Delete Yolov5-Deepsort

Yolov5-Deepsort/AIDetector_pytorch.py

@@ -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
-

Yolov5-Deepsort/DDM_DeepSort/.gitattributes

@@ -1,35 +0,0 @@
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-*tfevents* filter=lfs diff=lfs merge=lfs -text

Yolov5-Deepsort/DDM_DeepSort/README.md

@@ -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

Yolov5-Deepsort/DDM_DeepSort/app.py

@@ -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()

Yolov5-Deepsort/LICENSE

@@ -1,674 +0,0 @@
-GNU GENERAL PUBLIC LICENSE
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-if any, to sign a "copyright disclaimer" for the program, if necessary.
-For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
-
-  The GNU General Public License does not permit incorporating your program
-into proprietary programs.  If your program is a subroutine library, you
-may consider it more useful to permit linking proprietary applications with
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-Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.

Yolov5-Deepsort/README.md

@@ -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/
-
-

Yolov5-Deepsort/deep_sort/configs/deep_sort.yaml

@@ -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
-  

Yolov5-Deepsort/deep_sort/deep_sort/README.md

@@ -1,3 +0,0 @@
-# Deep Sort 
-
-This is the implemention of deep sort with pytorch.

Yolov5-Deepsort/deep_sort/deep_sort/__init__.py

@@ -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)
-    
-
-
-
-
-
-
-
-
-

Yolov5-Deepsort/deep_sort/deep_sort/deep/checkpoint/ckpt.t7

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:df75ddef42c3d1bda67bc94b093e7ce61de7f75a89f36a8f868a428462198316
-size 46034619

Yolov5-Deepsort/deep_sort/deep_sort/deep/evaluate.py

@@ -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)))
-
-

Yolov5-Deepsort/deep_sort/deep_sort/deep/feature_extractor.py

@@ -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)
-

Yolov5-Deepsort/deep_sort/deep_sort/deep/model.py

@@ -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()
-
-

Yolov5-Deepsort/deep_sort/deep_sort/deep/original_model.py

@@ -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()
-
-

Yolov5-Deepsort/deep_sort/deep_sort/deep/test.py

@@ -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")

Yolov5-Deepsort/deep_sort/deep_sort/deep/train.py

@@ -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()

Yolov5-Deepsort/deep_sort/deep_sort/deep_sort.py

@@ -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 [email protected] 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