效果展示

目录

 效果展示

核心源码

main.py

detect.py

安装环境

环境安装：

---

核心源码

main.py

# -*- coding: UTF-8 -*-

import random
import sys
import threading
import time

import cv2
import numpy
import torch
import torch.backends.cudnn as cudnn
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *

from models.experimental import attempt_load
from utils.datasets import LoadImages, LoadStreams
from utils.general import check_img_size, non_max_suppression, scale_coords
from utils.plots import plot_one_box
from utils.torch_utils import select_device, time_synchronized

model_path = 'runs/train/yolov5s/weights/best.pt'

# 添加一个关于界面
# 窗口主类
class MainWindow(QTabWidget):
    # 基本配置不动，然后只动第三个界面
    def __init__(self):
       # 初始化界面
       super().__init__()
       self.setWindowTitle('Yolov5检测系统')
       self.resize(1200, 800)
       self.setWindowIcon(QIcon("./UI/xf.jpg"))
       # 图片读取进程
       self.output_size = 480
       self.img2predict = ""
       # 空字符串会自己进行选择，首选cuda
       self.device = ''
       # # 初始化视频读取线程
       self.vid_source = '0'  # 初始设置为摄像头
       # 检测视频的线程
       self.threading = None
       # 是否跳出当前循环的线程
       self.jump_threading: bool = False

       self.image_size = 640
       self.confidence = 0.25
       self.iou_threshold = 0.45
       # 指明模型加载的位置的设备
       self.model = self.model_load(weights=model_path,
                                    device=self.device)
       self.initUI()
       self.reset_vid()

    @torch.no_grad()
    def model_load(self,
                   weights="",  # model.pt path(s)
                   device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
                   ):
       """
       模型初始化
       """
       device = self.device = select_device(device)
       half = device.type != 'cpu'  # half precision only supported on CUDA

       # Load model
       model = attempt_load(weights, device)  # load FP32 model
       self.stride = int(model.stride.max())  # model stride
       self.image_size = check_img_size(self.image_size, s=self.stride)  # check img_size
       if half:
          model.half()  # to FP16
       # Run inference
       if device.type != 'cpu':
          print("Run inference")
          model(torch.zeros(1, 3, self.image_size, self.image_size).to(device).type_as(
             next(model.parameters())))  # run once
       print("模型加载完成!")
       return model

    def reset_vid(self):
       """
       界面重置事件
       """
       self.webcam_detection_btn.setEnabled(True)
       self.mp4_detection_btn.setEnabled(True)
       self.left_vid_img.setPixmap(QPixmap("./UI/up.jpeg"))
       self.vid_source = '0'
       self.disable_btn(self.det_img_button)
       self.disable_btn(self.vid_start_stop_btn)
       self.jump_threading = False

    def initUI(self):
       """
       界面初始化
       """
       # 图片检测子界面
       font_title = QFont('楷体', 16)
       font_main = QFont('楷体', 14)
       font_general = QFont('楷体', 10)
       # 图片识别界面, 两个按钮，上传图片和显示结果
       img_detection_widget = QWidget()
       img_detection_layout = QVBoxLayout()
       img_detection_title = QLabel("图片识别功能")
       img_detection_title.setFont(font_title)
       mid_img_widget = QWidget()
       mid_img_layout = QHBoxLayout()
       self.left_img = QLabel()
       self.right_img = QLabel()
       self.left_img.setPixmap(QPixmap("./UI/up.jpeg"))
       self.right_img.setPixmap(QPixmap("./UI/right.jpeg"))
       self.left_img.setAlignment(Qt.AlignCenter)
       self.right_img.setAlignment(Qt.AlignCenter)
       self.left_img.setMinimumSize(480, 480)
       self.left_img.setStyleSheet("QLabel{background-color: #f6f8fa;}")
       mid_img_layout.addWidget(self.left_img)
       self.right_img.setMinimumSize(480, 480)
       self.right_img.setStyleSheet("QLabel{background-color: #f6f8fa;}")
       mid_img_layout.addStretch(0)
       mid_img_layout.addWidget(self.right_img)
       mid_img_widget.setLayout(mid_img_layout)
       self.up_img_button = QPushButton("上传图片")
       self.det_img_button = QPushButton("开始检测")
       self.up_img_button.clicked.connect(self.upload_img)
       self.det_img_button.clicked.connect(self.detect_img)
       self.up_img_button.setFont(font_main)
       self.det_img_button.setFont(font_main)
       self.up_img_button.setStyleSheet("QPushButton{color:white}"
                                        "QPushButton:hover{background-color: rgb(2,110,180);}"
                                        "QPushButton{background-color:rgb(48,124,208)}"
                                        "QPushButton{border:2px}"
                                        "QPushButton{border-radius:5px}"
                                        "QPushButton{padding:5px 5px}"
                                        "QPushButton{margin:5px 5px}")
       self.det_img_button.setStyleSheet("QPushButton{color:white}"
                                         "QPushButton:hover{background-color: rgb(2,110,180);}"
                                         "QPushButton{background-color:rgb(48,124,208)}"
                                         "QPushButton{border:2px}"
                                         "QPushButton{border-radius:5px}"
                                         "QPushButton{padding:5px 5px}"
                                         "QPushButton{margin:5px 5px}")
       img_detection_layout.addWidget(img_detection_title, alignment=Qt.AlignCenter)
       img_detection_layout.addWidget(mid_img_widget, alignment=Qt.AlignCenter)
       img_detection_layout.addWidget(self.up_img_button)
       img_detection_layout.addWidget(self.det_img_button)
       img_detection_widget.setLayout(img_detection_layout)

       # 视频识别界面
       # 视频识别界面的逻辑比较简单，基本就从上到下的逻辑
       vid_detection_widget = QWidget()
       vid_detection_layout = QVBoxLayout()
       vid_title = QLabel("视频检测功能")
       vid_title.setFont(font_title)
       self.left_vid_img = QLabel()
       self.right_vid_img = QLabel()
       self.left_vid_img.setPixmap(QPixmap("./UI/up.jpeg"))
       self.right_vid_img.setPixmap(QPixmap("./UI/right.jpeg"))
       self.left_vid_img.setAlignment(Qt.AlignCenter)
       self.left_vid_img.setMinimumSize(480, 480)
       self.left_vid_img.setStyleSheet("QLabel{background-color: #f6f8fa;}")
       self.right_vid_img.setAlignment(Qt.AlignCenter)
       self.right_vid_img.setMinimumSize(480, 480)
       self.right_vid_img.setStyleSheet("QLabel{background-color: #f6f8fa;}")
       mid_img_widget = QWidget()
       mid_img_layout = QHBoxLayout()
       mid_img_layout.addWidget(self.left_vid_img)
       mid_img_layout.addStretch(0)
       mid_img_layout.addWidget(self.right_vid_img)
       mid_img_widget.setLayout(mid_img_layout)
       self.webcam_detection_btn = QPushButton("摄像头实时监测")
       self.mp4_detection_btn = QPushButton("视频文件检测")
       self.vid_start_stop_btn = QPushButton("启动/停止检测")
       self.webcam_detection_btn.setFont(font_main)
       self.mp4_detection_btn.setFont(font_main)
       self.vid_start_stop_btn.setFont(font_main)
       self.webcam_detection_btn.setStyleSheet("QPushButton{color:white}"
                                               "QPushButton:hover{background-color: rgb(2,110,180);}"
                                               "QPushButton{background-color:rgb(48,124,208)}"
                                               "QPushButton{border:2px}"
                                               "QPushButton{border-radius:5px}"
                                               "QPushButton{padding:5px 5px}"
                                               "QPushButton{margin:5px 5px}")
       self.mp4_detection_btn.setStyleSheet("QPushButton{color:white}"
                                            "QPushButton:hover{background-color: rgb(2,110,180);}"
                                            "QPushButton{background-color:rgb(48,124,208)}"
                                            "QPushButton{border:1px}"
                                            "QPushButton{border-radius:5px}"
                                            "QPushButton{padding:5px 5px}"
                                            "QPushButton{margin:5px 5px}")
       self.vid_start_stop_btn.setStyleSheet("QPushButton{color:white}"
                                             "QPushButton:hover{background-color: rgb(2,110,180);}"
                                             "QPushButton{background-color:rgb(48,124,208)}"
                                             "QPushButton{border:2px}"
                                             "QPushButton{border-radius:5px}"
                                             "QPushButton{padding:5px 5px}"
                                             "QPushButton{margin:5px 5px}")
       self.webcam_detection_btn.clicked.connect(self.open_cam)
       self.mp4_detection_btn.clicked.connect(self.open_mp4)
       self.vid_start_stop_btn.clicked.connect(self.start_or_stop)

       # 添加fps显示
       fps_container = QWidget()
       fps_container.setStyleSheet("QWidget{background-color: #f6f8fa;}")
       fps_container_layout = QHBoxLayout()
       fps_container.setLayout(fps_container_layout)
       # 左容器
       fps_left_container = QWidget()
       fps_left_container.setStyleSheet("QWidget{background-color: #f6f8fa;}")
       fps_left_container_layout = QHBoxLayout()
       fps_left_container.setLayout(fps_left_container_layout)

       # 右容器
       fps_right_container = QWidget()
       fps_right_container.setStyleSheet("QWidget{background-color: #f6f8fa;}")
       fps_right_container_layout = QHBoxLayout()
       fps_right_container.setLayout(fps_right_container_layout)

       # 将左容器和右容器添加到fps_container_layout中
       fps_container_layout.addWidget(fps_left_container)
       fps_container_layout.addStretch(0)
       fps_container_layout.addWidget(fps_right_container)

       # 左容器中添加fps显示
       raw_fps_label = QLabel("原始帧率:")
       raw_fps_label.setFont(font_general)
       raw_fps_label.setAlignment(Qt.AlignLeft)
       raw_fps_label.setStyleSheet("QLabel{margin-left:80px}")
       self.raw_fps_value = QLabel("0")
       self.raw_fps_value.setFont(font_general)
       self.raw_fps_value.setAlignment(Qt.AlignLeft)
       fps_left_container_layout.addWidget(raw_fps_label)
       fps_left_container_layout.addWidget(self.raw_fps_value)

       # 右容器中添加fps显示
       detect_fps_label = QLabel("检测帧率:")
       detect_fps_label.setFont(font_general)
       detect_fps_label.setAlignment(Qt.AlignRight)
       self.detect_fps_value = QLabel("0")
       self.detect_fps_value.setFont(font_general)
       self.detect_fps_value.setAlignment(Qt.AlignRight)
       self.detect_fps_value.setStyleSheet("QLabel{margin-right:96px}")
       fps_right_container_layout.addWidget(detect_fps_label)
       fps_right_container_layout.addWidget(self.detect_fps_value)

       # 添加组件到布局上
       vid_detection_layout.addWidget(vid_title, alignment=Qt.AlignCenter)
       vid_detection_layout.addWidget(fps_container)
       vid_detection_layout.addWidget(mid_img_widget, alignment=Qt.AlignCenter)
       vid_detection_layout.addWidget(self.webcam_detection_btn)
       vid_detection_layout.addWidget(self.mp4_detection_btn)
       vid_detection_layout.addWidget(self.vid_start_stop_btn)
       vid_detection_widget.setLayout(vid_detection_layout)

       # 关于界面
       about_widget = QWidget()
       about_layout = QVBoxLayout()
       about_title = QLabel('YOLOv5安全帽检测系统\nCSDN搜索：人工智能_SYBH\n微信：sybh_0117')  # 修改欢迎词语
       about_title.setFont(QFont('楷体', 18))
       about_title.setAlignment(Qt.AlignCenter)
       about_img = QLabel()
       about_img.setPixmap(QPixmap('./UI/qq.png'))
       about_img.setAlignment(Qt.AlignCenter)

       label_super = QLabel()  # 更换作者信息
       label_super.setText("")
       label_super.setFont(QFont('楷体', 16))
       label_super.setOpenExternalLinks(True)
       # label_super.setOpenExternalLinks(True)
       label_super.setAlignment(Qt.AlignRight)
       about_layout.addWidget(about_title)
       about_layout.addStretch()
       about_layout.addWidget(about_img)
       about_layout.addStretch()
       about_layout.addWidget(label_super)
       about_widget.setLayout(about_layout)

       self.addTab(img_detection_widget, '图片检测')
       self.addTab(vid_detection_widget, '视频检测')
       self.addTab(about_widget, '联系我')
       self.setTabIcon(0, QIcon('./UI/lufei.png'))
       self.setTabIcon(1, QIcon('./UI/lufei.png'))

    def disable_btn(self, pushButton: QPushButton):
       pushButton.setDisabled(True)
       pushButton.setStyleSheet("QPushButton{background-color: rgb(2,110,180);}")

    def enable_btn(self, pushButton: QPushButton):
       pushButton.setEnabled(True)
       pushButton.setStyleSheet(
          "QPushButton{background-color: rgb(48,124,208);}"
          "QPushButton{color:white}"
       )

    def detect(self, source: str, left_img: QLabel, right_img: QLabel):
       """
       @param source: file/dir/URL/glob, 0 for webcam
       @param left_img: 将左侧QLabel对象传入，用于显示图片
       @param right_img: 将右侧QLabel对象传入，用于显示图片
       """
       model = self.model
       img_size = [self.image_size, self.image_size]  # inference size (pixels)
       conf_threshold = self.confidence  # confidence threshold
       iou_threshold = self.iou_threshold  # NMS IOU threshold
       device = self.device  # cuda device, i.e. 0 or 0,1,2,3 or cpu
       classes = None # filter by class: --class 0, or --class 0 2 3
       agnostic_nms = False  # class-agnostic NMS
       augment = False  # augmented inference

       half = device.type != 'cpu'  # half precision only supported on CUDA

       if source == "":
          self.disable_btn(self.det_img_button)
          QMessageBox.warning(self, "请上传", "请先上传视频或图片再进行检测")
       else:
          source = str(source)
          webcam = source.isnumeric()

          # Set Dataloader
          if webcam:
             cudnn.benchmark = True  # set True to speed up constant image size inference
             dataset = LoadStreams(source, img_size=img_size, stride=self.stride)
          else:
             dataset = LoadImages(source, img_size=img_size, stride=self.stride)
          # Get names and colors
          names = model.module.names if hasattr(model, 'module') else model.names
          colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]

          # 用来记录处理的图片数量
          count = 0
          # 计算帧率开始时间
          fps_start_time = time.time()
          for path, img, im0s, vid_cap in dataset:
             # 直接跳出for，结束线程
             if self.jump_threading:
                # 清除状态
                self.jump_threading = False
                break
             count += 1
             img = torch.from_numpy(img).to(device)
             img = img.half() if half else img.float()  # uint8 to fp16/32
             img /= 255.0  # 0 - 255 to 0.0 - 1.0
             if img.ndimension() == 3:
                img = img.unsqueeze(0)

             # Inference
             t1 = time_synchronized()
             pred = model(img, augment=augment)[0]

             # Apply NMS
             pred = non_max_suppression(pred, conf_threshold, iou_threshold, classes=classes, agnostic=agnostic_nms)
             t2 = time_synchronized()

             # Process detections
             for i, det in enumerate(pred):  # detections per image
                if webcam:  # batch_size >= 1
                   s, im0 = 'detect : ', im0s[i].copy()
                else:
                   s, im0 = 'detect : ', im0s.copy()

                # s += '%gx%g ' % img.shape[2:]  # print string
                if len(det):
                   # Rescale boxes from img_size to im0 size
                   det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                   # Print results
                   for c in det[:, -1].unique():
                      n = (det[:, -1] == c).sum()  # detections per class
                      s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                   # Write results
                   for *xyxy, conf, cls in reversed(det):
                      label = f'{names[int(cls)]} {conf:.2f}'
                      plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)

                if webcam or vid_cap is not None:
                   if webcam:  # batch_size >= 1
                      img = im0s[i]
                   else:
                      img = im0s
                   img = self.resize_img(img)
                   img = QImage(img.data, img.shape[1], img.shape[0], img.shape[2] * img.shape[1],
                                QImage.Format_RGB888)
                   left_img.setPixmap(QPixmap.fromImage(img))
                   # 计算一次帧率
                   if count % 10 == 0:
                      fps = int(10 / (time.time() - fps_start_time))
                      self.detect_fps_value.setText(str(fps))
                      fps_start_time = time.time()
                # 应该调整一下图片的大小
                # 时间显示
                timenumber = time.strftime('%Y/%m/%d/-%H:%M:%S', time.localtime(time.time()))
                im0 = cv2.putText(im0, timenumber, (50, 50), cv2.FONT_HERSHEY_SIMPLEX,
                              1, (0, 255, 0), 2, cv2.LINE_AA)
                im0 = cv2.putText(im0, s, (50, 80), cv2.FONT_HERSHEY_SIMPLEX,
                              1, (255, 0, 0), 2, cv2.LINE_AA)
                img = self.resize_img(im0)
                img = QImage(img.data, img.shape[1], img.shape[0], img.shape[2] * img.shape[1],
                             QImage.Format_RGB888)
                right_img.setPixmap(QPixmap.fromImage(img))

                # Print time (inference + NMS)
                print(f'{s}Done. ({t2 - t1:.3f}s)')

          # 使用完摄像头释放资源
          if webcam:
             for cap in dataset.caps:
                cap.release()
          else:
             dataset.cap and dataset.cap.release()

    def resize_img(self, img):
       """
       调整图片大小，方便用来显示
       @param img: 需要调整的图片
       """
       resize_scale = min(self.output_size / img.shape[0], self.output_size / img.shape[1])
       img = cv2.resize(img, (0, 0), fx=resize_scale, fy=resize_scale)
       img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
       return img

    def upload_img(self):
       """
       上传图片
       """
       # 选择录像文件进行读取
       fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.jpg *.png *.tif *.jpeg')
       if fileName:
          self.img2predict = fileName
          # 将上传照片和进行检测做成互斥的
          self.enable_btn(self.det_img_button)
          self.disable_btn(self.up_img_button)
          # 进行左侧原图展示
          img = cv2.imread(fileName)
          # 应该调整一下图片的大小
          img = self.resize_img(img)
          img = QImage(img.data, img.shape[1], img.shape[0], img.shape[2] * img.shape[1], QImage.Format_RGB888)
          self.left_img.setPixmap(QPixmap.fromImage(img))
          # 上传图片之后右侧的图片重置
          self.right_img.setPixmap(QPixmap("./UI/right.jpeg"))

    def detect_img(self):
       """
       检测图片
       """
       # 重置跳出线程状态，防止其他位置使用的影响
       self.jump_threading = False
       self.detect(self.img2predict, self.left_img, self.right_img)
       # 将上传照片和进行检测做成互斥的
       self.enable_btn(self.up_img_button)
       self.disable_btn(self.det_img_button)

    def open_mp4(self):
       """
       开启视频文件检测事件
       """
       print("开启视频文件检测")
       fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.mp4 *.avi')
       if fileName:
          self.disable_btn(self.webcam_detection_btn)
          self.disable_btn(self.mp4_detection_btn)
          self.enable_btn(self.vid_start_stop_btn)
          # 生成读取视频对象
          cap = cv2.VideoCapture(fileName)
          # 获取视频的帧率
          fps = cap.get(cv2.CAP_PROP_FPS)
          # 显示原始视频帧率
          self.raw_fps_value.setText(str(fps))
          if cap.isOpened():
             # 读取一帧用来提前左侧展示
             ret, raw_img = cap.read()
             cap.release()
          else:
             QMessageBox.warning(self, "需要重新上传", "请重新选择视频文件")
             self.disable_btn(self.vid_start_stop_btn)
             self.enable_btn(self.webcam_detection_btn)
             self.enable_btn(self.mp4_detection_btn)
             return
          # 应该调整一下图片的大小
          img = self.resize_img(numpy.array(raw_img))
          img = QImage(img.data, img.shape[1], img.shape[0], img.shape[2] * img.shape[1], QImage.Format_RGB888)
          self.left_vid_img.setPixmap(QPixmap.fromImage(img))
          # 上传图片之后右侧的图片重置
          self.right_vid_img.setPixmap(QPixmap("./UI/right.jpeg"))
          self.vid_source = fileName
          self.jump_threading = False

    def open_cam(self):
       """
       打开摄像头事件
       """
       print("打开摄像头")
       self.disable_btn(self.webcam_detection_btn)
       self.disable_btn(self.mp4_detection_btn)
       self.enable_btn(self.vid_start_stop_btn)
       self.vid_source = "0"
       self.jump_threading = False
       # 生成读取视频对象
       cap = cv2.VideoCapture(0)
       # 获取视频的帧率
       fps = cap.get(cv2.CAP_PROP_FPS)
       # 显示原始视频帧率
       self.raw_fps_value.setText(str(fps))
       if cap.isOpened():
          # 读取一帧用来提前左侧展示
          ret, raw_img = cap.read()
          cap.release()
       else:
          QMessageBox.warning(self, "需要重新上传", "请重新选择视频文件")
          self.disable_btn(self.vid_start_stop_btn)
          self.enable_btn(self.webcam_detection_btn)
          self.enable_btn(self.mp4_detection_btn)
          return
       # 应该调整一下图片的大小
       img = self.resize_img(numpy.array(raw_img))
       img = QImage(img.data, img.shape[1], img.shape[0], img.shape[2] * img.shape[1], QImage.Format_RGB888)
       self.left_vid_img.setPixmap(QPixmap.fromImage(img))
       # 上传图片之后右侧的图片重置
       self.right_vid_img.setPixmap(QPixmap("./UI/right.jpeg"))

    def start_or_stop(self):
       """
       启动或者停止事件
       """
       print("启动或者停止")
       if self.threading is None:
          # 创造并启动一个检测视频线程
          self.jump_threading = False
          self.threading = threading.Thread(target=self.detect_vid)
          self.threading.start()
          self.disable_btn(self.webcam_detection_btn)
          self.disable_btn(self.mp4_detection_btn)
       else:
          # 停止当前线程
          # 线程属性置空，恢复状态
          self.threading = None
          self.jump_threading = True
          self.enable_btn(self.webcam_detection_btn)
          self.enable_btn(self.mp4_detection_btn)

    def detect_vid(self):
       """
       视频检测
       视频和摄像头的主函数是一样的，不过是传入的source不同罢了
       """
       print("视频开始检测")
       self.detect(self.vid_source, self.left_vid_img, self.right_vid_img)
       print("视频检测结束")
       # 执行完进程，刷新一下和进程有关的状态，只有self.threading是None，
       # 才能说明是正常结束的线程，需要被刷新状态
       if self.threading is not None:
          self.start_or_stop()

    def closeEvent(self, event):
       """
       界面关闭事件
       """
       reply = QMessageBox.question(
          self,
          'quit',
          "Are you sure?",
          QMessageBox.Yes | QMessageBox.No,
          QMessageBox.No
       )
       if reply == QMessageBox.Yes:
          self.jump_threading = True
          self.close()
          event.accept()
       else:
          event.ignore()


if __name__ == "__main__":
    app = QApplication(sys.argv)
    mainWindow = MainWindow()
    mainWindow.show()
    sys.exit(app.exec_())

detect.py

# -*- coding:utf-8 -*-
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Run YOLOv5 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.

Usage - sources:
    $ python detect.py --weights yolov5s.pt --source 0                               # webcam
                                                     img.jpg                         # image
                                                     vid.mp4                         # video
                                                     path/                           # directory
                                                     'path/*.jpg'                    # glob
                                                     'https://youtu.be/Zgi9g1ksQHc'  # YouTube
                                                     'rtsp://example.com/media.mp4'  # RTSP, RTMP, HTTP stream

Usage - formats:
    $ python detect.py --weights yolov5s.pt                 # PyTorch
                                 yolov5s.torchscript        # TorchScript
                                 yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                                 yolov5s.xml                # OpenVINO
                                 yolov5s.engine             # TensorRT
                                 yolov5s.mlmodel            # CoreML (macOS-only)
                                 yolov5s_saved_model        # TensorFlow SavedModel
                                 yolov5s.pb                 # TensorFlow GraphDef
                                 yolov5s.tflite             # TensorFlow Lite
                                 yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                                 yolov5s_paddle_model       # PaddlePaddle
"""
import argparse
import os
import platform
import sys
from pathlib import Path

import torch

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from models.common import DetectMultiBackend
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                           increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, smart_inference_mode


@smart_inference_mode()
def run(
        weights=ROOT / 'yolov5s.pt',  # model.pt path(s)
        source=ROOT / 'data/images',  # file/dir/URL/glob, 0 for webcam
        data=ROOT / 'data/coco128.yaml',  # dataset.yaml path
        imgsz=(640, 640),  # inference size (height, width)
        conf_thres=0.25,  # confidence threshold
        iou_thres=0.45,  # NMS IOU threshold
        max_det=1000,  # maximum detections per image
        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        view_img=False,  # show results
        save_txt=False,  # save results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_crop=False,  # save cropped prediction boxes
        nosave=False,  # do not save images/videos
        classes=None,  # filter by class: --class 0, or --class 0 2 3
        agnostic_nms=False,  # class-agnostic NMS
        augment=False,  # augmented inference
        visualize=False,  # visualize features
        update=False,  # update all models
        project=ROOT / 'runs/detect',  # save results to project/name
        name='exp',  # save results to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        line_thickness=3,  # bounding box thickness (pixels)
        hide_labels=False,  # hide labels
        hide_conf=False,  # hide confidences
        half=False,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        vid_stride=1,  # video frame-rate stride
):
    source = str(source)
    save_img = not nosave and not source.endswith('.txt')  # save inference images
    is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
    is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
    webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
    if is_url and is_file:
        source = check_file(source)  # download

    # Directories
    save_dir = increment_path(Path(project) / name, exist_ok=exist_ok)  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

    # Load model
    device = select_device(device)
    model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
    stride, names, pt = model.stride, model.names, model.pt
    imgsz = check_img_size(imgsz, s=stride)  # check image size

    # Dataloader
    if webcam:
        view_img = check_imshow()
        dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = len(dataset)  # batch_size
    else:
        dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
        bs = 1  # batch_size
    vid_path, vid_writer = [None] * bs, [None] * bs

    # Run inference
    model.warmup(imgsz=(1 if pt else bs, 3, *imgsz))  # warmup
    seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
    for path, im, im0s, vid_cap, s in dataset:
        with dt[0]:
            im = torch.from_numpy(im).to(device)
            im = im.half() if model.fp16 else im.float()  # uint8 to fp16/32
            im /= 255  # 0 - 255 to 0.0 - 1.0
            if len(im.shape) == 3:
                im = im[None]  # expand for batch dim

        # Inference
        with dt[1]:
            visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
            pred = model(im, augment=augment, visualize=visualize)

        # NMS
        with dt[2]:
            pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)

        # Second-stage classifier (optional)
        # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)

        # Process predictions
        for i, det in enumerate(pred):  # per image
            seen += 1
            if webcam:  # batch_size >= 1
                p, im0, frame = path[i], im0s[i].copy(), dataset.count
                s += f'{i}: '
            else:
                p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # im.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}')  # im.txt
            s += '%gx%g ' % im.shape[2:]  # print string
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwh
            imc = im0.copy() if save_crop else im0  # for save_crop
            annotator = Annotator(im0, line_width=line_thickness, example=str(names))
            if len(det):
                # Rescale boxes from img_size to im0 size
                det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()

                # Print results
                for c in det[:, 5].unique():
                    n = (det[:, 5] == c).sum()  # detections per class
                    s += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string

                # Write results
                for *xyxy, conf, cls in reversed(det):
                    if save_txt:  # Write to file
                        xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                        line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                        with open(f'{txt_path}.txt', 'a') as f:
                            f.write(('%g ' * len(line)).rstrip() % line + '\n')

                    if save_img or save_crop or view_img:  # Add bbox to image
                        c = int(cls)  # integer class
                        label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                        annotator.box_label(xyxy, label, color=colors(c, True))
                    if save_crop:
                        save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

            # Stream results
            im0 = annotator.result()
            if view_img:
                if platform.system() == 'Linux' and p not in windows:
                    windows.append(p)
                    cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO)  # allow window resize (Linux)
                    cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
                cv2.imshow(str(p), im0)
                cv2.waitKey(1)  # 1 millisecond

            # Save results (image with detections)
            if save_img:
                if dataset.mode == 'image':
                    cv2.imwrite(save_path, im0)
                else:  # 'video' or 'stream'
                    if vid_path[i] != save_path:  # new video
                        vid_path[i] = save_path
                        if isinstance(vid_writer[i], cv2.VideoWriter):
                            vid_writer[i].release()  # release previous video writer
                        if vid_cap:  # video
                            fps = vid_cap.get(cv2.CAP_PROP_FPS)
                            w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
                            h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
                        else:  # stream
                            fps, w, h = 30, im0.shape[1], im0.shape[0]
                        save_path = str(Path(save_path).with_suffix('.mp4'))  # force *.mp4 suffix on results videos
                        vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
                    vid_writer[i].write(im0)

        # Print time (inference-only)
        LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")

    # Print results
    t = tuple(x.t / seen * 1E3 for x in dt)  # speeds per image
    LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
    if save_txt or save_img:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
    if update:
        strip_optimizer(weights[0])  # update model (to fix SourceChangeWarning)


def parse_opt():
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'weights/yolov5s.pt', help='model path(s)')
    parser.add_argument('--source', type=str, default=ROOT / '0', help='file/dir/URL/glob, 0 for webcam')
    parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
    parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
    parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
    parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
    parser.add_argument('--device', default='0', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--view-img', action='store_true', help='show results')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
    parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
    parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
    parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--visualize', action='store_true', help='visualize features')
    parser.add_argument('--update', action='store_true', help='update all models')
    parser.add_argument('--project', default=ROOT / 'runs/detect', help='save results to project/name')
    parser.add_argument('--name', default='exp', help='save results to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
    parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
    parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
    parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
    parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
    parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
    opt = parser.parse_args()
    opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1  # expand
    print_args(vars(opt))
    return opt


def main(opt):
    check_requirements(exclude=('tensorboard', 'thop'))
    run(**vars(opt))


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)

安装环境

runs文件夹中，存放训练和评估的结果图

环境安装：

请按照给定的python版本配置环境，否则可能会因依赖不兼容而出错，

在文件目录下cmd进入终端

(1)使用anaconda新建python3.10环境:
conda create -n env_rec python=3.10

(2)激活创建的环境:
conda activate env_rec

(3)使用pip安装所需的依赖，可通过requirements.txt:
pip install -r requirements.txt

在settings中找到project python interpreter 点击Add Interpreter

点击conda，在Use existing environment中选择刚才创建的虚拟环境 ，最后点击确定。如果conda Executable中路径没有，那就把anaconda3的路径添加上