人脸识别引擎SeetaFaceEngine中Identification模块使用的测试代码

人脸识别引擎SeetaFaceEngine中Identification模块用于比较两幅人脸图像的相似度，以下是测试代码：

int test_recognize()
{
const std::string path_images{ "E:/GitCode/Face_Test/testdata/recognization/" };
seeta::FaceDetection detector("E:/GitCode/Face_Test/src/SeetaFaceEngine/FaceDetection/model/seeta_fd_frontal_v1.0.bin");
seeta::FaceAlignment alignment("E:/GitCode/Face_Test/src/SeetaFaceEngine/FaceAlignment/model/seeta_fa_v1.1.bin");
seeta::FaceIdentification face_recognizer("E:/GitCode/Face_Test/src/SeetaFaceEngine/FaceIdentification/model/seeta_fr_v1.0.bin");

detector.SetMinFaceSize(20);
detector.SetMaxFaceSize(200);
detector.SetScoreThresh(2.f);
detector.SetImagePyramidScaleFactor(0.8f);
detector.SetWindowStep(4, 4);

std::vector<std::vector<seeta::FacialLandmark>> landmards;

// detect and alignment
for (int i = 0; i < 20; i++) {
std::string image = path_images + std::to_string(i) + ".jpg";
//fprintf(stderr, "start process image: %s\n", image.c_str());

cv::Mat src_ = cv::imread(image, 1);
if (src_.empty()) {
fprintf(stderr, "read image error: %s\n", image.c_str());
continue;
}

cv::Mat src;
cv::cvtColor(src_, src, CV_BGR2GRAY);

seeta::ImageData img_data;
img_data.data = src.data;
img_data.width = src.cols;
img_data.height = src.rows;
img_data.num_channels = 1;

std::vector<seeta::FaceInfo> faces = detector.Detect(img_data);
if (faces.size() == 0) {
fprintf(stderr, "%s don't detect face\n", image.c_str());
continue;
}

// Detect 5 facial landmarks: two eye centers, nose tip and two mouth corners
std::vector<seeta::FacialLandmark> landmard(5);
alignment.PointDetectLandmarks(img_data, faces[0], &landmard[0]);

landmards.push_back(landmard);

cv::rectangle(src_, cv::Rect(faces[0].bbox.x, faces[0].bbox.y,
faces[0].bbox.width, faces[0].bbox.height), cv::Scalar(0, 255, 0), 2);

for (auto point : landmard) {
cv::circle(src_, cv::Point(point.x, point.y), 2, cv::Scalar(0, 0, 255), 2);
}

std::string save_result = path_images + "_" + std::to_string(i) + ".jpg";
cv::imwrite(save_result, src_);
}

int width = 200;
int height = 200;
cv::Mat dst(height * 5, width * 4, CV_8UC3);
for (int i = 0; i < 20; i++) {
std::string input_image = path_images + "_" + std::to_string(i) + ".jpg";
cv::Mat src = cv::imread(input_image, 1);
if (src.empty()) {
fprintf(stderr, "read image error: %s\n", input_image.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_alignment.png";
cv::imwrite(output_image, dst);

// crop image
for (int i = 0; i < 20; i++) {
std::string image = path_images + std::to_string(i) + ".jpg";
//fprintf(stderr, "start process image: %s\n", image.c_str());

cv::Mat src_img = cv::imread(image, 1);
if (src_img.data == nullptr) {
fprintf(stderr, "Load image error: %s\n", image.c_str());
return -1;
}

if (face_recognizer.crop_channels() != src_img.channels()) {
fprintf(stderr, "channels dismatch: %d, %d\n", face_recognizer.crop_channels(), src_img.channels());
return -1;
}

// ImageData store data of an image without memory alignment.
seeta::ImageData src_img_data(src_img.cols, src_img.rows, src_img.channels());
src_img_data.data = src_img.data;

// 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()));
seeta::ImageData dst_img_data(dst_img.cols, dst_img.rows, dst_img.channels());
dst_img_data.data = dst_img.data;
// Crop Face
face_recognizer.CropFace(src_img_data, &landmards[i][0], dst_img_data);

std::string save_image_name = path_images + "crop_" + std::to_string(i) + ".jpg";
cv::imwrite(save_image_name, dst_img);
}

dst = cv::Mat(height * 5, width * 4, CV_8UC3);
for (int i = 0; i < 20; i++) {
std::string input_image = path_images + "crop_" + std::to_string(i) + ".jpg";
cv::Mat src_img = cv::imread(input_image, 1);
if (src_img.empty()) {
fprintf(stderr, "read image error: %s\n", input_image.c_str());
return -1;
}

cv::resize(src_img, src_img, 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_img.copyTo(part);
}
output_image = path_images + "result_crop.png";
cv::imwrite(output_image, dst);

// extract feature
int feat_size = face_recognizer.feature_size();
if (feat_size != 2048) {
fprintf(stderr, "feature size mismatch: %d\n", feat_size);
return -1;
}

float* feat_sdk = new float[feat_size * 20];

for (int i = 0; i < 20; i++) {
std::string input_image = path_images + "crop_" + std::to_string(i) + ".jpg";
cv::Mat src_img = cv::imread(input_image, 1);
if (src_img.empty()) {
fprintf(stderr, "read image error: %s\n", input_image.c_str());
return -1;
}

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.
seeta::ImageData src_img_data(src_img.cols, src_img.rows, src_img.channels());
src_img_data.data = src_img.data;

// Extract feature
face_recognizer.ExtractFeature(src_img_data, feat_sdk + i * feat_size);
}

float* feat1 = feat_sdk;
// varify(recognize)
for (int i = 1; i < 20; i++) {
std::string image = std::to_string(i) + ".jpg";
float* feat_other = feat_sdk + i * feat_size;

// Caculate similarity
float sim = face_recognizer.CalcSimilarity(feat1, feat_other);
fprintf(stdout, "0.jpg -- %s similarity: %f\n", image.c_str(), sim);
}

delete[] feat_sdk;

return 0;
}

从网上找了20张图像，前19张为周星驰，最后一张为汤唯，用于测试此模块，测试结果如下：

detect/alignment结果如下：



crop结果如下：



取上图中最左上图为标准图，与其它19幅图作验证，测试结果如下：



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