下面是OpenCV 3.3中基于CascadeClassifier类的LBP算法实现的人脸检测，从结果上看，不如其它开源库效果好，如libfacedetection，可参考 https://blog.csdn.net/fengbingchun/article/details/52964163

#include "funset.hpp" #include #include #include #include namespace { const std::string images_path_detect{ "E:/GitCode/Face_Test/testdata/detection/" }; const std::vector<:string> images_name_detect{ "1.jpg", "2.jpg", "3.jpg", "4.jpg", "5.jpg", "6.jpg", "7.jpg", "8.jpg", "9.jpg", "10.jpg", "11.jpg", "12.jpg", "13.jpg", "14.jpg", "15.jpg", "16.jpg", "17.jpg", "18.jpg", "19.jpg", "20.jpg" }; void save_mats() { const int width = 200, height = 200; cv::Mat dst(height * 5, width * 4, CV_8UC3); for (int i = 0; i < images_name_detect.size(); ++i) { std::string input_image = images_path_detect + "_" + images_name_detect[i]; cv::Mat src = cv::imread(input_image, 1); if (src.empty()) { fprintf(stderr, "read image error: %sn", input_image.c_str()); return; } cv::resize(src, src, cv::Size(width, height), 0, 0, 4); int x = (i * width) % (width * 4); int y = (i / 4) * height; cv::Mat part = dst(cv::Rect(x, y, width, height)); src.copyTo(part); } std::string output_image = images_path_detect + "result.png"; cv::imwrite(output_image, dst); } } // namespace int test_face_detect_LBP() { const std::string lbp_files_path{ "E:/GitCode/Face_Test/testdata/lbpcascades/" }; const std::vector<:string> lbpcascades_files{ "lbpcascade_frontalface.xml", "lbpcascade_frontalface_improved.xml", "lbpcascade_profileface.xml" }; cv::CascadeClassifier face_cascade(lbp_files_path+lbpcascades_files[0]); if (face_cascade.empty()) { fprintf(stderr, "classifier hasn't been loadedn"); return -1; } // Search for many objects in the one image. const int flags = cv::CASCADE_SCALE_IMAGE; // Smallest object size. const cv::Size min_feature_size = cv::Size(10, 10); // How detailed should the search be. Must be larger than 1.0. const float search_scale_factor = 1.1f; // How much the detections should be filtered out. This should depend on how bad false detections are to your system. // min_neighbors=2 means lots of good+bad detections, and min_neighbors=6 means only good detections are given but some are missed. const int min_neighbors = 2; const int scaled_width = 320; for (int i = 0; i < images_name_detect.size(); ++i) { std::string name = images_path_detect + images_name_detect[i]; cv::Mat bgr = cv::imread(name, 1); cv::Mat gray = cv::imread(name, 0); if (!bgr.data || bgr.channels() != 3 || !gray.data || gray.channels() != 1) { fprintf(stderr, "read image fail: %sn", name.c_str()); return -1; } fprintf(stdout, "image name: %s: size(width, height): (%d, %d)n", images_name_detect[i].c_str(), gray.cols, gray.rows); // possibly shrink the image to run much faster. cv::Mat resized, equalized; float scale = gray.cols / (float)scaled_width; if (gray.cols > scaled_width) { // Shrink the image while keeping the same aspect ratio. int scaled_height = cvRound(gray.rows / scale); cv::resize(gray, resized, cv::Size(scaled_width, scaled_height)); } else { // Access the input image directly, since it is already small. resized = gray; } // standardize the brightness and contrast to improve dark images. cv::equalizeHist(resized, equalized); std::vector<:rect> objects; // Detect objects in the small grayscale image. face_cascade.detectMultiScale(equalized, objects, search_scale_factor, min_neighbors, flags, min_feature_size); fprintf(stdout, "image name: %s: detect face count: %dn", images_name_detect[i].c_str(), objects.size()); // Enlarge the results if the image was temporarily shrunk before detection. if (gray.cols > scaled_width) { for (int j = 0; j < objects.size(); ++j) { objects[j].x = cvRound(objects[j].x * scale); objects[j].y = cvRound(objects[j].y * scale); objects[j].width = cvRound(objects[j].width * scale); objects[j].height = cvRound(objects[j].height * scale); } } for (int j = 0; j < objects.size(); ++j) { // Make sure the object is completely within the image, in case it was on a border. objects[j].x = std::max(objects[j].x, 0); objects[j].y = std::max(objects[j].y, 0); if (objects[j].x + objects[j].width > gray.cols) { objects[j].x = gray.cols - objects[j].width; } if (objects[j].y + objects[j].height > gray.rows) { objects[j].y = gray.rows - objects[j].height; } cv::rectangle(bgr, objects[j], cv::Scalar(0, 255, 0), 2); } std::string save_result = images_path_detect + "_" + images_name_detect[i]; cv::imwrite(save_result, bgr); } save_mats(); return 0; }

检测结果如下：