基于深度学习识别人脸性别和年龄

#include "pch.h" #include <tuple> #include <iostream> #include <opencv2/opencv.hpp> #include <opencv2/dnn.hpp> #include <iterator> using namespace cv; using namespace cv::dnn; using namespace std; /** * @brief Get the Face Box object 人脸定位 * * @param net 人脸检测网络 * @param frame 检测图像 * @param conf_threshold 阈值 * @return tuple<Mat, vector<vector<int>>> 元组容器，可返回多个值 */ tuple<Mat, vector<vector<int>>> getFaceBox(Net net, Mat &frame, double conf_threshold) { //图像复制 Mat frameOpenCVDNN = frame.clone(); int frameHeight = frameOpenCVDNN.rows; int frameWidth = frameOpenCVDNN.cols; //缩放尺寸 double inScaleFactor = 1.0; //检测图大小 Size size = Size(300, 300); // std::vector<int> meanVal = {104, 117, 123}; Scalar meanVal = Scalar(104, 117, 123); cv::Mat inputBlob; inputBlob = cv::dnn::blobFromImage(frameOpenCVDNN, inScaleFactor, size, meanVal, true, false); net.setInput(inputBlob, "data"); //四维矩阵输出 cv::Mat detection = net.forward("detection_out"); //提取结果信息 cv::Mat detectionMat(detection.size[2], detection.size[3], CV_32F, detection.ptr<float>()); vector<vector<int>> bboxes; for (int i = 0; i < detectionMat.rows; i++) { //预测概率 float confidence = detectionMat.at<float>(i, 2); if (confidence > conf_threshold) { //左上角点，坐标被归一化 int x1 = static_cast<int>(detectionMat.at<float>(i, 3) * frameWidth); int y1 = static_cast<int>(detectionMat.at<float>(i, 4) * frameHeight); //右下角角点，坐标被归一化 int x2 = static_cast<int>(detectionMat.at<float>(i, 5) * frameWidth); int y2 = static_cast<int>(detectionMat.at<float>(i, 6) * frameHeight); vector<int> box = { x1, y1, x2, y2 }; //人脸坐标 bboxes.push_back(box); //图像框选 cv::rectangle(frameOpenCVDNN, cv::Point(x1, y1), cv::Point(x2, y2), cv::Scalar(0, 255, 0), 2, 4); } } return make_tuple(frameOpenCVDNN, bboxes); } int main(void) { //人脸模型 string faceProto = "model/opencv_face_detector.pbtxt"; string faceModel = "model/opencv_face_detector_uint8.pb"; //年龄模型 string ageProto = "model/age_deploy.prototxt"; string ageModel = "model/age_net.caffemodel"; //性别模型 string genderProto = "model/gender_deploy.prototxt"; string genderModel = "model/gender_net.caffemodel"; //均值 Scalar MODEL_MEAN_VALUES = Scalar(78.4263377603, 87.7689143744, 114.895847746); //年龄段标签 vector<string> ageList = { "(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)", "(38-43)", "(48-53)", "(60-100)" }; //性别标签 vector<string> genderList = { "Male", "Female" }; //导入网络 Net ageNet = cv::dnn::readNet(ageProto, ageModel); Net genderNet = cv::dnn::readNet(genderProto, genderModel); Net faceNet = cv::dnn::readNetFromTensorflow(faceModel, faceProto); //打开摄像头 VideoCapture cap; cap.open(0); if (cap.isOpened()) { cout << "camera is opened!" << endl; } else { return 0; } int padding = 20; while (waitKey(1) < 0) { // read frame 读图 Mat frame; cap.read(frame); if (frame.empty()) { waitKey(); break; } frame = imread("./images/couple1.jpg"); //人脸坐标 vector<vector<int>> bboxes; //人脸检测结果图 Mat frameFace; //人脸定位 //tie()函数解包frameFace和bboxes tie(frameFace, bboxes) = getFaceBox(faceNet, frame, 0.7); //人脸判断 if (bboxes.size() == 0) { cout << "No face detected, checking next frame." << endl; continue; } //逐个提取人脸检测 for (auto it = begin(bboxes); it != end(bboxes); ++it) { //框选人脸 Rect rec(it->at(0) - padding, it->at(1) - padding, it->at(2) - it->at(0) + 2 * padding, it->at(3) - it->at(1) + 2 * padding); //避免人脸框选超过图像边缘 rec.width = ((rec.x + rec.width) > frame.cols) ? (frame.cols - rec.x - 1) : rec.width; rec.height = ((rec.y + rec.height) > frame.rows) ? (frame.rows - rec.y - 1) : rec.height; // take the ROI of box on the frame,原图中提取人脸 Mat face = frame(rec); //性别检测 Mat blob; blob = blobFromImage(face, 1, Size(227, 227), MODEL_MEAN_VALUES, false); genderNet.setInput(blob); // string gender_preds; 获取前向传播softmax结果 vector<float> genderPreds = genderNet.forward(); // find max element index max_ele�