【SeetaFace】中科院山世光老师开源的Seetaface人脸识别引擎测试

【知乎】如何评价中科院山世光老师开源的Seetaface人脸识别引擎？

【源码下载地址】seetaface/SeetaFaceEngine

【测试源码下载地址】

中科院山世光老师开源的Seetaface人脸识别引擎SeetaFacesTest.rar

中科院山世光老师开源的Seetaface人脸识别引擎测试SeetaFacesTest_面部五点特征.rar 

中科院山世光老师开源的Seetaface人脸识别引擎SeetaFacesTest人脸识别Identification.rar

SeetaFace大总结

人脸检测效果图：

（百度下载的图片）

面部特征检测效果图：

// SeetaFacesTest.cpp : 定义控制台应用程序的入口点。//#include "stdafx.h"#include <iostream>  #include <cstdint>#include <fstream>#include <string>#include <opencv2/core/core.hpp>  #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp>  // Gaussian Blur#include "face_detection.h"using namespace cv;using namespace std;int main(){Mat frame = imread("02.jpg");// 输入视频帧序列namedWindow("Test00");      //创建一个名为Test窗口imshow("Test00", frame);   //窗口中显示图像const char* img_path = "02.jpg";seeta::FaceDetection detector(".\\Model\\seeta_fd_frontal_v1.0.bin");detector.SetMinFaceSize(40);detector.SetScoreThresh(2.f);detector.SetImagePyramidScaleFactor(0.8f);detector.SetWindowStep(4, 4);cv::Mat img = cv::imread(img_path, cv::IMREAD_UNCHANGED);cv::Mat img_gray;if (img.channels() != 1)cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);elseimg_gray = img;seeta::ImageData img_data;img_data.data = img_gray.data;img_data.width = img_gray.cols;img_data.height = img_gray.rows;img_data.num_channels = 1;long t0 = cv::getTickCount();std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);long t1 = cv::getTickCount();double secs = (t1 - t0) / cv::getTickFrequency();cout << "Detections takes " << secs << " seconds " << endl;#ifdef USE_OPENMPcout << "OpenMP is used." << endl;#elsecout << "OpenMP is not used. " << endl;#endif#ifdef USE_SSEcout << "SSE is used." << endl;#elsecout << "SSE is not used." << endl;#endifcout << "Image size (wxh): " << img_data.width << "x"<< img_data.height << endl;cv::Rect face_rect;int32_t num_face = static_cast<int32_t>(faces.size());for (int32_t i = 0; i < num_face; i++) {face_rect.x = faces[i].bbox.x;face_rect.y = faces[i].bbox.y;face_rect.width = faces[i].bbox.width;face_rect.height = faces[i].bbox.height;cv::rectangle(img, face_rect, CV_RGB(0, 0, 255), 4, 8, 0);}cv::namedWindow("Test", cv::WINDOW_AUTOSIZE);cv::imshow("Test", img);cv::waitKey(0);cv::destroyAllWindows();return 0;}

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// SeetaFacesTest.cpp : 定义控制台应用程序的入口点。//#include "stdafx.h"#include <iostream>  #include <cstdint>#include <fstream>#include <string>#include <opencv2/core/core.hpp>  #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp>  // Gaussian Blur#include "face_detection.h"#include "face_alignment.h"using namespace cv;using namespace std;int main(){// Initialize face detection modelseeta::FaceDetection detector("seeta_fd_frontal_v1.0.bin");detector.SetMinFaceSize(40);detector.SetScoreThresh(2.f);detector.SetImagePyramidScaleFactor(0.8f);detector.SetWindowStep(4, 4);// Initialize face alignment model seeta::FaceAlignment point_detector("seeta_fa_v1.1.bin");//load imageconst char* picturename = "11.jpg";IplImage *img_grayscale = NULL;img_grayscale = cvLoadImage(picturename, 0);if (img_grayscale == NULL){return 0;}IplImage *img_color = cvLoadImage(picturename, 1);int pts_num = 5;int im_width = img_grayscale->width;int im_height = img_grayscale->height;unsigned char* data = new unsigned char[im_width * im_height];unsigned char* data_ptr = data;unsigned char* image_data_ptr = (unsigned char*)img_grayscale->imageData;int h = 0;for (h = 0; h < im_height; h++) {memcpy(data_ptr, image_data_ptr, im_width);data_ptr += im_width;image_data_ptr += img_grayscale->widthStep;}seeta::ImageData image_data;image_data.data = data;image_data.width = im_width;image_data.height = im_height;image_data.num_channels = 1;// Detect facesstd::vector<seeta::FaceInfo> faces = detector.Detect(image_data);int32_t face_num = static_cast<int32_t>(faces.size());if (face_num == 0){delete[]data;cvReleaseImage(&img_grayscale);cvReleaseImage(&img_color);return 0;}// Detect 5 facial landmarksseeta::FacialLandmark points[5];point_detector.PointDetectLandmarks(image_data, faces[0], points);// Visualize the resultscvRectangle(img_color, cvPoint(faces[0].bbox.x, faces[0].bbox.y), cvPoint(faces[0].bbox.x + faces[0].bbox.width - 1, faces[0].bbox.y + faces[0].bbox.height - 1), CV_RGB(255, 0, 0));for (int i = 0; i<pts_num; i++){cvCircle(img_color, cvPoint(points[i].x, points[i].y), 10, CV_RGB(0, 255, 0), CV_FILLED);}cvSaveImage("result.jpg", img_color);// Release memorycvReleaseImage(&img_color);cvReleaseImage(&img_grayscale);delete[]data;return 0;}

for (int m = 0; m<face_num; m++){// Detect 5 facial landmarksseeta::FacialLandmark points[5];point_detector.PointDetectLandmarks(image_data, faces[m], points);// Visualize the resultscvRectangle(img_color, cvPoint(faces[m].bbox.x, faces[m].bbox.y), cvPoint(faces[m].bbox.x + faces[m].bbox.width - 1, faces[m].bbox.y + faces[m].bbox.height - 1), CV_RGB(255, 0, 0));for (int i = 0; i<pts_num; i++){cvCircle(img_color, cvPoint(points[i].x, points[i].y), 10, CV_RGB(0, 255, 0), CV_FILLED);}}

在使用FaceAlignment里的源代码时，会报错：std::max......
是因为在max中两个参数的类型要一致；
#include <algorithm>
using namespace std;
int extend_lx = max((double)(floor(left_x - extend_factor*bbox_w)), double(0));
int extend_rx = min((double)(floor(right_x + extend_factor*bbox_w)), double(im_width - 1));
int extend_ly = max((double)(floor(left_y - (extend_factor - extend_revised_y)*bbox_h)), double(0));
int extend_ry = min((double)(floor(right_y + (extend_factor + extend_revised_y)*bbox_h)), double(im_height - 1));

// SeetaFacesTest.cpp : 定义控制台应用程序的入口点。//#include "stdafx.h"#include <iostream>  #include <cstdint>#include <fstream>#include <string>#include  <opencv2/opencv.hpp>#include "face_identification.h"#include "face_detection.h"#include <ctime>//#pragma comment(lib, "SeetaTest.lib")using namespace cv;using namespace std;using namespace seeta;#define TEST(major, minor) major##_##minor##_Tester()#define EXPECT_NE(a, b) if ((a) == (b)) std::cout << "ERROR: "#define EXPECT_EQ(a, b) if ((a) != (b)) std::cout << "ERROR: "void TEST(FaceRecognizerTest, CropFace) {FaceIdentification face_recognizer(".\\model\\seeta_fr_v1.0.bin");std::string test_dir = ".\\data\\";/* data initialize */std::ifstream ifs;std::string img_name;FacialLandmark pt5[5];ifs.open(".\\data\\test_file_list.txt", std::ifstream::in);clock_t start, count = 0;int img_num = 0;while (ifs >> img_name) {img_num++;// read imagestd::string imgpath = test_dir + img_name;cv::Mat src_img = cv::imread(imgpath, 1);EXPECT_NE(src_img.data, nullptr) << "Load image error!";// ImageData store data of an image without memory alignment.ImageData src_img_data(src_img.cols, src_img.rows, src_img.channels());src_img_data.data = src_img.data;// 5 located landmark points (left eye, right eye, nose, left and right // corner of mouse).for (int i = 0; i < 5; ++i) {ifs >> pt5[i].x >> pt5[i].y;}// Create a image to store crop face.cv::Mat dst_img(face_recognizer.crop_height(), face_recognizer.crop_width(), CV_8UC(face_recognizer.crop_channels()));ImageData dst_img_data(dst_img.cols, dst_img.rows, dst_img.channels());dst_img_data.data = dst_img.data;/* Crop Face */start = clock();uint8_t rst_cropface = face_recognizer.CropFace(src_img_data, pt5, dst_img_data);cout << "CropFace : " << rst_cropface << endl;count += clock() - start;// Show crop face/*cv::imshow("Crop Face", dst_img);cv::waitKey(0);cv::destroyWindow("Crop Face");*/}ifs.close();std::cout << "Test successful! \nAverage crop face time: "  << 1000.0 * count / CLOCKS_PER_SEC / img_num << "ms" << std::endl;}void TEST(FaceRecognizerTest, ExtractFeature) {FaceIdentification face_recognizer(".\\model\\seeta_fr_v1.0.bin");std::string test_dir = ".\\data\\";int feat_size = face_recognizer.feature_size();EXPECT_EQ(feat_size, 2048);FILE* feat_file = NULL;// Load features extract from caffefopen_s(&feat_file, ".\\data\\feats.dat", "rb");int n, c, h, w;EXPECT_EQ(fread(&n, sizeof(int), 1, feat_file), (unsigned int)1);EXPECT_EQ(fread(&c, sizeof(int), 1, feat_file), (unsigned int)1);EXPECT_EQ(fread(&h, sizeof(int), 1, feat_file), (unsigned int)1);EXPECT_EQ(fread(&w, sizeof(int), 1, feat_file), (unsigned int)1);float* feat_caffe = new float[n * c * h * w];float* feat_sdk = new float[n * c * h * w];EXPECT_EQ(fread(feat_caffe, sizeof(float), n * c * h * w, feat_file), n * c * h * w);EXPECT_EQ(feat_size, c * h * w);int cnt = 0;/* Data initialize */std::ifstream ifs(".\\data\\crop_file_list.txt");std::string img_name;clock_t start, count = 0;int img_num = 0, lb;double average_sim = 0.0;while (ifs >> img_name >> lb) {// read imagestd::string imgpath = test_dir + img_name;cv::Mat src_img = cv::imread(imgpath, 1);EXPECT_NE(src_img.data, nullptr) << "Load image error!";cv::resize(src_img, src_img, cv::Size(face_recognizer.crop_height(), face_recognizer.crop_width()));// ImageData store data of an image without memory alignment.ImageData src_img_data(src_img.cols, src_img.rows, src_img.channels());src_img_data.data = src_img.data;/* Extract feature */start = clock();face_recognizer.ExtractFeature(src_img_data, feat_sdk + img_num * feat_size);count += clock() - start;/* Caculate similarity*/float* feat1 = feat_caffe + img_num * feat_size;float* feat2 = feat_sdk + img_num * feat_size;float sim = face_recognizer.CalcSimilarity(feat1, feat2);std::cout << "CalcSimilarity : " << sim << endl;average_sim += sim;img_num++;}ifs.close();average_sim /= img_num;if (1.0 - average_sim >  0.01) {std::cout << "average similarity: " << average_sim << std::endl;}else {std::cout << "Test successful!\nAverage extract feature time: "<< 1000.0 * count / CLOCKS_PER_SEC / img_num << "ms" << std::endl;}delete[]feat_caffe;delete[]feat_sdk;}void TEST(FaceRecognizerTest, ExtractFeatureWithCrop) {FaceIdentification face_recognizer(".\\Model\\seeta_fr_v1.0.bin");std::string test_dir = ".\\data\\";int feat_size = face_recognizer.feature_size();EXPECT_EQ(feat_size, 2048);FILE* feat_file = NULL;// Load features extract from caffefopen_s(&feat_file, ".\\data\\feats.dat", "rb");int n, c, h, w;EXPECT_EQ(fread(&n, sizeof(int), 1, feat_file), (unsigned int)1);EXPECT_EQ(fread(&c, sizeof(int), 1, feat_file), (unsigned int)1);EXPECT_EQ(fread(&h, sizeof(int), 1, feat_file), (unsigned int)1);EXPECT_EQ(fread(&w, sizeof(int), 1, feat_file), (unsigned int)1);float* feat_caffe = new float[n * c * h * w];float* feat_sdk = new float[n * c * h * w];EXPECT_EQ(fread(feat_caffe, sizeof(float), n * c * h * w, feat_file),n * c * h * w);EXPECT_EQ(feat_size, c * h * w);int cnt = 0;/* Data initialize */std::ifstream ifs(".\\data\\test_file_list.txt");std::string img_name;FacialLandmark pt5[5];clock_t start, count = 0;int img_num = 0;double average_sim = 0.0;while (ifs >> img_name) {// read imagestd::string imgpath = test_dir + img_name;cv::Mat src_img = cv::imread(imgpath, 1);EXPECT_NE(src_img.data, nullptr) << "Load image error!";// ImageData store data of an image without memory alignment.ImageData src_img_data(src_img.cols, src_img.rows, src_img.channels());src_img_data.data = src_img.data;// 5 located landmark points (left eye, right eye, nose, left and right // corner of mouse).for (int i = 0; i < 5; ++i) {ifs >> pt5[i].x >> pt5[i].y;}/* Extract feature: ExtractFeatureWithCrop */start = clock();face_recognizer.ExtractFeatureWithCrop(src_img_data, pt5,feat_sdk + img_num * feat_size);count += clock() - start;/* Caculate similarity*/float* feat1 = feat_caffe + img_num * feat_size;float* feat2 = feat_sdk + img_num * feat_size;float sim = face_recognizer.CalcSimilarity(feat1, feat2);average_sim += sim;std::cout << "CalcSimilarity : " << sim << std::endl;img_num++;}ifs.close();average_sim /= img_num;if (1.0 - average_sim >  0.02) {std::cout << "average similarity: " << average_sim << std::endl;}else {std::cout << "Test successful!\nAverage extract feature time: "<< 1000.0 * count / CLOCKS_PER_SEC / img_num << "ms" << std::endl;}delete[]feat_caffe;delete[]feat_sdk;}int main(){TEST(FaceRecognizerTest, CropFace);TEST(FaceRecognizerTest, ExtractFeature);TEST(FaceRecognizerTest, ExtractFeatureWithCrop);/*const char* img_path = "12.jpg";//seeta::FaceDetection detector(".\\Model\\seeta_fd_frontal_v1.0.bin");seeta::FaceDetection detector(".\\Model\\seeta_fd_frontal_v1.0.bin");detector.SetMinFaceSize(100);detector.SetScoreThresh(2.f);detector.SetImagePyramidScaleFactor(1.0f);detector.SetWindowStep(4, 4);cv::Mat img = cv::imread(img_path, cv::IMREAD_UNCHANGED);cv::Mat img_gray;if (img.channels() != 1)cv::cvtColor(img, img_gray, cv::COLOR_BGR2GRAY);elseimg_gray = img;seeta::ImageData img_data;img_data.data = img_gray.data;img_data.width = img_gray.cols;img_data.height = img_gray.rows;img_data.num_channels = 1;long t0 = cv::getTickCount();std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);long t1 = cv::getTickCount();double secs = (t1 - t0) / cv::getTickFrequency();cv::Rect face_rect;int32_t num_face = static_cast<int32_t>(faces.size());for (int32_t i = 0; i < num_face; i++) {face_rect.x = faces[i].bbox.x;face_rect.y = faces[i].bbox.y;face_rect.width = faces[i].bbox.width;face_rect.height = faces[i].bbox.height;cv::rectangle(img, face_rect, CV_RGB(0, 0, 255), 10, 8, 0);}cout << "renlian number:" << num_face << endl;Size size(img_data.width/5 , img_data.height/5 );Mat dst;    cv:resize(img, dst, size);cv::namedWindow("Test", cv::WINDOW_AUTOSIZE);cv::imshow("Test", dst);cv::waitKey(0);cv::destroyAllWindows();*/return 0;}

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class FaceDetection { public:  SEETA_API explicit FaceDetection();  //SEETA_API FaceDetection(const char* model_path);  SEETA_API ~FaceDetection();  SEETA_API void LoadFaceDetectionModel(const char* model_path);

FaceDetection::FaceDetection()    : impl_(new seeta::FaceDetection::Impl()) {  }

void FaceDetection::LoadFaceDetectionModel(const char* model_path){impl_->detector_->LoadModel(model_path);}