opencv框选人脸测试代码c++ python

 

c++

#include <opencv2/opencv.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <opencv2/imgproc/imgproc.hpp>

#include <iostream>
#include <vector>

int main(int argc, char** argv) {

    cv::Mat image = cv::imread("d:/2.jpg");

    // 检查图像是否加载成功
    if (image.empty()) {
        std::cout << "Could not open or find the image!" << std::endl;
        return -1;
    }

    // 加载预训练的Haar Cascade分类器用于人脸检测
    // 你可能需要根据你的OpenCV安装路径修改此路径
    // 例如: "haarcascade_frontalface_default.xml"
    // 或者 "/usr/local/share/opencv4/haarcascades/haarcascade_frontalface_default.xml"
    cv::CascadeClassifier face_cascade;
    std::string cascade_path = "haarcascade_frontalface_default.xml"; // 假设它在同一目录或可访问
    if (!face_cascade.load(cascade_path)) {
        std::cout << "Error loading face cascade XML file. Make sure 'haarcascade_frontalface_default.xml' is in the same directory or provide its full path." << std::endl;
        return -1;
    }

    // 将图像转换为灰度图
    cv::Mat gray_image;
    cv::cvtColor(image, gray_image, cv::COLOR_BGR2GRAY);

    // 均衡直方图以获得更好的对比度
    cv::equalizeHist(gray_image, gray_image);

    // 检测人脸
    std::vector<cv::Rect> faces;
    face_cascade.detectMultiScale(gray_image, faces, 1.1, 2, 0 | cv::CASCADE_SCALE_IMAGE, cv::Size(30, 30));

    // 在人脸周围绘制绿色矩形
    for (const auto& face : faces) {
        cv::rectangle(image, face, cv::Scalar(0, 255, 0), 2); // 绿色 (B, G, R)
    }

    // 显示结果
    cv::imshow("Detected Faces", image);

    // 无限期等待按键
    cv::waitKey(0);

    // 销毁所有OpenCV窗口
    cv::destroyAllWindows();

    return 0;
}

或者

#include <opencv2/opencv.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main() {
    // 加载人脸检测器（Haar级联分类器）
    CascadeClassifier face_cascade;
    if (!face_cascade.load("haarcascade_frontalface_default.xml")) {
        cout << "Error loading face cascade classifier!" << endl;
        return -1;
    }

    // 读取输入图片
    Mat image = imread("d:/3.jpg");
    if (image.empty()) {
        cout << "Could not open or find the image" << endl;
        return -1;
    }

    // 将图像转换为灰度图，因为人脸检测器需要灰度图像
    Mat gray_image;
    cvtColor(image, gray_image, COLOR_BGR2GRAY);
    equalizeHist(gray_image, gray_image);

    // 检测人脸
    vector<Rect> faces;
    face_cascade.detectMultiScale(gray_image, faces, 1.1, 3, 0, Size(30, 30));

    // 在检测到的人脸周围绘制矩形
    for (size_t i = 0; i < faces.size(); i++) {
        rectangle(image, faces[i], Scalar(0, 255, 0), 2);
    }

    // 显示结果
    imshow("Face Detection", image);
    waitKey(0);

    // 保存结果
    if (!imwrite("output.jpg", image)) {
        cout << "Error saving the image!" << endl;
        return -1;
    }

    return 0;
}    

python

import cv2

# 加载预训练的人脸分类器
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')

# 加载图片
image_path = "d:/2.jpg" # 替换为你的图片路径
image = cv2.imread(image_path)

if image is None:
    print("无法加载图片，请检查路径！")
    exit()

# 转为灰度图像
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 检测人脸
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

# 绘制检测到的人脸
for (x, y, w, h) in faces:
	 cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)

# 显示结果
cv2.imshow("Detected Faces", image)
cv2.waitKey(0)
cv2.destroyAllWindows()