人臉識別引擎SeetaFaceEngine中Identification模塊用于比較兩幅人臉圖像的相似度，以下是測試代碼：

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 alignmentfor (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 cornersstd::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 imagefor (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 Faceface_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 featureint 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 featureface_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 similarityfloat sim = face_recognizer.CalcSimilarity(feat1, feat_other);fprintf(stdout, "0.jpg -- %s similarity: %f\n", image.c_str(), sim);}delete[] feat_sdk;return 0;
}

detect/alignment結果如下：

crop結果如下：

取上圖中最左上圖為標準圖，與其它19幅圖作驗證，測試結果如下：

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