YOLOV5 + PYQT5双目测距（二）_pyqt5与yolo结合代码

1. 测距源码

详见文章 YOLOV5 + 双目测距（python）

2. 测距原理

如果想了解双目测距原理，请移步该文章 双目三维测距（python）

3. PYQT环境配置

首先安装一下pyqt5

pip install PyQt5
pip install PyQt5-tools
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接着再pycharm设置里配置一下

添加下面两个工具:
工具1：Qt Designer

Program D:\Anaconda3\Lib\site-packages\qt5_applications\Qt\bin\designer.exe#代码所用环境路径
Arauments : $FileName$
Working directory :$FileDir$
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工具2：PyUIC

Program D:\Anaconda3\Scripts\pyuic5.exe 
Arguments : $FileName$ -o $FileNameWithoutExtension$.py
Working directory :$FileDir$
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4. 实验

4.1 下载源码1

实验采用的是一个博主的开源代码，以此代码为基础添加了测距部分，下载 开源代码

4.2 复制源码2文件夹

将我之前写的文章 YOLOV5 + 双目测距（python）里的源码2下载，将其中的stereo文件夹复制到下载的源码1文件夹

4.3 创建py文件

在文件目录下创建一个main-ceju-pyqt.py文件，将以下代码写入此文件

from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, QMenu, QAction
from main_win.win import Ui_mainWindow
from PyQt5.QtCore import Qt, QPoint, QTimer, QThread, pyqtSignal
from PyQt5.QtGui import QImage, QPixmap, QPainter, QIcon

import sys
import os
import json
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import os
import time
import cv2

from models.experimental import attempt_load
from utils.datasets import LoadImages, LoadWebcam
from utils.CustomMessageBox import MessageBox
# LoadWebcam 的最后一个返回值改为 self.cap
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, 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, plot_one_box_PIL
from utils.torch_utils import select_device, load_classifier, time_sync
from utils.capnums import Camera
from dialog.rtsp_win import Window
from stereo.dianyuntu_yolo import preprocess, undistortion, getRectifyTransform, draw_line, rectifyImage, \
    stereoMatchSGBM
from stereo import stereoconfig

class DetThread(QThread):
    send_img = pyqtSignal(np.ndarray)
    send_raw = pyqtSignal(np.ndarray)
    send_statistic = pyqtSignal(dict)
    # 发送信号：正在检测/暂停/停止/检测结束/错误报告
    send_msg = pyqtSignal(str)
    send_percent = pyqtSignal(int)
    send_fps = pyqtSignal(str)

    def __init__(self):
        super(DetThread, self).__init__()
        self.weights = './yolov5s.pt'           # 设置权重
        self.current_weight = './yolov5s.pt'    # 当前权重
        self.source = '0'                       # 视频源
        self.conf_thres = 0.25                  # 置信度
        self.iou_thres = 0.45                   # iou
        self.jump_out = False                   # 跳出循环
        self.is_continue = True                 # 继续/暂停
        self.percent_length = 1000              # 进度条
        self.rate_check = True                  # 是否启用延时
        self.rate = 100                         # 延时HZ

    @torch.no_grad()
    def run(self,
            imgsz=640,  # inference size (pixels)
            max_det=1000,  # maximum detections per image
            device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
            view_img=True,  # 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='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
            ):

        # Initialize
        try:
            device = select_device(device)
            half &= device.type != 'cpu'  # half precision only supported on CUDA

            # Load model
            model = attempt_load(self.weights, map_location=device)  # load FP32 model
            num_params = 0
            for param in model.parameters():
                num_params += param.numel()
            stride = int(model.stride.max())  # model stride
            imgsz = check_img_size(imgsz, s=stride)  # check image size
            names = model.module.names if hasattr(model, 'module') else model.names  # get class names
            if half:
                model.half()  # to FP16

            # Dataloader
            if self.source.isnumeric() or self.source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')):
                view_img = check_imshow()
                cudnn.benchmark = True  # set True to speed up constant image size inference
                dataset = LoadWebcam(self.source, img_size=imgsz, stride=stride)
                # bs = len(dataset)  # batch_size
            else:
                dataset = LoadImages(self.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
            count = 0
            # 跳帧检测
            jump_count = 0
            start_time = time.time()
            dataset = iter(dataset)
            while True:
                # 手动停止
                if self.jump_out:
                    self.vid_cap.release()
                    self.send_percent.emit(0)
                    self.send_msg.emit('停止')
                    break
                # 临时更换模型
                if self.current_weight != self.weights:
                    # Load model
                    model = attempt_load(self.weights, map_location=device)  # load FP32 model
                    num_params = 0
                    for param in model.parameters():
                        num_params += param.numel()
                    stride = int(model.stride.max())  # model stride
                    imgsz = check_img_size(imgsz, s=stride)  # check image size
                    names = model.module.names if hasattr(model, 'module') else model.names  # get class names
                    if half:
                        model.half()  # to FP16
                    # Run inference
                    if device.type != 'cpu':
                        model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))  # run once
                    self.current_weight = self.weights
                # 暂停开关
                if self.is_continue:
                    path, img, im0s, self.vid_cap = next(dataset)
                    # jump_count += 1
                    # if jump_count % 5 != 0:
                    #     continue
                    count += 1
                    # 每三十帧刷新一次输出帧率
                    if count % 30 == 0 and count >= 30:
                        fps = int(30/(time.time()-start_time))
                        self.send_fps.emit('fps：'+str(fps))
                        start_time = time.time()
                    if self.vid_cap:
                        percent = int(count/self.vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)*self.percent_length)
                        self.send_percent.emit(percent)
                    else:
                        percent = self.percent_length

                    statistic_dic = {name: 0 for name in names}
                    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)

                    pred = model(img, augment=augment)[0]

                    # Apply NMS
                    pred = non_max_suppression(pred, self.conf_thres, self.iou_thres, classes, agnostic_nms, max_det=max_det)
                    # Process detections
                    for i, det in enumerate(pred):  # detections per image
                        im0 = im0s.copy()

                        if len(det):
                            # Rescale boxes from img_size to im0 size
                            det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()

                            # Write results
                            for *xyxy, conf, cls in reversed(det):
                                x = (xyxy[0] + xyxy[2]) / 2
                                y = (xyxy[1] + xyxy[3]) / 2
                                if (0 < x <= 1280):
                                    height_0, width_0 = im0.shape[0:2]
                                    iml = im0[0:int(height_0), 0:int(width_0 / 2)]
                                    imr = im0[0:int(height_0), int(width_0 / 2):int(width_0)]

                                    height, width = iml.shape[0:2]
                                    config = stereoconfig.stereoCamera()
                                    map1x, map1y, map2x, map2y, Q = getRectifyTransform(720, 1280, config)
                                    iml_rectified, imr_rectified = rectifyImage(iml, imr, map1x, map1y, map2x,
                                                                                map2y)
                                    line = draw_line(iml_rectified, imr_rectified)
                                    iml = undistortion(iml, config.cam_matrix_left, config.distortion_l)
                                    imr = undistortion(imr, config.cam_matrix_right, config.distortion_r)
                                    iml_, imr_ = preprocess(iml, imr)
                                    iml_rectified_l, imr_rectified_r = rectifyImage(iml_, imr_, map1x, map1y, map2x,
                                                                                    map2y)
                                    disp, _ = stereoMatchSGBM(iml_rectified_l, imr_rectified_r, True)
                                    points_3d = cv2.reprojectImageTo3D(disp, Q)

                                    distance = ((points_3d[int(y), int(x), 0] ** 2 + points_3d[int(y), int(x), 1] ** 2 +
                                                 points_3d[int(y), int(x), 2] ** 2) ** 0.5) / 1000
                                    distance = '%.2f' % distance
                                    c = int(cls)  # integer class
                                    statistic_dic[names[c]] += 1
                                    label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
                                    label = label + "  " + "dis:" + str(distance) + "m"
                                    # im0 = plot_one_box_PIL(xyxy, im0, label=label, color=colors(c, True), line_thickness=line_thickness)  # 中文标签画框，但是耗时会增加
                                    plot_one_box(xyxy, im0, label=label, color=colors(c, True),
                                                 line_thickness=line_thickness)

                    # 控制视频发送频率
                    if self.rate_check:
                        time.sleep(1/self.rate)
                    # print(type(im0s))
                    self.send_img.emit(im0)
                    self.send_raw.emit(im0s if isinstance(im0s, np.ndarray) else im0s[0])
                    self.send_statistic.emit(statistic_dic)
                    if percent == self.percent_length:
                        self.send_percent.emit(0)
                        self.send_msg.emit('检测结束')
                        # 正常跳出循环
                        break

        except Exception as e:
            self.send_msg.emit('%s' % e)


class MainWindow(QMainWindow, Ui_mainWindow):
    def __init__(self, parent=None):
        super(MainWindow, self).__init__(parent)
        self.setupUi(self)
        self.m_flag = False
        # win10的CustomizeWindowHint模式，边框上面有一段空白。
        # 不想看到空白可以用FramelessWindowHint模式，但是需要重写鼠标事件才能通过鼠标拉伸窗口，比较麻烦
        # 不嫌麻烦可以试试, 写了一半不想写了，累死人
        self.setWindowFlags(Qt.CustomizeWindowHint)
        # self.setWindowFlags(Qt.FramelessWindowHint)
        # 自定义标题栏按钮
        self.minButton.clicked.connect(self.showMinimized)
        self.maxButton.clicked.connect(self.max_or_restore)
        self.closeButton.clicked.connect(self.close)

        # 定时清空自定义状态栏上的文字
        self.qtimer = QTimer(self)
        self.qtimer.setSingleShot(True)
        self.qtimer.timeout.connect(lambda: self.statistic_label.clear())

        # 自动搜索模型
        self.comboBox.clear()
        self.pt_list = os.listdir('./pt')
        self.pt_list = [file for file in self.pt_list if file.endswith('.pt')]
        self.pt_list.sort(key=lambda x: os.path.getsize('./pt/'+x))
        self.comboBox.clear()
        self.comboBox.addItems(self.pt_list)
        self.qtimer_search = QTimer(self)
        self.qtimer_search.timeout.connect(lambda: self.search_pt())
        self.qtimer_search.start(2000)

        # yolov5线程
        self.det_thread = DetThread()
        self.model_type = self.comboBox.currentText()
        self.det_thread.weights = "./pt/%s" % self.model_type           # 权重
        self.det_thread.source = '0'                                    # 默认打开本机摄像头，无需保存到配置文件
        self.det_thread.percent_length = self.progressBar.maximum()
        self.det_thread.send_raw.connect(lambda x: self.show_image(x, self.raw_video))
        self.det_thread.send_img.connect(lambda x: self.show_image(x, self.out_video))
        self.det_thread.send_statistic.connect(self.show_statistic)
        self.det_thread.send_msg.connect(lambda x: self.show_msg(x))
        self.det_thread.send_percent.connect(lambda x: self.progressBar.setValue(x))
        self.det_thread.send_fps.connect(lambda x: self.fps_label.setText(x))

        self.fileButton.clicked.connect(self.open_file)
        self.cameraButton.clicked.connect(self.chose_cam)
        self.rtspButton.clicked.connect(self.chose_rtsp)

        self.runButton.clicked.connect(self.run_or_continue)
        self.stopButton.clicked.connect(self.stop)

        self.comboBox.currentTextChanged.connect(self.change_model)
        # self.comboBox.currentTextChanged.connect(lambda x: self.statistic_msg('模型切换为%s' % x))
        self.confSpinBox.valueChanged.connect(lambda x: self.change_val(x, 'confSpinBox'))
        self.confSlider.valueChanged.connect(lambda x: self.change_val(x, 'confSlider'))
        self.iouSpinBox.valueChanged.connect(lambda x: self.change_val(x, 'iouSpinBox'))
        self.iouSlider.valueChanged.connect(lambda x: self.change_val(x, 'iouSlider'))
        self.rateSpinBox.valueChanged.connect(lambda x: self.change_val(x, 'rateSpinBox'))
        self.rateSlider.valueChanged.connect(lambda x: self.change_val(x, 'rateSlider'))

        self.checkBox.clicked.connect(self.checkrate)
        self.load_setting()

    def search_pt(self):
        pt_list = os.listdir('./pt')
        pt_list = [file for file in pt_list if file.endswith('.pt')]
        pt_list.sort(key=lambda x: os.path.getsize('./pt/' + x))

        if pt_list != self.pt_list:
            self.pt_list = pt_list
            self.comboBox.clear()
            self.comboBox.addItems(self.pt_list)

    def checkrate(self):
        if self.checkBox.isChecked():
            # 选中时
            self.det_thread.rate_check = True
        else:
            self.det_thread.rate_check = False

    def chose_rtsp(self):
        self.rtsp_window = Window()
        config_file = 'config/ip.json'
        if not os.path.exists(config_file):
            ip = "rtsp://admin:admin888@192.168.1.67:555"
            new_config = {"ip": ip}
            new_json = json.dumps(new_config, ensure_ascii=False, indent=2)
            with open(config_file, 'w', encoding='utf-8') as f:
                f.write(new_json)
        else:
            config = json.load(open(config_file, 'r', encoding='utf-8'))
            ip = config['ip']
        self.rtsp_window.rtspEdit.setText(ip)
        self.rtsp_window.show()
        self.rtsp_window.rtspButton.clicked.connect(lambda: self.load_rtsp(self.rtsp_window.rtspEdit.text()))

    def load_rtsp(self, ip):
        try:
            self.stop()
            MessageBox(
                self.closeButton, title='提示', text='请稍等，正在加载rtsp视频流', time=1000, auto=True).exec_()
            self.det_thread.source = ip
            new_config = {"ip": ip}
            new_json = json.dumps(new_config, ensure_ascii=False, indent=2)
            with open('config/ip.json', 'w', encoding='utf-8') as f:
                f.write(new_json)
            self.statistic_msg('加载rtsp：{}'.format(ip))
            self.rtsp_window.close()
        except Exception as e:
            self.statistic_msg('%s' % e)

    def chose_cam(self):
        try:
            self.stop()
            # MessageBox的作用：留出2秒，让上一次摄像头安全release
            MessageBox(
                self.closeButton, title='提示', text='请稍等，正在检测摄像头设备', time=2000, auto=True).exec_()
            # 自动检测本机有哪些摄像头
            _, cams = Camera().get_cam_num()
            popMenu = QMenu()
            popMenu.setFixedWidth(self.cameraButton.width())
            popMenu.setStyleSheet('''
                                            QMenu {
                                            font-size: 16px;
                                            font-family: "Microsoft YaHei UI";
                                            font-weight: light;
                                            color:white;
                                            padding-left: 5px;
                                            padding-right: 5px;
                                            padding-top: 4px;
                                            padding-bottom: 4px;
                                            border-style: solid;
                                            border-width: 0px;
                                            border-color: rgba(255, 255, 255, 255);
                                            border-radius: 3px;
                                            background-color: rgba(200, 200, 200,50);}
                                            ''')

            for cam in cams:
                exec("action_%s = QAction('%s')" % (cam, cam))
                exec("popMenu.addAction(action_%s)" % cam)

            x = self.groupBox_5.mapToGlobal(self.cameraButton.pos()).x()
            y = self.groupBox_5.mapToGlobal(self.cameraButton.pos()).y()
            y = y + self.cameraButton.frameGeometry().height()
            pos = QPoint(x, y)
            action = popMenu.exec_(pos)
            if action:
                self.det_thread.source = action.text()
                self.statistic_msg('加载摄像头：{}'.format(action.text()))
        except Exception as e:
            self.statistic_msg('%s' % e)

    # 导入配置文件
    def load_setting(self):
        config_file = 'config/setting.json'
        if not os.path.exists(config_file):
            iou = 0.26
            conf = 0.33
            rate = 10
            check = 0
            new_config = {"iou": 0.26,
                          "conf": 0.33,
                          "rate": 10,
                          "check": 0
                          }
            new_json = json.dumps(new_config, ensure_ascii=False, indent=2)
            with open(config_file, 'w', encoding='utf-8') as f:
                f.write(new_json)
        else:
            config = json.load(open(config_file, 'r', encoding='utf-8'))
            iou = config['iou']
            conf = config['conf']
            rate = config['rate']
            check = config['check']
        self.confSpinBox.setValue(iou)
        self.iouSpinBox.setValue(conf)
        self.rateSpinBox.setValue(rate)
        self.checkBox.setCheckState(check)
        self.det_thread.rate_check = check

    def change_val(self, x, flag):
        if flag == 'confSpinBox':
            self.confSlider.setValue(int(x*100))
        elif flag == 'confSlider':
            self.confSpinBox.setValue(x/100)
            self.det_thread.conf_thres = x/100
        elif flag == 'iouSpinBox':
            self.iouSlider.setValue(int(x*100))
        elif flag == 'iouSlider':
            self.iouSpinBox.setValue(x/100)
            self.det_thread.iou_thres = x/100
        elif flag == 'rateSpinBox':
            self.rateSlider.setValue(x)
        elif flag == 'rateSlider':
            self.rateSpinBox.setValue(x)
            self.det_thread.rate = x * 10
        else:
            pass

    def statistic_msg(self, msg):
        self.statistic_label.setText(msg)
        # self.qtimer.start(3000)   # 3秒后自动清除

    def show_msg(self, msg):
        self.runButton.setChecked(Qt.Unchecked)
        self.statistic_msg(msg)

    def change_model(self, x):
        self.model_type = self.comboBox.currentText()
        self.det_thread.weights = "./pt/%s" % self.model_type
        self.statistic_msg('模型切换为%s' % x)

    def open_file(self):
        # source = QFileDialog.getOpenFileName(self, '选取视频或图片', os.getcwd(), "Pic File(*.mp4 *.mkv *.avi *.flv "
        #                                                                    "*.jpg *.png)")
        config_file = 'config/fold.json'
        # config = json.load(open(config_file, 'r', encoding='utf-8'))
        config = json.load(open(config_file, 'r', encoding='utf-8'))
        open_fold = config['open_fold']
        if not os.path.exists(open_fold):
            open_fold = os.getcwd()
        name, _ = QFileDialog.getOpenFileName(self, '选取视频或图片', open_fold, "Pic File(*.mp4 *.mkv *.avi *.flv "
                                                                          "*.jpg *.png)")
        if name:
            self.det_thread.source = name
            self.statistic_msg('加载文件：{}'.format(os.path.basename(name)))
            config['open_fold'] = os.path.dirname(name)
            config_json = json.dumps(config, ensure_ascii=False, indent=2)
            with open(config_file, 'w', encoding='utf-8') as f:
                f.write(config_json)
            # 切换文件后，上一次检测停止
            self.stop()

    def max_or_restore(self):
        if self.maxButton.isChecked():
            self.showMaximized()
        else:
            self.showNormal()

    # 继续/暂停
    def run_or_continue(self):
        self.det_thread.jump_out = False
        if self.runButton.isChecked():
            self.det_thread.is_continue = True
            if not self.det_thread.isRunning():
                self.det_thread.start()
            source = os.path.basename(self.det_thread.source)
            source = '摄像头设备' if source.isnumeric() else source
            self.statistic_msg('正在检测 >> 模型：{}，文件：{}'.
                               format(os.path.basename(self.det_thread.weights),
                                      source))
        else:
            self.det_thread.is_continue = False
            self.statistic_msg('暂停')

    # 退出检测循环
    def stop(self):
        self.det_thread.jump_out = True

    def mousePressEvent(self, event):
        self.m_Position = event.pos()
        if event.button() == Qt.LeftButton:
            if 0 < self.m_Position.x() < self.groupBox.pos().x() + self.groupBox.width() and \
                    0 < self.m_Position.y() < self.groupBox.pos().y() + self.groupBox.height():
                self.m_flag = True

    def mouseMoveEvent(self, QMouseEvent):
        if Qt.LeftButton and self.m_flag:
            self.move(QMouseEvent.globalPos() - self.m_Position)  # 更改窗口位置
            # QMouseEvent.accept()

    def mouseReleaseEvent(self, QMouseEvent):
        self.m_flag = False
        # self.setCursor(QCursor(Qt.ArrowCursor))

    @staticmethod
    def show_image(img_src, label):
        try:
            ih, iw, _ = img_src.shape
            w = label.geometry().width()
            h = label.geometry().height()
            # 保持纵横比
            # 找出长边
            if iw > ih:
                scal = w / iw
                nw = w
                nh = int(scal * ih)
                img_src_ = cv2.resize(img_src, (nw, nh))

            else:
                scal = h / ih
                nw = int(scal * iw)
                nh = h
                img_src_ = cv2.resize(img_src, (nw, nh))

            frame = cv2.cvtColor(img_src_, cv2.COLOR_BGR2RGB)
            img = QImage(frame.data, frame.shape[1], frame.shape[0], frame.shape[2] * frame.shape[1],
                         QImage.Format_RGB888)
            label.setPixmap(QPixmap.fromImage(img))

        except Exception as e:
            print(repr(e))

    # 实时统计
    def show_statistic(self, statistic_dic):
        try:
            self.resultWidget.clear()
            statistic_dic = sorted(statistic_dic.items(), key=lambda x: x[1], reverse=True)
            statistic_dic = [i for i in statistic_dic if i[1] > 0]
            results = [' '+str(i[0]) + '：' + str(i[1]) for i in statistic_dic]
            self.resultWidget.addItems(results)

        except Exception as e:
            print(repr(e))

    def closeEvent(self, event):
        # 如果摄像头开着，先把摄像头关了再退出，否则极大可能可能导致检测线程未退出
        self.det_thread.jump_out = True
        # 退出时，保存设置
        config_file = 'config/setting.json'
        config = dict()
        config['iou'] = self.confSpinBox.value()
        config['conf'] = self.iouSpinBox.value()
        config['rate'] = self.rateSpinBox.value()
        config['check'] = self.checkBox.checkState()
        config_json = json.dumps(config, ensure_ascii=False, indent=2)
        with open(config_file, 'w', encoding='utf-8') as f:
            f.write(config_json)
        MessageBox(
            self.closeButton, title='提示', text='请稍等，正在关闭程序。。。', time=2000, auto=True).exec_()
        sys.exit(0)

if __name__ == "__main__":
    app = QApplication(sys.argv)
    myWin = MainWindow()
    myWin.show()
    sys.exit(app.exec_())
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4.4 实验结果

若想要更换背景图，在class MainWindow(QMainWindow, Ui_mainWindow):下方添加一个set_background函数

def set_background(self):
   # Load the background image
   background_image = QPixmap("2.jpg")
   # Set up a QLabel to hold the background image
   background_label = QLabel(self)
   background_label.setPixmap(background_image)
   background_label.setGeometry(0, 0, self.width(), self.height())
   background_label.lower()
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并在def init(self, parent=None):函数下调用set_background函数：

self.set_background()
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工程源码下载：https://github.com/up-up-up-up/pyqt5-yolov5-v3.0-stereo-ceju

文章内容后续会慢慢完善…