【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.
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SeetaFace2 Detection. See SeetaFace Detection for more details.
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SeetaFace2 Alignment. See SeetaFace Alignment for more details.
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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
#include
#include
#include
#include
#include
#include // 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);
else
img_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 faces = detector.Detect(img_data);
long t1 = cv::getTickCount();
double secs = (t1 - t0) / cv::getTickFrequency();
cout << "Detections takes " << secs << " seconds " << endl;
#ifdef USE_OPENMP
cout << "OpenMP is used." << endl;
#else
cout << "OpenMP is not used. " << endl;
#endif
#ifdef USE_SSE
cout << "SSE is used." << endl;
#else
cout << "SSE is not used." << endl;
#endif
cout << "Image size (wxh): " << img_data.width << "x"
<< img_data.height << endl;
cv::Rect face_rect;
int32_t num_face = static_cast(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
#include
#include
#include
#include
#include
#include // Gaussian Blur
#include "face_detection.h"
#include "face_alignment.h"
using namespace cv;
using namespace std;
int main()
{
// Initialize face detection model
seeta::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 image
const 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 faces
std::vector faces = detector.Detect(image_data);
int32_t face_num = static_cast(faces.size());
if (face_num == 0)
{
delete[]data;
cvReleaseImage(&img_grayscale);
cvReleaseImage(&img_color);
return 0;
}
// Detect 5 facial landmarks
seeta::FacialLandmark points[5];
point_detector.PointDetectLandmarks(image_data, faces[0], points);
// Visualize the results
cvRectangle(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
for (int m = 0; m
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
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
#include
#include
#include
#include
#include "face_identification.h"
#include "face_detection.h"
#include
//#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 image
std::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 caffe
fopen_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 image
std::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 caffe
fopen_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 image
std::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);
else
img_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 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(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);
}Taily老段的微信公众号，欢迎交流学习
https://blog.csdn.net/taily_duan/article/details/81214815