Pyqt搭建YOLOV5目标检测界面

2023-03-11 03:04| 来源: 网络整理| 查看: 265

Pyqt搭建YOLOV5目标检测界面（超详细+源代码） 2022.5.25更新2022.4.9更新2021.11.19 更新实现效果如下所示，可以检测图片、视频以及摄像头实时检测。

本项目1.0版本github地址：https://github.com/chenanga/qt5_yolov5_1.0 本项目2.0版本github地址：https://github.com/chenanga/qt5_yolov5_2.0 （2.0版本是优化后的，视频检测和图片检测都比较快）

大家觉得有用的话，帮忙点点star，感谢大家！

2022.5.25更新

大家有问题的话尽量在评论区问，问之前可以看一下评论区有没有类似错误的解决方法。

2022.4.9更新

必读：本篇文章给出了基于yolov5的实现，具体思路以及如何从零开始搭建界面可以参考上一篇博客：Pyqt搭建YOLOV3目标检测界面（超详细+源代码）

2021.11.19 更新

下面的代码片段大家可以参考着实现，如果直接拖拽到最新版的yolov5文件夹中运行可能会出错，应该我当时那个代码片段写的比较早，后续yolov5更新了，有些函数名有变动，所以直接运行会出错。我这里有当时和这个代码片段对应的yolov5的代码，但是不太知道这是哪个版本的yolov5。所以有需要的朋友直接在公众号：万能的小陈后台回复 qtv5，获取整个文件夹以及模型，配置环境后可以直接运行，配置环境教程可以参考这里注：压缩包名字为qt5_yolov5_1.0的对应原始版本，也就是下面代码片段可以直接用的，qt5_yolov5_2.0对应的是优化后的。这两个压缩包中的yolov5也不是同一个版本的，一个是2021年上半年的，一个是2021年下半年的

以下是正文

实现效果如下所示，可以检测图片、视频以及摄像头实时检测。

yolov5界面检测效果（pyqt5搭建）

测试平台：显卡1080ti。视频检测是优化后的版本，之前版本也可以视频检测，但是没这么流畅，优化后的版本在公众号：万能的小陈后台回复 qtv5。在这里插入图片描述

具体细节实现可以参考上一篇博客：Pyqt搭建YOLOV3目标检测界面（超详细+源代码）使用的yolov5版本为https://github.com/ultralytics/yolov5 这里直接贴出具体代码。

方法1：共两个文件，ui_yolov5.py 、detect_qt5.py，然后把yolov5的代码下载下来，直接把这两个文件拷贝到yolov5根目录，下载yolov5官方的yolov5s.pt权重，放置根目录，然后运行ui_yolov5.py 即可。

方法2：整个yolov5以及两个文件都已上传在github，点这里。无法访问github的关注公众号：万能的小陈,回复qtv5即可获取下载链接。（包含所有代码以及权重文件），只需要配置一下环境，配置环境可以参考这里，如果环境配置困难的或者失败的，在公众号后台回复pyqt5即可获取完整环境。

文件1：ui_yolov5.py

#!/usr/bin/env python # -*- coding:utf-8 -*- # @author : ChenAng # @file : ui_yolov5.py # @Time : 2021/8/27 10:13 import time import os from PyQt5 import QtWidgets, QtCore, QtGui from PyQt5.QtGui import * import cv2 import sys from PyQt5.QtWidgets import * from detect_qt5 import main_detect,my_lodelmodel '''摄像头和视频实时检测界面''' class Ui_MainWindow(QWidget): def __init__(self, parent=None): super(Ui_MainWindow, self).__init__(parent) # self.face_recong = face.Recognition() self.timer_camera1 = QtCore.QTimer() self.timer_camera2 = QtCore.QTimer() self.timer_camera3 = QtCore.QTimer() self.timer_camera4 = QtCore.QTimer() self.cap = cv2.VideoCapture() self.CAM_NUM = 0 # self.slot_init() self.__flag_work = 0 self.x = 0 self.count = 0 self.setWindowTitle("yolov5检测") self.setWindowIcon(QIcon(os.getcwd() + '\\data\\source_image\\Detective.ico')) # self.resize(300, 150) # 宽×高 window_pale = QtGui.QPalette() window_pale.setBrush(self.backgroundRole(), QtGui.QBrush( QtGui.QPixmap(os.getcwd() + '\\data\\source_image\\backgroud.jpg'))) self.setPalette(window_pale) self.setFixedSize(1600, 900) self.my_model = my_lodelmodel() self.button_open_camera = QPushButton(self) self.button_open_camera.setText(u'打开摄像头') self.button_open_camera.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') self.button_open_camera.move(10, 40) self.button_open_camera.clicked.connect(self.button_open_camera_click) #self.button_open_camera.clicked.connect(self.button_open_camera_click1) # btn.clicked.connect(self.openimage) self.btn1 = QPushButton(self) self.btn1.setText("检测摄像头") self.btn1.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') self.btn1.move(10, 80) self.btn1.clicked.connect(self.button_open_camera_click1) # print("QPushButton构建") self.open_video = QPushButton(self) self.open_video.setText("打开视频") self.open_video.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') self.open_video.move(10, 160) self.open_video.clicked.connect(self.open_video_button) print("QPushButton构建") self.btn1 = QPushButton(self) self.btn1.setText("检测视频文件") self.btn1.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') self.btn1.move(10, 200) self.btn1.clicked.connect(self.detect_video) print("QPushButton构建") # btn1.clicked.connect(self.detect()) # btn1.clicked.connect(self.button1_test) #btn1.clicked.connect(self.detect()) # btn1.clicked.connect(self.button1_test) btn2 = QPushButton(self) btn2.setText("返回上一界面") btn2.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') btn2.move(10, 240) btn2.clicked.connect(self.back_lastui) # 信息显示 self.label_show_camera = QLabel(self) self.label_move = QLabel() self.label_move.setFixedSize(100, 100) # self.label_move.setText(" 11 待检测图片") self.label_show_camera.setFixedSize(700, 500) self.label_show_camera.setAutoFillBackground(True) self.label_show_camera.move(110,80) self.label_show_camera.setStyleSheet("QLabel{background:#F5F5DC;}" "QLabel{color:rgb(300,300,300,120);font-size:10px;font-weight:bold;font-family:宋体;}" ) self.label_show_camera1 = QLabel(self) self.label_show_camera1.setFixedSize(700, 500) self.label_show_camera1.setAutoFillBackground(True) self.label_show_camera1.move(850, 80) self.label_show_camera1.setStyleSheet("QLabel{background:#F5F5DC;}" "QLabel{color:rgb(300,300,300,120);font-size:10px;font-weight:bold;font-family:宋体;}" ) self.timer_camera1.timeout.connect(self.show_camera) self.timer_camera2.timeout.connect(self.show_camera1) # self.timer_camera3.timeout.connect(self.show_camera2) self.timer_camera4.timeout.connect(self.show_camera2) self.timer_camera4.timeout.connect(self.show_camera3) self.clicked = False # self.setWindowTitle(u'摄像头') self.frame_s=3 ''' # 设置背景图片 palette1 = QPalette() palette1.setBrush(self.backgroundRole(), QBrush(QPixmap('background.jpg'))) self.setPalette(palette1) ''' def back_lastui(self): self.timer_camera1.stop() self.cap.release() self.label_show_camera.clear() self.timer_camera2.stop() self.label_show_camera1.clear() cam_t.close() ui_p.show() '''摄像头''' def button_open_camera_click(self): if self.timer_camera1.isActive() == False: flag = self.cap.open(self.CAM_NUM) if flag == False: msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"请检测相机与电脑是否连接正确", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok) else: self.timer_camera1.start(30) self.button_open_camera.setText(u'关闭摄像头') else: self.timer_camera1.stop() self.cap.release() self.label_show_camera.clear() self.timer_camera2.stop() self.label_show_camera1.clear() self.button_open_camera.setText(u'打开摄像头') def show_camera(self): #摄像头左边 flag, self.image = self.cap.read() dir_path=os.getcwd() camera_source =dir_path+ "\\data\\test\\2.jpg" cv2.imwrite(camera_source, self.image) width = self.image.shape[1] height = self.image.shape[0] # 设置新的图片分辨率框架 width_new = 700 height_new = 500 # 判断图片的长宽比率 if width / height >= width_new / height_new: show = cv2.resize(self.image, (width_new, int(height * width_new / width))) else: show = cv2.resize(self.image, (int(width * height_new / height), height_new)) show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB) showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0],3 * show.shape[1], QtGui.QImage.Format_RGB888) self.label_show_camera.setPixmap(QtGui.QPixmap.fromImage(showImage)) def button_open_camera_click1(self): if self.timer_camera2.isActive() == False: flag = self.cap.open(self.CAM_NUM) if flag == False: msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"请检测相机与电脑是否连接正确", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok) else: self.timer_camera2.start(30) self.button_open_camera.setText(u'关闭摄像头') else: self.timer_camera2.stop() self.cap.release() self.label_show_camera1.clear() self.button_open_camera.setText(u'打开摄像头') def show_camera1(self): flag, self.image = self.cap.read() dir_path = os.getcwd() camera_source = dir_path + "\\data\\test\\2.jpg" cv2.imwrite(camera_source, self.image) im0, label = main_detect(self.my_model, camera_source) if label=='debug': print("labelkong") width = im0.shape[1] height = im0.shape[0] # 设置新的图片分辨率框架 width_new = 700 height_new = 500 # 判断图片的长宽比率 if width / height >= width_new / height_new: show = cv2.resize(im0, (width_new, int(height * width_new / width))) else: show = cv2.resize(im0, (int(width * height_new / height), height_new)) im0 = cv2.cvtColor(show, cv2.COLOR_RGB2BGR) # print("debug2") showImage = QtGui.QImage(im0, im0.shape[1], im0.shape[0], 3 * im0.shape[1], QtGui.QImage.Format_RGB888) self.label_show_camera1.setPixmap(QtGui.QPixmap.fromImage(showImage)) '''视频检测''' def open_video_button(self): if self.timer_camera4.isActive() == False: imgName, imgType = QFileDialog.getOpenFileName(self, "打开视频", "", "*.mp4;;*.AVI;;*.rmvb;;All Files(*)") self.cap_video = cv2.VideoCapture(imgName) flag = self.cap_video.isOpened() if flag == False: msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"请检测相机与电脑是否连接正确", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok) else: # self.timer_camera3.start(30) self.show_camera2() self.open_video.setText(u'关闭视频') else: # self.timer_camera3.stop() self.cap_video.release() self.label_show_camera.clear() self.timer_camera4.stop() self.frame_s=3 self.label_show_camera1.clear() self.open_video.setText(u'打开视频') def detect_video(self): if self.timer_camera4.isActive() == False: flag = self.cap_video.isOpened() if flag == False: msg = QtWidgets.QMessageBox.warning(self, u"Warning", u"请检测相机与电脑是否连接正确", buttons=QtWidgets.QMessageBox.Ok, defaultButton=QtWidgets.QMessageBox.Ok) else: self.timer_camera4.start(30) else: self.timer_camera4.stop() self.cap_video.release() self.label_show_camera1.clear() def show_camera2(self): #显示视频的左边 #抽帧 length = int(self.cap_video.get(cv2.CAP_PROP_FRAME_COUNT)) #抽帧 print(self.frame_s,length) #抽帧 flag, self.image1 = self.cap_video.read() #image1是视频的 if flag == True: if self.frame_s%3==0: #抽帧 dir_path=os.getcwd() # print("dir_path",dir_path) camera_source =dir_path+ "\\data\\test\\video.jpg" cv2.imwrite(camera_source, self.image1) width=self.image1.shape[1] height=self.image1.shape[0] # 设置新的图片分辨率框架 width_new = 700 height_new = 500 # 判断图片的长宽比率 if width / height >= width_new / height_new: show = cv2.resize(self.image1, (width_new, int(height * width_new / width))) else: show = cv2.resize(self.image1, (int(width * height_new / height), height_new)) show = cv2.cvtColor(show, cv2.COLOR_BGR2RGB) showImage = QtGui.QImage(show.data, show.shape[1], show.shape[0],3 * show.shape[1], QtGui.QImage.Format_RGB888) self.label_show_camera.setPixmap(QtGui.QPixmap.fromImage(showImage)) else: self.cap_video.release() self.label_show_camera.clear() self.timer_camera4.stop() self.label_show_camera1.clear() self.open_video.setText(u'打开视频') def show_camera3(self): flag, self.image1 = self.cap_video.read() self.frame_s += 1 if flag==True: if self.frame_s % 3 == 0: #抽帧 # face = self.face_detect.align(self.image) # if face: # pass dir_path = os.getcwd() camera_source = dir_path + "\\data\\test\\video.jpg" cv2.imwrite(camera_source, self.image1) # print("im01") im0, label = main_detect(self.my_model, camera_source) # print("imo",im0) # print(label) if label=='debug': print("labelkong") # print("debug") # im0, label = slef.detect() # print("debug1") width = im0.shape[1] height = im0.shape[0] # 设置新的图片分辨率框架 width_new = 700 height_new = 500 # 判断图片的长宽比率 if width / height >= width_new / height_new: show = cv2.resize(im0, (width_new, int(height * width_new / width))) else: show = cv2.resize(im0, (int(width * height_new / height), height_new)) im0 = cv2.cvtColor(show, cv2.COLOR_RGB2BGR) # print("debug2") showImage = QtGui.QImage(im0, im0.shape[1], im0.shape[0], 3 * im0.shape[1], QtGui.QImage.Format_RGB888) self.label_show_camera1.setPixmap(QtGui.QPixmap.fromImage(showImage)) '''单张图片检测''' class picture(QWidget): def __init__(self): super(picture, self).__init__() self.str_name = '0' self.my_model=my_lodelmodel() self.resize(1600, 900) self.setWindowIcon(QIcon(os.getcwd() + '\\data\\source_image\\Detective.ico')) self.setWindowTitle("yolov5目标检测平台") window_pale = QtGui.QPalette() window_pale.setBrush(self.backgroundRole(), QtGui.QBrush( QtGui.QPixmap(os.getcwd() + '\\data\\source_image\\backgroud.jpg'))) self.setPalette(window_pale) camera_or_video_save_path = 'data\\test' if not os.path.exists(camera_or_video_save_path): os.makedirs(camera_or_video_save_path) self.label1 = QLabel(self) self.label1.setText(" 待检测图片") self.label1.setFixedSize(700, 500) self.label1.move(110, 80) self.label1.setStyleSheet("QLabel{background:#7A6969;}" "QLabel{color:rgb(300,300,300,120);font-size:20px;font-weight:bold;font-family:宋体;}" ) self.label2 = QLabel(self) self.label2.setText(" 检测结果") self.label2.setFixedSize(700, 500) self.label2.move(850, 80) self.label2.setStyleSheet("QLabel{background:#7A6969;}" "QLabel{color:rgb(300,300,300,120);font-size:20px;font-weight:bold;font-family:宋体;}" ) self.label3 = QLabel(self) self.label3.setText("") self.label3.move(1200, 620) self.label3.setStyleSheet("font-size:20px;") self.label3.adjustSize() btn = QPushButton(self) btn.setText("打开图片") btn.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') btn.move(10, 30) btn.clicked.connect(self.openimage) btn1 = QPushButton(self) btn1.setText("检测图片") btn1.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') btn1.move(10, 80) # print("QPushButton构建") btn1.clicked.connect(self.button1_test) btn3 = QPushButton(self) btn3.setText("") btn3.setStyleSheet(''' QPushButton {text-align : center; background-color : white; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} QPushButton:pressed {text-align : center; background-color : light gray; font: bold; border-color: gray; border-width: 2px; border-radius: 10px; padding: 6px; height : 14px; border-style: outset; font : 14px;} ''') btn3.move(10, 160) btn3.clicked.connect(self.camera_find) self.imgname1='0' def camera_find(self): ui_p.close() cam_t.show() def openimage(self): imgName, imgType = QFileDialog.getOpenFileName(self, "打开图片", "", "*.jpg;;*.png;;All Files(*)") if imgName!='': self.imgname1=imgName # print("imgName",imgName,type(imgName)) im0=cv2.imread(imgName) width = im0.shape[1] height = im0.shape[0] # 设置新的图片分辨率框架 width_new = 700 height_new = 500 # 判断图片的长宽比率 if width / height >= width_new / height_new: show = cv2.resize(im0, (width_new, int(height * width_new / width))) else: show = cv2.resize(im0, (int(width * height_new / height), height_new)) im0 = cv2.cvtColor(show, cv2.COLOR_RGB2BGR) showImage = QtGui.QImage(im0, im0.shape[1], im0.shape[0], 3 * im0.shape[1], QtGui.QImage.Format_RGB888) self.label1.setPixmap(QtGui.QPixmap.fromImage(showImage)) # jpg = QtGui.QPixmap(imgName).scaled(self.label1.width(), self.label1.height()) # self.label1.setPixmap(jpg) def button1_test(self): if self.imgname1!='0': QApplication.processEvents() im0,label=main_detect(self.my_model,self.imgname1) QApplication.processEvents() width = im0.shape[1] height = im0.shape[0] # 设置新的图片分辨率框架 width_new = 700 height_new = 500 # 判断图片的长宽比率 if width / height >= width_new / height_new: show = cv2.resize(im0, (width_new, int(height * width_new / width))) else: show = cv2.resize(im0, (int(width * height_new / height), height_new)) im0 = cv2.cvtColor(show, cv2.COLOR_RGB2BGR) image_name = QtGui.QImage(im0, im0.shape[1], im0.shape[0], 3 * im0.shape[1], QtGui.QImage.Format_RGB888) # label=label.split(' ')[0] #label 59 0.96 分割字符串取前一个 self.label2.setPixmap(QtGui.QPixmap.fromImage(image_name)) # jpg = QtGui.QPixmap(image_name).scaled(self.label1.width(), self.label1.height()) # self.label2.setPixmap(jpg) else: QMessageBox.information(self, '错误', '请先选择一个图片文件', QMessageBox.Yes, QMessageBox.Yes) if __name__ == '__main__': app = QApplication(sys.argv) splash = QSplashScreen(QPixmap(".\\data\\source_image\\logo.png")) # 设置画面中的文字的字体 splash.setFont(QFont('Microsoft YaHei UI', 12)) # 显示画面 splash.show() # 显示信息 splash.showMessage("程序初始化中... 0%", QtCore.Qt.AlignLeft | QtCore.Qt.AlignBottom, QtCore.Qt.black) time.sleep(0.3) splash.showMessage("正在加载模型配置文件...60%", QtCore.Qt.AlignLeft | QtCore.Qt.AlignBottom, QtCore.Qt.black) cam_t=Ui_MainWindow() splash.showMessage("正在加载模型配置文件...100%", QtCore.Qt.AlignLeft | QtCore.Qt.AlignBottom, QtCore.Qt.black) ui_p = picture() ui_p.show() splash.close() sys.exit(app.exec_())

文件2：detect_qt5.py

import argparse import time from pathlib import Path import cv2 import torch import torch.backends.cudnn as cudnn from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box from utils.plots import colors, plot_one_box from utils.torch_utils import select_device, load_classifier, time_synchronized def my_lodelmodel(): parser = argparse.ArgumentParser() parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') opt = parser.parse_args() device = select_device(opt.device) ''' 打包为exe 时候这个select——device可能会出错，所以替换为 # device ='cuda:0' ''' # device ='cuda:0' print("device", device) weights = opt.weights # Load model model = attempt_load(weights, map_location=device) # load FP32 model return model @torch.no_grad() def detect(opt, my_model, source_open): source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size save_img = not opt.nosave and not source.endswith('.txt') # save inference images webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( ('rtsp://', 'rtmp://', 'http://', 'https://')) label = 'debug' # # Directories save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok) # increment run (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir # Initialize set_logging() device = select_device(opt.device) half = opt.half and device.type != 'cpu' # half precision only supported on CUDA # Load model # model = attempt_load(weights, map_location=device) # load FP32 model model = my_model stride = int(model.stride.max()) # model stride imgsz = check_img_size(imgsz, s=stride) # check img_size names = model.module.names if hasattr(model, 'module') else model.names # get class names if half: model.half() # to FP16 # Second-stage classifier classify = False if classify: modelc = load_classifier(name='resnet101', n=2) # initialize modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() # Set Dataloader vid_path, vid_writer = None, None source = source_open if webcam: view_img = check_imshow() cudnn.benchmark = True # set True to speed up constant image size inference dataset = LoadStreams(source, img_size=imgsz, stride=stride) else: dataset = LoadImages(source, img_size=imgsz, stride=stride) # Run inference if device.type != 'cpu': model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once t0 = time.time() for path, img, im0s, vid_cap in dataset: 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=opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, opt.classes, opt.agnostic_nms, max_det=opt.max_det) t2 = time_synchronized() # Apply Classifier if classify: pred = apply_classifier(pred, modelc, img, im0s) # Process detections for i, det in enumerate(pred): # detections per image if webcam: # batch_size >= 1 p, s, im0, frame = path[i], f'{i}: ', im0s[i].copy(), dataset.count else: p, s, im0, frame = path, '', im0s.copy(), getattr(dataset, 'frame', 0) p = Path(p) # to Path save_path = str(save_dir / p.name) # img.jpg txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt s += '%gx%g ' % img.shape[2:] # print string gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh imc = im0.copy() if opt.save_crop else im0 # for opt.save_crop 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): 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 opt.save_conf else (cls, *xywh) # label format with open(txt_path + '.txt', 'a') as f: f.write(('%g ' * len(line)).rstrip() % line + '\n') if save_img or opt.save_crop or view_img: # Add bbox to image c = int(cls) # integer class label = None if opt.hide_labels else (names[c] if opt.hide_conf else f'{names[c]} {conf:.2f}') plot_one_box(xyxy, im0, label=label, color=colors(c, True), line_thickness=opt.line_thickness) # if opt.save_crop: # save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) # # # Print time (inference + NMS) # print(f'{s}Done. ({t2 - t1:.3f}s)') # Stream results # if view_img: # 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 != save_path: # new video # vid_path = save_path # if isinstance(vid_writer, cv2.VideoWriter): # vid_writer.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 += '.mp4' # vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) # vid_writer.write(im0) # 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 '' # print(f"Results saved to {save_dir}{s}") print(f'Done. ({time.time() - t0:.3f}s)') return im0,label def main_detect(my_model,source_open): # if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') parser.add_argument('--max-det', type=int, default=1000, help='maximum number of detections per image') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--view-img', action='store_true', help='display 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: --class 0, or --class 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('--update', action='store_true', help='update all models') parser.add_argument('--project', default='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') opt = parser.parse_args() print(opt) im0, label = detect(opt, my_model, source_open) print("detect") return im0, label

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