init app

app.py

@@ -0,0 +1,237 @@
+# Gradio YOLOv5 Det v0.1
+# 创建人：曾逸夫
+# 创建时间：2022-04-03
+
+import argparse
+import csv
+import sys
+
+import gradio as gr
+import torch
+import yaml
+from PIL import Image
+from zmq import device
+
+ROOT_PATH = sys.path[0]  # 根目录
+
+# 本地模型路径
+local_model_path = f"{ROOT_PATH}/yolov5"
+
+
+# 模型名称临时变量
+model_name_tmp = ""
+
+# 设备临时变量
+device_tmp = ""
+
+
+def parse_args(known=False):
+    parser = argparse.ArgumentParser(description="Gradio YOLOv5 Det v0.1")
+    parser.add_argument(
+        "--model_name", "-mn", default="yolov5s", type=str, help="model name"
+    )
+    parser.add_argument(
+        "--model_cfg",
+        "-mc",
+        default="./model_config/model_name_p5_all.yaml",
+        type=str,
+        help="model config",
+    )
+    parser.add_argument(
+        "--cls_name",
+        "-cls",
+        default="./cls_name/cls_name.yaml",
+        type=str,
+        help="cls name",
+    )
+    parser.add_argument(
+        "--nms_conf",
+        "-conf",
+        default=0.5,
+        type=float,
+        help="model NMS confidence threshold",
+    )
+    parser.add_argument(
+        "--nms_iou", "-iou", default=0.45, type=float, help="model NMS IoU threshold"
+    )
+
+    parser.add_argument(
+        "--label_dnt_show",
+        "-lds",
+        action="store_false",
+        default=True,
+        help="label show",
+    )
+    parser.add_argument(
+        "--device",
+        "-dev",
+        default="0",
+        type=str,
+        help="cuda or cpu",
+    )
+
+    args = parser.parse_known_args()[0] if known else parser.parse_args()
+    return args
+
+
+#  模型加载
+def model_loading(model_name, device):
+
+    # 加载本地模型
+    model = torch.hub.load(
+        local_model_path,
+        "custom",
+        path=f"{local_model_path}/{model_name}",
+        source="local",
+        device=device,
+    )
+
+    return model
+
+
+# 检测信息
+def export_json(results, model, img_size):
+
+    return [
+        [
+            {
+                "id": int(i),
+                "class": int(result[i][5]),
+                "class_name": model.model.names[int(result[i][5])],
+                "normalized_box": {
+                    "x0": round(result[i][:4].tolist()[0], 6),
+                    "y0": round(result[i][:4].tolist()[1], 6),
+                    "x1": round(result[i][:4].tolist()[2], 6),
+                    "y1": round(result[i][:4].tolist()[3], 6),
+                },
+                "confidence": round(float(result[i][4]), 2),
+                "fps": round(1000 / float(results.t[1]), 2),
+                "width": img_size[0],
+                "height": img_size[1],
+            }
+            for i in range(len(result))
+        ]
+        for result in results.xyxyn
+    ]
+
+
+# YOLOv5图片检测函数
+def yolo_det(img, device, model_name, conf, iou, label_opt, model_cls):
+
+    global model, model_name_tmp, device_tmp
+
+    if model_name_tmp != model_name:
+        # 模型判断，避免反复加载
+        model_name_tmp = model_name
+        model = model_loading(model_name_tmp, device)
+    elif device_tmp != device:
+        device_tmp = device
+        model = model_loading(model_name_tmp, device)
+
+    # -----------模型调参-----------
+    model.conf = conf  # NMS 置信度阈值
+    model.iou = iou  # NMS IOU阈值
+    model.max_det = 1000  # 最大检测框数
+    model.classes = model_cls  # 模型类别
+
+    results = model(img)  # 检测
+    results.render(labels=label_opt)  # 渲染
+
+    det_img = Image.fromarray(results.imgs[0])  # 检测图片
+
+    det_json = export_json(results, model, img.size)[0]  # 检测信息
+
+    return det_img, det_json
+
+
+# yaml文件解析
+def yaml_parse(file_path):
+    return yaml.load(
+        open(file_path, "r", encoding="utf-8").read(), Loader=yaml.FullLoader
+    )
+
+
+def main(args):
+    global model
+
+    slider_step = 0.05  # 滑动步长
+
+    nms_conf = args.nms_conf
+    nms_iou = args.nms_iou
+    label_opt = args.label_dnt_show
+    model_name = args.model_name
+    model_cfg = args.model_cfg
+    cls_name = args.cls_name
+    device = args.device
+
+    # 模型加载
+    model = model_loading(model_name, device)
+    # 模型名称
+    # model_names = [i[0] for i in list(csv.reader(open(model_cfg)))] # csv版
+    model_names = yaml_parse(model_cfg).get("model_names")  # yaml版
+
+    # 类别名称
+    # model_cls_name = [i[0] for i in list(csv.reader(open(cls_name)))] # csv版
+    model_cls_name = yaml_parse(cls_name).get("model_cls_name")  # yaml版
+
+    # -------------------输入组件-------------------
+    inputs_img = gr.inputs.Image(type="pil", label="原始图片")
+    device = gr.inputs.Dropdown(
+        choices=["0", "cpu"], default=device, type="value", label="设备"
+    )
+    inputs_model = gr.inputs.Dropdown(
+        choices=model_names, default=model_name, type="value", label="模型"
+    )
+    input_conf = gr.inputs.Slider(
+        0, 1, step=slider_step, default=nms_conf, label="置信度阈值"
+    )
+    inputs_iou = gr.inputs.Slider(
+        0, 1, step=slider_step, default=nms_iou, label="IoU 阈值"
+    )
+    inputs_label = gr.inputs.Checkbox(default=label_opt, label="标签显示")
+    inputs_clsName = gr.inputs.CheckboxGroup(
+        choices=model_cls_name, default=model_cls_name, type="index", label="类别"
+    )
+
+    # 输入参数
+    inputs = [
+        inputs_img,  # 输入图片
+        device,  # 设备
+        inputs_model,  # 模型
+        input_conf,  # 置信度阈值
+        inputs_iou,  # IoU阈值
+        inputs_label,  # 标签显示
+        inputs_clsName,  # 类别
+    ]
+    # 输出参数
+    outputs = gr.outputs.Image(type="pil", label="检测图片")
+    outputs02 = gr.outputs.JSON(label="检测信息")
+
+    # 标题
+    title = "基于Gradio的YOLOv5通用目标检测系统"
+    # 描述
+    description = "<div align='center'>可自定义目标检测模型、安装简单、使用方便</div>"
+
+    gr.close_all()
+
+    # 接口
+    gr.Interface(
+        fn=yolo_det,
+        inputs=inputs,
+        outputs=[outputs, outputs02],
+        title=title,
+        description=description,
+        theme="seafoam",
+        # live=True, # 实时变更输出
+        flagging_dir="run"  # 输出目录
+        # ).launch(inbrowser=True, auth=['admin', 'admin'])
+    ).launch(
+        inbrowser=True,  # 自动打开默认浏览器
+        show_tips=True,  # 自动显示gradio最新功能
+        favicon_path="./icon/logo.ico",
+    )
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

cls_name/cls_name.csv

@@ -0,0 +1,80 @@
+人
+自行车
+汽车
+摩托车
+飞机
+公交车
+火车
+卡车
+船
+红绿灯
+消防栓
+停止标志
+停车收费表
+长凳
+鸟
+猫
+狗
+马
+羊
+牛
+象
+熊
+斑马
+长颈鹿
+背包
+雨伞
+手提包
+领带
+手提箱
+飞盘
+滑雪板
+单板滑雪
+运动球
+风筝
+棒球棒
+棒球手套
+滑板
+冲浪板
+网球拍
+瓶子
+红酒杯
+杯子
+叉子
+刀
+勺
+碗
+香蕉
+苹果
+三明治
+橙子
+西兰花
+胡萝卜
+热狗
+比萨
+甜甜圈
+蛋糕
+椅子
+长椅
+盆栽
+床
+餐桌
+马桶
+电视
+笔记本电脑
+鼠标
+遥控器
+键盘
+手机
+微波炉
+烤箱
+烤面包机
+洗碗槽
+冰箱
+书
+时钟
+花瓶
+剪刀
+泰迪熊
+吹风机
+牙刷

cls_name/cls_name.yaml

@@ -0,0 +1,7 @@
+model_cls_name: ['人', '自行车', '汽车', '摩托车', '飞机', '公交车', '火车', '卡车', '船', '红绿灯', '消防栓', '停止标志',
+                '停车收费表', '长凳', '鸟', '猫', '狗', '马', '羊', '牛', '象', '熊', '斑马', '长颈鹿', '背包', '雨伞', '手提包', '领带',
+                '手提箱', '飞盘', '滑雪板', '单板滑雪', '运动球', '风筝', '棒球棒', '棒球手套', '滑板', '冲浪板', '网球拍', '瓶子', '红酒杯',
+                '杯子', '叉子', '刀', '勺', '碗', '香蕉', '苹果', '三明治', '橙子', '西兰花', '胡萝卜', '热狗', '比萨', '甜甜圈', '蛋糕',
+                '椅子', '长椅', '盆栽', '床', '餐桌', '马桶', '电视', '笔记本电脑', '鼠标', '遥控器', '键盘', '手机', '微波炉', '烤箱',
+                '烤面包机', '洗碗槽', '冰箱', '书', '时钟', '花瓶', '剪刀', '泰迪熊', '吹风机', '牙刷'
+            ]

model_config/model_name_p5_all.csv

@@ -0,0 +1,5 @@
+yolov5n
+yolov5s
+yolov5m
+yolov5l
+yolov5x

model_config/model_name_p5_all.yaml

@@ -0,0 +1 @@
+model_names: ["yolov5n", "yolov5s", "yolov5m", "yolov5l", "yolov5x"]

model_config/model_name_p5_n.csv

@@ -0,0 +1 @@
+yolov5n

model_config/model_name_p5_n.yaml

@@ -0,0 +1 @@
+model_names: ["yolov5n"]

model_config/model_name_p6_all.csv

@@ -0,0 +1,5 @@
+yolov5n6
+yolov5s6
+yolov5m6
+yolov5l6
+yolov5x6

model_config/model_name_p6_all.yaml

@@ -0,0 +1 @@
+model_names: ["yolov5n6", "yolov5s6", "yolov5m6", "yolov5l6", "yolov5x6"]

model_download/yolov5_model_p5_all.sh

@@ -0,0 +1,8 @@
+cd ./yolov5
+
+# 下载YOLOv5模型
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x.pt

model_download/yolov5_model_p5_n.sh

@@ -0,0 +1,4 @@
+cd ./yolov5
+
+# 下载YOLOv5模型
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n.pt

model_download/yolov5_model_p6_all.sh

@@ -0,0 +1,8 @@
+cd ./yolov5
+
+# 下载YOLOv5模型
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5n6.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s6.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5m6.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5l6.pt
+wget -c -t 0 https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5x6.pt