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retinaface optim

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CMakeLists.txt 0 → 100644
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4 # include_directories( ${OpenCV_INCLUDE_DIRS} )
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build/facerecognize 0 → 100755
No preview for this file type
facerecognize.cpp
...@@ -39,29 +39,29 @@ vector<vector<float>> FaceRecognize::priorBox(vector<float> image_size){ ...@@ -39,29 +39,29 @@ vector<vector<float>> FaceRecognize::priorBox(vector<float> image_size){
39 } 39 }
40 40
41 // 解析bounding box 包含置信度 41 // 解析bounding box 包含置信度
42 vector<Bbox> FaceRecognize::decode(vector<vector<float>> loc,vector<vector<float>> score,vector<vector<float>>pre,vector<vector<float>> priors,vector<float> variances){ 42 vector<Bbox> FaceRecognize::decode(Mat loc,Mat score,Mat pre,vector<vector<float>> priors,vector<float> variances){
43 vector<Bbox> boxes; 43 vector<Bbox> boxes;
44 for(int i=0;i<priors.size();++i){ 44 for(int i=0;i<priors.size();++i){
45 float b1=priors[i][0]+loc[i][0]*variances[0]*priors[i][2]; 45 float b1=priors[i][0]+loc.at<float>(i,0)*variances[0]*priors[i][2];
46 float b2=priors[i][1]+loc[i][1]*variances[0]*priors[i][3]; 46 float b2=priors[i][1]+loc.at<float>(i,1)*variances[0]*priors[i][3];
47 float b3=priors[i][2]*exp(loc[i][2]*variances[1]); 47 float b3=priors[i][2]*exp(loc.at<float>(i,2)*variances[1]);
48 float b4=priors[i][3]*exp(loc[i][3]*variances[1]); 48 float b4=priors[i][3]*exp(loc.at<float>(i,3)*variances[1]);
49 b1=b1-b3/float(2); 49 b1=b1-b3/float(2);
50 b2=b2-b4/float(2); 50 b2=b2-b4/float(2);
51 b3=b3+b1; 51 b3=b3+b1;
52 b4=b4+b2; 52 b4=b4+b2;
53 float l1=priors[i][0]+pre[i][0]*variances[0]*priors[i][2]; 53 float l1=priors[i][0]+pre.at<float>(i,0)*variances[0]*priors[i][2];
54 float l2=priors[i][1]+pre[i][1]*variances[0]*priors[i][3]; 54 float l2=priors[i][1]+pre.at<float>(i,1)*variances[0]*priors[i][3];
55 float l3=priors[i][0]+pre[i][2]*variances[0]*priors[i][2]; 55 float l3=priors[i][0]+pre.at<float>(i,2)*variances[0]*priors[i][2];
56 float l4=priors[i][1]+pre[i][3]*variances[0]*priors[i][3]; 56 float l4=priors[i][1]+pre.at<float>(i,3)*variances[0]*priors[i][3];
57 float l5=priors[i][0]+pre[i][4]*variances[0]*priors[i][2]; 57 float l5=priors[i][0]+pre.at<float>(i,4)*variances[0]*priors[i][2];
58 float l6=priors[i][1]+pre[i][5]*variances[0]*priors[i][3]; 58 float l6=priors[i][1]+pre.at<float>(i,5)*variances[0]*priors[i][3];
59 float l7=priors[i][0]+pre[i][6]*variances[0]*priors[i][2]; 59 float l7=priors[i][0]+pre.at<float>(i,6)*variances[0]*priors[i][2];
60 float l8=priors[i][1]+pre[i][7]*variances[0]*priors[i][3]; 60 float l8=priors[i][1]+pre.at<float>(i,7)*variances[0]*priors[i][3];
61 float l9=priors[i][0]+pre[i][8]*variances[0]*priors[i][2]; 61 float l9=priors[i][0]+pre.at<float>(i,8)*variances[0]*priors[i][2];
62 float l10=priors[i][1]+pre[i][9]*variances[0]*priors[i][3]; 62 float l10=priors[i][1]+pre.at<float>(i,9)*variances[0]*priors[i][3];
63 Bbox tmp_box={.xmin=b1*input_size[0]/resize_scale,.ymin=b2*input_size[1]/resize_scale,.xmax=b3*input_size[0]/resize_scale,.ymax=b4*input_size[1]/resize_scale, 63 Bbox tmp_box={.xmin=b1*input_size[0]/resize_scale,.ymin=b2*input_size[1]/resize_scale,.xmax=b3*input_size[0]/resize_scale,.ymax=b4*input_size[1]/resize_scale,
64 .score=float(score[i][0]),.x1=(l1*input_size[0])/resize_scale,.y1=l2*input_size[1]/resize_scale,.x2=l3*input_size[0]/resize_scale,.y2=l4*input_size[1]/resize_scale, 64 .score=score.at<float>(i,0),.x1=(l1*input_size[0])/resize_scale,.y1=l2*input_size[1]/resize_scale,.x2=l3*input_size[0]/resize_scale,.y2=l4*input_size[1]/resize_scale,
65 .x3=l5*input_size[0]/resize_scale,.y3=l6*input_size[1]/resize_scale,.x4=l7*input_size[0]/resize_scale,.y4=l8*input_size[1]/resize_scale,.x5=l9*input_size[0]/resize_scale,.y5=l10*input_size[1]/resize_scale}; 65 .x3=l5*input_size[0]/resize_scale,.y3=l6*input_size[1]/resize_scale,.x4=l7*input_size[0]/resize_scale,.y4=l8*input_size[1]/resize_scale,.x5=l9*input_size[0]/resize_scale,.y5=l10*input_size[1]/resize_scale};
66 boxes.push_back(tmp_box); 66 boxes.push_back(tmp_box);
67 } 67 }
...@@ -177,27 +177,18 @@ vector<Bbox> FaceRecognize::detect(string image_path){ ...@@ -177,27 +177,18 @@ vector<Bbox> FaceRecognize::detect(string image_path){
177 det_net.setInput(blob); // 设置模型输入 177 det_net.setInput(blob); // 设置模型输入
178 std::vector<cv::Mat> det_netOutputImg; 178 std::vector<cv::Mat> det_netOutputImg;
179 det_net.forward(det_netOutputImg,det_net.getUnconnectedOutLayersNames()); // 推理出结果 179 det_net.forward(det_netOutputImg,det_net.getUnconnectedOutLayersNames()); // 推理出结果
180 cv::Mat scores_ = det_netOutputImg[2].reshape(1,16800); 180 cv::Mat scores = det_netOutputImg[2].reshape(1,16800);
181 cv::Mat boxes_ = det_netOutputImg[0].reshape(1,16800); 181 cv::Mat boxes = det_netOutputImg[0].reshape(1,16800);
182 cv::Mat landms_ = det_netOutputImg[1].reshape(1,16800); 182 cv::Mat landms = det_netOutputImg[1].reshape(1,16800);
183 std::vector<vector<float>> scores,boxes,landms;
184
185 scores=mat2vector(scores_);
186 boxes=mat2vector(boxes_);
187 landms=mat2vector(landms_);
188
189 183
190 vector<vector<float>> anchors=priorBox(input_size); 184 vector<vector<float>> anchors=priorBox(input_size);
191 vector<Bbox> result_boxes=decode(boxes,scores,landms,anchors,variances); 185 vector<Bbox> result_boxes=decode(boxes,scores,landms,anchors,variances);
192 vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r); 186 vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r);
193 187
194 // vector<Bbox> result_bboxes=vec2Bbox(boxes,w_r,h_r);
195
196 nms_cpu(results,nms_threshold); 188 nms_cpu(results,nms_threshold);
197 if(is_bbox_process){ 189 if(is_bbox_process){
198 vector<Bbox> res_bboxes=bbox_process(results,input_size[0],input_size[1]); 190 vector<Bbox> res_bboxes=bbox_process(results,input_size[0],input_size[1]);
199 return res_bboxes; 191 return res_bboxes;
200
201 }else{ 192 }else{
202 return results; 193 return results;
203 } 194 }
...@@ -210,14 +201,9 @@ vector<Bbox> FaceRecognize::detect_image(Mat image){ ...@@ -210,14 +201,9 @@ vector<Bbox> FaceRecognize::detect_image(Mat image){
210 det_net.setInput(blob); // 设置模型输入 201 det_net.setInput(blob); // 设置模型输入
211 std::vector<cv::Mat> det_netOutputImg; 202 std::vector<cv::Mat> det_netOutputImg;
212 det_net.forward(det_netOutputImg,det_net.getUnconnectedOutLayersNames()); // 推理出结果 203 det_net.forward(det_netOutputImg,det_net.getUnconnectedOutLayersNames()); // 推理出结果
213 cv::Mat scores_ = det_netOutputImg[2].reshape(1,16800); 204 cv::Mat scores = det_netOutputImg[2].reshape(1,16800);
214 cv::Mat boxes_ = det_netOutputImg[0].reshape(1,16800); 205 cv::Mat boxes = det_netOutputImg[0].reshape(1,16800);
215 cv::Mat landms_ = det_netOutputImg[1].reshape(1,16800); 206 cv::Mat landms = det_netOutputImg[1].reshape(1,16800);
216 std::vector<vector<float>> scores,boxes,landms;
217
218 scores=mat2vector(scores_);
219 boxes=mat2vector(boxes_);
220 landms=mat2vector(landms_);
221 207
222 208
223 vector<vector<float>> anchors=priorBox(input_size); 209 vector<vector<float>> anchors=priorBox(input_size);
......
facerecognize.h
...@@ -22,7 +22,7 @@ struct Bbox{ ...@@ -22,7 +22,7 @@ struct Bbox{
22 float y5; 22 float y5;
23 }; 23 };
24 class FaceRecognize{ 24 class FaceRecognize{
25 25
26 private: 26 private:
27 //是否使用gpu? 27 //是否使用gpu?
28 bool use_gpu=true; 28 bool use_gpu=true;
...@@ -41,8 +41,7 @@ class FaceRecognize{ ...@@ -41,8 +41,7 @@ class FaceRecognize{
41 //人脸识别 41 //人脸识别
42 double face_recongnize_thr = 0.2; //人脸相似度阈值 42 double face_recongnize_thr = 0.2; //人脸相似度阈值
43 cv::dnn::Net det_net,landm_net,rec_net; 43 cv::dnn::Net det_net,landm_net,rec_net;
44 44
45
46 public: 45 public:
47 FaceRecognize(); 46 FaceRecognize();
48 FaceRecognize(string face_det_model_path,string face_landm_model_path,string face_rec_model_path){ 47 FaceRecognize(string face_det_model_path,string face_landm_model_path,string face_rec_model_path){
...@@ -59,9 +58,11 @@ class FaceRecognize{ ...@@ -59,9 +58,11 @@ class FaceRecognize{
59 } 58 }
60 59
61 } 60 }
61
62 private:
62 //人脸检测部分 63 //人脸检测部分
63 vector<vector<float>> priorBox(vector<float> image_size); 64 vector<vector<float>> priorBox(vector<float> image_size);
64 vector<Bbox> decode(vector<vector<float>> loc,vector<vector<float>> score,vector<vector<float>>pre,vector<vector<float>> priors,vector<float> variances); 65 vector<Bbox> decode(Mat loc,Mat score,Mat pre,vector<vector<float>> priors,vector<float> variances);
65 void nms_cpu(std::vector<Bbox> &bboxes, float threshold); 66 void nms_cpu(std::vector<Bbox> &bboxes, float threshold);
66 vector<vector<float>> mat2vector(Mat mat); 67 vector<vector<float>> mat2vector(Mat mat);
67 vector<Bbox> select_score(vector<Bbox> bboxes,float threshold,float w_r,float h_r); 68 vector<Bbox> select_score(vector<Bbox> bboxes,float threshold,float w_r,float h_r);
...@@ -86,8 +87,8 @@ class FaceRecognize{ ...@@ -86,8 +87,8 @@ class FaceRecognize{
86 double cos_distance(const vector<double>& base, const vector<double>& target); 87 double cos_distance(const vector<double>& base, const vector<double>& target);
87 double get_samilar(Mat image1,Mat image2); 88 double get_samilar(Mat image1,Mat image2);
88 89
89 90 public:
90 //整体 91 //人脸识别整体推理
91 bool face_recognize(string image1_path,string image2_path); 92 bool face_recognize(string image1_path,string image2_path);
92 bool face_recognize_image(Mat image1,Mat image2); 93 bool face_recognize_image(Mat image1,Mat image2);
93 }; 94 };
......
libfacerecognize.so
No preview for this file type
main.cpp
...@@ -11,7 +11,7 @@ int main(){ ...@@ -11,7 +11,7 @@ int main(){
11 bool result=face_rec.face_recognize_image(image1,image2); 11 bool result=face_rec.face_recognize_image(image1,image2);
12 end = clock(); 12 end = clock();
13 double elapsedTime = static_cast<double>(end-start) / CLOCKS_PER_SEC ; 13 double elapsedTime = static_cast<double>(end-start) / CLOCKS_PER_SEC ;
14 14 printf("result : %d \n",result);
15 printf("PROCESSING TIME: %f",elapsedTime); 15 printf("PROCESSING TIME: %f",elapsedTime);
16 16
17 } 17 }
......
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