PyQT5实现车辆检测实例

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目前许多车辆检测实例已经非常完善，yolov5对于车辆的检测准确度已经非常高，但是其框架相对来说还是比较大的，相对于opencv来说显得就有些笨重了，许多时候我们利用预训练好的文件，使用opencv也可以完成一系列的检测过程，例如人脸检测时，opencv自带的一些关于人脸的权重数据，我们直接可以使用，车辆检测也一样，关于下面代码中所使用的车辆检测的预训练文件我放到了百度网盘，有需要的可以自行下载。（链接：pan.baidu.com/s/14V0VCSKU… 提取码：jxzv）

下面先上程序的界面文件

# -*- coding: utf-8 -*-

# Form implementation generated from reading ui file 'carDetect.ui'
#
# Created by: PyQt5 UI code generator 5.15.6
#
# WARNING: Any manual changes made to this file will be lost when pyuic5 is
# run again.  Do not edit this file unless you know what you are doing.


from PyQt5 import QtCore, QtGui, QtWidgets


class Ui_Form(object):
    def setupUi(self, Form):
        Form.setObjectName("Form")
        Form.resize(810, 638)
        self.horizontalLayoutWidget = QtWidgets.QWidget(Form)
        self.horizontalLayoutWidget.setGeometry(QtCore.QRect(10, 10, 791, 31))
        self.horizontalLayoutWidget.setObjectName("horizontalLayoutWidget")
        self.horizontalLayout = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget)
        self.horizontalLayout.setContentsMargins(0, 0, 0, 0)
        self.horizontalLayout.setObjectName("horizontalLayout")
        self.openImg = QtWidgets.QPushButton(self.horizontalLayoutWidget)
        self.openImg.setObjectName("openImg")
        self.horizontalLayout.addWidget(self.openImg)
        self.carDetect = QtWidgets.QPushButton(self.horizontalLayoutWidget)
        self.carDetect.setObjectName("carDetect")
        self.horizontalLayout.addWidget(self.carDetect)
        self.closeWindow = QtWidgets.QPushButton(self.horizontalLayoutWidget)
        self.closeWindow.setObjectName("closeWindow")
        self.horizontalLayout.addWidget(self.closeWindow)
        self.horizontalLayoutWidget_2 = QtWidgets.QWidget(Form)
        self.horizontalLayoutWidget_2.setGeometry(QtCore.QRect(10, 40, 791, 591))
        self.horizontalLayoutWidget_2.setObjectName("horizontalLayoutWidget_2")
        self.horizontalLayout_2 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget_2)
        self.horizontalLayout_2.setContentsMargins(0, 0, 0, 0)
        self.horizontalLayout_2.setObjectName("horizontalLayout_2")
        self.label = QtWidgets.QLabel(self.horizontalLayoutWidget_2)
        self.label.setObjectName("label")
        self.horizontalLayout_2.addWidget(self.label)

        self.retranslateUi(Form)
        self.closeWindow.clicked.connect(Form.close) # type: ignore
        self.carDetect.clicked.connect(Form.detect) # type: ignore
        self.openImg.clicked.connect(Form.openImage) # type: ignore
        QtCore.QMetaObject.connectSlotsByName(Form)

    def retranslateUi(self, Form):
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate("Form", "Form"))
        self.openImg.setText(_translate("Form", "打开图片"))
        self.carDetect.setText(_translate("Form", "车辆检测"))
        self.closeWindow.setText(_translate("Form", "关闭窗口"))
        self.label.setText(_translate("Form", "TextLabel"))

相对来说整个工程还是非常简单的，下面是主函数

from carDetect import Ui_Form
from PyQt5.QtWidgets import QApplication, QMainWindow, QCheckBox
from PyQt5.QtWidgets import QFileDialog
import sys
import cv2
from PyQt5.QtGui import *


class MainWindow(QMainWindow, Ui_Form):
    def __init__(self, parent=None):
        super(MainWindow, self).__init__(parent=parent)
        self.setupUi(self)
        self.imgPath = None

    def openImage(self):
        try:
            self.imgPath = QFileDialog.getOpenFileNames(self, "select file", "./", "*.*")
        except Exception as e:
            print(e)
        img = cv2.imread(self.imgPath[0][0])
        img_dis = QImage(img, img.shape[1], img.shape[0], QImage.Format_RGB888)
        img = QPixmap(img_dis)
        self.label.setScaledContents(True)
        self.label.setPixmap(img)

    def detect(self):
        car_cascade = cv2.CascadeClassifier('cars.xml')
        frames = cv2.imread(self.imgPath[0][0])
        gray = cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)
        cars = car_cascade.detectMultiScale(gray, 1.1, 9)
        for (x, y, w, h) in cars:
            plate = frames[y:y + h, x:x + w]
            cv2.rectangle(frames, (x, y), (x + w, y + h), (51, 51, 255), 2)
            cv2.rectangle(frames, (x, y - 40), (x + w, y), (51, 51, 255), -2)
            cv2.putText(frames, 'Car', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 255, 255), 2)
        img_dis = QImage(frames, frames.shape[1], frames.shape[0], QImage.Format_RGB888)
        img = QPixmap(img_dis)
        self.label.setScaledContents(True)
        self.label.setPixmap(img)


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

程序运行时的界面如下

估计是因为训练权重文件的问题，导致检测的结果并不理想，但是在训练时看结果感觉还可以，保存后通过opencv调用并识别的结果并不是很理想。

可以通过自己的训练，将文件替换为自己的测试一下，可能是训练过程不理想导致的。