self.samples = {} - - def partial_fit(self, features, targets, active_targets): - """Update the distance metric with new data. - - Parameters - ---------- - features : ndarray - An NxM matrix of N features of dimensionality M. - targets : ndarray - An integer array of associated target identities. - active_targets : List[int] - A list of targets that are currently present in the scene. - - """ - for feature, target in zip(features, targets): - self.samples.setdefault(target, []).append(feature) - if self.budget is not None: - self.samples[target] = self.samples[target][-self.budget:] - self.samples = {k: self.samples[k] for k in active_targets} - - def distance(self, features, targets): - """Compute distance between features and targets. - - Parameters - ---------- - features : ndarray - An NxM matrix of N features of dimensionality M. - targets : List[int] - A list of targets to match the given `features` against. - - Returns - ------- - ndarray - Returns a cost matrix of shape len(targets), len(features), where - element (i, j) contains the closest squared distance between - `targets[i]` and `features[j]`. - - """ - cost_matrix = np.zeros((len(targets), len(features))) - for i, target in enumerate(targets): - cost_matrix[i, :] = self._metric(self.samples[target], features) - return cost_matrix diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/preprocessing.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/preprocessing.py deleted file mode 100644 index 5493b127f602dec398efac4269c00d31a3650ce9..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/preprocessing.py +++ /dev/null @@ -1,73 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -import numpy as np -import cv2 - - -def non_max_suppression(boxes, max_bbox_overlap, scores=None): - """Suppress overlapping detections. - - Original code from [1]_ has been adapted to include confidence score. - - .. [1] http://www.pyimagesearch.com/2015/02/16/ - faster-non-maximum-suppression-python/ - - Examples - -------- - - >>> boxes = [d.roi for d in detections] - >>> scores = [d.confidence for d in detections] - >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores) - >>> detections = [detections[i] for i in indices] - - Parameters - ---------- - boxes : ndarray - Array of ROIs (x, y, width, height). - max_bbox_overlap : float - ROIs that overlap more than this values are suppressed. - scores : Optional[array_like] - Detector confidence score. - - Returns - ------- - List[int] - Returns indices of detections that have survived non-maxima suppression. - - """ - if len(boxes) == 0: - return [] - - boxes = boxes.astype(np.float) - pick = [] - - x1 = boxes[:, 0] - y1 = boxes[:, 1] - x2 = boxes[:, 2] + boxes[:, 0] - y2 = boxes[:, 3] + boxes[:, 1] - - area = (x2 - x1 + 1) * (y2 - y1 + 1) - if scores is not None: - idxs = np.argsort(scores) - else: - idxs = np.argsort(y2) - - while len(idxs) > 0: - last = len(idxs) - 1 - i = idxs[last] - pick.append(i) - - xx1 = np.maximum(x1[i], x1[idxs[:last]]) - yy1 = np.maximum(y1[i], y1[idxs[:last]]) - xx2 = np.minimum(x2[i], x2[idxs[:last]]) - yy2 = np.minimum(y2[i], y2[idxs[:last]]) - - w = np.maximum(0, xx2 - xx1 + 1) - h = np.maximum(0, yy2 - yy1 + 1) - - overlap = (w * h) / area[idxs[:last]] - - idxs = np.delete( - idxs, np.concatenate( - ([last], np.where(overlap > max_bbox_overlap)[0]))) - - return pick diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/track.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/track.py deleted file mode 100644 index e46d391a1bd7667d3e9fe52d88fe8de51c2e70ac..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/track.py +++ /dev/null @@ -1,168 +0,0 @@ -# vim: expandtab:ts=4:sw=4 - - -class TrackState: - """ - Enumeration type for the single target track state. Newly created tracks are - classified as `tentative` until enough evidence has been collected. Then, - the track state is changed to `confirmed`. Tracks that are no longer alive - are classified as `deleted` to mark them for removal from the set of active - tracks. - - """ - - Tentative = 1 - Confirmed = 2 - Deleted = 3 - - -class Track: - """ - A single target track with state space `(x, y, a, h)` and associated - velocities, where `(x, y)` is the center of the bounding box, `a` is the - aspect ratio and `h` is the height. - - Parameters - ---------- - mean : ndarray - Mean vector of the initial state distribution. - covariance : ndarray - Covariance matrix of the initial state distribution. - track_id : int - A unique track identifier. - n_init : int - Number of consecutive detections before the track is confirmed. The - track state is set to `Deleted` if a miss occurs within the first - `n_init` frames. - max_age : int - The maximum number of consecutive misses before the track state is - set to `Deleted`. - feature : Optional[ndarray] - Feature vector of the detection this track originates from. If not None, - this feature is added to the `features` cache. - - Attributes - ---------- - mean : ndarray - Mean vector of the initial state distribution. - covariance : ndarray - Covariance matrix of the initial state distribution. - track_id : int - A unique track identifier. - hits : int - Total number of measurement updates. - age : int - Total number of frames since first occurance. - time_since_update : int - Total number of frames since last measurement update. - state : TrackState - The current track state. - features : List[ndarray] - A cache of features. On each measurement update, the associated feature - vector is added to this list. - - """ - - def __init__(self, mean, cls_, covariance, track_id, n_init, max_age, - feature=None): - self.mean = mean - self.cls_ = cls_ - self.covariance = covariance - self.track_id = track_id - self.hits = 1 - self.age = 1 - self.time_since_update = 0 - - self.state = TrackState.Tentative - self.features = [] - if feature is not None: - self.features.append(feature) - - self._n_init = n_init - self._max_age = max_age - - def to_tlwh(self): - """Get current position in bounding box format `(top left x, top left y, - width, height)`. - - Returns - ------- - ndarray - The bounding box. - - """ - ret = self.mean[:4].copy() - ret[2] *= ret[3] - ret[:2] -= ret[2:] / 2 - return ret - - def to_tlbr(self): - """Get current position in bounding box format `(min x, miny, max x, - max y)`. - - Returns - ------- - ndarray - The bounding box. - - """ - ret = self.to_tlwh() - ret[2:] = ret[:2] + ret[2:] - return ret - - def predict(self, kf): - """Propagate the state distribution to the current time step using a - Kalman filter prediction step. - - Parameters - ---------- - kf : kalman_filter.KalmanFilter - The Kalman filter. - - """ - self.mean, self.covariance = kf.predict(self.mean, self.covariance) - self.age += 1 - self.time_since_update += 1 - - def update(self, kf, detection): - """Perform Kalman filter measurement update step and update the feature - cache. - - Parameters - ---------- - kf : kalman_filter.KalmanFilter - The Kalman filter. - detection : Detection - The associated detection. - - """ - self.mean, self.covariance = kf.update( - self.mean, self.covariance, detection.to_xyah()) - self.features.append(detection.feature) - self.cls_ = detection.cls_ - - self.hits += 1 - self.time_since_update = 0 - if self.state == TrackState.Tentative and self.hits >= self._n_init: - self.state = TrackState.Confirmed - - def mark_missed(self): - """Mark this track as missed (no association at the current time step). - """ - if self.state == TrackState.Tentative: - self.state = TrackState.Deleted - elif self.time_since_update > self._max_age: - self.state = TrackState.Deleted - - def is_tentative(self): - """Returns True if this track is tentative (unconfirmed). - """ - return self.state == TrackState.Tentative - - def is_confirmed(self): - """Returns True if this track is confirmed.""" - return self.state == TrackState.Confirmed - - def is_deleted(self): - """Returns True if this track is dead and should be deleted.""" - return self.state == TrackState.Deleted diff --git a/Yolov5-Deepsort/deep_sort/deep_sort/sort/tracker.py b/Yolov5-Deepsort/deep_sort/deep_sort/sort/tracker.py deleted file mode 100644 index d27c9b35d298744f43b2466b45b793677712346b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/deep_sort/sort/tracker.py +++ /dev/null @@ -1,435 +0,0 @@ -# vim: expandtab:ts=4:sw=4 -from __future__ import absolute_import -import numpy as np -from . import kalman_filter -from . import linear_assignment -from .detection import Detection -from . import iou_matching -from .track import Track -import rich - -class Tracker: - - def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3, window_size=10): - self.metric = metric - self.max_iou_distance = max_iou_distance - self.max_age = max_age - self.n_init = n_init - - self.kf = kalman_filter.KalmanFilter() - self.tracks = [] - self._next_id = 1 - - - # DDM parameters - self.match_history = [] # Store match history - self.evidence_threshold = 3 # Example threshold - self.window_size = window_size - self.evidence = {} # Initialize evidence dictionary - self.DDM = DDM() - - - def predict(self): - """Propagate track state distributions one time step forward. - - This function should be called once every time step, before `update`. - """ - for track in self.tracks: - track.predict(self.kf) - - def update(self, detections): - """Perform measurement update and track management. - - Parameters - ---------- - detections : List[deep_sort.detection.Detection] - A list of detections at the current time step. - - """ - - # matches, unmatched_tracks, unmatched_detections = self._match(detections) - - # # Record current matches - # self.match_history.append(matches) - # if len(self.match_history) > self.window_size: - # self.match_history.pop(0) - - # # Use evidence accumulation to finalize matches - # final_matches = self.accumulate_evidence(matches) - - # # Update track set with final matches - # for track_idx, detection_idx in final_matches: - - # self.tracks[track_idx].update(self.kf, detections[detection_idx]) - # for track_idx in unmatched_tracks: - # self.tracks[track_idx].mark_missed() - # for detection_idx in unmatched_detections: - # self._initiate_track(detections[detection_idx]) - # self.tracks = [t for t in self.tracks if not t.is_deleted()] - - # # Update distance metric - # active_targets = [t.track_id for t in self.tracks if t.is_confirmed()] - # features, targets = [], [] - # for track in self.tracks: - # if not track.is_confirmed(): - # continue - # features += track.features - # targets += [track.track_id for _ in track.features] - # track.features = [] - # self.metric.partial_fit( - # np.asarray(features), np.asarray(targets), active_targets) - #################################################################################### - # Run matching cascade. - matches, unmatched_tracks, unmatched_detections = \ - self._match(detections) - - #开始为DDM积累证据喵 - self.DDM.add_matches(matches) - self.DDM.add_detections(detections=detections) - self.DDM.add_tracks(self.tracks) - self.DDM.remove_old() - self.DDM.log_info() - final_matches, new_unmatched_tracks, new_unmatched_detections = self.DDM.final_judge() - matches = final_matches - for new_unmatched_track in new_unmatched_tracks: - unmatched_tracks.append(new_unmatched_track) - for new_unmatched_detection in new_unmatched_detections: - unmatched_detections.append(new_unmatched_detection) - #self.DDM.final_judge() - #rich.print(matches,unmatched_detections,unmatched_tracks) - - # Record current matches - self.match_history.append(matches) - if len(self.match_history) > self.window_size: - self.match_history.pop(0) - - # Update track set. - for track_idx, detection_idx in matches: - self.tracks[track_idx].update( - self.kf, detections[detection_idx]) - for track_idx in unmatched_tracks: - self.tracks[track_idx].mark_missed() - for detection_idx in unmatched_detections: - self._initiate_track(detections[detection_idx]) - self.tracks = [t for t in self.tracks if not t.is_deleted()] - - # Update distance metric. - active_targets = [t.track_id for t in self.tracks if t.is_confirmed()] - features, targets = [], [] - for track in self.tracks: - if not track.is_confirmed(): - continue - features += track.features - targets += [track.track_id for _ in track.features] - track.features = [] - self.metric.partial_fit( - np.asarray(features), np.asarray(targets), active_targets) - - def _match(self, detections): - - def gated_metric(tracks, dets, track_indices, detection_indices): - features = np.array([dets[i].feature for i in detection_indices]) - targets = np.array([tracks[i].track_id for i in track_indices]) - cost_matrix = self.metric.distance(features, targets) - cost_matrix = linear_assignment.gate_cost_matrix( - self.kf, cost_matrix, tracks, dets, track_indices, - detection_indices) - - - - return cost_matrix - - # Split track set into confirmed and unconfirmed tracks. - confirmed_tracks = [ - i for i, t in enumerate(self.tracks) if t.is_confirmed()] - unconfirmed_tracks = [ - i for i, t in enumerate(self.tracks) if not t.is_confirmed()] - - # Associate confirmed tracks using appearance features. - matches_a, unmatched_tracks_a, unmatched_detections = \ - linear_assignment.matching_cascade( - gated_metric, self.metric.matching_threshold, self.max_age, - self.tracks, detections, confirmed_tracks) - - # Associate remaining tracks together with unconfirmed tracks using IOU. - iou_track_candidates = unconfirmed_tracks + [ - k for k in unmatched_tracks_a if - self.tracks[k].time_since_update == 1] - unmatched_tracks_a = [ - k for k in unmatched_tracks_a if - self.tracks[k].time_since_update != 1] - matches_b, unmatched_tracks_b, unmatched_detections = \ - linear_assignment.min_cost_matching( - iou_matching.iou_cost, self.max_iou_distance, self.tracks, - detections, iou_track_candidates, unmatched_detections) - matches = matches_a + matches_b - unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b)) - - - - return matches, unmatched_tracks, unmatched_detections - - def _initiate_track(self, detection): - mean, covariance = self.kf.initiate(detection.to_xyah()) - self.tracks.append(Track( - mean, detection.cls_, covariance, self._next_id, self.n_init, self.max_age, - detection.feature)) - self._next_id += 1 - - - - - def accumulate_evidence(self, current_matches): - rich.print(current_matches) - return current_matches - evidence = {} - - # 初始化evidence字典，包含所有可能的匹配对，包括不在current_matches中的 - for past_matches in reversed(self.match_history): - for track_idx, det_idx in past_matches: - evidence[(track_idx, det_idx)] = evidence.get((track_idx, det_idx), 0) - - # 初始化current_matches中的匹配对 - for track_idx, det_idx in current_matches: - evidence[(track_idx, det_idx)] = 1 # 初始分数为1 - - # 积累证据 - for past_matches in reversed(self.match_history): - for track_idx, det_idx in past_matches: - evidence[(track_idx, det_idx)] += 10 # 如果在过去的匹配中出现过，则增加1分 - - # 调整证据 - for key in evidence: - evidence[key] -= 0.2 # 所有匹配对减去0.2分 - - # 过滤匹配 - final_matches = [ - (track_idx, det_idx) for (track_idx, det_idx), score in evidence.items() - if score >= self.evidence_threshold - ] - - return final_matches - - -class DDM: - def __init__(self, history_lenth = 10, threshold = 5, evidence_rate = 1): - self.history_lenth = history_lenth - self.threshold = threshold - self.evidence_rate = evidence_rate - self.history_detections =[] - self.history_tracks =[] - self.history_matches = [] - self.frame_id = 0 - - def add_detections(self, detections): - if detections is None: - detections = [] - self.history_detections.append(detections) - # self.remove_old() - - def add_matches(self, matches): - if matches is None: - matches = [] - self.history_matches.append(matches) - # self.remove_old() - - def add_tracks(self, tracks): - if tracks is None: - tracks = [] - self.history_tracks.append(tracks) - # self.remove_old() - - def log_info(self): - # rich.print("DDM当前长度",len(self.history_matches)) - # rich.print("DDM历史匹配：",self.history_matches[-1]) - # rich.print("DDM历史Detecions：",self.history_detections[-1]) - # rich.print("DDM历史Tracks:", self.history_tracks[-1]) - # rich.print("随便一组的tlbr") - if self.frame_id>5: - self.total_detection_match() - rich.print("-------------------------------------------------------------") - self.total_track_match() - #self.final_judge() - self.frame_id = self.frame_id+1 - - - - - def remove_old(self): - if len(self.history_detections)!=len(self.history_matches) or len(self.history_detections)!=len(self.history_tracks): - rich.print("DDM历史长度不匹配，请检查代码捏qaq") - rich.print("Detection长度", len(self.history_detections)) - rich.print("Matches长度", len(self.history_matches)) - rich.print("Tracks长度", len(self.history_tracks)) - - while True: - if len(self.history_matches) > self.history_lenth: - self.history_matches.pop(0) - self.history_detections.pop(0) - self.history_tracks.pop(0) - else: - break - - def total_detection_match(self): - #current_detection = self.history_detections[-1] - output = [] - for current_detection in self.history_detections[-1]: - detection_result=[] - for track in self.history_tracks: - track_matches = [] - for detection in track: - match = self.iou_match(current_detection, detection) - track_matches.append(match) - detection_result.append(track_matches) - output.append(detection_result) - #rich.print(output) - return output - - def total_track_match(self): - output = [] - for current_track in self.history_tracks[-1]: - detection_result=[] - for tracks in self.history_tracks: - track_matches = [] - for track in tracks: - match = self.iou_match(current_track, track) - track_matches.append(match) - detection_result.append(track_matches) - output.append(detection_result) - # rich.print(output) - return output - - - - - def final_judge(self):#最终裁决 - if self.frame_id<10: - return self.history_matches[-1],[],[] - current_detections = self.history_detections[-1] - current_tracks = self.history_tracks[-1] - current_matches = self.history_matches - iou_detection_history_tracks_result = self.total_detection_match() - iou_track_history_tracks_result = self.total_track_match() - - iou_detection_history_tracks_result = [sublist[::-1] for sublist in iou_detection_history_tracks_result] - iou_track_history_tracks_result = [sublist[::-1] for sublist in iou_track_history_tracks_result] - #iou_detection_history_tracks_result.reverse() - #iou_track_history_tracks_result.reverse() - - - evidence = {} - - #证据初始化捏 - - # final_evidence = 0 - - for now_detection_id, now_detection_id_detections in enumerate(iou_detection_history_tracks_result): - for now_track_id, now_track_id_tracks in enumerate(iou_track_history_tracks_result): - result_matrix = self.logical_and_lists(now_detection_id_detections, now_track_id_tracks) - true_count = sum(sum(row) for row in result_matrix) - #rich.print(true_count) - evidence[(now_track_id,now_detection_id)] = self.evidence_rate*true_count - - # rich.print(current_matches[-1]) - # rich.print(evidence) - # rich.print(iou_detection_history_tracks_result[-1]) - rich.print(len(iou_detection_history_tracks_result)) - new_unmatched_tracks = [] - new_unmatched_detections = [] - for (track,detection) in current_matches[-1]: - if evidence[(track, detection)] <1: - #pass - new_unmatched_tracks.append(track) - new_unmatched_detections.append(detection) - #rich.print('niuniu', match) - - - - fianl_matches = [match for match in current_matches[-1] if evidence[match] >= 1] - return fianl_matches, new_unmatched_tracks, new_unmatched_detections - - - - - - - # rich.print(iou_track_history_tracks_result) - # for i in range(len(iou_detection_history_tracks_result)): - # for detections_ids,detections in enumerate(iou_detection_history_tracks_result[i]): - # for tracks_ids, tracks in enumerate(iou_track_history_tracks_result[i]): - # for match_id, match in enumerate(current_matches): - # for detection_id, detection in enumerate(detections): - # for track_id, track in enumerate(tracks): - # if(track == True) and (detection == True) and (detections_ids == tracks_ids) : - # final_evidence += self.evidence_rate + final_evidence - - - # if(detection == True) and (track == True): - # final_evidence +=self.evidence_rate - #rich.print(detection, track, match, "牛子") - - #rich.print("当前得分：", final_evidence) - - - - - - - - - def iou_match(self, detection, track ):#单组的detect与track的iou匹配哦 - ##rich.print(detection.to_tlbr()) - ##rich.print(track.to_tlbr()) - det_tlbr = detection.to_tlbr() - track_tlbr = track.to_tlbr() - - # Calculate the intersection coordinates - x_left = max(det_tlbr[0], track_tlbr[0]) - y_top = max(det_tlbr[1], track_tlbr[1]) - x_right = min(det_tlbr[2], track_tlbr[2]) - y_bottom = min(det_tlbr[3], track_tlbr[3]) - - # Calculate the area of intersection - if x_right < x_left or y_bottom < y_top: - return False # No overlap - - intersection_area = (x_right - x_left) * (y_bottom - y_top) - - # Calculate the area of both rectangles - det_area = (det_tlbr[2] - det_tlbr[0]) * (det_tlbr[3] - det_tlbr[1]) - track_area = (track_tlbr[2] - track_tlbr[0]) * (track_tlbr[3] - track_tlbr[1]) - - # Calculate the union area - union_area = det_area + track_area - intersection_area - - # Calculate the IoU - iou = intersection_area / union_area - - if iou> 0.1: - return True - - - return False - - - def logical_and_lists(self, list1, list2): - if len(list1) != len(list2) or len(list1[0]) != len(list2[0]): - return "Error: Input lists are not of the same size" - - result = [[list1[i][j] and list2[i][j] for j in range(len(list1[i]))] for i in range(len(list1))] - - return result - - - - - - - - - - - - - - \ No newline at end of file diff --git a/Yolov5-Deepsort/deep_sort/utils/__init__.py b/Yolov5-Deepsort/deep_sort/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Yolov5-Deepsort/deep_sort/utils/__pycache__/__init__.cpython-36.pyc b/Yolov5-Deepsort/deep_sort/utils/__pycache__/__init__.cpython-36.pyc deleted file mode 100644 index 8b78a3774e297bfa3a92ff2ae77f138373a30f67..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/utils/__pycache__/__init__.cpython-36.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/utils/__pycache__/__init__.cpython-37.pyc b/Yolov5-Deepsort/deep_sort/utils/__pycache__/__init__.cpython-37.pyc deleted file mode 100644 index cab1729ff02daa25ac6cb7308e45fcc703a877be..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/utils/__pycache__/__init__.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/utils/__pycache__/parser.cpython-36.pyc b/Yolov5-Deepsort/deep_sort/utils/__pycache__/parser.cpython-36.pyc deleted file mode 100644 index b5c82da3dbebf17fc41a3c56f9c8f9bdbe0bc8e0..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/utils/__pycache__/parser.cpython-36.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/utils/__pycache__/parser.cpython-37.pyc b/Yolov5-Deepsort/deep_sort/utils/__pycache__/parser.cpython-37.pyc deleted file mode 100644 index 875847c85eb0f2b8c05dd41a3d4651a69e2e690a..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/deep_sort/utils/__pycache__/parser.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/deep_sort/utils/asserts.py b/Yolov5-Deepsort/deep_sort/utils/asserts.py deleted file mode 100644 index 59a73cc04025762d6490fcd2945a747d963def32..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/asserts.py +++ /dev/null @@ -1,13 +0,0 @@ -from os import environ - - -def assert_in(file, files_to_check): - if file not in files_to_check: - raise AssertionError("{} does not exist in the list".format(str(file))) - return True - - -def assert_in_env(check_list: list): - for item in check_list: - assert_in(item, environ.keys()) - return True diff --git a/Yolov5-Deepsort/deep_sort/utils/draw.py b/Yolov5-Deepsort/deep_sort/utils/draw.py deleted file mode 100644 index bc7cb537978e86805d5d9789785a8afe67df9030..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/draw.py +++ /dev/null @@ -1,36 +0,0 @@ -import numpy as np -import cv2 - -palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) - - -def compute_color_for_labels(label): - """ - Simple function that adds fixed color depending on the class - """ - color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] - return tuple(color) - - -def draw_boxes(img, bbox, identities=None, offset=(0,0)): - for i,box in enumerate(bbox): - x1,y1,x2,y2 = [int(i) for i in box] - x1 += offset[0] - x2 += offset[0] - y1 += offset[1] - y2 += offset[1] - # box text and bar - id = int(identities[i]) if identities is not None else 0 - color = compute_color_for_labels(id) - label = '{}{:d}'.format("", id) - t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0] - cv2.rectangle(img,(x1, y1),(x2,y2),color,3) - cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1) - cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2) - return img - - - -if __name__ == '__main__': - for i in range(82): - print(compute_color_for_labels(i)) diff --git a/Yolov5-Deepsort/deep_sort/utils/evaluation.py b/Yolov5-Deepsort/deep_sort/utils/evaluation.py deleted file mode 100644 index 100179407181933d59809b25400d115cfa789867..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/evaluation.py +++ /dev/null @@ -1,103 +0,0 @@ -import os -import numpy as np -import copy -import motmetrics as mm -mm.lap.default_solver = 'lap' -from utils.io import read_results, unzip_objs - - -class Evaluator(object): - - def __init__(self, data_root, seq_name, data_type): - self.data_root = data_root - self.seq_name = seq_name - self.data_type = data_type - - self.load_annotations() - self.reset_accumulator() - - def load_annotations(self): - assert self.data_type == 'mot' - - gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt') - self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True) - self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True) - - def reset_accumulator(self): - self.acc = mm.MOTAccumulator(auto_id=True) - - def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False): - # results - trk_tlwhs = np.copy(trk_tlwhs) - trk_ids = np.copy(trk_ids) - - # gts - gt_objs = self.gt_frame_dict.get(frame_id, []) - gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2] - - # ignore boxes - ignore_objs = self.gt_ignore_frame_dict.get(frame_id, []) - ignore_tlwhs = unzip_objs(ignore_objs)[0] - - - # remove ignored results - keep = np.ones(len(trk_tlwhs), dtype=bool) - iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5) - if len(iou_distance) > 0: - match_is, match_js = mm.lap.linear_sum_assignment(iou_distance) - match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js]) - match_ious = iou_distance[match_is, match_js] - - match_js = np.asarray(match_js, dtype=int) - match_js = match_js[np.logical_not(np.isnan(match_ious))] - keep[match_js] = False - trk_tlwhs = trk_tlwhs[keep] - trk_ids = trk_ids[keep] - - # get distance matrix - iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5) - - # acc - self.acc.update(gt_ids, trk_ids, iou_distance) - - if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'): - events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics - else: - events = None - return events - - def eval_file(self, filename): - self.reset_accumulator() - - result_frame_dict = read_results(filename, self.data_type, is_gt=False) - frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys()))) - for frame_id in frames: - trk_objs = result_frame_dict.get(frame_id, []) - trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2] - self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False) - - return self.acc - - @staticmethod - def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')): - names = copy.deepcopy(names) - if metrics is None: - metrics = mm.metrics.motchallenge_metrics - metrics = copy.deepcopy(metrics) - - mh = mm.metrics.create() - summary = mh.compute_many( - accs, - metrics=metrics, - names=names, - generate_overall=True - ) - - return summary - - @staticmethod - def save_summary(summary, filename): - import pandas as pd - writer = pd.ExcelWriter(filename) - summary.to_excel(writer) - writer.save() diff --git a/Yolov5-Deepsort/deep_sort/utils/io.py b/Yolov5-Deepsort/deep_sort/utils/io.py deleted file mode 100644 index 2dc9afd24019cd930eef6c21ab9f579313dd3b3a..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/io.py +++ /dev/null @@ -1,133 +0,0 @@ -import os -from typing import Dict -import numpy as np - -# from utils.log import get_logger - - -def write_results(filename, results, data_type): - if data_type == 'mot': - save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n' - elif data_type == 'kitti': - save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' - else: - raise ValueError(data_type) - - with open(filename, 'w') as f: - for frame_id, tlwhs, track_ids in results: - if data_type == 'kitti': - frame_id -= 1 - for tlwh, track_id in zip(tlwhs, track_ids): - if track_id < 0: - continue - x1, y1, w, h = tlwh - x2, y2 = x1 + w, y1 + h - line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h) - f.write(line) - - -# def write_results(filename, results_dict: Dict, data_type: str): -# if not filename: -# return -# path = os.path.dirname(filename) -# if not os.path.exists(path): -# os.makedirs(path) - -# if data_type in ('mot', 'mcmot', 'lab'): -# save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n' -# elif data_type == 'kitti': -# save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n' -# else: -# raise ValueError(data_type) - -# with open(filename, 'w') as f: -# for frame_id, frame_data in results_dict.items(): -# if data_type == 'kitti': -# frame_id -= 1 -# for tlwh, track_id in frame_data: -# if track_id < 0: -# continue -# x1, y1, w, h = tlwh -# x2, y2 = x1 + w, y1 + h -# line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0) -# f.write(line) -# logger.info('Save results to {}'.format(filename)) - - -def read_results(filename, data_type: str, is_gt=False, is_ignore=False): - if data_type in ('mot', 'lab'): - read_fun = read_mot_results - else: - raise ValueError('Unknown data type: {}'.format(data_type)) - - return read_fun(filename, is_gt, is_ignore) - - -""" -labels={'ped', ... % 1 -'person_on_vhcl', ... % 2 -'car', ... % 3 -'bicycle', ... % 4 -'mbike', ... % 5 -'non_mot_vhcl', ... % 6 -'static_person', ... % 7 -'distractor', ... % 8 -'occluder', ... % 9 -'occluder_on_grnd', ... %10 -'occluder_full', ... % 11 -'reflection', ... % 12 -'crowd' ... % 13 -}; -""" - - -def read_mot_results(filename, is_gt, is_ignore): - valid_labels = {1} - ignore_labels = {2, 7, 8, 12} - results_dict = dict() - if os.path.isfile(filename): - with open(filename, 'r') as f: - for line in f.readlines(): - linelist = line.split(',') - if len(linelist) < 7: - continue - fid = int(linelist[0]) - if fid < 1: - continue - results_dict.setdefault(fid, list()) - - if is_gt: - if 'MOT16-' in filename or 'MOT17-' in filename: - label = int(float(linelist[7])) - mark = int(float(linelist[6])) - if mark == 0 or label not in valid_labels: - continue - score = 1 - elif is_ignore: - if 'MOT16-' in filename or 'MOT17-' in filename: - label = int(float(linelist[7])) - vis_ratio = float(linelist[8]) - if label not in ignore_labels and vis_ratio >= 0: - continue - else: - continue - score = 1 - else: - score = float(linelist[6]) - - tlwh = tuple(map(float, linelist[2:6])) - target_id = int(linelist[1]) - - results_dict[fid].append((tlwh, target_id, score)) - - return results_dict - - -def unzip_objs(objs): - if len(objs) > 0: - tlwhs, ids, scores = zip(*objs) - else: - tlwhs, ids, scores = [], [], [] - tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4) - - return tlwhs, ids, scores \ No newline at end of file diff --git a/Yolov5-Deepsort/deep_sort/utils/json_logger.py b/Yolov5-Deepsort/deep_sort/utils/json_logger.py deleted file mode 100644 index 0afd0b45df736866c49473db78286685d77660ac..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/json_logger.py +++ /dev/null @@ -1,383 +0,0 @@ -""" -References: - https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f -""" -import json -from os import makedirs -from os.path import exists, join -from datetime import datetime - - -class JsonMeta(object): - HOURS = 3 - MINUTES = 59 - SECONDS = 59 - PATH_TO_SAVE = 'LOGS' - DEFAULT_FILE_NAME = 'remaining' - - -class BaseJsonLogger(object): - """ - This is the base class that returns __dict__ of its own - it also returns the dicts of objects in the attributes that are list instances - - """ - - def dic(self): - # returns dicts of objects - out = {} - for k, v in self.__dict__.items(): - if hasattr(v, 'dic'): - out[k] = v.dic() - elif isinstance(v, list): - out[k] = self.list(v) - else: - out[k] = v - return out - - @staticmethod - def list(values): - # applies the dic method on items in the list - return [v.dic() if hasattr(v, 'dic') else v for v in values] - - -class Label(BaseJsonLogger): - """ - For each bounding box there are various categories with confidences. Label class keeps track of that information. - """ - - def __init__(self, category: str, confidence: float): - self.category = category - self.confidence = confidence - - -class Bbox(BaseJsonLogger): - """ - This module stores the information for each frame and use them in JsonParser - Attributes: - labels (list): List of label module. - top (int): - left (int): - width (int): - height (int): - - Args: - bbox_id (float): - top (int): - left (int): - width (int): - height (int): - - References: - Check Label module for better understanding. - - - """ - - def __init__(self, bbox_id, top, left, width, height): - self.labels = [] - self.bbox_id = bbox_id - self.top = top - self.left = left - self.width = width - self.height = height - - def add_label(self, category, confidence): - # adds category and confidence only if top_k is not exceeded. - self.labels.append(Label(category, confidence)) - - def labels_full(self, value): - return len(self.labels) == value - - -class Frame(BaseJsonLogger): - """ - This module stores the information for each frame and use them in JsonParser - Attributes: - timestamp (float): The elapsed time of captured frame - frame_id (int): The frame number of the captured video - bboxes (list of Bbox objects): Stores the list of bbox objects. - - References: - Check Bbox class for better information - - Args: - timestamp (float): - frame_id (int): - - """ - - def __init__(self, frame_id: int, timestamp: float = None): - self.frame_id = frame_id - self.timestamp = timestamp - self.bboxes = [] - - def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int): - bboxes_ids = [bbox.bbox_id for bbox in self.bboxes] - if bbox_id not in bboxes_ids: - self.bboxes.append(Bbox(bbox_id, top, left, width, height)) - else: - raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id)) - - def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float): - bboxes = {bbox.id: bbox for bbox in self.bboxes} - if bbox_id in bboxes.keys(): - res = bboxes.get(bbox_id) - res.add_label(category, confidence) - else: - raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id)) - - -class BboxToJsonLogger(BaseJsonLogger): - """ - ُ This module is designed to automate the task of logging jsons. An example json is used - to show the contents of json file shortly - Example: - { - "video_details": { - "frame_width": 1920, - "frame_height": 1080, - "frame_rate": 20, - "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi" - }, - "frames": [ - { - "frame_id": 329, - "timestamp": 3365.1254 - "bboxes": [ - { - "labels": [ - { - "category": "pedestrian", - "confidence": 0.9 - } - ], - "bbox_id": 0, - "top": 1257, - "left": 138, - "width": 68, - "height": 109 - } - ] - }], - - Attributes: - frames (dict): It's a dictionary that maps each frame_id to json attributes. - video_details (dict): information about video file. - top_k_labels (int): shows the allowed number of labels - start_time (datetime object): we use it to automate the json output by time. - - Args: - top_k_labels (int): shows the allowed number of labels - - """ - - def __init__(self, top_k_labels: int = 1): - self.frames = {} - self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None, - video_name=None) - self.top_k_labels = top_k_labels - self.start_time = datetime.now() - - def set_top_k(self, value): - self.top_k_labels = value - - def frame_exists(self, frame_id: int) -> bool: - """ - Args: - frame_id (int): - - Returns: - bool: true if frame_id is recognized - """ - return frame_id in self.frames.keys() - - def add_frame(self, frame_id: int, timestamp: float = None) -> None: - """ - Args: - frame_id (int): - timestamp (float): opencv captured frame time property - - Raises: - ValueError: if frame_id would not exist in class frames attribute - - Returns: - None - - """ - if not self.frame_exists(frame_id): - self.frames[frame_id] = Frame(frame_id, timestamp) - else: - raise ValueError("Frame id: {} already exists".format(frame_id)) - - def bbox_exists(self, frame_id: int, bbox_id: int) -> bool: - """ - Args: - frame_id: - bbox_id: - - Returns: - bool: if bbox exists in frame bboxes list - """ - bboxes = [] - if self.frame_exists(frame_id=frame_id): - bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes] - return bbox_id in bboxes - - def find_bbox(self, frame_id: int, bbox_id: int): - """ - - Args: - frame_id: - bbox_id: - - Returns: - bbox_id (int): - - Raises: - ValueError: if bbox_id does not exist in the bbox list of specific frame. - """ - if not self.bbox_exists(frame_id, bbox_id): - raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id)) - bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes} - return bboxes.get(bbox_id) - - def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None: - """ - - Args: - frame_id (int): - bbox_id (int): - top (int): - left (int): - width (int): - height (int): - - Returns: - None - - Raises: - ValueError: if bbox_id already exist in frame information with frame_id - ValueError: if frame_id does not exist in frames attribute - """ - if self.frame_exists(frame_id): - frame = self.frames[frame_id] - if not self.bbox_exists(frame_id, bbox_id): - frame.add_bbox(bbox_id, top, left, width, height) - else: - raise ValueError( - "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id)) - else: - raise ValueError("frame with frame_id: {} does not exist".format(frame_id)) - - def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float): - """ - Args: - frame_id: - bbox_id: - category: - confidence: the confidence value returned from yolo detection - - Returns: - None - - Raises: - ValueError: if labels quota (top_k_labels) exceeds. - """ - bbox = self.find_bbox(frame_id, bbox_id) - if not bbox.labels_full(self.top_k_labels): - bbox.add_label(category, confidence) - else: - raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id)) - - def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None, - video_name: str = None): - self.video_details['frame_width'] = frame_width - self.video_details['frame_height'] = frame_height - self.video_details['frame_rate'] = frame_rate - self.video_details['video_name'] = video_name - - def output(self): - output = {'video_details': self.video_details} - result = list(self.frames.values()) - output['frames'] = [item.dic() for item in result] - return output - - def json_output(self, output_name): - """ - Args: - output_name: - - Returns: - None - - Notes: - It creates the json output with `output_name` name. - """ - if not output_name.endswith('.json'): - output_name += '.json' - with open(output_name, 'w') as file: - json.dump(self.output(), file) - file.close() - - def set_start(self): - self.start_time = datetime.now() - - def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0, - seconds: int = 60) -> None: - """ - Notes: - Creates folder and then periodically stores the jsons on that address. - - Args: - output_dir (str): the directory where output files will be stored - hours (int): - minutes (int): - seconds (int): - - Returns: - None - - """ - end = datetime.now() - interval = 0 - interval += abs(min([hours, JsonMeta.HOURS]) * 3600) - interval += abs(min([minutes, JsonMeta.MINUTES]) * 60) - interval += abs(min([seconds, JsonMeta.SECONDS])) - diff = (end - self.start_time).seconds - - if diff > interval: - output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json' - if not exists(output_dir): - makedirs(output_dir) - output = join(output_dir, output_name) - self.json_output(output_name=output) - self.frames = {} - self.start_time = datetime.now() - - def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE): - """ - saves as the number of frames quota increases higher. - :param frames_quota: - :param frame_counter: - :param output_dir: - :return: - """ - pass - - def flush(self, output_dir): - """ - Notes: - We use this function to output jsons whenever possible. - like the time that we exit the while loop of opencv. - - Args: - output_dir: - - Returns: - None - - """ - filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json' - output = join(output_dir, filename) - self.json_output(output_name=output) diff --git a/Yolov5-Deepsort/deep_sort/utils/log.py b/Yolov5-Deepsort/deep_sort/utils/log.py deleted file mode 100644 index 0d48757dca88f35e9ea2cd1ca16e41bac9976a45..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/log.py +++ /dev/null @@ -1,17 +0,0 @@ -import logging - - -def get_logger(name='root'): - formatter = logging.Formatter( - # fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s') - fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') - - handler = logging.StreamHandler() - handler.setFormatter(formatter) - - logger = logging.getLogger(name) - logger.setLevel(logging.INFO) - logger.addHandler(handler) - return logger - - diff --git a/Yolov5-Deepsort/deep_sort/utils/parser.py b/Yolov5-Deepsort/deep_sort/utils/parser.py deleted file mode 100644 index c217ecb2c8c2b9a3c8cc10e2be2f149aba326b60..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/parser.py +++ /dev/null @@ -1,38 +0,0 @@ -import os -import yaml -from easydict import EasyDict as edict - -class YamlParser(edict): - """ - This is yaml parser based on EasyDict. - """ - def __init__(self, cfg_dict=None, config_file=None): - if cfg_dict is None: - cfg_dict = {} - - if config_file is not None: - assert(os.path.isfile(config_file)) - with open(config_file, 'r') as fo: - cfg_dict.update(yaml.safe_load(fo.read())) - - super(YamlParser, self).__init__(cfg_dict) - - - def merge_from_file(self, config_file): - with open(config_file, 'r') as fo: - self.update(yaml.safe_load(fo.read())) - - - def merge_from_dict(self, config_dict): - self.update(config_dict) - - -def get_config(config_file=None): - return YamlParser(config_file=config_file) - - -if __name__ == "__main__": - cfg = YamlParser(config_file="../configs/yolov3.yaml") - cfg.merge_from_file("../configs/deep_sort.yaml") - - import ipdb; ipdb.set_trace() \ No newline at end of file diff --git a/Yolov5-Deepsort/deep_sort/utils/tools.py b/Yolov5-Deepsort/deep_sort/utils/tools.py deleted file mode 100644 index 965fb69c2df41510fd740a4ab57d8fc7b81012de..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/deep_sort/utils/tools.py +++ /dev/null @@ -1,39 +0,0 @@ -from functools import wraps -from time import time - - -def is_video(ext: str): - """ - Returns true if ext exists in - allowed_exts for video files. - - Args: - ext: - - Returns: - - """ - - allowed_exts = ('.mp4', '.webm', '.ogg', '.avi', '.wmv', '.mkv', '.3gp') - return any((ext.endswith(x) for x in allowed_exts)) - - -def tik_tok(func): - """ - keep track of time for each process. - Args: - func: - - Returns: - - """ - @wraps(func) - def _time_it(*args, **kwargs): - start = time() - try: - return func(*args, **kwargs) - finally: - end_ = time() - print("time: {:.03f}s, fps: {:.03f}".format(end_ - start, 1 / (end_ - start))) - - return _time_it diff --git a/Yolov5-Deepsort/demo.py b/Yolov5-Deepsort/demo.py deleted file mode 100644 index 3c68e34a2d00daec71f4eee6ca31263ec1591b8b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/demo.py +++ /dev/null @@ -1,112 +0,0 @@ -from AIDetector_pytorch import Detector -import imutils -import rich -import cv2 -import time - -def main(): - - name = 'demo' - - det = Detector() - cap = cv2.VideoCapture('mot.mp4') - fps = int(cap.get(5)) - print('fps:', fps) - t = int(1000/fps) - frame_count = 0 - total_time = 0 - - videoWriter = None - - while True: - - start_time = time.time() - - # try: - _, im = cap.read() - if im is None: - break - #rich.print("im:",im) - result = det.feedCap(im) - #rich.print(result) - result = result['frame'] - result = imutils.resize(result, height=500) - if videoWriter is None: - fourcc = cv2.VideoWriter_fourcc( - 'm', 'p', '4', 'v') # opencv3.0 - videoWriter = cv2.VideoWriter( - 'result.mp4', fourcc, fps, (result.shape[1], result.shape[0])) - - videoWriter.write(result) - cv2.imshow(name, result) - cv2.waitKey(t) - - end_time = time.time() - total_time += (end_time - start_time) - frame_count +=1 - - if frame_count > 0: - processing_fps = frame_count / total_time - rich.print('Processing fps:', processing_fps) - - - if cv2.getWindowProperty(name, cv2.WND_PROP_AUTOSIZE) < 1: - # 点x退出 - break - # except Exception as e: - # print(e) - # break - - cap.release() - videoWriter.release() - cv2.destroyAllWindows() - - -def app_main(video): - name = 'demo' - - det = Detector() - cap = cv2.VideoCapture(video) - fps = int(cap.get(cv2.CAP_PROP_FPS)) - print('fps:', fps) - t = int(1000/fps) - frame_count = 0 - total_time = 0 - - videoWriter = None - output_filename = 'result.mp4' - - while True: - start_time = time.time() - - ret, im = cap.read() - if not ret: - break - - result = det.feedCap(im) - result = result['frame'] - result = imutils.resize(result, height=500) - - if videoWriter is None: - fourcc = cv2.VideoWriter_fourcc(*'mp4v') - videoWriter = cv2.VideoWriter( - output_filename, fourcc, fps, (result.shape[1], result.shape[0])) - - videoWriter.write(result) - - end_time = time.time() - total_time += (end_time - start_time) - frame_count += 1 - - if frame_count > 0: - processing_fps = frame_count / total_time - print('Processing fps:', processing_fps) - - cap.release() - videoWriter.release() - - return output_filename - -if __name__ == '__main__': - - main() \ No newline at end of file diff --git a/Yolov5-Deepsort/eval.py b/Yolov5-Deepsort/eval.py deleted file mode 100644 index 52af1ee1aed9cad895e3aa71c4bc8f7e8e6d3bb6..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/eval.py +++ /dev/null @@ -1,33 +0,0 @@ -import motmetrics as mm -import pandas as pd - -# 读取ground truth和结果文件 -gt = pd.read_csv('/home/suml/docker/su/Yolov5-Deepsort/gt.txt', header=None) -res = pd.read_csv('/home/suml/docker/su/Yolov5-Deepsort/gt.txt', header=None) - -# 创建一个accumulator对象 -acc = mm.MOTAccumulator(auto_id=True) - -# 假设文件格式为：, , , , , , , , -for frame in gt[0].unique(): - gt_frame = gt[gt[0] == frame] - res_frame = res[res[0] == frame] - - # 提取ID和位置 - gt_ids = gt_frame[1].values - gt_positions = gt_frame[[2, 3, 4, 5]].values - - res_ids = res_frame[1].values - res_positions = res_frame[[2, 3, 4, 5]].values - - # 更新accumulator - acc.update( - gt_ids, res_ids, - mm.distances.iou_matrix(gt_positions, res_positions, max_iou=0.5) - ) - -# 计算指标 -mh = mm.metrics.create() -summary = mh.compute(acc, metrics=['idf1', 'mota', 'motp'], name='DeepSORT') - -print(summary) diff --git a/Yolov5-Deepsort/gt.txt b/Yolov5-Deepsort/gt.txt deleted file mode 100644 index f8c1ad7181ccf4b07620a53afd98e7b61045d709..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/gt.txt +++ /dev/null @@ -1,18718 +0,0 @@ -1,1,1376,485,37,28,0,11,1 -2,1,1379,486,37,28,0,11,1 -3,1,1382,487,38,29,0,11,1 -4,1,1386,488,38,29,0,11,1 -5,1,1389,490,38,29,0,11,1 -6,1,1393,491,38,30,0,11,1 -7,1,1396,492,39,30,0,11,1 -8,1,1399,494,39,30,0,11,1 -9,1,1403,495,39,30,0,11,1 -10,1,1406,496,40,31,0,11,1 -11,1,1410,498,40,31,0,11,1 -12,1,1413,497,40,31,0,11,1 -13,1,1416,497,40,31,0,11,1 -14,1,1419,497,40,31,0,11,1 -15,1,1422,497,40,31,0,11,1 -16,1,1426,497,39,31,0,11,1 -17,1,1429,496,39,31,0,11,1 -18,1,1432,496,39,31,0,11,1 -19,1,1435,496,39,31,0,11,1 -20,1,1438,496,39,31,0,11,1 -21,1,1442,496,39,31,0,11,1 -22,1,1445,497,39,32,0,11,1 -23,1,1448,499,40,32,0,11,1 -24,1,1451,501,41,32,0,11,1 -25,1,1454,503,41,32,0,11,1 -26,1,1458,505,41,32,0,11,1 -27,1,1461,507,42,32,0,11,1 -28,1,1464,509,42,32,0,11,1 -29,1,1467,511,43,32,0,11,1 -30,1,1470,513,44,32,0,11,1 -31,1,1474,515,44,33,0,11,1 -32,1,1476,518,45,36,0,11,1 -33,1,1476,508,45,36,0,11,1 -34,1,1488,509,46,36,0,11,1 -35,1,1488,509,46,36,0,11,1 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b/Yolov5-Deepsort/models/__pycache__/yolo.cpython-37.pyc deleted file mode 100644 index 7c4abfe75e25b31af450916e37b66ea4aeb10c72..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/models/__pycache__/yolo.cpython-37.pyc and /dev/null differ diff --git a/Yolov5-Deepsort/models/common.py b/Yolov5-Deepsort/models/common.py deleted file mode 100644 index 4211db406c3d85e652bc11280cf9856f500ef835..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/common.py +++ /dev/null @@ -1,395 +0,0 @@ -# YOLOv5 common modules - -import math -from copy import copy -from pathlib import Path - -import numpy as np -import pandas as pd -import requests -import torch -import torch.nn as nn -from PIL import Image -from torch.cuda import amp - -from utils.datasets import letterbox -from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box -from utils.plots import colors, plot_one_box -from utils.torch_utils import time_synchronized - - -def autopad(k, p=None): # kernel, padding - # Pad to 'same' - if p is None: - p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad - return p - - -def DWConv(c1, c2, k=1, s=1, act=True): - # Depthwise convolution - return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) - - -class Conv(nn.Module): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super(Conv, self).__init__() - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) - - def forward(self, x): - return self.act(self.bn(self.conv(x))) - - def fuseforward(self, x): - return self.act(self.conv(x)) - - -class TransformerLayer(nn.Module): - # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) - def __init__(self, c, num_heads): - super().__init__() - self.q = nn.Linear(c, c, bias=False) - self.k = nn.Linear(c, c, bias=False) - self.v = nn.Linear(c, c, bias=False) - self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) - self.fc1 = nn.Linear(c, c, bias=False) - self.fc2 = nn.Linear(c, c, bias=False) - - def forward(self, x): - x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x - x = self.fc2(self.fc1(x)) + x - return x - - -class TransformerBlock(nn.Module): - # Vision Transformer https://arxiv.org/abs/2010.11929 - def __init__(self, c1, c2, num_heads, num_layers): - super().__init__() - self.conv = None - if c1 != c2: - self.conv = Conv(c1, c2) - self.linear = nn.Linear(c2, c2) # learnable position embedding - self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) - self.c2 = c2 - - def forward(self, x): - if self.conv is not None: - x = self.conv(x) - b, _, w, h = x.shape - p = x.flatten(2) - p = p.unsqueeze(0) - p = p.transpose(0, 3) - p = p.squeeze(3) - e = self.linear(p) - x = p + e - - x = self.tr(x) - x = x.unsqueeze(3) - x = x.transpose(0, 3) - x = x.reshape(b, self.c2, w, h) - return x - - -class Bottleneck(nn.Module): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion - super(Bottleneck, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_, c2, 3, 1, g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class BottleneckCSP(nn.Module): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(BottleneckCSP, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) - self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) - self.cv4 = Conv(2 * c_, c2, 1, 1) - self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) - self.act = nn.LeakyReLU(0.1, inplace=True) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - - def forward(self, x): - y1 = self.cv3(self.m(self.cv1(x))) - y2 = self.cv2(x) - return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) - - -class C3(nn.Module): - # CSP Bottleneck with 3 convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion - super(C3, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c1, c_, 1, 1) - self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) - self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) - # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) - - def forward(self, x): - return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) - - -class C3TR(C3): - # C3 module with TransformerBlock() - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) - self.m = TransformerBlock(c_, c_, 4, n) - - -class SPP(nn.Module): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, k=(5, 9, 13)): - super(SPP, self).__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) - self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) - - def forward(self, x): - x = self.cv1(x) - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) - - -class Focus(nn.Module): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super(Focus, self).__init__() - self.conv = Conv(c1 * 4, c2, k, s, p, g, act) - # self.contract = Contract(gain=2) - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) - # return self.conv(self.contract(x)) - - -class Contract(nn.Module): - # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' - s = self.gain - x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) - return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) - - -class Expand(nn.Module): - # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' - s = self.gain - x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80) - x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) - return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160) - - -class Concat(nn.Module): - # Concatenate a list of tensors along dimension - def __init__(self, dimension=1): - super(Concat, self).__init__() - self.d = dimension - - def forward(self, x): - return torch.cat(x, self.d) - - -class NMS(nn.Module): - # Non-Maximum Suppression (NMS) module - conf = 0.25 # confidence threshold - iou = 0.45 # IoU threshold - classes = None # (optional list) filter by class - max_det = 1000 # maximum number of detections per image - - def __init__(self): - super(NMS, self).__init__() - - def forward(self, x): - return non_max_suppression(x[0], self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) - - -class AutoShape(nn.Module): - # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - classes = None # (optional list) filter by class - max_det = 1000 # maximum number of detections per image - - def __init__(self, model): - super(AutoShape, self).__init__() - self.model = model.eval() - - def autoshape(self): - print('AutoShape already enabled, skipping... ') # model already converted to model.autoshape() - return self - - @torch.no_grad() - def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=640, width=1280, RGB images example inputs are: - # filename: imgs = 'data/images/zidane.jpg' - # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) - # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) - # numpy: = np.zeros((640,1280,3)) # HWC - # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - t = [time_synchronized()] - p = next(self.model.parameters()) # for device and type - if isinstance(imgs, torch.Tensor): # torch - with amp.autocast(enabled=p.device.type != 'cpu'): - return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference - - # Pre-process - n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images - shape0, shape1, files = [], [], [] # image and inference shapes, filenames - for i, im in enumerate(imgs): - f = f'image{i}' # filename - if isinstance(im, str): # filename or uri - im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im - elif isinstance(im, Image.Image): # PIL Image - im, f = np.asarray(im), getattr(im, 'filename', f) or f - files.append(Path(f).with_suffix('.jpg').name) - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = (size / max(s)) # gain - shape1.append([y * g for y in s]) - imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update - shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape - x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad - x = np.stack(x, 0) if n > 1 else x[0][None] # stack - x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 - t.append(time_synchronized()) - - with amp.autocast(enabled=p.device.type != 'cpu'): - # Inference - y = self.model(x, augment, profile)[0] # forward - t.append(time_synchronized()) - - # Post-process - y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) # NMS - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) - - t.append(time_synchronized()) - return Detections(imgs, y, files, t, self.names, x.shape) - - -class Detections: - # detections class for YOLOv5 inference results - def __init__(self, imgs, pred, files, times=None, names=None, shape=None): - super(Detections, self).__init__() - d = pred[0].device # device - gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations - self.imgs = imgs # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.files = files # image filenames - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) # number of images (batch size) - self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) - self.s = shape # inference BCHW shape - - def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): - for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): - str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' - if pred is not None: - for c in pred[:, -1].unique(): - n = (pred[:, -1] == c).sum() # detections per class - str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string - if show or save or render or crop: - for *box, conf, cls in pred: # xyxy, confidence, class - label = f'{self.names[int(cls)]} {conf:.2f}' - if crop: - save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i]) - else: # all others - plot_one_box(box, im, label=label, color=colors(cls)) - - im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np - if pprint: - print(str.rstrip(', ')) - if show: - im.show(self.files[i]) # show - if save: - f = self.files[i] - im.save(save_dir / f) # save - print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') - if render: - self.imgs[i] = np.asarray(im) - - def print(self): - self.display(pprint=True) # print results - print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) - - def show(self): - self.display(show=True) # show results - - def save(self, save_dir='runs/hub/exp'): - save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir - self.display(save=True, save_dir=save_dir) # save results - - def crop(self, save_dir='runs/hub/exp'): - save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir - self.display(crop=True, save_dir=save_dir) # crop results - print(f'Saved results to {save_dir}\n') - - def render(self): - self.display(render=True) # render results - return self.imgs - - def pandas(self): - # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) - new = copy(self) # return copy - ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns - cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns - for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): - a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update - setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) - return new - - def tolist(self): - # return a list of Detections objects, i.e. 'for result in results.tolist():' - x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] - for d in x: - for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: - setattr(d, k, getattr(d, k)[0]) # pop out of list - return x - - def __len__(self): - return self.n - - -class Classify(nn.Module): - # Classification head, i.e. x(b,c1,20,20) to x(b,c2) - def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups - super(Classify, self).__init__() - self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) - self.flat = nn.Flatten() - - def forward(self, x): - z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list - return self.flat(self.conv(z)) # flatten to x(b,c2) diff --git a/Yolov5-Deepsort/models/experimental.py b/Yolov5-Deepsort/models/experimental.py deleted file mode 100644 index afa787907104f1134335ed3ac7bfdd8ac4fd4ae9..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/experimental.py +++ /dev/null @@ -1,137 +0,0 @@ -# YOLOv5 experimental modules - -import numpy as np -import torch -import torch.nn as nn - -from models.common import Conv, DWConv -from utils.google_utils import attempt_download - - -class CrossConv(nn.Module): - # Cross Convolution Downsample - def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): - # ch_in, ch_out, kernel, stride, groups, expansion, shortcut - super(CrossConv, self).__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, (1, k), (1, s)) - self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class Sum(nn.Module): - # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 - def __init__(self, n, weight=False): # n: number of inputs - super(Sum, self).__init__() - self.weight = weight # apply weights boolean - self.iter = range(n - 1) # iter object - if weight: - self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights - - def forward(self, x): - y = x[0] # no weight - if self.weight: - w = torch.sigmoid(self.w) * 2 - for i in self.iter: - y = y + x[i + 1] * w[i] - else: - for i in self.iter: - y = y + x[i + 1] - return y - - -class GhostConv(nn.Module): - # Ghost Convolution https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups - super(GhostConv, self).__init__() - c_ = c2 // 2 # hidden channels - self.cv1 = Conv(c1, c_, k, s, None, g, act) - self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) - - def forward(self, x): - y = self.cv1(x) - return torch.cat([y, self.cv2(y)], 1) - - -class GhostBottleneck(nn.Module): - # Ghost Bottleneck https://github.com/huawei-noah/ghostnet - def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride - super(GhostBottleneck, self).__init__() - c_ = c2 // 2 - self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw - DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw - GhostConv(c_, c2, 1, 1, act=False)) # pw-linear - self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), - Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() - - def forward(self, x): - return self.conv(x) + self.shortcut(x) - - -class MixConv2d(nn.Module): - # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 - def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): - super(MixConv2d, self).__init__() - groups = len(k) - if equal_ch: # equal c_ per group - i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices - c_ = [(i == g).sum() for g in range(groups)] # intermediate channels - else: # equal weight.numel() per group - b = [c2] + [0] * groups - a = np.eye(groups + 1, groups, k=-1) - a -= np.roll(a, 1, axis=1) - a *= np.array(k) ** 2 - a[0] = 1 - c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b - - self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.LeakyReLU(0.1, inplace=True) - - def forward(self, x): - return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) - - -class Ensemble(nn.ModuleList): - # Ensemble of models - def __init__(self): - super(Ensemble, self).__init__() - - def forward(self, x, augment=False): - y = [] - for module in self: - y.append(module(x, augment)[0]) - # y = torch.stack(y).max(0)[0] # max ensemble - # y = torch.stack(y).mean(0) # mean ensemble - y = torch.cat(y, 1) # nms ensemble - return y, None # inference, train output - - -def attempt_load(weights, map_location=None, inplace=True): - from models.yolo import Detect, Model - - # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a - model = Ensemble() - for w in weights if isinstance(weights, list) else [weights]: - attempt_download(w) - ckpt = torch.load(w, map_location=map_location) # load - model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model - - # Compatibility updates - for m in model.modules(): - if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: - m.inplace = inplace # pytorch 1.7.0 compatibility - elif type(m) is Conv: - m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility - - if len(model) == 1: - return model[-1] # return model - else: - print(f'Ensemble created with {weights}\n') - for k in ['names']: - setattr(model, k, getattr(model[-1], k)) - model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride - return model # return ensemble diff --git a/Yolov5-Deepsort/models/export.py b/Yolov5-Deepsort/models/export.py deleted file mode 100644 index 65721f65d88835bfba6a7b5318af26ae8890bd8a..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/export.py +++ /dev/null @@ -1,143 +0,0 @@ -"""Exports a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats - -Usage: - $ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1 -""" - -import argparse -import sys -import time -from pathlib import Path - -sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories - -import torch -import torch.nn as nn -from torch.utils.mobile_optimizer import optimize_for_mobile - -import models -from models.experimental import attempt_load -from utils.activations import Hardswish, SiLU -from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging -from utils.torch_utils import select_device - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') - parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--include', nargs='+', default=['torchscript', 'onnx', 'coreml'], help='include formats') - parser.add_argument('--half', action='store_true', help='FP16 half-precision export') - parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') - parser.add_argument('--train', action='store_true', help='model.train() mode') - parser.add_argument('--optimize', action='store_true', help='optimize TorchScript for mobile') # TorchScript-only - parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only - parser.add_argument('--simplify', action='store_true', help='simplify ONNX model') # ONNX-only - parser.add_argument('--opset-version', type=int, default=12, help='ONNX opset version') # ONNX-only - opt = parser.parse_args() - opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand - opt.include = [x.lower() for x in opt.include] - print(opt) - set_logging() - t = time.time() - - # Load PyTorch model - device = select_device(opt.device) - model = attempt_load(opt.weights, map_location=device) # load FP32 model - labels = model.names - - # Checks - gs = int(max(model.stride)) # grid size (max stride) - opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples - assert not (opt.device.lower() == 'cpu' and opt.half), '--half only compatible with GPU export, i.e. use --device 0' - - # Input - img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection - - # Update model - if opt.half: - img, model = img.half(), model.half() # to FP16 - if opt.train: - model.train() # training mode (no grid construction in Detect layer) - for k, m in model.named_modules(): - m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility - if isinstance(m, models.common.Conv): # assign export-friendly activations - if isinstance(m.act, nn.Hardswish): - m.act = Hardswish() - elif isinstance(m.act, nn.SiLU): - m.act = SiLU() - elif isinstance(m, models.yolo.Detect): - m.inplace = opt.inplace - m.onnx_dynamic = opt.dynamic - # m.forward = m.forward_export # assign forward (optional) - - for _ in range(2): - y = model(img) # dry runs - print(f"\n{colorstr('PyTorch:')} starting from {opt.weights} ({file_size(opt.weights):.1f} MB)") - - # TorchScript export ----------------------------------------------------------------------------------------------- - if 'torchscript' in opt.include or 'coreml' in opt.include: - prefix = colorstr('TorchScript:') - try: - print(f'\n{prefix} starting export with torch {torch.__version__}...') - f = opt.weights.replace('.pt', '.torchscript.pt') # filename - ts = torch.jit.trace(model, img, strict=False) - (optimize_for_mobile(ts) if opt.optimize else ts).save(f) - print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - print(f'{prefix} export failure: {e}') - - # ONNX export ------------------------------------------------------------------------------------------------------ - if 'onnx' in opt.include: - prefix = colorstr('ONNX:') - try: - import onnx - - print(f'{prefix} starting export with onnx {onnx.__version__}...') - f = opt.weights.replace('.pt', '.onnx') # filename - torch.onnx.export(model, img, f, verbose=False, opset_version=opt.opset_version, input_names=['images'], - dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) - 'output': {0: 'batch', 2: 'y', 3: 'x'}} if opt.dynamic else None) - - # Checks - model_onnx = onnx.load(f) # load onnx model - onnx.checker.check_model(model_onnx) # check onnx model - # print(onnx.helper.printable_graph(model_onnx.graph)) # print - - # Simplify - if opt.simplify: - try: - check_requirements(['onnx-simplifier']) - import onnxsim - - print(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') - model_onnx, check = onnxsim.simplify( - model_onnx, - dynamic_input_shape=opt.dynamic, - input_shapes={'images': list(img.shape)} if opt.dynamic else None) - assert check, 'assert check failed' - onnx.save(model_onnx, f) - except Exception as e: - print(f'{prefix} simplifier failure: {e}') - print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - print(f'{prefix} export failure: {e}') - - # CoreML export ---------------------------------------------------------------------------------------------------- - if 'coreml' in opt.include: - prefix = colorstr('CoreML:') - try: - import coremltools as ct - - print(f'{prefix} starting export with coremltools {ct.__version__}...') - assert opt.train, 'CoreML exports should be placed in model.train() mode with `python export.py --train`' - model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) - f = opt.weights.replace('.pt', '.mlmodel') # filename - model.save(f) - print(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - print(f'{prefix} export failure: {e}') - - # Finish - print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.') diff --git a/Yolov5-Deepsort/models/hub/anchors.yaml b/Yolov5-Deepsort/models/hub/anchors.yaml deleted file mode 100644 index a07a4dc72387ef79ee0d473ac1055d23c6543ee9..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/anchors.yaml +++ /dev/null @@ -1,58 +0,0 @@ -# Default YOLOv5 anchors for COCO data - - -# P5 ------------------------------------------------------------------------------------------------------------------- -# P5-640: -anchors_p5_640: - - [ 10,13, 16,30, 33,23 ] # P3/8 - - [ 30,61, 62,45, 59,119 ] # P4/16 - - [ 116,90, 156,198, 373,326 ] # P5/32 - - -# P6 ------------------------------------------------------------------------------------------------------------------- -# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 -anchors_p6_640: - - [ 9,11, 21,19, 17,41 ] # P3/8 - - [ 43,32, 39,70, 86,64 ] # P4/16 - - [ 65,131, 134,130, 120,265 ] # P5/32 - - [ 282,180, 247,354, 512,387 ] # P6/64 - -# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 -anchors_p6_1280: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 - -# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 -anchors_p6_1920: - - [ 28,41, 67,59, 57,141 ] # P3/8 - - [ 144,103, 129,227, 270,205 ] # P4/16 - - [ 209,452, 455,396, 358,812 ] # P5/32 - - [ 653,922, 1109,570, 1387,1187 ] # P6/64 - - -# P7 ------------------------------------------------------------------------------------------------------------------- -# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 -anchors_p7_640: - - [ 11,11, 13,30, 29,20 ] # P3/8 - - [ 30,46, 61,38, 39,92 ] # P4/16 - - [ 78,80, 146,66, 79,163 ] # P5/32 - - [ 149,150, 321,143, 157,303 ] # P6/64 - - [ 257,402, 359,290, 524,372 ] # P7/128 - -# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 -anchors_p7_1280: - - [ 19,22, 54,36, 32,77 ] # P3/8 - - [ 70,83, 138,71, 75,173 ] # P4/16 - - [ 165,159, 148,334, 375,151 ] # P5/32 - - [ 334,317, 251,626, 499,474 ] # P6/64 - - [ 750,326, 534,814, 1079,818 ] # P7/128 - -# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 -anchors_p7_1920: - - [ 29,34, 81,55, 47,115 ] # P3/8 - - [ 105,124, 207,107, 113,259 ] # P4/16 - - [ 247,238, 222,500, 563,227 ] # P5/32 - - [ 501,476, 376,939, 749,711 ] # P6/64 - - [ 1126,489, 801,1222, 1618,1227 ] # P7/128 diff --git a/Yolov5-Deepsort/models/hub/yolov3-spp.yaml b/Yolov5-Deepsort/models/hub/yolov3-spp.yaml deleted file mode 100644 index 38dcc449f0d0c1b85b4e6ff426da0d9e9df07d4e..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov3-spp.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# darknet53 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [32, 3, 1]], # 0 - [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 - [-1, 1, Bottleneck, [64]], - [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 - [-1, 2, Bottleneck, [128]], - [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 - [-1, 8, Bottleneck, [256]], - [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 - [-1, 8, Bottleneck, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 - [-1, 4, Bottleneck, [1024]], # 10 - ] - -# YOLOv3-SPP head -head: - [[-1, 1, Bottleneck, [1024, False]], - [-1, 1, SPP, [512, [5, 9, 13]]], - [-1, 1, Conv, [1024, 3, 1]], - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) - - [-2, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) - - [-2, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P3 - [-1, 1, Bottleneck, [256, False]], - [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) - - [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov3-tiny.yaml b/Yolov5-Deepsort/models/hub/yolov3-tiny.yaml deleted file mode 100644 index ff7638cad3beb5a0186cc67b84e23799ffb53bae..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov3-tiny.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [10,14, 23,27, 37,58] # P4/16 - - [81,82, 135,169, 344,319] # P5/32 - -# YOLOv3-tiny backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [16, 3, 1]], # 0 - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 - [-1, 1, Conv, [32, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 - [-1, 1, Conv, [64, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 - [-1, 1, Conv, [128, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 - [-1, 1, Conv, [256, 3, 1]], - [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 - [-1, 1, Conv, [512, 3, 1]], - [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 - [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 - ] - -# YOLOv3-tiny head -head: - [[-1, 1, Conv, [1024, 3, 1]], - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) - - [-2, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) - - [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov3.yaml b/Yolov5-Deepsort/models/hub/yolov3.yaml deleted file mode 100644 index f2e76135546945f3ccbb3311c99bf3882a90c199..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov3.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# darknet53 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [32, 3, 1]], # 0 - [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 - [-1, 1, Bottleneck, [64]], - [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 - [-1, 2, Bottleneck, [128]], - [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 - [-1, 8, Bottleneck, [256]], - [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 - [-1, 8, Bottleneck, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 - [-1, 4, Bottleneck, [1024]], # 10 - ] - -# YOLOv3 head -head: - [[-1, 1, Bottleneck, [1024, False]], - [-1, 1, Conv, [512, [1, 1]]], - [-1, 1, Conv, [1024, 3, 1]], - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) - - [-2, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Bottleneck, [512, False]], - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) - - [-2, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P3 - [-1, 1, Bottleneck, [256, False]], - [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) - - [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5-fpn.yaml b/Yolov5-Deepsort/models/hub/yolov5-fpn.yaml deleted file mode 100644 index e772bffecbbca75c536cd43d9e1659e41910b337..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5-fpn.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, Bottleneck, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, BottleneckCSP, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, BottleneckCSP, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 6, BottleneckCSP, [1024]], # 9 - ] - -# YOLOv5 FPN head -head: - [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) - - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 1, Conv, [512, 1, 1]], - [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) - - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 1, Conv, [256, 1, 1]], - [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) - - [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5-p2.yaml b/Yolov5-Deepsort/models/hub/yolov5-p2.yaml deleted file mode 100644 index 0633a90fd065efce8d9c771da36815ae353fa2f2..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5-p2.yaml +++ /dev/null @@ -1,54 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: 3 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 9 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 13 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small) - - [ -1, 1, Conv, [ 128, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 2 ], 1, Concat, [ 1 ] ], # cat backbone P2 - [ -1, 1, C3, [ 128, False ] ], # 21 (P2/4-xsmall) - - [ -1, 1, Conv, [ 128, 3, 2 ] ], - [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P3 - [ -1, 3, C3, [ 256, False ] ], # 24 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 27 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 1024, False ] ], # 30 (P5/32-large) - - [ [ 24, 27, 30 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5-p6.yaml b/Yolov5-Deepsort/models/hub/yolov5-p6.yaml deleted file mode 100644 index 3728a118f09016eebc68538e7651134e070bd79f..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5-p6.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: 3 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P5/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5-p7.yaml b/Yolov5-Deepsort/models/hub/yolov5-p7.yaml deleted file mode 100644 index ca8f8492ce0e9750e1856ac67f19ff4bf92d1948..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5-p7.yaml +++ /dev/null @@ -1,67 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: 3 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 3, C3, [ 1024 ] ], - [ -1, 1, Conv, [ 1280, 3, 2 ] ], # 11-P7/128 - [ -1, 1, SPP, [ 1280, [ 3, 5 ] ] ], - [ -1, 3, C3, [ 1280, False ] ], # 13 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 1024, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat backbone P6 - [ -1, 3, C3, [ 1024, False ] ], # 17 - - [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 21 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 25 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 29 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 26 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 32 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 22 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 35 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 18 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 38 (P6/64-xlarge) - - [ -1, 1, Conv, [ 1024, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P7 - [ -1, 3, C3, [ 1280, False ] ], # 41 (P7/128-xxlarge) - - [ [ 29, 32, 35, 38, 41 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6, P7) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5-panet.yaml b/Yolov5-Deepsort/models/hub/yolov5-panet.yaml deleted file mode 100644 index 340f95a4dbc9a7a3250cd6e41422ea4c520991c8..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5-panet.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, BottleneckCSP, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, BottleneckCSP, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, BottleneckCSP, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 3, BottleneckCSP, [1024, False]], # 9 - ] - -# YOLOv5 PANet head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, BottleneckCSP, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5l6.yaml b/Yolov5-Deepsort/models/hub/yolov5l6.yaml deleted file mode 100644 index 11298b01f47915d729c747714caf308b676ec579..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5l6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5m6.yaml b/Yolov5-Deepsort/models/hub/yolov5m6.yaml deleted file mode 100644 index 48afc865593ae964493e8fbd042a72209ab6487f..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5m6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple - -# anchors -anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5s-transformer.yaml b/Yolov5-Deepsort/models/hub/yolov5s-transformer.yaml deleted file mode 100644 index f2d666722b30508eea865cf4d51ce607589a60ec..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5s-transformer.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module - ] - -# YOLOv5 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5s6.yaml b/Yolov5-Deepsort/models/hub/yolov5s6.yaml deleted file mode 100644 index 1df577a2cc97c66763eafd37c70b97f6e893d4fd..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5s6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple - -# anchors -anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) - ] diff --git a/Yolov5-Deepsort/models/hub/yolov5x6.yaml b/Yolov5-Deepsort/models/hub/yolov5x6.yaml deleted file mode 100644 index 5ebc02124fe785d730316fde42bde40c830eac66..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/hub/yolov5x6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple - -# anchors -anchors: - - [ 19,27, 44,40, 38,94 ] # P3/8 - - [ 96,68, 86,152, 180,137 ] # P4/16 - - [ 140,301, 303,264, 238,542 ] # P5/32 - - [ 436,615, 739,380, 925,792 ] # P6/64 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 9, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 768, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 768 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P6/64 - [ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ], - [ -1, 3, C3, [ 1024, False ] ], # 11 - ] - -# YOLOv5 head -head: - [ [ -1, 1, Conv, [ 768, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P5 - [ -1, 3, C3, [ 768, False ] ], # 15 - - [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 512, False ] ], # 19 - - [ -1, 1, Conv, [ 256, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3 - [ -1, 3, C3, [ 256, False ] ], # 23 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 20 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 26 (P4/16-medium) - - [ -1, 1, Conv, [ 512, 3, 2 ] ], - [ [ -1, 16 ], 1, Concat, [ 1 ] ], # cat head P5 - [ -1, 3, C3, [ 768, False ] ], # 29 (P5/32-large) - - [ -1, 1, Conv, [ 768, 3, 2 ] ], - [ [ -1, 12 ], 1, Concat, [ 1 ] ], # cat head P6 - [ -1, 3, C3, [ 1024, False ] ], # 32 (P6/64-xlarge) - - [ [ 23, 26, 29, 32 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6) - ] diff --git a/Yolov5-Deepsort/models/yolo.py b/Yolov5-Deepsort/models/yolo.py deleted file mode 100644 index 06b80032d3d3b6d84162e040e45e0e276a619a34..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/yolo.py +++ /dev/null @@ -1,304 +0,0 @@ -# YOLOv5 YOLO-specific modules - -import argparse -import logging -import sys -from copy import deepcopy -from pathlib import Path - -sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories -logger = logging.getLogger(__name__) - -from models.common import * -from models.experimental import * -from utils.autoanchor import check_anchor_order -from utils.general import make_divisible, check_file, set_logging -from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ - select_device, copy_attr - -try: - import thop # for FLOPS computation -except ImportError: - thop = None - - -class Detect(nn.Module): - stride = None # strides computed during build - onnx_dynamic = False # ONNX export parameter - - def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer - super(Detect, self).__init__() - self.nc = nc # number of classes - self.no = nc + 5 # number of outputs per anchor - self.nl = len(anchors) # number of detection layers - self.na = len(anchors[0]) // 2 # number of anchors - self.grid = [torch.zeros(1)] * self.nl # init grid - a = torch.tensor(anchors).float().view(self.nl, -1, 2) - self.register_buffer('anchors', a) # shape(nl,na,2) - self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) - self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv - self.inplace = inplace # use in-place ops (e.g. slice assignment) - - def forward(self, x): - # x = x.copy() # for profiling - z = [] # inference output - for i in range(self.nl): - x[i] = self.m[i](x[i]) # conv - bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) - x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() - - if not self.training: # inference - if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic: - self.grid[i] = self._make_grid(nx, ny).to(x[i].device) - - y = x[i].sigmoid() - if self.inplace: - y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy - y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh - else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 - xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy - wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh - y = torch.cat((xy, wh, y[..., 4:]), -1) - z.append(y.view(bs, -1, self.no)) - - return x if self.training else (torch.cat(z, 1), x) - - @staticmethod - def _make_grid(nx=20, ny=20): - yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) - return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() - - -class Model(nn.Module): - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes - super(Model, self).__init__() - if isinstance(cfg, dict): - self.yaml = cfg # model dict - else: # is *.yaml - import yaml # for torch hub - self.yaml_file = Path(cfg).name - with open(cfg) as f: - self.yaml = yaml.safe_load(f) # model dict - - # Define model - ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels - if nc and nc != self.yaml['nc']: - logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value - if anchors: - logger.info(f'Overriding model.yaml anchors with anchors={anchors}') - self.yaml['anchors'] = round(anchors) # override yaml value - self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist - self.names = [str(i) for i in range(self.yaml['nc'])] # default names - self.inplace = self.yaml.get('inplace', True) - # logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) - - # Build strides, anchors - m = self.model[-1] # Detect() - if isinstance(m, Detect): - s = 256 # 2x min stride - m.inplace = self.inplace - m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward - m.anchors /= m.stride.view(-1, 1, 1) - check_anchor_order(m) - self.stride = m.stride - self._initialize_biases() # only run once - # logger.info('Strides: %s' % m.stride.tolist()) - - # Init weights, biases - initialize_weights(self) - self.info() - logger.info('') - - def forward(self, x, augment=False, profile=False): - if augment: - return self.forward_augment(x) # augmented inference, None - else: - return self.forward_once(x, profile) # single-scale inference, train - - def forward_augment(self, x): - img_size = x.shape[-2:] # height, width - s = [1, 0.83, 0.67] # scales - f = [None, 3, None] # flips (2-ud, 3-lr) - y = [] # outputs - for si, fi in zip(s, f): - xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) - yi = self.forward_once(xi)[0] # forward - # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save - yi = self._descale_pred(yi, fi, si, img_size) - y.append(yi) - return torch.cat(y, 1), None # augmented inference, train - - def forward_once(self, x, profile=False): - y, dt = [], [] # outputs - for m in self.model: - if m.f != -1: # if not from previous layer - x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - - if profile: - o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS - t = time_synchronized() - for _ in range(10): - _ = m(x) - dt.append((time_synchronized() - t) * 100) - if m == self.model[0]: - logger.info(f"{'time (ms)':>10s} {'GFLOPS':>10s} {'params':>10s} {'module'}") - logger.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') - - x = m(x) # run - y.append(x if m.i in self.save else None) # save output - - if profile: - logger.info('%.1fms total' % sum(dt)) - return x - - def _descale_pred(self, p, flips, scale, img_size): - # de-scale predictions following augmented inference (inverse operation) - if self.inplace: - p[..., :4] /= scale # de-scale - if flips == 2: - p[..., 1] = img_size[0] - p[..., 1] # de-flip ud - elif flips == 3: - p[..., 0] = img_size[1] - p[..., 0] # de-flip lr - else: - x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale - if flips == 2: - y = img_size[0] - y # de-flip ud - elif flips == 3: - x = img_size[1] - x # de-flip lr - p = torch.cat((x, y, wh, p[..., 4:]), -1) - return p - - def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency - # https://arxiv.org/abs/1708.02002 section 3.3 - # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. - m = self.model[-1] # Detect() module - for mi, s in zip(m.m, m.stride): # from - b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) - b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) - b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls - mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) - - def _print_biases(self): - m = self.model[-1] # Detect() module - for mi in m.m: # from - b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) - logger.info( - ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) - - # def _print_weights(self): - # for m in self.model.modules(): - # if type(m) is Bottleneck: - # logger.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights - - def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers - logger.info('Fusing layers... ') - for m in self.model.modules(): - if type(m) is Conv and hasattr(m, 'bn'): - m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv - delattr(m, 'bn') # remove batchnorm - m.forward = m.fuseforward # update forward - self.info() - return self - - def nms(self, mode=True): # add or remove NMS module - present = type(self.model[-1]) is NMS # last layer is NMS - if mode and not present: - logger.info('Adding NMS... ') - m = NMS() # module - m.f = -1 # from - m.i = self.model[-1].i + 1 # index - self.model.add_module(name='%s' % m.i, module=m) # add - self.eval() - elif not mode and present: - logger.info('Removing NMS... ') - self.model = self.model[:-1] # remove - return self - - def autoshape(self): # add AutoShape module - logger.info('Adding AutoShape... ') - m = AutoShape(self) # wrap model - copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes - return m - - def info(self, verbose=False, img_size=640): # print model information - model_info(self, verbose, img_size) - - -def parse_model(d, ch): # model_dict, input_channels(3) - logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) - anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] - na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors - no = na * (nc + 5) # number of outputs = anchors * (classes + 5) - - layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out - for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args - m = eval(m) if isinstance(m, str) else m # eval strings - for j, a in enumerate(args): - try: - args[j] = eval(a) if isinstance(a, str) else a # eval strings - except: - pass - - n = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, - C3, C3TR]: - c1, c2 = ch[f], args[0] - if c2 != no: # if not output - c2 = make_divisible(c2 * gw, 8) - - args = [c1, c2, *args[1:]] - if m in [BottleneckCSP, C3, C3TR]: - args.insert(2, n) # number of repeats - n = 1 - elif m is nn.BatchNorm2d: - args = [ch[f]] - elif m is Concat: - c2 = sum([ch[x] for x in f]) - elif m is Detect: - args.append([ch[x] for x in f]) - if isinstance(args[1], int): # number of anchors - args[1] = [list(range(args[1] * 2))] * len(f) - elif m is Contract: - c2 = ch[f] * args[0] ** 2 - elif m is Expand: - c2 = ch[f] // args[0] ** 2 - else: - c2 = ch[f] - - m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module - t = str(m)[8:-2].replace('__main__.', '') # module type - np = sum([x.numel() for x in m_.parameters()]) # number params - m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params - logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print - save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist - layers.append(m_) - if i == 0: - ch = [] - ch.append(c2) - return nn.Sequential(*layers), sorted(save) - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - opt = parser.parse_args() - opt.cfg = check_file(opt.cfg) # check file - set_logging() - device = select_device(opt.device) - - # Create model - model = Model(opt.cfg).to(device) - model.train() - - # Profile - # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 320, 320).to(device) - # y = model(img, profile=True) - - # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898) - # from torch.utils.tensorboard import SummaryWriter - # tb_writer = SummaryWriter('.') - # logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/") - # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph - # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/Yolov5-Deepsort/models/yolov5l.yaml b/Yolov5-Deepsort/models/yolov5l.yaml deleted file mode 100644 index 71ebf86e57918ca222264d47fa6f04205be445a7..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/yolov5l.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 3, C3, [1024, False]], # 9 - ] - -# YOLOv5 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/yolov5m.yaml b/Yolov5-Deepsort/models/yolov5m.yaml deleted file mode 100644 index 3c749c916246815fbfaa3ac96506c85a65466c02..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/yolov5m.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 3, C3, [1024, False]], # 9 - ] - -# YOLOv5 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/yolov5s.yaml b/Yolov5-Deepsort/models/yolov5s.yaml deleted file mode 100644 index aca669d60d8b5db54963302b1ae629afe4499565..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/yolov5s.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 3, C3, [1024, False]], # 9 - ] - -# YOLOv5 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/models/yolov5x.yaml b/Yolov5-Deepsort/models/yolov5x.yaml deleted file mode 100644 index d3babdf7baf01a95a2a8da760a6db41bb8c7dd79..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/models/yolov5x.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple - -# anchors -anchors: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - -# YOLOv5 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Focus, [64, 3]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 9, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 1, SPP, [1024, [5, 9, 13]]], - [-1, 3, C3, [1024, False]], # 9 - ] - -# YOLOv5 head -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 13 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/Yolov5-Deepsort/mot.mp4 b/Yolov5-Deepsort/mot.mp4 deleted file mode 100644 index ad233d76ae04637c5d8950a7e50482f82400168f..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/mot.mp4 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:19075cdf256ef23ce5432e16ab4b6a8c5a75b5d8f0c5548955f6eacf672a06c5 -size 7869562 diff --git a/Yolov5-Deepsort/myresult.txt b/Yolov5-Deepsort/myresult.txt deleted file mode 100644 index 8535951ffaed1fefe8d6a09be3a3c00d3074f50c..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/myresult.txt +++ /dev/null @@ -1,6619 +0,0 @@ -3, 1, 477, 269, 43, 35, 1, 2, 1 -3, 2, 0, 339, 20, 108, 1, 0, 1 -3, 3, 540, 269, 56, 53, 1, 2, 1 -3, 4, 686, 256, 18, 49, 1, 0, 1 -3, 5, 810, 272, 28, 61, 1, 0, 1 -3, 6, 767, 277, 25, 58, 1, 0, 1 -3, 7, 250, 272, 17, 47, 1, 0, 1 -3, 8, 725, 243, 15, 44, 1, 0, 1 -3, 9, 267, 268, 19, 52, 1, 0, 1 -3, 10, 744, 255, 13, 35, 1, 0, 1 -3, 11, 52, 359, 24, 47, 1, 0, 1 -3, 12, 130, 298, 22, 68, 1, 0, 1 -4, 1, 478, 270, 43, 35, 1, 2, 1 -4, 2, 0, 346, 17, 92, 1, 0, 1 -4, 3, 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31, 31, 1, 2, 1 -684, 720, 604, 282, 72, 49, 1, 2, 1 -684, 748, 665, 299, 92, 56, 1, 2, 1 -684, 753, 407, 245, 27, 22, 1, 2, 1 -684, 770, 313, 299, 30, 62, 1, 0, 1 -684, 789, 234, 260, 16, 41, 1, 0, 1 -684, 800, 742, 290, 35, 25, 1, 2, 1 -684, 803, 304, 245, 13, 28, 1, 0, 1 diff --git a/Yolov5-Deepsort/niuzi.mp4 b/Yolov5-Deepsort/niuzi.mp4 deleted file mode 100644 index f7ccbc70b3f6fb159dd97fabc2ee7068f29c20bc..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/niuzi.mp4 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6c29e6dcecff5ad08dcb96ca2a3f24860d2ab420bff194598ed51a674fc41447 -size 5859018 diff --git a/Yolov5-Deepsort/other_method.py b/Yolov5-Deepsort/other_method.py deleted file mode 100644 index 63357d519a09e9972d8d6775f240fbed16dc737b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/other_method.py +++ /dev/null @@ -1,46 +0,0 @@ -import cv2 - -# Initialize video capture -cap = cv2.VideoCapture('mot.mp4') - -# Initialize tracker -tracker = cv2.TrackerCSRT_create() - -# Read the first frame -ret, frame = cap.read() -if not ret: - print("Failed to read video") - exit() - -# Detect initial bounding box -bbox = cv2.selectROI(frame, False) - -# Initialize tracker with first frame and bounding box -tracker.init(frame, bbox) - -while True: - ret, frame = cap.read() - if not ret: - break - - # Update tracker - success, bbox = tracker.update(frame) - - if success: - # Draw bounding box - p1 = (int(bbox[0]), int(bbox[1])) - p2 = (int(bbox[0] + bbox[2]), int(bbox[1] + bbox[3])) - cv2.rectangle(frame, p1, p2, (255,0,0), 2, 1) - else: - # Tracking failure - cv2.putText(frame, "Tracking failure detected", (100,80), cv2.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2) - - # Display result - cv2.imshow("Tracking", frame) - - # Exit if ESC pressed - if cv2.waitKey(1) & 0xFF == 27: # ESC key - break - -cap.release() -cv2.destroyAllWindows() diff --git a/Yolov5-Deepsort/result.mp4 b/Yolov5-Deepsort/result.mp4 deleted file mode 100644 index bd0ce22bd6b02a363521a3c2301c5e3c1fddc495..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/result.mp4 +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:363c4066a0672367bef8be6dfe6c882e709712911776644947d5f9813c9fa016 -size 23811638 diff --git a/Yolov5-Deepsort/su_demo.py b/Yolov5-Deepsort/su_demo.py deleted file mode 100644 index 3c5b99ad9265b3f83aa2a0f26980177fde06d1bb..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/su_demo.py +++ /dev/null @@ -1,23 +0,0 @@ -import cv2 -import rich - -if __name__ == '__main__': - videoCapture = cv2.VideoCapture('niuzi.mp4') - - # 创建一个窗口，并设置大小 - cv2.namedWindow('sudemo', cv2.WINDOW_NORMAL) - cv2.resizeWindow('sudemo', 800, 600) # 设置窗口的宽度和高度 - - while True: - ret, frame = videoCapture.read() - if not ret: - rich.print("播放完毕") - break - else: - cv2.imshow('sudemo', frame) - - if cv2.waitKey(25) & 0xFF == ord('q'): - break - - videoCapture.release() - cv2.destroyAllWindows() diff --git a/Yolov5-Deepsort/tracker.py b/Yolov5-Deepsort/tracker.py deleted file mode 100644 index 6f91daeed5afad2b6e228c5237d942446495ea67..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/tracker.py +++ /dev/null @@ -1,129 +0,0 @@ -from deep_sort.utils.parser import get_config -from deep_sort.deep_sort import DeepSort -import torch -import rich -import os -import cv2 - -palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) -cfg = get_config() -cfg.merge_from_file("deep_sort/configs/deep_sort.yaml") -deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT, - max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, - nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, - max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, - use_cuda=True) - - -def plot_bboxes(image, bboxes, line_thickness=None): - # Plots one bounding box on image img - tl = line_thickness or round( - 0.002 * (image.shape[0] + image.shape[1]) / 2) + 1 # line/font thickness - for (x1, y1, x2, y2, cls_id, pos_id) in bboxes: - if cls_id in ['person']: - color = (0, 0, 255) - else: - color = (0, 255, 0) - c1, c2 = (x1, y1), (x2, y2) - cv2.rectangle(image, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) - tf = max(tl - 1, 1) # font thickness - t_size = cv2.getTextSize(cls_id, 0, fontScale=tl / 3, thickness=tf)[0] - c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 - cv2.rectangle(image, c1, c2, color, -1, cv2.LINE_AA) # filled - cv2.putText(image, '{} ID-{}'.format(cls_id, pos_id), (c1[0], c1[1] - 2), 0, tl / 3, - [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) - - return image - - -def update_tracker(target_detector, image, framecounter): - - new_faces = [] - _, bboxes = target_detector.detect(image) - - - - bbox_xywh = [] - confs = [] - clss = [] - - for x1, y1, x2, y2, cls_id, conf in bboxes: - - obj = [ - int((x1+x2)/2), int((y1+y2)/2), - x2-x1, y2-y1 - ] - bbox_xywh.append(obj) - confs.append(conf) - clss.append(cls_id) - - xywhs = torch.Tensor(bbox_xywh) - confss = torch.Tensor(confs) - - outputs = deepsort.update(xywhs, confss, clss, image) - #rich.print("该帧的输出",outputs) - transfer_result_to_txt(current_frame=framecounter, current_output=outputs) - - bboxes2draw = [] - face_bboxes = [] - current_ids = [] - for value in list(outputs): - x1, y1, x2, y2, cls_, track_id = value - bboxes2draw.append( - (x1, y1, x2, y2, cls_, track_id) - ) - current_ids.append(track_id) - if cls_ == 'face': - if not track_id in target_detector.faceTracker: - target_detector.faceTracker[track_id] = 0 - face = image[y1:y2, x1:x2] - new_faces.append((face, track_id)) - face_bboxes.append( - (x1, y1, x2, y2) - ) - - ids2delete = [] - for history_id in target_detector.faceTracker: - if not history_id in current_ids: - target_detector.faceTracker[history_id] -= 1 - if target_detector.faceTracker[history_id] < -5: - ids2delete.append(history_id) - - for ids in ids2delete: - target_detector.faceTracker.pop(ids) - print('-[INFO] Delete track id:', ids) - - image = plot_bboxes(image, bboxes2draw) - - return image, new_faces, face_bboxes - - - -def transfer_result_to_txt(current_output, current_frame: int): - if current_frame == 1: - with open("myresult.txt",'w') as file: - for det in current_output: - x_min, y_min, x_max, y_max, obj_class, obj_id = det - width = x_max - x_min - height = y_max - y_min - conf = 1 # 置信度，通常在ground truth中为1 - class_id = 1 if obj_class == 'person' else 2 # 假设1代表person, 2代表car - visibility = 1 # 假设目标完全可见 - - # 写入格式：, , , , , , , , - file.write(f"{current_frame}, {obj_id}, {x_min}, {y_min}, {width}, {height}, {conf}, {class_id}, {visibility}\n") - else: - with open("myresult.txt",'a') as file: - for det in current_output: - x_min, y_min, x_max, y_max, obj_class, obj_id = det - width = x_max - x_min - height = y_max - y_min - conf = 1 # 置信度，通常在ground truth中为1 - class_id = 0 if obj_class == 'person' else 2 # 假设1代表person, 2代表car - visibility = 1 # 假设目标完全可见 - - # 写入格式：, , , , , , , , - file.write(f"{current_frame}, {obj_id}, {x_min}, {y_min}, {width}, {height}, {conf}, {class_id}, {visibility}\n") - - - diff --git a/Yolov5-Deepsort/utils/BaseDetector.py b/Yolov5-Deepsort/utils/BaseDetector.py deleted file mode 100644 index 1fd77f962e6860a8c708439d9be5059beca12f2f..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/BaseDetector.py +++ /dev/null @@ -1,50 +0,0 @@ -from tracker import update_tracker -import cv2 - - -class baseDet(object): - - def __init__(self): - - self.img_size = 640 - self.threshold = 0.3 - self.stride = 1 - - def build_config(self): - - self.faceTracker = {} - self.faceClasses = {} - self.faceLocation1 = {} - self.faceLocation2 = {} - self.frameCounter = 0 - self.currentCarID = 0 - self.recorded = [] - - self.font = cv2.FONT_HERSHEY_SIMPLEX - - def feedCap(self, im): - - retDict = { - 'frame': None, - 'faces': None, - 'list_of_ids': None, - 'face_bboxes': [] - } - self.frameCounter += 1 - - im, faces, face_bboxes = update_tracker(self, im, self.frameCounter) - - retDict['frame'] = im - retDict['faces'] = faces - retDict['face_bboxes'] = face_bboxes - - return retDict - - def init_model(self): - raise EOFError("Undefined model type.") - - def preprocess(self): - raise EOFError("Undefined model type.") - - def detect(self): - raise EOFError("Undefined model type.") diff --git a/Yolov5-Deepsort/utils/__init__.py b/Yolov5-Deepsort/utils/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Yolov5-Deepsort/utils/__pycache__/BaseDetector.cpython-37.pyc b/Yolov5-Deepsort/utils/__pycache__/BaseDetector.cpython-37.pyc deleted file mode 100644 index bc358656fde98374d185612891a20a0249968609..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/utils/__pycache__/BaseDetector.cpython-37.pyc and /dev/null differ diff --git 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a/Yolov5-Deepsort/utils/activations.py +++ /dev/null @@ -1,98 +0,0 @@ -# Activation functions - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -# SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- -class SiLU(nn.Module): # export-friendly version of nn.SiLU() - @staticmethod - def forward(x): - return x * torch.sigmoid(x) - - -class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() - @staticmethod - def forward(x): - # return x * F.hardsigmoid(x) # for torchscript and CoreML - return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX - - -# Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- -class Mish(nn.Module): - @staticmethod - def forward(x): - return x * F.softplus(x).tanh() - - -class MemoryEfficientMish(nn.Module): - class F(torch.autograd.Function): - @staticmethod - def forward(ctx, x): - ctx.save_for_backward(x) - return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) - - @staticmethod - def backward(ctx, grad_output): - x = ctx.saved_tensors[0] - sx = torch.sigmoid(x) - fx = F.softplus(x).tanh() - return grad_output * (fx + x * sx * (1 - fx * fx)) - - def forward(self, x): - return self.F.apply(x) - - -# FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- -class FReLU(nn.Module): - def __init__(self, c1, k=3): # ch_in, kernel - super().__init__() - self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) - self.bn = nn.BatchNorm2d(c1) - - def forward(self, x): - return torch.max(x, self.bn(self.conv(x))) - - -# ACON https://arxiv.org/pdf/2009.04759.pdf ---------------------------------------------------------------------------- -class AconC(nn.Module): - r""" ACON activation (activate or not). - AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter - according to "Activate or Not: Learning Customized Activation" . - """ - - def __init__(self, c1): - super().__init__() - self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) - - def forward(self, x): - dpx = (self.p1 - self.p2) * x - return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x - - -class MetaAconC(nn.Module): - r""" ACON activation (activate or not). - MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network - according to "Activate or Not: Learning Customized Activation" . - """ - - def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r - super().__init__() - c2 = max(r, c1 // r) - self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) - self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) - # self.bn1 = nn.BatchNorm2d(c2) - # self.bn2 = nn.BatchNorm2d(c1) - - def forward(self, x): - y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) - # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 - # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable - beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed - dpx = (self.p1 - self.p2) * x - return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/Yolov5-Deepsort/utils/autoanchor.py b/Yolov5-Deepsort/utils/autoanchor.py deleted file mode 100644 index 87dc394c832e628a0b1c7b39aeb1e7c6584c63b3..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/autoanchor.py +++ /dev/null @@ -1,161 +0,0 @@ -# Auto-anchor utils - -import numpy as np -import torch -import yaml -from tqdm import tqdm - -from utils.general import colorstr - - -def check_anchor_order(m): - # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary - a = m.anchor_grid.prod(-1).view(-1) # anchor area - da = a[-1] - a[0] # delta a - ds = m.stride[-1] - m.stride[0] # delta s - if da.sign() != ds.sign(): # same order - print('Reversing anchor order') - m.anchors[:] = m.anchors.flip(0) - m.anchor_grid[:] = m.anchor_grid.flip(0) - - -def check_anchors(dataset, model, thr=4.0, imgsz=640): - # Check anchor fit to data, recompute if necessary - prefix = colorstr('autoanchor: ') - print(f'\n{prefix}Analyzing anchors... ', end='') - m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() - shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) - scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale - wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh - - def metric(k): # compute metric - r = wh[:, None] / k[None] - x = torch.min(r, 1. / r).min(2)[0] # ratio metric - best = x.max(1)[0] # best_x - aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold - bpr = (best > 1. / thr).float().mean() # best possible recall - return bpr, aat - - anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors - bpr, aat = metric(anchors) - print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') - if bpr < 0.98: # threshold to recompute - print('. Attempting to improve anchors, please wait...') - na = m.anchor_grid.numel() // 2 # number of anchors - try: - anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) - except Exception as e: - print(f'{prefix}ERROR: {e}') - new_bpr = metric(anchors)[0] - if new_bpr > bpr: # replace anchors - anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) - m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference - m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss - check_anchor_order(m) - print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') - else: - print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') - print('') # newline - - -def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): - """ Creates kmeans-evolved anchors from training dataset - - Arguments: - path: path to dataset *.yaml, or a loaded dataset - n: number of anchors - img_size: image size used for training - thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 - gen: generations to evolve anchors using genetic algorithm - verbose: print all results - - Return: - k: kmeans evolved anchors - - Usage: - from utils.autoanchor import *; _ = kmean_anchors() - """ - from scipy.cluster.vq import kmeans - - thr = 1. / thr - prefix = colorstr('autoanchor: ') - - def metric(k, wh): # compute metrics - r = wh[:, None] / k[None] - x = torch.min(r, 1. / r).min(2)[0] # ratio metric - # x = wh_iou(wh, torch.tensor(k)) # iou metric - return x, x.max(1)[0] # x, best_x - - def anchor_fitness(k): # mutation fitness - _, best = metric(torch.tensor(k, dtype=torch.float32), wh) - return (best * (best > thr).float()).mean() # fitness - - def print_results(k): - k = k[np.argsort(k.prod(1))] # sort small to large - x, best = metric(k, wh0) - bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr - print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') - print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' - f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') - for i, x in enumerate(k): - print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg - return k - - if isinstance(path, str): # *.yaml file - with open(path) as f: - data_dict = yaml.safe_load(f) # model dict - from utils.datasets import LoadImagesAndLabels - dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) - else: - dataset = path # dataset - - # Get label wh - shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) - wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh - - # Filter - i = (wh0 < 3.0).any(1).sum() - if i: - print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') - wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels - # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 - - # Kmeans calculation - print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') - s = wh.std(0) # sigmas for whitening - k, dist = kmeans(wh / s, n, iter=30) # points, mean distance - assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') - k *= s - wh = torch.tensor(wh, dtype=torch.float32) # filtered - wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered - k = print_results(k) - - # Plot - # k, d = [None] * 20, [None] * 20 - # for i in tqdm(range(1, 21)): - # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance - # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) - # ax = ax.ravel() - # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') - # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh - # ax[0].hist(wh[wh[:, 0]<100, 0],400) - # ax[1].hist(wh[wh[:, 1]<100, 1],400) - # fig.savefig('wh.png', dpi=200) - - # Evolve - npr = np.random - f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma - pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar - for _ in pbar: - v = np.ones(sh) - while (v == 1).all(): # mutate until a change occurs (prevent duplicates) - v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) - kg = (k.copy() * v).clip(min=2.0) - fg = anchor_fitness(kg) - if fg > f: - f, k = fg, kg.copy() - pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' - if verbose: - print_results(k) - - return print_results(k) diff --git a/Yolov5-Deepsort/utils/aws/__init__.py b/Yolov5-Deepsort/utils/aws/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Yolov5-Deepsort/utils/aws/mime.sh b/Yolov5-Deepsort/utils/aws/mime.sh deleted file mode 100644 index c319a83cfbdf09bea634c3bd9fca737c0b1dd505..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/aws/mime.sh +++ /dev/null @@ -1,26 +0,0 @@ -# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ -# This script will run on every instance restart, not only on first start -# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- - -Content-Type: multipart/mixed; boundary="//" -MIME-Version: 1.0 - ---// -Content-Type: text/cloud-config; charset="us-ascii" -MIME-Version: 1.0 -Content-Transfer-Encoding: 7bit -Content-Disposition: attachment; filename="cloud-config.txt" - -#cloud-config -cloud_final_modules: -- [scripts-user, always] - ---// -Content-Type: text/x-shellscript; charset="us-ascii" -MIME-Version: 1.0 -Content-Transfer-Encoding: 7bit -Content-Disposition: attachment; filename="userdata.txt" - -#!/bin/bash -# --- paste contents of userdata.sh here --- ---// diff --git a/Yolov5-Deepsort/utils/aws/resume.py b/Yolov5-Deepsort/utils/aws/resume.py deleted file mode 100644 index 4b0d4246b594acddbecf065956fc8729bb96ec36..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/aws/resume.py +++ /dev/null @@ -1,37 +0,0 @@ -# Resume all interrupted trainings in yolov5/ dir including DDP trainings -# Usage: $ python utils/aws/resume.py - -import os -import sys -from pathlib import Path - -import torch -import yaml - -sys.path.append('./') # to run '$ python *.py' files in subdirectories - -port = 0 # --master_port -path = Path('').resolve() -for last in path.rglob('*/**/last.pt'): - ckpt = torch.load(last) - if ckpt['optimizer'] is None: - continue - - # Load opt.yaml - with open(last.parent.parent / 'opt.yaml') as f: - opt = yaml.safe_load(f) - - # Get device count - d = opt['device'].split(',') # devices - nd = len(d) # number of devices - ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel - - if ddp: # multi-GPU - port += 1 - cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' - else: # single-GPU - cmd = f'python train.py --resume {last}' - - cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread - print(cmd) - os.system(cmd) diff --git a/Yolov5-Deepsort/utils/aws/userdata.sh b/Yolov5-Deepsort/utils/aws/userdata.sh deleted file mode 100644 index 5846fedb16f971c194c80ff533bc8c237de6815c..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/aws/userdata.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html -# This script will run only once on first instance start (for a re-start script see mime.sh) -# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir -# Use >300 GB SSD - -cd home/ubuntu -if [ ! -d yolov5 ]; then - echo "Running first-time script." # install dependencies, download COCO, pull Docker - git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 - cd yolov5 - bash data/scripts/get_coco.sh && echo "Data done." & - sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & - python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & - wait && echo "All tasks done." # finish background tasks -else - echo "Running re-start script." # resume interrupted runs - i=0 - list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' - while IFS= read -r id; do - ((i++)) - echo "restarting container $i: $id" - sudo docker start $id - # sudo docker exec -it $id python train.py --resume # single-GPU - sudo docker exec -d $id python utils/aws/resume.py # multi-scenario - done <<<"$list" -fi diff --git a/Yolov5-Deepsort/utils/datasets.py b/Yolov5-Deepsort/utils/datasets.py deleted file mode 100644 index 36416b14e1387a3e3b6b30acf6254bb727d96ced..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/datasets.py +++ /dev/null @@ -1,1067 +0,0 @@ -# Dataset utils and dataloaders - -import glob -import logging -import math -import os -import random -import shutil -import time -from itertools import repeat -from multiprocessing.pool import ThreadPool -from pathlib import Path -from threading import Thread - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -from PIL import Image, ExifTags -from torch.utils.data import Dataset -from tqdm import tqdm - -from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \ - resample_segments, clean_str -from utils.torch_utils import torch_distributed_zero_first - -# Parameters -help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' -img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes -vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes -logger = logging.getLogger(__name__) - -# Get orientation exif tag -for orientation in ExifTags.TAGS.keys(): - if ExifTags.TAGS[orientation] == 'Orientation': - break - - -def get_hash(files): - # Returns a single hash value of a list of files - return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) - - -def exif_size(img): - # Returns exif-corrected PIL size - s = img.size # (width, height) - try: - rotation = dict(img._getexif().items())[orientation] - if rotation == 6: # rotation 270 - s = (s[1], s[0]) - elif rotation == 8: # rotation 90 - s = (s[1], s[0]) - except: - pass - - return s - - -def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, - rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''): - # Make sure only the first process in DDP process the dataset first, and the following others can use the cache - with torch_distributed_zero_first(rank): - dataset = LoadImagesAndLabels(path, imgsz, batch_size, - augment=augment, # augment images - hyp=hyp, # augmentation hyperparameters - rect=rect, # rectangular training - cache_images=cache, - single_cls=opt.single_cls, - stride=int(stride), - pad=pad, - image_weights=image_weights, - prefix=prefix) - - batch_size = min(batch_size, len(dataset)) - nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers - sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None - loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader - # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() - dataloader = loader(dataset, - batch_size=batch_size, - num_workers=nw, - sampler=sampler, - pin_memory=True, - collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn) - return dataloader, dataset - - -class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): - """ Dataloader that reuses workers - - Uses same syntax as vanilla DataLoader - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) - self.iterator = super().__iter__() - - def __len__(self): - return len(self.batch_sampler.sampler) - - def __iter__(self): - for i in range(len(self)): - yield next(self.iterator) - - -class _RepeatSampler(object): - """ Sampler that repeats forever - - Args: - sampler (Sampler) - """ - - def __init__(self, sampler): - self.sampler = sampler - - def __iter__(self): - while True: - yield from iter(self.sampler) - - -class LoadImages: # for inference - def __init__(self, path, img_size=640, stride=32): - p = str(Path(path).absolute()) # os-agnostic absolute path - if '*' in p: - files = sorted(glob.glob(p, recursive=True)) # glob - elif os.path.isdir(p): - files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir - elif os.path.isfile(p): - files = [p] # files - else: - raise Exception(f'ERROR: {p} does not exist') - - images = [x for x in files if x.split('.')[-1].lower() in img_formats] - videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] - ni, nv = len(images), len(videos) - - self.img_size = img_size - self.stride = stride - self.files = images + videos - self.nf = ni + nv # number of files - self.video_flag = [False] * ni + [True] * nv - self.mode = 'image' - if any(videos): - self.new_video(videos[0]) # new video - else: - self.cap = None - assert self.nf > 0, f'No images or videos found in {p}. ' \ - f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}' - - def __iter__(self): - self.count = 0 - return self - - def __next__(self): - if self.count == self.nf: - raise StopIteration - path = self.files[self.count] - - if self.video_flag[self.count]: - # Read video - self.mode = 'video' - ret_val, img0 = self.cap.read() - if not ret_val: - self.count += 1 - self.cap.release() - if self.count == self.nf: # last video - raise StopIteration - else: - path = self.files[self.count] - self.new_video(path) - ret_val, img0 = self.cap.read() - - self.frame += 1 - print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ', end='') - - else: - # Read image - self.count += 1 - img0 = cv2.imread(path) # BGR - assert img0 is not None, 'Image Not Found ' + path - print(f'image {self.count}/{self.nf} {path}: ', end='') - - # Padded resize - img = letterbox(img0, self.img_size, stride=self.stride)[0] - - # Convert - img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 - img = np.ascontiguousarray(img) - - return path, img, img0, self.cap - - def new_video(self, path): - self.frame = 0 - self.cap = cv2.VideoCapture(path) - self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) - - def __len__(self): - return self.nf # number of files - - -class LoadWebcam: # for inference - def __init__(self, pipe='0', img_size=640, stride=32): - self.img_size = img_size - self.stride = stride - - if pipe.isnumeric(): - pipe = eval(pipe) # local camera - # pipe = 'rtsp://192.168.1.64/1' # IP camera - # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login - # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera - - self.pipe = pipe - self.cap = cv2.VideoCapture(pipe) # video capture object - self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size - - def __iter__(self): - self.count = -1 - return self - - def __next__(self): - self.count += 1 - if cv2.waitKey(1) == ord('q'): # q to quit - self.cap.release() - cv2.destroyAllWindows() - raise StopIteration - - # Read frame - if self.pipe == 0: # local camera - ret_val, img0 = self.cap.read() - img0 = cv2.flip(img0, 1) # flip left-right - else: # IP camera - n = 0 - while True: - n += 1 - self.cap.grab() - if n % 30 == 0: # skip frames - ret_val, img0 = self.cap.retrieve() - if ret_val: - break - - # Print - assert ret_val, f'Camera Error {self.pipe}' - img_path = 'webcam.jpg' - print(f'webcam {self.count}: ', end='') - - # Padded resize - img = letterbox(img0, self.img_size, stride=self.stride)[0] - - # Convert - img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 - img = np.ascontiguousarray(img) - - return img_path, img, img0, None - - def __len__(self): - return 0 - - -class LoadStreams: # multiple IP or RTSP cameras - def __init__(self, sources='streams.txt', img_size=640, stride=32): - self.mode = 'stream' - self.img_size = img_size - self.stride = stride - - if os.path.isfile(sources): - with open(sources, 'r') as f: - sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] - else: - sources = [sources] - - n = len(sources) - self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n - self.sources = [clean_str(x) for x in sources] # clean source names for later - for i, s in enumerate(sources): # index, source - # Start thread to read frames from video stream - print(f'{i + 1}/{n}: {s}... ', end='') - if 'youtube.com/' in s or 'youtu.be/' in s: # if source is YouTube video - check_requirements(('pafy', 'youtube_dl')) - import pafy - s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL - s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam - cap = cv2.VideoCapture(s) - assert cap.isOpened(), f'Failed to open {s}' - w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) - h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) - self.fps[i] = max(cap.get(cv2.CAP_PROP_FPS) % 100, 0) or 30.0 # 30 FPS fallback - self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback - - _, self.imgs[i] = cap.read() # guarantee first frame - self.threads[i] = Thread(target=self.update, args=([i, cap]), daemon=True) - print(f" success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") - self.threads[i].start() - print('') # newline - - # check for common shapes - s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0) # shapes - self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal - if not self.rect: - print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') - - def update(self, i, cap): - # Read stream `i` frames in daemon thread - n, f = 0, self.frames[i] - while cap.isOpened() and n < f: - n += 1 - # _, self.imgs[index] = cap.read() - cap.grab() - if n % 4: # read every 4th frame - success, im = cap.retrieve() - self.imgs[i] = im if success else self.imgs[i] * 0 - time.sleep(1 / self.fps[i]) # wait time - - def __iter__(self): - self.count = -1 - return self - - def __next__(self): - self.count += 1 - if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit - cv2.destroyAllWindows() - raise StopIteration - - # Letterbox - img0 = self.imgs.copy() - img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0] - - # Stack - img = np.stack(img, 0) - - # Convert - img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 - img = np.ascontiguousarray(img) - - return self.sources, img, img0, None - - def __len__(self): - return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years - - -def img2label_paths(img_paths): - # Define label paths as a function of image paths - sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings - return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths] - - -class LoadImagesAndLabels(Dataset): # for training/testing - def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False, - cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''): - self.img_size = img_size - self.augment = augment - self.hyp = hyp - self.image_weights = image_weights - self.rect = False if image_weights else rect - self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) - self.mosaic_border = [-img_size // 2, -img_size // 2] - self.stride = stride - self.path = path - - try: - f = [] # image files - for p in path if isinstance(path, list) else [path]: - p = Path(p) # os-agnostic - if p.is_dir(): # dir - f += glob.glob(str(p / '**' / '*.*'), recursive=True) - # f = list(p.rglob('**/*.*')) # pathlib - elif p.is_file(): # file - with open(p, 'r') as t: - t = t.read().strip().splitlines() - parent = str(p.parent) + os.sep - f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path - # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) - else: - raise Exception(f'{prefix}{p} does not exist') - self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) - # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib - assert self.img_files, f'{prefix}No images found' - except Exception as e: - raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}') - - # Check cache - self.label_files = img2label_paths(self.img_files) # labels - cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') # cached labels - if cache_path.is_file(): - cache, exists = torch.load(cache_path), True # load - if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache: # changed - cache, exists = self.cache_labels(cache_path, prefix), False # re-cache - else: - cache, exists = self.cache_labels(cache_path, prefix), False # cache - - # Display cache - nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total - if exists: - d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" - tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results - assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}' - - # Read cache - cache.pop('hash') # remove hash - cache.pop('version') # remove version - labels, shapes, self.segments = zip(*cache.values()) - self.labels = list(labels) - self.shapes = np.array(shapes, dtype=np.float64) - self.img_files = list(cache.keys()) # update - self.label_files = img2label_paths(cache.keys()) # update - if single_cls: - for x in self.labels: - x[:, 0] = 0 - - n = len(shapes) # number of images - bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index - nb = bi[-1] + 1 # number of batches - self.batch = bi # batch index of image - self.n = n - self.indices = range(n) - - # Rectangular Training - if self.rect: - # Sort by aspect ratio - s = self.shapes # wh - ar = s[:, 1] / s[:, 0] # aspect ratio - irect = ar.argsort() - self.img_files = [self.img_files[i] for i in irect] - self.label_files = [self.label_files[i] for i in irect] - self.labels = [self.labels[i] for i in irect] - self.shapes = s[irect] # wh - ar = ar[irect] - - # Set training image shapes - shapes = [[1, 1]] * nb - for i in range(nb): - ari = ar[bi == i] - mini, maxi = ari.min(), ari.max() - if maxi < 1: - shapes[i] = [maxi, 1] - elif mini > 1: - shapes[i] = [1, 1 / mini] - - self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride - - # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) - self.imgs = [None] * n - if cache_images: - gb = 0 # Gigabytes of cached images - self.img_hw0, self.img_hw = [None] * n, [None] * n - results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) # 8 threads - pbar = tqdm(enumerate(results), total=n) - for i, x in pbar: - self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i) - gb += self.imgs[i].nbytes - pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)' - pbar.close() - - def cache_labels(self, path=Path('./labels.cache'), prefix=''): - # Cache dataset labels, check images and read shapes - x = {} # dict - nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate - pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) - for i, (im_file, lb_file) in enumerate(pbar): - try: - # verify images - im = Image.open(im_file) - im.verify() # PIL verify - shape = exif_size(im) # image size - segments = [] # instance segments - assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels' - assert im.format.lower() in img_formats, f'invalid image format {im.format}' - - # verify labels - if os.path.isfile(lb_file): - nf += 1 # label found - with open(lb_file, 'r') as f: - l = [x.split() for x in f.read().strip().splitlines()] - if any([len(x) > 8 for x in l]): # is segment - classes = np.array([x[0] for x in l], dtype=np.float32) - segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) - l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) - l = np.array(l, dtype=np.float32) - if len(l): - assert l.shape[1] == 5, 'labels require 5 columns each' - assert (l >= 0).all(), 'negative labels' - assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' - assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' - else: - ne += 1 # label empty - l = np.zeros((0, 5), dtype=np.float32) - else: - nm += 1 # label missing - l = np.zeros((0, 5), dtype=np.float32) - x[im_file] = [l, shape, segments] - except Exception as e: - nc += 1 - logging.info(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}') - - pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \ - f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" - pbar.close() - - if nf == 0: - logging.info(f'{prefix}WARNING: No labels found in {path}. See {help_url}') - - x['hash'] = get_hash(self.label_files + self.img_files) - x['results'] = nf, nm, ne, nc, i + 1 - x['version'] = 0.1 # cache version - try: - torch.save(x, path) # save for next time - logging.info(f'{prefix}New cache created: {path}') - except Exception as e: - logging.info(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}') # path not writeable - return x - - def __len__(self): - return len(self.img_files) - - # def __iter__(self): - # self.count = -1 - # print('ran dataset iter') - # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) - # return self - - def __getitem__(self, index): - index = self.indices[index] # linear, shuffled, or image_weights - - hyp = self.hyp - mosaic = self.mosaic and random.random() < hyp['mosaic'] - if mosaic: - # Load mosaic - img, labels = load_mosaic(self, index) - shapes = None - - # MixUp https://arxiv.org/pdf/1710.09412.pdf - if random.random() < hyp['mixup']: - img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1)) - r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 - img = (img * r + img2 * (1 - r)).astype(np.uint8) - labels = np.concatenate((labels, labels2), 0) - - else: - # Load image - img, (h0, w0), (h, w) = load_image(self, index) - - # Letterbox - shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape - img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) - shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling - - labels = self.labels[index].copy() - if labels.size: # normalized xywh to pixel xyxy format - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) - - if self.augment: - # Augment imagespace - if not mosaic: - img, labels = random_perspective(img, labels, - degrees=hyp['degrees'], - translate=hyp['translate'], - scale=hyp['scale'], - shear=hyp['shear'], - perspective=hyp['perspective']) - - # Augment colorspace - augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) - - # Apply cutouts - # if random.random() < 0.9: - # labels = cutout(img, labels) - - nL = len(labels) # number of labels - if nL: - labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh - labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 - labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 - - if self.augment: - # flip up-down - if random.random() < hyp['flipud']: - img = np.flipud(img) - if nL: - labels[:, 2] = 1 - labels[:, 2] - - # flip left-right - if random.random() < hyp['fliplr']: - img = np.fliplr(img) - if nL: - labels[:, 1] = 1 - labels[:, 1] - - labels_out = torch.zeros((nL, 6)) - if nL: - labels_out[:, 1:] = torch.from_numpy(labels) - - # Convert - img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 - img = np.ascontiguousarray(img) - - return torch.from_numpy(img), labels_out, self.img_files[index], shapes - - @staticmethod - def collate_fn(batch): - img, label, path, shapes = zip(*batch) # transposed - for i, l in enumerate(label): - l[:, 0] = i # add target image index for build_targets() - return torch.stack(img, 0), torch.cat(label, 0), path, shapes - - @staticmethod - def collate_fn4(batch): - img, label, path, shapes = zip(*batch) # transposed - n = len(shapes) // 4 - img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] - - ho = torch.tensor([[0., 0, 0, 1, 0, 0]]) - wo = torch.tensor([[0., 0, 1, 0, 0, 0]]) - s = torch.tensor([[1, 1, .5, .5, .5, .5]]) # scale - for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW - i *= 4 - if random.random() < 0.5: - im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[ - 0].type(img[i].type()) - l = label[i] - else: - im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) - l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s - img4.append(im) - label4.append(l) - - for i, l in enumerate(label4): - l[:, 0] = i # add target image index for build_targets() - - return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 - - -# Ancillary functions -------------------------------------------------------------------------------------------------- -def load_image(self, index): - # loads 1 image from dataset, returns img, original hw, resized hw - img = self.imgs[index] - if img is None: # not cached - path = self.img_files[index] - img = cv2.imread(path) # BGR - assert img is not None, 'Image Not Found ' + path - h0, w0 = img.shape[:2] # orig hw - r = self.img_size / max(h0, w0) # ratio - if r != 1: # if sizes are not equal - img = cv2.resize(img, (int(w0 * r), int(h0 * r)), - interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR) - return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized - else: - return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized - - -def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): - r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains - hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) - dtype = img.dtype # uint8 - - x = np.arange(0, 256, dtype=np.int16) - lut_hue = ((x * r[0]) % 180).astype(dtype) - lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) - lut_val = np.clip(x * r[2], 0, 255).astype(dtype) - - img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) - cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed - - -def hist_equalize(img, clahe=True, bgr=False): - # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255 - yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) - if clahe: - c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) - yuv[:, :, 0] = c.apply(yuv[:, :, 0]) - else: - yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram - return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB - - -def load_mosaic(self, index): - # loads images in a 4-mosaic - - labels4, segments4 = [], [] - s = self.img_size - yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y - indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices - for i, index in enumerate(indices): - # Load image - img, _, (h, w) = load_image(self, index) - - # place img in img4 - if i == 0: # top left - img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles - x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) - x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) - elif i == 1: # top right - x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc - x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h - elif i == 2: # bottom left - x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) - x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) - elif i == 3: # bottom right - x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) - x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) - - img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] - padw = x1a - x1b - padh = y1a - y1b - - # Labels - labels, segments = self.labels[index].copy(), self.segments[index].copy() - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padw, padh) for x in segments] - labels4.append(labels) - segments4.extend(segments) - - # Concat/clip labels - labels4 = np.concatenate(labels4, 0) - for x in (labels4[:, 1:], *segments4): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img4, labels4 = replicate(img4, labels4) # replicate - - # Augment - img4, labels4 = random_perspective(img4, labels4, segments4, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove - - return img4, labels4 - - -def load_mosaic9(self, index): - # loads images in a 9-mosaic - - labels9, segments9 = [], [] - s = self.img_size - indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices - for i, index in enumerate(indices): - # Load image - img, _, (h, w) = load_image(self, index) - - # place img in img9 - if i == 0: # center - img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles - h0, w0 = h, w - c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates - elif i == 1: # top - c = s, s - h, s + w, s - elif i == 2: # top right - c = s + wp, s - h, s + wp + w, s - elif i == 3: # right - c = s + w0, s, s + w0 + w, s + h - elif i == 4: # bottom right - c = s + w0, s + hp, s + w0 + w, s + hp + h - elif i == 5: # bottom - c = s + w0 - w, s + h0, s + w0, s + h0 + h - elif i == 6: # bottom left - c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h - elif i == 7: # left - c = s - w, s + h0 - h, s, s + h0 - elif i == 8: # top left - c = s - w, s + h0 - hp - h, s, s + h0 - hp - - padx, pady = c[:2] - x1, y1, x2, y2 = [max(x, 0) for x in c] # allocate coords - - # Labels - labels, segments = self.labels[index].copy(), self.segments[index].copy() - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padx, pady) for x in segments] - labels9.append(labels) - segments9.extend(segments) - - # Image - img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] - hp, wp = h, w # height, width previous - - # Offset - yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border] # mosaic center x, y - img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] - - # Concat/clip labels - labels9 = np.concatenate(labels9, 0) - labels9[:, [1, 3]] -= xc - labels9[:, [2, 4]] -= yc - c = np.array([xc, yc]) # centers - segments9 = [x - c for x in segments9] - - for x in (labels9[:, 1:], *segments9): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img9, labels9 = replicate(img9, labels9) # replicate - - # Augment - img9, labels9 = random_perspective(img9, labels9, segments9, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove - - return img9, labels9 - - -def replicate(img, labels): - # Replicate labels - h, w = img.shape[:2] - boxes = labels[:, 1:].astype(int) - x1, y1, x2, y2 = boxes.T - s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) - for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices - x1b, y1b, x2b, y2b = boxes[i] - bh, bw = y2b - y1b, x2b - x1b - yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y - x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] - img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] - labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) - - return img, labels - - -def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): - # Resize and pad image while meeting stride-multiple constraints - shape = img.shape[:2] # current shape [height, width] - if isinstance(new_shape, int): - new_shape = (new_shape, new_shape) - - # Scale ratio (new / old) - r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) - if not scaleup: # only scale down, do not scale up (for better test mAP) - r = min(r, 1.0) - - # Compute padding - ratio = r, r # width, height ratios - new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) - dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding - if auto: # minimum rectangle - dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding - elif scaleFill: # stretch - dw, dh = 0.0, 0.0 - new_unpad = (new_shape[1], new_shape[0]) - ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios - - dw /= 2 # divide padding into 2 sides - dh /= 2 - - if shape[::-1] != new_unpad: # resize - img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) - top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) - left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) - img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border - return img, ratio, (dw, dh) - - -def random_perspective(img, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, - border=(0, 0)): - # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) - # targets = [cls, xyxy] - - height = img.shape[0] + border[0] * 2 # shape(h,w,c) - width = img.shape[1] + border[1] * 2 - - # Center - C = np.eye(3) - C[0, 2] = -img.shape[1] / 2 # x translation (pixels) - C[1, 2] = -img.shape[0] / 2 # y translation (pixels) - - # Perspective - P = np.eye(3) - P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) - P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) - - # Rotation and Scale - R = np.eye(3) - a = random.uniform(-degrees, degrees) - # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations - s = random.uniform(1 - scale, 1 + scale) - # s = 2 ** random.uniform(-scale, scale) - R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) - - # Shear - S = np.eye(3) - S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) - S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) - - # Translation - T = np.eye(3) - T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) - T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) - - # Combined rotation matrix - M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT - if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed - if perspective: - img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) - else: # affine - img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) - - # Visualize - # import matplotlib.pyplot as plt - # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() - # ax[0].imshow(img[:, :, ::-1]) # base - # ax[1].imshow(img2[:, :, ::-1]) # warped - - # Transform label coordinates - n = len(targets) - if n: - use_segments = any(x.any() for x in segments) - new = np.zeros((n, 4)) - if use_segments: # warp segments - segments = resample_segments(segments) # upsample - for i, segment in enumerate(segments): - xy = np.ones((len(segment), 3)) - xy[:, :2] = segment - xy = xy @ M.T # transform - xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine - - # clip - new[i] = segment2box(xy, width, height) - - else: # warp boxes - xy = np.ones((n * 4, 3)) - xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 - xy = xy @ M.T # transform - xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine - - # create new boxes - x = xy[:, [0, 2, 4, 6]] - y = xy[:, [1, 3, 5, 7]] - new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T - - # clip - new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) - new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) - - # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) - targets = targets[i] - targets[:, 1:5] = new[i] - - return img, targets - - -def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) - # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio - w1, h1 = box1[2] - box1[0], box1[3] - box1[1] - w2, h2 = box2[2] - box2[0], box2[3] - box2[1] - ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio - return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates - - -def cutout(image, labels): - # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 - h, w = image.shape[:2] - - def bbox_ioa(box1, box2): - # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 - box2 = box2.transpose() - - # Get the coordinates of bounding boxes - b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] - b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] - - # Intersection area - inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ - (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) - - # box2 area - box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 - - # Intersection over box2 area - return inter_area / box2_area - - # create random masks - scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction - for s in scales: - mask_h = random.randint(1, int(h * s)) - mask_w = random.randint(1, int(w * s)) - - # box - xmin = max(0, random.randint(0, w) - mask_w // 2) - ymin = max(0, random.randint(0, h) - mask_h // 2) - xmax = min(w, xmin + mask_w) - ymax = min(h, ymin + mask_h) - - # apply random color mask - image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] - - # return unobscured labels - if len(labels) and s > 0.03: - box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) - ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area - labels = labels[ioa < 0.60] # remove >60% obscured labels - - return labels - - -def create_folder(path='./new'): - # Create folder - if os.path.exists(path): - shutil.rmtree(path) # delete output folder - os.makedirs(path) # make new output folder - - -def flatten_recursive(path='../coco128'): - # Flatten a recursive directory by bringing all files to top level - new_path = Path(path + '_flat') - create_folder(new_path) - for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)): - shutil.copyfile(file, new_path / Path(file).name) - - -def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_boxes('../coco128') - # Convert detection dataset into classification dataset, with one directory per class - - path = Path(path) # images dir - shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing - files = list(path.rglob('*.*')) - n = len(files) # number of files - for im_file in tqdm(files, total=n): - if im_file.suffix[1:] in img_formats: - # image - im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB - h, w = im.shape[:2] - - # labels - lb_file = Path(img2label_paths([str(im_file)])[0]) - if Path(lb_file).exists(): - with open(lb_file, 'r') as f: - lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels - - for j, x in enumerate(lb): - c = int(x[0]) # class - f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename - if not f.parent.is_dir(): - f.parent.mkdir(parents=True) - - b = x[1:] * [w, h, w, h] # box - # b[2:] = b[2:].max() # rectangle to square - b[2:] = b[2:] * 1.2 + 3 # pad - b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) - - b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image - b[[1, 3]] = np.clip(b[[1, 3]], 0, h) - assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' - - -def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0), annotated_only=False): - """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files - Usage: from utils.datasets import *; autosplit('../coco128') - Arguments - path: Path to images directory - weights: Train, val, test weights (list) - annotated_only: Only use images with an annotated txt file - """ - path = Path(path) # images dir - files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only - n = len(files) # number of files - indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split - - txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files - [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing - - print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) - for i, img in tqdm(zip(indices, files), total=n): - if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label - with open(path / txt[i], 'a') as f: - f.write(str(img) + '\n') # add image to txt file diff --git a/Yolov5-Deepsort/utils/flask_rest_api/README.md b/Yolov5-Deepsort/utils/flask_rest_api/README.md deleted file mode 100644 index 324c2416dcd9fa83b18286c33ce309f4f5573637..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/flask_rest_api/README.md +++ /dev/null @@ -1,68 +0,0 @@ -# Flask REST API -[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). - -## Requirements - -[Flask](https://palletsprojects.com/p/flask/) is required. Install with: -```shell -$ pip install Flask -``` - -## Run - -After Flask installation run: - -```shell -$ python3 restapi.py --port 5000 -``` - -Then use [curl](https://curl.se/) to perform a request: - -```shell -$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'` -``` - -The model inference results are returned as a JSON response: - -```json -[ - { - "class": 0, - "confidence": 0.8900438547, - "height": 0.9318675399, - "name": "person", - "width": 0.3264600933, - "xcenter": 0.7438579798, - "ycenter": 0.5207948685 - }, - { - "class": 0, - "confidence": 0.8440024257, - "height": 0.7155083418, - "name": "person", - "width": 0.6546785235, - "xcenter": 0.427829951, - "ycenter": 0.6334488392 - }, - { - "class": 27, - "confidence": 0.3771208823, - "height": 0.3902671337, - "name": "tie", - "width": 0.0696444362, - "xcenter": 0.3675483763, - "ycenter": 0.7991207838 - }, - { - "class": 27, - "confidence": 0.3527112305, - "height": 0.1540903747, - "name": "tie", - "width": 0.0336618312, - "xcenter": 0.7814827561, - "ycenter": 0.5065554976 - } -] -``` - -An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py` diff --git a/Yolov5-Deepsort/utils/flask_rest_api/example_request.py b/Yolov5-Deepsort/utils/flask_rest_api/example_request.py deleted file mode 100644 index ff21f30f93ca37578ce45366a1ddbe3f3eadaa79..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/flask_rest_api/example_request.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Perform test request""" -import pprint - -import requests - -DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" -TEST_IMAGE = "zidane.jpg" - -image_data = open(TEST_IMAGE, "rb").read() - -response = requests.post(DETECTION_URL, files={"image": image_data}).json() - -pprint.pprint(response) diff --git a/Yolov5-Deepsort/utils/flask_rest_api/restapi.py b/Yolov5-Deepsort/utils/flask_rest_api/restapi.py deleted file mode 100644 index a54e2309715ce5d3d41e9e2e76a347db3cdb7ccb..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/flask_rest_api/restapi.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -Run a rest API exposing the yolov5s object detection model -""" -import argparse -import io - -import torch -from PIL import Image -from flask import Flask, request - -app = Flask(__name__) - -DETECTION_URL = "/v1/object-detection/yolov5s" - - -@app.route(DETECTION_URL, methods=["POST"]) -def predict(): - if not request.method == "POST": - return - - if request.files.get("image"): - image_file = request.files["image"] - image_bytes = image_file.read() - - img = Image.open(io.BytesIO(image_bytes)) - - results = model(img, size=640) # reduce size=320 for faster inference - return results.pandas().xyxy[0].to_json(orient="records") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") - parser.add_argument("--port", default=5000, type=int, help="port number") - args = parser.parse_args() - - model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache - app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat diff --git a/Yolov5-Deepsort/utils/general.py b/Yolov5-Deepsort/utils/general.py deleted file mode 100644 index 9a882715f0ad43a0271252fca3aeb06f6169d1ff..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/general.py +++ /dev/null @@ -1,692 +0,0 @@ -# YOLOv5 general utils - -import glob -import logging -import math -import os -import platform -import random -import re -import subprocess -import time -from itertools import repeat -from multiprocessing.pool import ThreadPool -from pathlib import Path - -import cv2 -import numpy as np -import pandas as pd -import pkg_resources as pkg -import torch -import torchvision -import yaml - -from utils.google_utils import gsutil_getsize -from utils.metrics import fitness -from utils.torch_utils import init_torch_seeds - -# Settings -torch.set_printoptions(linewidth=320, precision=5, profile='long') -np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 -pd.options.display.max_columns = 10 -cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) -os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8)) # NumExpr max threads - - -def set_logging(rank=-1, verbose=True): - logging.basicConfig( - format="%(message)s", - level=logging.INFO if (verbose and rank in [-1, 0]) else logging.WARN) - - -def init_seeds(seed=0): - # Initialize random number generator (RNG) seeds - random.seed(seed) - np.random.seed(seed) - init_torch_seeds(seed) - - -def get_latest_run(search_dir='.'): - # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) - last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) - return max(last_list, key=os.path.getctime) if last_list else '' - - -def is_docker(): - # Is environment a Docker container - return Path('/workspace').exists() # or Path('/.dockerenv').exists() - - -def is_colab(): - # Is environment a Google Colab instance - try: - import google.colab - return True - except Exception as e: - return False - - -def emojis(str=''): - # Return platform-dependent emoji-safe version of string - return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str - - -def file_size(file): - # Return file size in MB - return Path(file).stat().st_size / 1e6 - - -def check_online(): - # Check internet connectivity - import socket - try: - socket.create_connection(("1.1.1.1", 443), 5) # check host accesability - return True - except OSError: - return False - - -def check_git_status(): - # Recommend 'git pull' if code is out of date - print(colorstr('github: '), end='') - try: - assert Path('.git').exists(), 'skipping check (not a git repository)' - assert not is_docker(), 'skipping check (Docker image)' - assert check_online(), 'skipping check (offline)' - - cmd = 'git fetch && git config --get remote.origin.url' - url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git') # github repo url - branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out - n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind - if n > 0: - s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \ - f"Use 'git pull' to update or 'git clone {url}' to download latest." - else: - s = f'up to date with {url} ✅' - print(emojis(s)) # emoji-safe - except Exception as e: - print(e) - - -def check_python(minimum='3.7.0', required=True): - # Check current python version vs. required python version - current = platform.python_version() - result = pkg.parse_version(current) >= pkg.parse_version(minimum) - if required: - assert result, f'Python {minimum} required by YOLOv5, but Python {current} is currently installed' - return result - - -def check_requirements(requirements='requirements.txt', exclude=()): - # Check installed dependencies meet requirements (pass *.txt file or list of packages) - prefix = colorstr('red', 'bold', 'requirements:') - check_python() # check python version - if isinstance(requirements, (str, Path)): # requirements.txt file - file = Path(requirements) - if not file.exists(): - print(f"{prefix} {file.resolve()} not found, check failed.") - return - requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude] - else: # list or tuple of packages - requirements = [x for x in requirements if x not in exclude] - - n = 0 # number of packages updates - for r in requirements: - try: - pkg.require(r) - except Exception as e: # DistributionNotFound or VersionConflict if requirements not met - n += 1 - print(f"{prefix} {r} not found and is required by YOLOv5, attempting auto-update...") - try: - print(subprocess.check_output(f"pip install '{r}'", shell=True).decode()) - except Exception as e: - print(f'{prefix} {e}') - - if n: # if packages updated - source = file.resolve() if 'file' in locals() else requirements - s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ - f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" - print(emojis(s)) # emoji-safe - - -def check_img_size(img_size, s=32): - # Verify img_size is a multiple of stride s - new_size = make_divisible(img_size, int(s)) # ceil gs-multiple - if new_size != img_size: - print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) - return new_size - - -def check_imshow(): - # Check if environment supports image displays - try: - assert not is_docker(), 'cv2.imshow() is disabled in Docker environments' - assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments' - cv2.imshow('test', np.zeros((1, 1, 3))) - cv2.waitKey(1) - cv2.destroyAllWindows() - cv2.waitKey(1) - return True - except Exception as e: - print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') - return False - - -def check_file(file): - # Search for file if not found - if Path(file).is_file() or file == '': - return file - else: - files = glob.glob('./**/' + file, recursive=True) # find file - assert len(files), f'File Not Found: {file}' # assert file was found - assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique - return files[0] # return file - - -def check_dataset(dict): - # Download dataset if not found locally - val, s = dict.get('val'), dict.get('download') - if val and len(val): - val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path - if not all(x.exists() for x in val): - print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) - if s and len(s): # download script - if s.startswith('http') and s.endswith('.zip'): # URL - f = Path(s).name # filename - print(f'Downloading {s} ...') - torch.hub.download_url_to_file(s, f) - r = os.system(f'unzip -q {f} -d ../ && rm {f}') # unzip - elif s.startswith('bash '): # bash script - print(f'Running {s} ...') - r = os.system(s) - else: # python script - r = exec(s) # return None - print('Dataset autodownload %s\n' % ('success' if r in (0, None) else 'failure')) # print result - else: - raise Exception('Dataset not found.') - - -def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1): - # Multi-threaded file download and unzip function - def download_one(url, dir): - # Download 1 file - f = dir / Path(url).name # filename - if not f.exists(): - print(f'Downloading {url} to {f}...') - if curl: - os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail - else: - torch.hub.download_url_to_file(url, f, progress=True) # torch download - if unzip and f.suffix in ('.zip', '.gz'): - print(f'Unzipping {f}...') - if f.suffix == '.zip': - s = f'unzip -qo {f} -d {dir} && rm {f}' # unzip -quiet -overwrite - elif f.suffix == '.gz': - s = f'tar xfz {f} --directory {f.parent}' # unzip - if delete: # delete zip file after unzip - s += f' && rm {f}' - os.system(s) - - dir = Path(dir) - dir.mkdir(parents=True, exist_ok=True) # make directory - if threads > 1: - pool = ThreadPool(threads) - pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multi-threaded - pool.close() - pool.join() - else: - for u in tuple(url) if isinstance(url, str) else url: - download_one(u, dir) - - -def make_divisible(x, divisor): - # Returns x evenly divisible by divisor - return math.ceil(x / divisor) * divisor - - -def clean_str(s): - # Cleans a string by replacing special characters with underscore _ - return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) - - -def one_cycle(y1=0.0, y2=1.0, steps=100): - # lambda function for sinusoidal ramp from y1 to y2 - return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 - - -def colorstr(*input): - # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') - *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string - colors = {'black': '\033[30m', # basic colors - 'red': '\033[31m', - 'green': '\033[32m', - 'yellow': '\033[33m', - 'blue': '\033[34m', - 'magenta': '\033[35m', - 'cyan': '\033[36m', - 'white': '\033[37m', - 'bright_black': '\033[90m', # bright colors - 'bright_red': '\033[91m', - 'bright_green': '\033[92m', - 'bright_yellow': '\033[93m', - 'bright_blue': '\033[94m', - 'bright_magenta': '\033[95m', - 'bright_cyan': '\033[96m', - 'bright_white': '\033[97m', - 'end': '\033[0m', # misc - 'bold': '\033[1m', - 'underline': '\033[4m'} - return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] - - -def labels_to_class_weights(labels, nc=80): - # Get class weights (inverse frequency) from training labels - if labels[0] is None: # no labels loaded - return torch.Tensor() - - labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO - classes = labels[:, 0].astype(np.int) # labels = [class xywh] - weights = np.bincount(classes, minlength=nc) # occurrences per class - - # Prepend gridpoint count (for uCE training) - # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image - # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start - - weights[weights == 0] = 1 # replace empty bins with 1 - weights = 1 / weights # number of targets per class - weights /= weights.sum() # normalize - return torch.from_numpy(weights) - - -def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): - # Produces image weights based on class_weights and image contents - class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels]) - image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) - # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample - return image_weights - - -def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) - # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ - # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') - # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') - # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco - # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet - x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, - 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, - 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] - return x - - -def xyxy2xywh(x): - # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center - y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center - y[:, 2] = x[:, 2] - x[:, 0] # width - y[:, 3] = x[:, 3] - x[:, 1] # height - return y - - -def xywh2xyxy(x): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x - y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y - y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x - y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y - return y - - -def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): - # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x - y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y - y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x - y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y - return y - - -def xyn2xy(x, w=640, h=640, padw=0, padh=0): - # Convert normalized segments into pixel segments, shape (n,2) - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * x[:, 0] + padw # top left x - y[:, 1] = h * x[:, 1] + padh # top left y - return y - - -def segment2box(segment, width=640, height=640): - # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) - x, y = segment.T # segment xy - inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) - x, y, = x[inside], y[inside] - return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy - - -def segments2boxes(segments): - # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) - boxes = [] - for s in segments: - x, y = s.T # segment xy - boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy - return xyxy2xywh(np.array(boxes)) # cls, xywh - - -def resample_segments(segments, n=1000): - # Up-sample an (n,2) segment - for i, s in enumerate(segments): - x = np.linspace(0, len(s) - 1, n) - xp = np.arange(len(s)) - segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy - return segments - - -def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - coords[:, [0, 2]] -= pad[0] # x padding - coords[:, [1, 3]] -= pad[1] # y padding - coords[:, :4] /= gain - clip_coords(coords, img0_shape) - return coords - - -def clip_coords(boxes, img_shape): - # Clip bounding xyxy bounding boxes to image shape (height, width) - boxes[:, 0].clamp_(0, img_shape[1]) # x1 - boxes[:, 1].clamp_(0, img_shape[0]) # y1 - boxes[:, 2].clamp_(0, img_shape[1]) # x2 - boxes[:, 3].clamp_(0, img_shape[0]) # y2 - - -def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): - # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 - box2 = box2.T - - # Get the coordinates of bounding boxes - if x1y1x2y2: # x1, y1, x2, y2 = box1 - b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] - b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] - else: # transform from xywh to xyxy - b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 - b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 - b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 - b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 - - # Intersection area - inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ - (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) - - # Union Area - w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps - w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps - union = w1 * h1 + w2 * h2 - inter + eps - - iou = inter / union - if GIoU or DIoU or CIoU: - cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width - ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height - if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 - c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared - rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + - (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared - if DIoU: - return iou - rho2 / c2 # DIoU - elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 - v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) - with torch.no_grad(): - alpha = v / (v - iou + (1 + eps)) - return iou - (rho2 / c2 + v * alpha) # CIoU - else: # GIoU https://arxiv.org/pdf/1902.09630.pdf - c_area = cw * ch + eps # convex area - return iou - (c_area - union) / c_area # GIoU - else: - return iou # IoU - - -def box_iou(box1, box2): - # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - box1 (Tensor[N, 4]) - box2 (Tensor[M, 4]) - Returns: - iou (Tensor[N, M]): the NxM matrix containing the pairwise - IoU values for every element in boxes1 and boxes2 - """ - - def box_area(box): - # box = 4xn - return (box[2] - box[0]) * (box[3] - box[1]) - - area1 = box_area(box1.T) - area2 = box_area(box2.T) - - # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) - inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) - return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) - - -def wh_iou(wh1, wh2): - # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 - wh1 = wh1[:, None] # [N,1,2] - wh2 = wh2[None] # [1,M,2] - inter = torch.min(wh1, wh2).prod(2) # [N,M] - return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) - - -def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, - labels=(), max_det=300): - """Runs Non-Maximum Suppression (NMS) on inference results - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - nc = prediction.shape[2] - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Checks - assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' - assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' - - # Settings - min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - time_limit = 10.0 # seconds to quit after - redundant = True # require redundant detections - multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) - merge = False # use merge-NMS - - t = time.time() - output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] - for xi, x in enumerate(prediction): # image index, image inference - # Apply constraints - # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height - x = x[xc[xi]] # confidence - - # Cat apriori labels if autolabelling - if labels and len(labels[xi]): - l = labels[xi] - v = torch.zeros((len(l), nc + 5), device=x.device) - v[:, :4] = l[:, 1:5] # box - v[:, 4] = 1.0 # conf - v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls - x = torch.cat((x, v), 0) - - # If none remain process next image - if not x.shape[0]: - continue - - # Compute conf - x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf - - # Box (center x, center y, width, height) to (x1, y1, x2, y2) - box = xywh2xyxy(x[:, :4]) - - # Detections matrix nx6 (xyxy, conf, cls) - if multi_label: - i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T - x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) - else: # best class only - conf, j = x[:, 5:].max(1, keepdim=True) - x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] - - # Filter by class - if classes is not None: - x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] - - # Apply finite constraint - # if not torch.isfinite(x).all(): - # x = x[torch.isfinite(x).all(1)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - elif n > max_nms: # excess boxes - x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence - - # Batched NMS - c = x[:, 5:6] * (0 if agnostic else max_wh) # classes - boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores - i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS - if i.shape[0] > max_det: # limit detections - i = i[:max_det] - if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) - # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) - iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix - weights = iou * scores[None] # box weights - x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes - if redundant: - i = i[iou.sum(1) > 1] # require redundancy - - output[xi] = x[i] - if (time.time() - t) > time_limit: - print(f'WARNING: NMS time limit {time_limit}s exceeded') - break # time limit exceeded - - return output - - -def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_optimizer() - # Strip optimizer from 'f' to finalize training, optionally save as 's' - x = torch.load(f, map_location=torch.device('cpu')) - if x.get('ema'): - x['model'] = x['ema'] # replace model with ema - for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates': # keys - x[k] = None - x['epoch'] = -1 - x['model'].half() # to FP16 - for p in x['model'].parameters(): - p.requires_grad = False - torch.save(x, s or f) - mb = os.path.getsize(s or f) / 1E6 # filesize - print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") - - -def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): - # Print mutation results to evolve.txt (for use with train.py --evolve) - a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys - b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values - c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) - print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) - - if bucket: - url = 'gs://%s/evolve.txt' % bucket - if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): - os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local - - with open('evolve.txt', 'a') as f: # append result - f.write(c + b + '\n') - x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows - x = x[np.argsort(-fitness(x))] # sort - np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness - - # Save yaml - for i, k in enumerate(hyp.keys()): - hyp[k] = float(x[0, i + 7]) - with open(yaml_file, 'w') as f: - results = tuple(x[0, :7]) - c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) - f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') - yaml.safe_dump(hyp, f, sort_keys=False) - - if bucket: - os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload - - -def apply_classifier(x, model, img, im0): - # Apply a second stage classifier to yolo outputs - im0 = [im0] if isinstance(im0, np.ndarray) else im0 - for i, d in enumerate(x): # per image - if d is not None and len(d): - d = d.clone() - - # Reshape and pad cutouts - b = xyxy2xywh(d[:, :4]) # boxes - b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square - b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad - d[:, :4] = xywh2xyxy(b).long() - - # Rescale boxes from img_size to im0 size - scale_coords(img.shape[2:], d[:, :4], im0[i].shape) - - # Classes - pred_cls1 = d[:, 5].long() - ims = [] - for j, a in enumerate(d): # per item - cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] - im = cv2.resize(cutout, (224, 224)) # BGR - # cv2.imwrite('test%i.jpg' % j, cutout) - - im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 - im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 - im /= 255.0 # 0 - 255 to 0.0 - 1.0 - ims.append(im) - - pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction - x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections - - return x - - -def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True): - # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop - xyxy = torch.tensor(xyxy).view(-1, 4) - b = xyxy2xywh(xyxy) # boxes - if square: - b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square - b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad - xyxy = xywh2xyxy(b).long() - clip_coords(xyxy, im.shape) - crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] - if save: - cv2.imwrite(str(increment_path(file, mkdir=True).with_suffix('.jpg')), crop) - return crop - - -def increment_path(path, exist_ok=False, sep='', mkdir=False): - # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. - path = Path(path) # os-agnostic - if path.exists() and not exist_ok: - suffix = path.suffix - path = path.with_suffix('') - dirs = glob.glob(f"{path}{sep}*") # similar paths - matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] - i = [int(m.groups()[0]) for m in matches if m] # indices - n = max(i) + 1 if i else 2 # increment number - path = Path(f"{path}{sep}{n}{suffix}") # update path - dir = path if path.suffix == '' else path.parent # directory - if not dir.exists() and mkdir: - dir.mkdir(parents=True, exist_ok=True) # make directory - return path diff --git a/Yolov5-Deepsort/utils/google_app_engine/Dockerfile b/Yolov5-Deepsort/utils/google_app_engine/Dockerfile deleted file mode 100644 index 0155618f475104e9858b81470339558156c94e13..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/google_app_engine/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -FROM gcr.io/google-appengine/python - -# Create a virtualenv for dependencies. This isolates these packages from -# system-level packages. -# Use -p python3 or -p python3.7 to select python version. Default is version 2. -RUN virtualenv /env -p python3 - -# Setting these environment variables are the same as running -# source /env/bin/activate. -ENV VIRTUAL_ENV /env -ENV PATH /env/bin:$PATH - -RUN apt-get update && apt-get install -y python-opencv - -# Copy the application's requirements.txt and run pip to install all -# dependencies into the virtualenv. -ADD requirements.txt /app/requirements.txt -RUN pip install -r /app/requirements.txt - -# Add the application source code. -ADD . /app - -# Run a WSGI server to serve the application. gunicorn must be declared as -# a dependency in requirements.txt. -CMD gunicorn -b :$PORT main:app diff --git a/Yolov5-Deepsort/utils/google_app_engine/additional_requirements.txt b/Yolov5-Deepsort/utils/google_app_engine/additional_requirements.txt deleted file mode 100644 index 5fcc30524a59ca2d3356b07725df7e2b64f81422..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/google_app_engine/additional_requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -# add these requirements in your app on top of the existing ones -pip==18.1 -Flask==1.0.2 -gunicorn==19.9.0 diff --git a/Yolov5-Deepsort/utils/google_app_engine/app.yaml b/Yolov5-Deepsort/utils/google_app_engine/app.yaml deleted file mode 100644 index ac29d104b144abd634482b35282725d694e84a2b..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/google_app_engine/app.yaml +++ /dev/null @@ -1,14 +0,0 @@ -runtime: custom -env: flex - -service: yolov5app - -liveness_check: - initial_delay_sec: 600 - -manual_scaling: - instances: 1 -resources: - cpu: 1 - memory_gb: 4 - disk_size_gb: 20 \ No newline at end of file diff --git a/Yolov5-Deepsort/utils/google_utils.py b/Yolov5-Deepsort/utils/google_utils.py deleted file mode 100644 index 63d3e5b212f3813847eeeb5cd6524155a7058f27..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/google_utils.py +++ /dev/null @@ -1,127 +0,0 @@ -# Google utils: https://cloud.google.com/storage/docs/reference/libraries - -import os -import platform -import subprocess -import time -from pathlib import Path - -import requests -import torch - - -def gsutil_getsize(url=''): - # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du - s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') - return eval(s.split(' ')[0]) if len(s) else 0 # bytes - - -def attempt_download(file, repo='ultralytics/yolov5'): - # Attempt file download if does not exist - file = Path(str(file).strip().replace("'", '')) - - if not file.exists(): - file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) - try: - response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api - assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] - tag = response['tag_name'] # i.e. 'v1.0' - except: # fallback plan - assets = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', - 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt'] - try: - tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] - except: - tag = 'v5.0' # current release - - name = file.name - if name in assets: - msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/' - redundant = False # second download option - try: # GitHub - url = f'https://github.com/{repo}/releases/download/{tag}/{name}' - print(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, file) - assert file.exists() and file.stat().st_size > 1E6 # check - except Exception as e: # GCP - print(f'Download error: {e}') - assert redundant, 'No secondary mirror' - url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' - print(f'Downloading {url} to {file}...') - os.system(f"curl -L '{url}' -o '{file}' --retry 3 -C -") # curl download, retry and resume on fail - finally: - if not file.exists() or file.stat().st_size < 1E6: # check - file.unlink(missing_ok=True) # remove partial downloads - print(f'ERROR: Download failure: {msg}') - print('') - return - - -def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): - # Downloads a file from Google Drive. from yolov5.utils.google_utils import *; gdrive_download() - t = time.time() - file = Path(file) - cookie = Path('cookie') # gdrive cookie - print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') - file.unlink(missing_ok=True) # remove existing file - cookie.unlink(missing_ok=True) # remove existing cookie - - # Attempt file download - out = "NUL" if platform.system() == "Windows" else "/dev/null" - os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') - if os.path.exists('cookie'): # large file - s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' - else: # small file - s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' - r = os.system(s) # execute, capture return - cookie.unlink(missing_ok=True) # remove existing cookie - - # Error check - if r != 0: - file.unlink(missing_ok=True) # remove partial - print('Download error ') # raise Exception('Download error') - return r - - # Unzip if archive - if file.suffix == '.zip': - print('unzipping... ', end='') - os.system(f'unzip -q {file}') # unzip - file.unlink() # remove zip to free space - - print(f'Done ({time.time() - t:.1f}s)') - return r - - -def get_token(cookie="./cookie"): - with open(cookie) as f: - for line in f: - if "download" in line: - return line.split()[-1] - return "" - -# def upload_blob(bucket_name, source_file_name, destination_blob_name): -# # Uploads a file to a bucket -# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python -# -# storage_client = storage.Client() -# bucket = storage_client.get_bucket(bucket_name) -# blob = bucket.blob(destination_blob_name) -# -# blob.upload_from_filename(source_file_name) -# -# print('File {} uploaded to {}.'.format( -# source_file_name, -# destination_blob_name)) -# -# -# def download_blob(bucket_name, source_blob_name, destination_file_name): -# # Uploads a blob from a bucket -# storage_client = storage.Client() -# bucket = storage_client.get_bucket(bucket_name) -# blob = bucket.blob(source_blob_name) -# -# blob.download_to_filename(destination_file_name) -# -# print('Blob {} downloaded to {}.'.format( -# source_blob_name, -# destination_file_name)) diff --git a/Yolov5-Deepsort/utils/loss.py b/Yolov5-Deepsort/utils/loss.py deleted file mode 100644 index 9e78df17fdf31c23c70a5d1fce8b17fb7b78726a..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/loss.py +++ /dev/null @@ -1,216 +0,0 @@ -# Loss functions - -import torch -import torch.nn as nn - -from utils.general import bbox_iou -from utils.torch_utils import is_parallel - - -def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 - # return positive, negative label smoothing BCE targets - return 1.0 - 0.5 * eps, 0.5 * eps - - -class BCEBlurWithLogitsLoss(nn.Module): - # BCEwithLogitLoss() with reduced missing label effects. - def __init__(self, alpha=0.05): - super(BCEBlurWithLogitsLoss, self).__init__() - self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() - self.alpha = alpha - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - pred = torch.sigmoid(pred) # prob from logits - dx = pred - true # reduce only missing label effects - # dx = (pred - true).abs() # reduce missing label and false label effects - alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) - loss *= alpha_factor - return loss.mean() - - -class FocalLoss(nn.Module): - # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(FocalLoss, self).__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - # p_t = torch.exp(-loss) - # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability - - # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py - pred_prob = torch.sigmoid(pred) # prob from logits - p_t = true * pred_prob + (1 - true) * (1 - pred_prob) - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = (1.0 - p_t) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - - -class QFocalLoss(nn.Module): - # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) - def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): - super(QFocalLoss, self).__init__() - self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() - self.gamma = gamma - self.alpha = alpha - self.reduction = loss_fcn.reduction - self.loss_fcn.reduction = 'none' # required to apply FL to each element - - def forward(self, pred, true): - loss = self.loss_fcn(pred, true) - - pred_prob = torch.sigmoid(pred) # prob from logits - alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) - modulating_factor = torch.abs(true - pred_prob) ** self.gamma - loss *= alpha_factor * modulating_factor - - if self.reduction == 'mean': - return loss.mean() - elif self.reduction == 'sum': - return loss.sum() - else: # 'none' - return loss - - -class ComputeLoss: - # Compute losses - def __init__(self, model, autobalance=False): - super(ComputeLoss, self).__init__() - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets): # predictions, targets, model - device = targets.device - lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) - tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - ps = pi[b, a, gj, gi] # prediction subset corresponding to targets - - # Regression - pxy = ps[:, :2].sigmoid() * 2. - 0.5 - pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] - pbox = torch.cat((pxy, pwh), 1) # predicted box - iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) - lbox += (1.0 - iou).mean() # iou loss - - # Objectness - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), tcls[i]] = self.cp - lcls += self.BCEcls(ps[:, 5:], t) # BCE - - # Append targets to text file - # with open('targets.txt', 'a') as file: - # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - loss = lbox + lobj + lcls - return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() - - def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - gain = torch.ones(7, device=targets.device) # normalized to gridspace gain - ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) - targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices - - g = 0.5 # bias - off = torch.tensor([[0, 0], - [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m - # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm - ], device=targets.device).float() * g # offsets - - for i in range(self.nl): - anchors = self.anchors[i] - gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain - - # Match targets to anchors - t = targets * gain - if nt: - # Matches - r = t[:, :, 4:6] / anchors[:, None] # wh ratio - j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1. < g) & (gxy > 1.)).T - l, m = ((gxi % 1. < g) & (gxi > 1.)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - b, c = t[:, :2].long().T # image, class - gxy = t[:, 2:4] # grid xy - gwh = t[:, 4:6] # grid wh - gij = (gxy - offsets).long() - gi, gj = gij.T # grid xy indices - - # Append - a = t[:, 6].long() # anchor indices - indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices - tbox.append(torch.cat((gxy - gij, gwh), 1)) # box - anch.append(anchors[a]) # anchors - tcls.append(c) # class - - return tcls, tbox, indices, anch diff --git a/Yolov5-Deepsort/utils/metrics.py b/Yolov5-Deepsort/utils/metrics.py deleted file mode 100644 index 323c84b6c873c87a3530a3caec60982a7d83a3b8..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/metrics.py +++ /dev/null @@ -1,223 +0,0 @@ -# Model validation metrics - -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch - -from . import general - - -def fitness(x): - # Model fitness as a weighted combination of metrics - w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] - return (x[:, :4] * w).sum(1) - - -def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): - """ Compute the average precision, given the recall and precision curves. - Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. - # Arguments - tp: True positives (nparray, nx1 or nx10). - conf: Objectness value from 0-1 (nparray). - pred_cls: Predicted object classes (nparray). - target_cls: True object classes (nparray). - plot: Plot precision-recall curve at mAP@0.5 - save_dir: Plot save directory - # Returns - The average precision as computed in py-faster-rcnn. - """ - - # Sort by objectness - i = np.argsort(-conf) - tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] - - # Find unique classes - unique_classes = np.unique(target_cls) - nc = unique_classes.shape[0] # number of classes, number of detections - - # Create Precision-Recall curve and compute AP for each class - px, py = np.linspace(0, 1, 1000), [] # for plotting - ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) - for ci, c in enumerate(unique_classes): - i = pred_cls == c - n_l = (target_cls == c).sum() # number of labels - n_p = i.sum() # number of predictions - - if n_p == 0 or n_l == 0: - continue - else: - # Accumulate FPs and TPs - fpc = (1 - tp[i]).cumsum(0) - tpc = tp[i].cumsum(0) - - # Recall - recall = tpc / (n_l + 1e-16) # recall curve - r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases - - # Precision - precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score - - # AP from recall-precision curve - for j in range(tp.shape[1]): - ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and j == 0: - py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - - # Compute F1 (harmonic mean of precision and recall) - f1 = 2 * p * r / (p + r + 1e-16) - if plot: - plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) - plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') - plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') - plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') - - i = f1.mean(0).argmax() # max F1 index - return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') - - -def compute_ap(recall, precision): - """ Compute the average precision, given the recall and precision curves - # Arguments - recall: The recall curve (list) - precision: The precision curve (list) - # Returns - Average precision, precision curve, recall curve - """ - - # Append sentinel values to beginning and end - mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) - mpre = np.concatenate(([1.], precision, [0.])) - - # Compute the precision envelope - mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) - - # Integrate area under curve - method = 'interp' # methods: 'continuous', 'interp' - if method == 'interp': - x = np.linspace(0, 1, 101) # 101-point interp (COCO) - ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate - else: # 'continuous' - i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes - ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve - - return ap, mpre, mrec - - -class ConfusionMatrix: - # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix - def __init__(self, nc, conf=0.25, iou_thres=0.45): - self.matrix = np.zeros((nc + 1, nc + 1)) - self.nc = nc # number of classes - self.conf = conf - self.iou_thres = iou_thres - - def process_batch(self, detections, labels): - """ - Return intersection-over-union (Jaccard index) of boxes. - Both sets of boxes are expected to be in (x1, y1, x2, y2) format. - Arguments: - detections (Array[N, 6]), x1, y1, x2, y2, conf, class - labels (Array[M, 5]), class, x1, y1, x2, y2 - Returns: - None, updates confusion matrix accordingly - """ - detections = detections[detections[:, 4] > self.conf] - gt_classes = labels[:, 0].int() - detection_classes = detections[:, 5].int() - iou = general.box_iou(labels[:, 1:], detections[:, :4]) - - x = torch.where(iou > self.iou_thres) - if x[0].shape[0]: - matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() - if x[0].shape[0] > 1: - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 1], return_index=True)[1]] - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 0], return_index=True)[1]] - else: - matches = np.zeros((0, 3)) - - n = matches.shape[0] > 0 - m0, m1, _ = matches.transpose().astype(np.int16) - for i, gc in enumerate(gt_classes): - j = m0 == i - if n and sum(j) == 1: - self.matrix[detection_classes[m1[j]], gc] += 1 # correct - else: - self.matrix[self.nc, gc] += 1 # background FP - - if n: - for i, dc in enumerate(detection_classes): - if not any(m1 == i): - self.matrix[dc, self.nc] += 1 # background FN - - def matrix(self): - return self.matrix - - def plot(self, save_dir='', names=()): - try: - import seaborn as sn - - array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize - array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) - - fig = plt.figure(figsize=(12, 9), tight_layout=True) - sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size - labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels - sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, - xticklabels=names + ['background FP'] if labels else "auto", - yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) - fig.axes[0].set_xlabel('True') - fig.axes[0].set_ylabel('Predicted') - fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) - except Exception as e: - pass - - def print(self): - for i in range(self.nc + 1): - print(' '.join(map(str, self.matrix[i]))) - - -# Plots ---------------------------------------------------------------------------------------------------------------- - -def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): - # Precision-recall curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - py = np.stack(py, axis=1) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) - else: - ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) - - ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) - ax.set_xlabel('Recall') - ax.set_ylabel('Precision') - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir), dpi=250) - - -def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): - # Metric-confidence curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py): - ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) - else: - ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) - - y = py.mean(0) - ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir), dpi=250) diff --git a/Yolov5-Deepsort/utils/plots.py b/Yolov5-Deepsort/utils/plots.py deleted file mode 100644 index 8313ef210f9047c327cea7ac63c59e6f6140f2d1..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/plots.py +++ /dev/null @@ -1,446 +0,0 @@ -# Plotting utils - -import glob -import math -import os -import random -from copy import copy -from pathlib import Path - -import cv2 -import matplotlib -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -import torch -import yaml -from PIL import Image, ImageDraw, ImageFont - -from utils.general import xywh2xyxy, xyxy2xywh -from utils.metrics import fitness - -# Settings -matplotlib.rc('font', **{'size': 11}) -matplotlib.use('Agg') # for writing to files only - - -class Colors: - # Ultralytics color palette https://ultralytics.com/ - def __init__(self): - # hex = matplotlib.colors.TABLEAU_COLORS.values() - hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', - '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') - self.palette = [self.hex2rgb('#' + c) for c in hex] - self.n = len(self.palette) - - def __call__(self, i, bgr=False): - c = self.palette[int(i) % self.n] - return (c[2], c[1], c[0]) if bgr else c - - @staticmethod - def hex2rgb(h): # rgb order (PIL) - return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) - - -colors = Colors() # create instance for 'from utils.plots import colors' - - -def hist2d(x, y, n=100): - # 2d histogram used in labels.png and evolve.png - xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) - hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) - xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) - yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) - return np.log(hist[xidx, yidx]) - - -def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): - from scipy.signal import butter, filtfilt - - # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy - def butter_lowpass(cutoff, fs, order): - nyq = 0.5 * fs - normal_cutoff = cutoff / nyq - return butter(order, normal_cutoff, btype='low', analog=False) - - b, a = butter_lowpass(cutoff, fs, order=order) - return filtfilt(b, a, data) # forward-backward filter - - -def plot_one_box(x, im, color=(128, 128, 128), label=None, line_thickness=3): - # Plots one bounding box on image 'im' using OpenCV - assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to plot_on_box() input image.' - tl = line_thickness or round(0.002 * (im.shape[0] + im.shape[1]) / 2) + 1 # line/font thickness - c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) - cv2.rectangle(im, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) - if label: - tf = max(tl - 1, 1) # font thickness - t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] - c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 - cv2.rectangle(im, c1, c2, color, -1, cv2.LINE_AA) # filled - cv2.putText(im, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) - - -def plot_one_box_PIL(box, im, color=(128, 128, 128), label=None, line_thickness=None): - # Plots one bounding box on image 'im' using PIL - im = Image.fromarray(im) - draw = ImageDraw.Draw(im) - line_thickness = line_thickness or max(int(min(im.size) / 200), 2) - draw.rectangle(box, width=line_thickness, outline=color) # plot - if label: - font = ImageFont.truetype("Arial.ttf", size=max(round(max(im.size) / 40), 12)) - txt_width, txt_height = font.getsize(label) - draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=color) - draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) - return np.asarray(im) - - -def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() - # Compares the two methods for width-height anchor multiplication - # https://github.com/ultralytics/yolov3/issues/168 - x = np.arange(-4.0, 4.0, .1) - ya = np.exp(x) - yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 - - fig = plt.figure(figsize=(6, 3), tight_layout=True) - plt.plot(x, ya, '.-', label='YOLOv3') - plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2') - plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6') - plt.xlim(left=-4, right=4) - plt.ylim(bottom=0, top=6) - plt.xlabel('input') - plt.ylabel('output') - plt.grid() - plt.legend() - fig.savefig('comparison.png', dpi=200) - - -def output_to_target(output): - # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] - targets = [] - for i, o in enumerate(output): - for *box, conf, cls in o.cpu().numpy(): - targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) - return np.array(targets) - - -def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): - # Plot image grid with labels - - if isinstance(images, torch.Tensor): - images = images.cpu().float().numpy() - if isinstance(targets, torch.Tensor): - targets = targets.cpu().numpy() - - # un-normalise - if np.max(images[0]) <= 1: - images *= 255 - - tl = 3 # line thickness - tf = max(tl - 1, 1) # font thickness - bs, _, h, w = images.shape # batch size, _, height, width - bs = min(bs, max_subplots) # limit plot images - ns = np.ceil(bs ** 0.5) # number of subplots (square) - - # Check if we should resize - scale_factor = max_size / max(h, w) - if scale_factor < 1: - h = math.ceil(scale_factor * h) - w = math.ceil(scale_factor * w) - - mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init - for i, img in enumerate(images): - if i == max_subplots: # if last batch has fewer images than we expect - break - - block_x = int(w * (i // ns)) - block_y = int(h * (i % ns)) - - img = img.transpose(1, 2, 0) - if scale_factor < 1: - img = cv2.resize(img, (w, h)) - - mosaic[block_y:block_y + h, block_x:block_x + w, :] = img - if len(targets) > 0: - image_targets = targets[targets[:, 0] == i] - boxes = xywh2xyxy(image_targets[:, 2:6]).T - classes = image_targets[:, 1].astype('int') - labels = image_targets.shape[1] == 6 # labels if no conf column - conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) - - if boxes.shape[1]: - if boxes.max() <= 1.01: # if normalized with tolerance 0.01 - boxes[[0, 2]] *= w # scale to pixels - boxes[[1, 3]] *= h - elif scale_factor < 1: # absolute coords need scale if image scales - boxes *= scale_factor - boxes[[0, 2]] += block_x - boxes[[1, 3]] += block_y - for j, box in enumerate(boxes.T): - cls = int(classes[j]) - color = colors(cls) - cls = names[cls] if names else cls - if labels or conf[j] > 0.25: # 0.25 conf thresh - label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) - plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) - - # Draw image filename labels - if paths: - label = Path(paths[i]).name[:40] # trim to 40 char - t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] - cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, - lineType=cv2.LINE_AA) - - # Image border - cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) - - if fname: - r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size - mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) - # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save - Image.fromarray(mosaic).save(fname) # PIL save - return mosaic - - -def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): - # Plot LR simulating training for full epochs - optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals - y = [] - for _ in range(epochs): - scheduler.step() - y.append(optimizer.param_groups[0]['lr']) - plt.plot(y, '.-', label='LR') - plt.xlabel('epoch') - plt.ylabel('LR') - plt.grid() - plt.xlim(0, epochs) - plt.ylim(0) - plt.savefig(Path(save_dir) / 'LR.png', dpi=200) - plt.close() - - -def plot_test_txt(): # from utils.plots import *; plot_test() - # Plot test.txt histograms - x = np.loadtxt('test.txt', dtype=np.float32) - box = xyxy2xywh(x[:, :4]) - cx, cy = box[:, 0], box[:, 1] - - fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) - ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) - ax.set_aspect('equal') - plt.savefig('hist2d.png', dpi=300) - - fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) - ax[0].hist(cx, bins=600) - ax[1].hist(cy, bins=600) - plt.savefig('hist1d.png', dpi=200) - - -def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() - # Plot targets.txt histograms - x = np.loadtxt('targets.txt', dtype=np.float32).T - s = ['x targets', 'y targets', 'width targets', 'height targets'] - fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) - ax = ax.ravel() - for i in range(4): - ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) - ax[i].legend() - ax[i].set_title(s[i]) - plt.savefig('targets.jpg', dpi=200) - - -def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() - # Plot study.txt generated by test.py - fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) - # ax = ax.ravel() - - fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: - for f in sorted(Path(path).glob('study*.txt')): - y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T - x = np.arange(y.shape[1]) if x is None else np.array(x) - s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] - # for i in range(7): - # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) - # ax[i].set_title(s[i]) - - j = y[3].argmax() + 1 - ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, - label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) - - ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], - 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') - - ax2.grid(alpha=0.2) - ax2.set_yticks(np.arange(20, 60, 5)) - ax2.set_xlim(0, 57) - ax2.set_ylim(30, 55) - ax2.set_xlabel('GPU Speed (ms/img)') - ax2.set_ylabel('COCO AP val') - ax2.legend(loc='lower right') - plt.savefig(str(Path(path).name) + '.png', dpi=300) - - -def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): - # plot dataset labels - print('Plotting labels... ') - c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes - nc = int(c.max() + 1) # number of classes - x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) - - # seaborn correlogram - sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) - plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) - plt.close() - - # matplotlib labels - matplotlib.use('svg') # faster - ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() - y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) - # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195 - ax[0].set_ylabel('instances') - if 0 < len(names) < 30: - ax[0].set_xticks(range(len(names))) - ax[0].set_xticklabels(names, rotation=90, fontsize=10) - else: - ax[0].set_xlabel('classes') - sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) - sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) - - # rectangles - labels[:, 1:3] = 0.5 # center - labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 - img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) - for cls, *box in labels[:1000]: - ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot - ax[1].imshow(img) - ax[1].axis('off') - - for a in [0, 1, 2, 3]: - for s in ['top', 'right', 'left', 'bottom']: - ax[a].spines[s].set_visible(False) - - plt.savefig(save_dir / 'labels.jpg', dpi=200) - matplotlib.use('Agg') - plt.close() - - # loggers - for k, v in loggers.items() or {}: - if k == 'wandb' and v: - v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) - - -def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() - # Plot hyperparameter evolution results in evolve.txt - with open(yaml_file) as f: - hyp = yaml.safe_load(f) - x = np.loadtxt('evolve.txt', ndmin=2) - f = fitness(x) - # weights = (f - f.min()) ** 2 # for weighted results - plt.figure(figsize=(10, 12), tight_layout=True) - matplotlib.rc('font', **{'size': 8}) - for i, (k, v) in enumerate(hyp.items()): - y = x[:, i + 7] - # mu = (y * weights).sum() / weights.sum() # best weighted result - mu = y[f.argmax()] # best single result - plt.subplot(6, 5, i + 1) - plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') - plt.plot(mu, f.max(), 'k+', markersize=15) - plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters - if i % 5 != 0: - plt.yticks([]) - print('%15s: %.3g' % (k, mu)) - plt.savefig('evolve.png', dpi=200) - print('\nPlot saved as evolve.png') - - -def profile_idetection(start=0, stop=0, labels=(), save_dir=''): - # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() - ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() - s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] - files = list(Path(save_dir).glob('frames*.txt')) - for fi, f in enumerate(files): - try: - results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows - n = results.shape[1] # number of rows - x = np.arange(start, min(stop, n) if stop else n) - results = results[:, x] - t = (results[0] - results[0].min()) # set t0=0s - results[0] = x - for i, a in enumerate(ax): - if i < len(results): - label = labels[fi] if len(labels) else f.stem.replace('frames_', '') - a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) - a.set_title(s[i]) - a.set_xlabel('time (s)') - # if fi == len(files) - 1: - # a.set_ylim(bottom=0) - for side in ['top', 'right']: - a.spines[side].set_visible(False) - else: - a.remove() - except Exception as e: - print('Warning: Plotting error for %s; %s' % (f, e)) - - ax[1].legend() - plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) - - -def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() - # Plot training 'results*.txt', overlaying train and val losses - s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends - t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles - for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): - results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T - n = results.shape[1] # number of rows - x = range(start, min(stop, n) if stop else n) - fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) - ax = ax.ravel() - for i in range(5): - for j in [i, i + 5]: - y = results[j, x] - ax[i].plot(x, y, marker='.', label=s[j]) - # y_smooth = butter_lowpass_filtfilt(y) - # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) - - ax[i].set_title(t[i]) - ax[i].legend() - ax[i].set_ylabel(f) if i == 0 else None # add filename - fig.savefig(f.replace('.txt', '.png'), dpi=200) - - -def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): - # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') - fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) - ax = ax.ravel() - s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', - 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] - if bucket: - # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] - files = ['results%g.txt' % x for x in id] - c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) - os.system(c) - else: - files = list(Path(save_dir).glob('results*.txt')) - assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) - for fi, f in enumerate(files): - try: - results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T - n = results.shape[1] # number of rows - x = range(start, min(stop, n) if stop else n) - for i in range(10): - y = results[i, x] - if i in [0, 1, 2, 5, 6, 7]: - y[y == 0] = np.nan # don't show zero loss values - # y /= y[0] # normalize - label = labels[fi] if len(labels) else f.stem - ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) - ax[i].set_title(s[i]) - # if i in [5, 6, 7]: # share train and val loss y axes - # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) - except Exception as e: - print('Warning: Plotting error for %s; %s' % (f, e)) - - ax[1].legend() - fig.savefig(Path(save_dir) / 'results.png', dpi=200) diff --git a/Yolov5-Deepsort/utils/torch_utils.py b/Yolov5-Deepsort/utils/torch_utils.py deleted file mode 100644 index 5074fa95ae4be7118a8de66b729192887058285f..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/torch_utils.py +++ /dev/null @@ -1,304 +0,0 @@ -# YOLOv5 PyTorch utils - -import datetime -import logging -import math -import os -import platform -import subprocess -import time -from contextlib import contextmanager -from copy import deepcopy -from pathlib import Path - -import torch -import torch.backends.cudnn as cudnn -import torch.nn as nn -import torch.nn.functional as F -import torchvision - -try: - import thop # for FLOPS computation -except ImportError: - thop = None -logger = logging.getLogger(__name__) - - -@contextmanager -def torch_distributed_zero_first(local_rank: int): - """ - Decorator to make all processes in distributed training wait for each local_master to do something. - """ - if local_rank not in [-1, 0]: - torch.distributed.barrier() - yield - if local_rank == 0: - torch.distributed.barrier() - - -def init_torch_seeds(seed=0): - # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html - torch.manual_seed(seed) - if seed == 0: # slower, more reproducible - cudnn.benchmark, cudnn.deterministic = False, True - else: # faster, less reproducible - cudnn.benchmark, cudnn.deterministic = True, False - - -def date_modified(path=__file__): - # return human-readable file modification date, i.e. '2021-3-26' - t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) - return f'{t.year}-{t.month}-{t.day}' - - -def git_describe(path=Path(__file__).parent): # path must be a directory - # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe - s = f'git -C {path} describe --tags --long --always' - try: - return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] - except subprocess.CalledProcessError as e: - return '' # not a git repository - - -def select_device(device='', batch_size=None): - # device = 'cpu' or '0' or '0,1,2,3' - s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string - cpu = device.lower() == 'cpu' - if cpu: - os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False - elif device: # non-cpu device requested - os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability - - cuda = not cpu and torch.cuda.is_available() - if cuda: - devices = device.split(',') if device else range(torch.cuda.device_count()) # i.e. 0,1,6,7 - n = len(devices) # device count - if n > 1 and batch_size: # check batch_size is divisible by device_count - assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' - space = ' ' * len(s) - for i, d in enumerate(devices): - p = torch.cuda.get_device_properties(i) - s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB - else: - s += 'CPU\n' - - logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe - return torch.device('cuda:0' if cuda else 'cpu') - - -def time_synchronized(): - # pytorch-accurate time - if torch.cuda.is_available(): - torch.cuda.synchronize() - return time.time() - - -def profile(x, ops, n=100, device=None): - # profile a pytorch module or list of modules. Example usage: - # x = torch.randn(16, 3, 640, 640) # input - # m1 = lambda x: x * torch.sigmoid(x) - # m2 = nn.SiLU() - # profile(x, [m1, m2], n=100) # profile speed over 100 iterations - - device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') - x = x.to(device) - x.requires_grad = True - print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') - print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") - for m in ops if isinstance(ops, list) else [ops]: - m = m.to(device) if hasattr(m, 'to') else m # device - m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type - dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward - try: - flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS - except: - flops = 0 - - for _ in range(n): - t[0] = time_synchronized() - y = m(x) - t[1] = time_synchronized() - try: - _ = y.sum().backward() - t[2] = time_synchronized() - except: # no backward method - t[2] = float('nan') - dtf += (t[1] - t[0]) * 1000 / n # ms per op forward - dtb += (t[2] - t[1]) * 1000 / n # ms per op backward - - s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' - s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' - p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') - - -def is_parallel(model): - return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) - - -def intersect_dicts(da, db, exclude=()): - # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values - return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} - - -def initialize_weights(model): - for m in model.modules(): - t = type(m) - if t is nn.Conv2d: - pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif t is nn.BatchNorm2d: - m.eps = 1e-3 - m.momentum = 0.03 - elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: - m.inplace = True - - -def find_modules(model, mclass=nn.Conv2d): - # Finds layer indices matching module class 'mclass' - return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] - - -def sparsity(model): - # Return global model sparsity - a, b = 0., 0. - for p in model.parameters(): - a += p.numel() - b += (p == 0).sum() - return b / a - - -def prune(model, amount=0.3): - # Prune model to requested global sparsity - import torch.nn.utils.prune as prune - print('Pruning model... ', end='') - for name, m in model.named_modules(): - if isinstance(m, nn.Conv2d): - prune.l1_unstructured(m, name='weight', amount=amount) # prune - prune.remove(m, 'weight') # make permanent - print(' %.3g global sparsity' % sparsity(model)) - - -def fuse_conv_and_bn(conv, bn): - # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ - fusedconv = nn.Conv2d(conv.in_channels, - conv.out_channels, - kernel_size=conv.kernel_size, - stride=conv.stride, - padding=conv.padding, - groups=conv.groups, - bias=True).requires_grad_(False).to(conv.weight.device) - - # prepare filters - w_conv = conv.weight.clone().view(conv.out_channels, -1) - w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) - fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) - - # prepare spatial bias - b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias - b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) - fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) - - return fusedconv - - -def model_info(model, verbose=False, img_size=640): - # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] - n_p = sum(x.numel() for x in model.parameters()) # number parameters - n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients - if verbose: - print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) - for i, (name, p) in enumerate(model.named_parameters()): - name = name.replace('module_list.', '') - print('%5g %40s %9s %12g %20s %10.3g %10.3g' % - (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) - - try: # FLOPS - from thop import profile - stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 - img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input - flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS - img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float - fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS - except (ImportError, Exception): - fs = '' - - logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") - - -def load_classifier(name='resnet101', n=2): - # Loads a pretrained model reshaped to n-class output - model = torchvision.models.__dict__[name](pretrained=True) - - # ResNet model properties - # input_size = [3, 224, 224] - # input_space = 'RGB' - # input_range = [0, 1] - # mean = [0.485, 0.456, 0.406] - # std = [0.229, 0.224, 0.225] - - # Reshape output to n classes - filters = model.fc.weight.shape[1] - model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) - model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) - model.fc.out_features = n - return model - - -def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) - # scales img(bs,3,y,x) by ratio constrained to gs-multiple - if ratio == 1.0: - return img - else: - h, w = img.shape[2:] - s = (int(h * ratio), int(w * ratio)) # new size - img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize - if not same_shape: # pad/crop img - h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] - return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean - - -def copy_attr(a, b, include=(), exclude=()): - # Copy attributes from b to a, options to only include [...] and to exclude [...] - for k, v in b.__dict__.items(): - if (len(include) and k not in include) or k.startswith('_') or k in exclude: - continue - else: - setattr(a, k, v) - - -class ModelEMA: - """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models - Keep a moving average of everything in the model state_dict (parameters and buffers). - This is intended to allow functionality like - https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage - A smoothed version of the weights is necessary for some training schemes to perform well. - This class is sensitive where it is initialized in the sequence of model init, - GPU assignment and distributed training wrappers. - """ - - def __init__(self, model, decay=0.9999, updates=0): - # Create EMA - self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA - # if next(model.parameters()).device.type != 'cpu': - # self.ema.half() # FP16 EMA - self.updates = updates # number of EMA updates - self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) - for p in self.ema.parameters(): - p.requires_grad_(False) - - def update(self, model): - # Update EMA parameters - with torch.no_grad(): - self.updates += 1 - d = self.decay(self.updates) - - msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict - for k, v in self.ema.state_dict().items(): - if v.dtype.is_floating_point: - v *= d - v += (1. - d) * msd[k].detach() - - def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): - # Update EMA attributes - copy_attr(self.ema, model, include, exclude) diff --git a/Yolov5-Deepsort/utils/wandb_logging/__init__.py b/Yolov5-Deepsort/utils/wandb_logging/__init__.py deleted file mode 100644 index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000 diff --git a/Yolov5-Deepsort/utils/wandb_logging/log_dataset.py b/Yolov5-Deepsort/utils/wandb_logging/log_dataset.py deleted file mode 100644 index f45a23011f15920e0cee33783504299337604a8d..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/wandb_logging/log_dataset.py +++ /dev/null @@ -1,24 +0,0 @@ -import argparse - -import yaml - -from wandb_utils import WandbLogger - -WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' - - -def create_dataset_artifact(opt): - with open(opt.data) as f: - data = yaml.safe_load(f) # data dict - logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') - parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') - parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') - opt = parser.parse_args() - opt.resume = False # Explicitly disallow resume check for dataset upload job - - create_dataset_artifact(opt) diff --git a/Yolov5-Deepsort/utils/wandb_logging/wandb_utils.py b/Yolov5-Deepsort/utils/wandb_logging/wandb_utils.py deleted file mode 100644 index 57ce9035a77736b744d9f74186cec592aba25200..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/utils/wandb_logging/wandb_utils.py +++ /dev/null @@ -1,318 +0,0 @@ -"""Utilities and tools for tracking runs with Weights & Biases.""" -import json -import sys -from pathlib import Path - -import torch -import yaml -from tqdm import tqdm - -sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path -from utils.datasets import LoadImagesAndLabels -from utils.datasets import img2label_paths -from utils.general import colorstr, xywh2xyxy, check_dataset, check_file - -try: - import wandb - from wandb import init, finish -except ImportError: - wandb = None - -WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' - - -def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): - return from_string[len(prefix):] - - -def check_wandb_config_file(data_config_file): - wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path - if Path(wandb_config).is_file(): - return wandb_config - return data_config_file - - -def get_run_info(run_path): - run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) - run_id = run_path.stem - project = run_path.parent.stem - entity = run_path.parent.parent.stem - model_artifact_name = 'run_' + run_id + '_model' - return entity, project, run_id, model_artifact_name - - -def check_wandb_resume(opt): - process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None - if isinstance(opt.resume, str): - if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - if opt.global_rank not in [-1, 0]: # For resuming DDP runs - entity, project, run_id, model_artifact_name = get_run_info(opt.resume) - api = wandb.Api() - artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest') - modeldir = artifact.download() - opt.weights = str(Path(modeldir) / "last.pt") - return True - return None - - -def process_wandb_config_ddp_mode(opt): - with open(check_file(opt.data)) as f: - data_dict = yaml.safe_load(f) # data dict - train_dir, val_dir = None, None - if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): - api = wandb.Api() - train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) - train_dir = train_artifact.download() - train_path = Path(train_dir) / 'data/images/' - data_dict['train'] = str(train_path) - - if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): - api = wandb.Api() - val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) - val_dir = val_artifact.download() - val_path = Path(val_dir) / 'data/images/' - data_dict['val'] = str(val_path) - if train_dir or val_dir: - ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') - with open(ddp_data_path, 'w') as f: - yaml.safe_dump(data_dict, f) - opt.data = ddp_data_path - - -class WandbLogger(): - """Log training runs, datasets, models, and predictions to Weights & Biases. - - This logger sends information to W&B at wandb.ai. By default, this information - includes hyperparameters, system configuration and metrics, model metrics, - and basic data metrics and analyses. - - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. - - For more on how this logger is used, see the Weights & Biases documentation: - https://docs.wandb.com/guides/integrations/yolov5 - """ - def __init__(self, opt, name, run_id, data_dict, job_type='Training'): - # Pre-training routine -- - self.job_type = job_type - self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict - # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call - if isinstance(opt.resume, str): # checks resume from artifact - if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - entity, project, run_id, model_artifact_name = get_run_info(opt.resume) - model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name - assert wandb, 'install wandb to resume wandb runs' - # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config - self.wandb_run = wandb.init(id=run_id, project=project, entity=entity, resume='allow') - opt.resume = model_artifact_name - elif self.wandb: - self.wandb_run = wandb.init(config=opt, - resume="allow", - project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, - entity=opt.entity, - name=name, - job_type=job_type, - id=run_id) if not wandb.run else wandb.run - if self.wandb_run: - if self.job_type == 'Training': - if not opt.resume: - wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict - # Info useful for resuming from artifacts - self.wandb_run.config.opt = vars(opt) - self.wandb_run.config.data_dict = wandb_data_dict - self.data_dict = self.setup_training(opt, data_dict) - if self.job_type == 'Dataset Creation': - self.data_dict = self.check_and_upload_dataset(opt) - else: - prefix = colorstr('wandb: ') - print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") - - def check_and_upload_dataset(self, opt): - assert wandb, 'Install wandb to upload dataset' - check_dataset(self.data_dict) - config_path = self.log_dataset_artifact(check_file(opt.data), - opt.single_cls, - 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) - print("Created dataset config file ", config_path) - with open(config_path) as f: - wandb_data_dict = yaml.safe_load(f) - return wandb_data_dict - - def setup_training(self, opt, data_dict): - self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants - self.bbox_interval = opt.bbox_interval - if isinstance(opt.resume, str): - modeldir, _ = self.download_model_artifact(opt) - if modeldir: - self.weights = Path(modeldir) / "last.pt" - config = self.wandb_run.config - opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( - self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ - config.opt['hyp'] - data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume - if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download - self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), - opt.artifact_alias) - self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), - opt.artifact_alias) - self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None - if self.train_artifact_path is not None: - train_path = Path(self.train_artifact_path) / 'data/images/' - data_dict['train'] = str(train_path) - if self.val_artifact_path is not None: - val_path = Path(self.val_artifact_path) / 'data/images/' - data_dict['val'] = str(val_path) - self.val_table = self.val_artifact.get("val") - self.map_val_table_path() - if self.val_artifact is not None: - self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") - self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) - if opt.bbox_interval == -1: - self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 - return data_dict - - def download_dataset_artifact(self, path, alias): - if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): - artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) - dataset_artifact = wandb.use_artifact(artifact_path.as_posix()) - assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" - datadir = dataset_artifact.download() - return datadir, dataset_artifact - return None, None - - def download_model_artifact(self, opt): - if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") - assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' - modeldir = model_artifact.download() - epochs_trained = model_artifact.metadata.get('epochs_trained') - total_epochs = model_artifact.metadata.get('total_epochs') - is_finished = total_epochs is None - assert not is_finished, 'training is finished, can only resume incomplete runs.' - return modeldir, model_artifact - return None, None - - def log_model(self, path, opt, epoch, fitness_score, best_model=False): - model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ - 'original_url': str(path), - 'epochs_trained': epoch + 1, - 'save period': opt.save_period, - 'project': opt.project, - 'total_epochs': opt.epochs, - 'fitness_score': fitness_score - }) - model_artifact.add_file(str(path / 'last.pt'), name='last.pt') - wandb.log_artifact(model_artifact, - aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) - print("Saving model artifact on epoch ", epoch + 1) - - def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): - with open(data_file) as f: - data = yaml.safe_load(f) # data dict - nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) - names = {k: v for k, v in enumerate(names)} # to index dictionary - self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( - data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None - self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( - data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None - if data.get('train'): - data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') - if data.get('val'): - data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') - path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path - data.pop('download', None) - with open(path, 'w') as f: - yaml.safe_dump(data, f) - - if self.job_type == 'Training': # builds correct artifact pipeline graph - self.wandb_run.use_artifact(self.val_artifact) - self.wandb_run.use_artifact(self.train_artifact) - self.val_artifact.wait() - self.val_table = self.val_artifact.get('val') - self.map_val_table_path() - else: - self.wandb_run.log_artifact(self.train_artifact) - self.wandb_run.log_artifact(self.val_artifact) - return path - - def map_val_table_path(self): - self.val_table_map = {} - print("Mapping dataset") - for i, data in enumerate(tqdm(self.val_table.data)): - self.val_table_map[data[3]] = data[0] - - def create_dataset_table(self, dataset, class_to_id, name='dataset'): - # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging - artifact = wandb.Artifact(name=name, type="dataset") - img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None - img_files = tqdm(dataset.img_files) if not img_files else img_files - for img_file in img_files: - if Path(img_file).is_dir(): - artifact.add_dir(img_file, name='data/images') - labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) - artifact.add_dir(labels_path, name='data/labels') - else: - artifact.add_file(img_file, name='data/images/' + Path(img_file).name) - label_file = Path(img2label_paths([img_file])[0]) - artifact.add_file(str(label_file), - name='data/labels/' + label_file.name) if label_file.exists() else None - table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) - class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) - for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): - box_data, img_classes = [], {} - for cls, *xywh in labels[:, 1:].tolist(): - cls = int(cls) - box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]}, - "class_id": cls, - "box_caption": "%s" % (class_to_id[cls])}) - img_classes[cls] = class_to_id[cls] - boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space - table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), - Path(paths).name) - artifact.add(table, name) - return artifact - - def log_training_progress(self, predn, path, names): - if self.val_table and self.result_table: - class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) - box_data = [] - total_conf = 0 - for *xyxy, conf, cls in predn.tolist(): - if conf >= 0.25: - box_data.append( - {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, - "class_id": int(cls), - "box_caption": "%s %.3f" % (names[cls], conf), - "scores": {"class_score": conf}, - "domain": "pixel"}) - total_conf = total_conf + conf - boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space - id = self.val_table_map[Path(path).name] - self.result_table.add_data(self.current_epoch, - id, - wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), - total_conf / max(1, len(box_data)) - ) - - def log(self, log_dict): - if self.wandb_run: - for key, value in log_dict.items(): - self.log_dict[key] = value - - def end_epoch(self, best_result=False): - if self.wandb_run: - wandb.log(self.log_dict) - self.log_dict = {} - if self.result_artifact: - train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") - self.result_artifact.add(train_results, 'result') - wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), - ('best' if best_result else '')]) - self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) - self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") - - def finish_run(self): - if self.wandb_run: - if self.log_dict: - wandb.log(self.log_dict) - wandb.run.finish() diff --git a/Yolov5-Deepsort/weights/yolov5s.pt b/Yolov5-Deepsort/weights/yolov5s.pt deleted file mode 100644 index a58e3fcde809e747c541b532cda255034e18e7e3..0000000000000000000000000000000000000000 --- a/Yolov5-Deepsort/weights/yolov5s.pt +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f1610cfd81f8cab94254b35f6b7da2981fa40f93ad1bd3dd1803c52e7f44753e -size 14795158 diff --git a/Yolov5-Deepsort/zhoushao.mp4 b/Yolov5-Deepsort/zhoushao.mp4 deleted file mode 100644 index cd5e0135133b90765a9eaab74a37286318f8c351..0000000000000000000000000000000000000000 Binary files a/Yolov5-Deepsort/zhoushao.mp4 and /dev/null differ