? ? ? ? ?Dlib庫中提供了正臉人臉檢測的接口，這里參考dlib/examples/face_detection_ex.cpp中的代碼，通過調(diào)用Dlib中的接口，實(shí)現(xiàn)正臉人臉檢測的測試代碼，測試代碼如下：

#include "funset.hpp"
#include <string>
#include <dlib/image_processing/frontal_face_detector.h>
#include <dlib/gui_widgets.h>
#include <dlib/image_io.h>
#include <opencv2/opencv.hpp>/* reference: dlib/examples/face_detection_ex.cppThis face detector is made using the now classic Histogram of OrientedGradients (HOG) feature combined with a linear classifier, an image pyramid,and sliding window detection scheme.  This type of object detector is fairlygeneral and capable of detecting many types of semi-rigid objects inaddition to human faces.
*/
int test_face_detect()
{dlib::frontal_face_detector detector = dlib::get_frontal_face_detector();dlib::image_window win;std::vector<std::string> images{ "1.jpg", "2.jpg", "3.jpg", "4.jpeg", "5.jpeg", "6.jpg", "7.jpg", "8.jpg", "9.jpg", "10.jpg","11.jpeg", "12.jpg", "13.jpeg", "14.jpg", "15.jpeg", "16.jpg", "17.jpg", "18.jpg", "19.jpg", "20.jpg" };std::vector<int> count_faces{ 1, 2, 6, 0, 1, 1, 1, 2, 1, 1,1, 1, 1, 1, 1, 1, 1, 0, 8, 2 };std::string path_images{ "E:/GitCode/Face_Test/testdata/" };if (images.size() != count_faces.size()) {fprintf(stderr, "their size that images and count_faces are mismatch\n");return -1;}for (int i = 0; i < images.size(); i++) {dlib::array2d<unsigned char> img;dlib::load_image(img, path_images + images[i]);// Make the image bigger by a factor of two.  This is useful since// the face detector looks for faces that are about 80 by 80 pixels// or larger.  Therefore, if you want to find faces that are smaller// than that then you need to upsample the image as we do here by// calling pyramid_up().  So this will allow it to detect faces that// are at least 40 by 40 pixels in size.  We could call pyramid_up()// again to find even smaller faces, but note that every time we// upsample the image we make the detector run slower since it must// process a larger image.pyramid_up(img);// Now tell the face detector to give us a list of bounding boxes// around all the faces it can find in the image.std::vector<dlib::rectangle> dets = detector(img);fprintf(stderr, "detect face count: %d, actual face count: %d\n", dets.size(), count_faces[i]);cv::Mat matSrc = cv::imread(path_images + images[i], 1);if (matSrc.empty()) {fprintf(stderr, "read image error: %s\n", images[i].c_str());return -1;}for (auto faces : dets) {int x = faces.left() / 2;int y = faces.top() / 2;int w = faces.width() / 2;int h = faces.height() / 2;cv::rectangle(matSrc, cv::Rect(x, y, w, h), cv::Scalar(0, 255, 0), 2);}std::string save_result = path_images + "_" + images[i];cv::imwrite(save_result, matSrc);}int width = 200;int height = 200;cv::Mat dst(height * 5, width * 4, CV_8UC3);for (int i = 0; i < images.size(); i++) {std::string input_image = path_images + "_" + images[i];cv::Mat src = cv::imread(input_image, 1);if (src.empty()) {fprintf(stderr, "read image error: %s\n", images[i].c_str());return -1;}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 = path_images + "result.png";cv::imwrite(output_image, dst);fprintf(stderr, "ok\n");return 0;
}

執(zhí)行結(jié)果如下圖：

人臉檢測結(jié)果如下：

GitHub：https://github.com/fengbingchun/Face_Test

總結(jié)

以上是生活随笔為你收集整理的Dlib库中实现正脸人脸检测的测试代码的全部內(nèi)容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網(wǎng)站內(nèi)容還不錯(cuò)，歡迎將生活随笔推薦給好友。