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

Seetaface发布了第二个版本——Seetaface2：

https://github.com/seetaface/SeetaFaceEngine2

SeetaFace2 Engine contains three key parts, i.e., SeetaFace2 Detection, SeetaFace2 Alignment and SeetaFace2 Identification. It can run in Android OS, Windows and Linux.

SeetaFace2 Detection. See SeetaFace Detection for more details.
SeetaFace2 Alignment. See SeetaFace Alignment for more details.
SeetaFace2 Identification. See SeetaFace Identification for more details.

【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;
}

//---------------------------------------------------------

// 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;
}

————————————————————————————————————————————————————————

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);
}

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