cv::CascadeClassifier() yolov5

cv::CascadeClassifier() 和 yolov5 都是计算机视觉领域中常用的目标检测算法。

cv::CascadeClassifier() 是 OpenCV 库中的一个基于 Haar 特征的级联分类器，它采用了一种叫做 AdaBoost 的机器学习算法来训练分类器，主要用于人脸检测、车牌识别等应用场景。

而 yolov5 则是一种基于深度学习的目标检测算法，它采用了一个类似于神经网络的结构，可以同时预测图像中多个目标的位置和类别，并且在速度和精度上都有很好的表现。

两者都是用于目标检测的算法，但实现方式和原理都不同。选择哪种算法，取决于应用场景和需求。

相关问题

undefined reference to `cv::CascadeClassifier::CascadeClassifier()'

This error message usually occurs when you are trying to use the cv::CascadeClassifier class in your code, but have not properly included the necessary OpenCV libraries or have not linked to them correctly.

To fix this error, you need to make sure that you have included the following header file at the top of your code:

#include <opencv2/objdetect/objdetect.hpp>

Additionally, you need to link to the OpenCV libraries when compiling your code. This can be done by adding the following flag to your compile command:

-lopencv_objdetect

If you are using an IDE like Visual Studio or Xcode, you should be able to add the necessary libraries to your project settings.

Once you have properly included the necessary libraries and linked to them correctly, the error should be resolved and you should be able to use the cv::CascadeClassifier class in your code without any issues.

python代码实现：Qt界面链接yolov5目标检测

以下是使用 PyQT5 实现 Qt 界面链接 YOLOv5 目标检测的 Python 代码示例：

import sys
from PyQt5.QtWidgets import QApplication, QWidget, QLabel, QPushButton, QFileDialog
from PyQt5.QtGui import QPixmap
import cv2
from yolov5.detect import YOLOv5Detector

class App(QWidget):
    def __init__(self):
        super().__init__()
        self.title = 'YOLOv5 Object Detection'
        self.left = 50
        self.top = 50
        self.width = 640
        self.height = 480
        self.image_path = ''
        self.initUI()

    def initUI(self):
        self.setWindowTitle(self.title)
        self.setGeometry(self.left, self.top, self.width, self.height)

        # 选择图像文件按钮
        self.select_button = QPushButton('Select Image', self)
        self.select_button.move(20, 20)
        self.select_button.clicked.connect(self.select_image)

        # 显示图像
        self.image_label = QLabel(self)
        self.image_label.move(20, 60)
        self.image_label.resize(600, 400)

        # 检测目标按钮
        self.detect_button = QPushButton('Detect Objects', self)
        self.detect_button.move(20, 470)
        self.detect_button.clicked.connect(self.detect_objects)

        self.show()

    def select_image(self):
        options = QFileDialog.Options()
        options |= QFileDialog.DontUseNativeDialog
        file_name, _ = QFileDialog.getOpenFileName(self, 'Select Image', '', 'Images (*.png *.xpm *.jpg *.bmp *.gif)', options=options)
        if file_name:
            self.image_path = file_name
            pixmap = QPixmap(file_name)
            self.image_label.setPixmap(pixmap)
            self.image_label.setScaledContents(True)

    def detect_objects(self):
        if not self.image_path:
            return
        detector = YOLOv5Detector()
        image = cv2.imread(self.image_path)
        result_image, _ = detector.detect(image)
        cv2.imwrite('result.jpg', result_image)
        pixmap = QPixmap('result.jpg')
        self.image_label.setPixmap(pixmap)
        self.image_label.setScaledContents(True)

if __name__ == '__main__':
    app = QApplication(sys.argv)
    ex = App()
    sys.exit(app.exec_())

其中 `yolov5.detect` 模块的代码可以参考 YOLOv5 的官方实现。在 `detect` 函数中，需要对输入的图像进行目标检测，并返回检测结果。在本例中，我们将检测结果保存为 `result.jpg`，并在界面中显示出来。

注意，需要将模型文件和权重文件放在适当的位置，并在 `YOLOv5Detector` 类中设置正确的路径。