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... | @@ -6,8 +6,13 @@ set(CMAKE_CXX_STANDARD 11) | ... | @@ -6,8 +6,13 @@ set(CMAKE_CXX_STANDARD 11) |
6 | find_package(OpenCV REQUIRED) | 6 | find_package(OpenCV REQUIRED) |
7 | set(MNN_DIR /home/situ/MNN/MNN1.0/MNN) | 7 | set(MNN_DIR /home/situ/MNN/MNN1.0/MNN) |
8 | include_directories(${MNN_DIR}/include) | 8 | include_directories(${MNN_DIR}/include) |
9 | include_directories(/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/include) | ||
9 | LINK_DIRECTORIES(${MNN_DIR}/build) | 10 | LINK_DIRECTORIES(${MNN_DIR}/build) |
10 | add_executable(main main.cpp faceLandmarks.cpp) | 11 | set(FACECOMPARISON_DIR /home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/lib) |
12 | # add_library(facecomparison SHARED facerecognize.cpp retinaface.cpp facelandmarks.cpp facecomparison.cpp) | ||
13 | # add_executable(main main.cpp facerecognize.cpp retinaface.cpp facelandmarks.cpp facecomparison.cpp) | ||
14 | add_executable(main main.cpp) | ||
11 | # add_executable(main z.cpp) | 15 | # add_executable(main z.cpp) |
12 | target_link_libraries(main -lMNN ${OpenCV_LIBS}) | 16 | target_link_libraries(main ${FACECOMPARISON_DIR}/libfacecomparison.so -lMNN ${OpenCV_LIBS}) |
17 | # target_link_libraries(facecomparison -lMNN ${OpenCV_LIBS}) | ||
13 | 18 | ... | ... |
README.md
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1 | |||
2 | MNN1.0.0+opencv | ||
3 | |||
4 | 模型初始化 | ||
5 | |||
6 | FaceComparison face_rec=FaceComparison(det_model_path,landm_model_path,rec_model_path) | ||
7 | |||
8 | det_model_path:人脸检测模型retinaface的模型路径 | ||
9 | landm_model_path:106人脸关键点模型的模型路径 | ||
10 | rec_model_path:人脸识别模型的模型路径 | ||
11 | |||
12 | 重要参数(include/facecomparison.h文件) | ||
13 | |||
14 | // 人脸检测阈值 | ||
15 | float confidence_threshold = 0.5; | ||
16 | // 是否进行人脸外廓 | ||
17 | bool is_bbox_process = true; | ||
18 | // 人脸比对相似度阈值 | ||
19 | float face_recongnize_thr = 0.5; | ||
20 | // 线程个数 | ||
21 | int num_thread = 2; | ||
22 | |||
23 | 接口(返回结果 bool:true/false) | ||
24 | bool face_compare(string image_path1,string image_path2);参数为两张图像地址,其中iamge1_path为face_id图像输入 | ||
25 | bool face_compare_image(Mat image1,Mat image2);参数为两张opencv读取的图像矩阵,其中iamge1为face_id图像输入 | ||
26 | |||
27 | 编译 | ||
28 | 将CMakeLists.txt 中 set(FACECOMPARISON_DIR /home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/lib) | ||
29 | 改为lib文件夹所在的绝对地址 | ||
30 | mkdir build | ||
31 | cd build | ||
32 | cmake .. | ||
33 | make | ||
34 | ./main | ||
35 |
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build/CMakeFiles/main.dir/main.cpp.o
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No preview for this file type
build/CMakeFiles/main.dir/progress.make
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22 | |||
23 | .SUFFIXES: .hpux_make_needs_suffix_list | ||
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27 | $(VERBOSE).SILENT: | ||
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29 | |||
30 | # A target that is always out of date. | ||
31 | cmake_force: | ||
32 | |||
33 | .PHONY : cmake_force | ||
34 | |||
35 | #============================================================================= | ||
36 | # Set environment variables for the build. | ||
37 | |||
38 | # The shell in which to execute make rules. | ||
39 | SHELL = /bin/sh | ||
40 | |||
41 | # The CMake executable. | ||
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44 | # The command to remove a file. | ||
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49 | |||
50 | # The top-level source directory on which CMake was run. | ||
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52 | |||
53 | # The top-level build directory on which CMake was run. | ||
54 | CMAKE_BINARY_DIR = /home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/build | ||
55 | |||
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57 | # Targets provided globally by CMake. | ||
58 | |||
59 | # Special rule for the target rebuild_cache | ||
60 | rebuild_cache: | ||
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66 | rebuild_cache/fast: rebuild_cache | ||
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157 | @echo "... clean" | ||
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168 | |||
169 | #============================================================================= | ||
170 | # Special targets to cleanup operation of make. | ||
171 | |||
172 | # Special rule to run CMake to check the build system integrity. | ||
173 | # No rule that depends on this can have commands that come from listfiles | ||
174 | # because they might be regenerated. | ||
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build/cmake_install.cmake
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2 | |||
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5 | set(CMAKE_INSTALL_PREFIX "/usr/local") | ||
6 | endif() | ||
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8 | |||
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11 | if(BUILD_TYPE) | ||
12 | string(REGEX REPLACE "^[^A-Za-z0-9_]+" "" | ||
13 | CMAKE_INSTALL_CONFIG_NAME "${BUILD_TYPE}") | ||
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18 | endif() | ||
19 | |||
20 | # Set the component getting installed. | ||
21 | if(NOT CMAKE_INSTALL_COMPONENT) | ||
22 | if(COMPONENT) | ||
23 | message(STATUS "Install component: \"${COMPONENT}\"") | ||
24 | set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") | ||
25 | else() | ||
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29 | |||
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34 | |||
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37 | set(CMAKE_CROSSCOMPILING "FALSE") | ||
38 | endif() | ||
39 | |||
40 | if(CMAKE_INSTALL_COMPONENT) | ||
41 | set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt") | ||
42 | else() | ||
43 | set(CMAKE_INSTALL_MANIFEST "install_manifest.txt") | ||
44 | endif() | ||
45 | |||
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build/main
0 → 100755
No preview for this file type
faceLandmarks.cpp
deleted
100644 → 0
1 | #include "faceLandmarks.h" | ||
2 | |||
3 | |||
4 | vector<vector<float>> FaceLandmarks::detect_landmarks(std::string image_path){ | ||
5 | |||
6 | Mat input_data_=cv::imread(image_path); | ||
7 | float w_r=float(input_data_.cols)/112.0f; | ||
8 | float h_r=float(input_data_.rows)/112.0f; | ||
9 | |||
10 | Mat input_data; | ||
11 | cv::resize(input_data_,input_data,Size2d(112,112)); | ||
12 | input_data.convertTo(input_data, CV_32F); | ||
13 | input_data = input_data /256.0f; | ||
14 | std::vector<std::vector<cv::Mat>> nChannels; | ||
15 | std::vector<cv::Mat> rgbChannels(3); | ||
16 | cv::split(input_data, rgbChannels); | ||
17 | nChannels.push_back(rgbChannels); // NHWC 转NCHW | ||
18 | auto *pvData = malloc(1 * 3 * 112 * 112 *sizeof(float)); | ||
19 | int nPlaneSize = 112 * 112; | ||
20 | for (int c = 0; c < 3; ++c) | ||
21 | { | ||
22 | cv::Mat matPlane = nChannels[0][c]; | ||
23 | memcpy((float *)(pvData) + c * nPlaneSize,\ | ||
24 | matPlane.data, nPlaneSize * sizeof(float)); | ||
25 | } | ||
26 | auto inTensor = net->getSessionInput(session, NULL); | ||
27 | net->resizeTensor(inTensor, {1, 3, 112,112}); | ||
28 | net->resizeSession(session); | ||
29 | auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE); | ||
30 | ::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float)); | ||
31 | inTensor->copyFromHostTensor(nchwTensor); | ||
32 | // //推理 | ||
33 | net->runSession(session); | ||
34 | auto output= net->getSessionOutput(session, NULL); | ||
35 | |||
36 | MNN::Tensor feat_tensor(output, output->getDimensionType()); | ||
37 | output->copyToHostTensor(&feat_tensor); | ||
38 | |||
39 | vector<vector<float>> landmarks; | ||
40 | for(int idx =0;idx<106;++idx){ | ||
41 | float x_= *(feat_tensor.host<float>()+2*idx)*w_r; | ||
42 | float y_= *(feat_tensor.host<float>()+2*idx+1)*h_r; | ||
43 | vector<float> tmp={x_,y_}; | ||
44 | landmarks.push_back(tmp); | ||
45 | } | ||
46 | return landmarks; | ||
47 | } | ||
48 | |||
49 | vector<vector<float>> FaceLandmarks::detect_image_landmarks(Mat image){ | ||
50 | |||
51 | Mat input_data_=image; | ||
52 | float w_r=float(input_data_.cols)/112.0f; | ||
53 | float h_r=float(input_data_.rows)/112.0f; | ||
54 | |||
55 | Mat input_data; | ||
56 | cv::resize(input_data_,input_data,Size2d(112,112)); | ||
57 | input_data.convertTo(input_data, CV_32F); | ||
58 | input_data = input_data /256.0f; | ||
59 | std::vector<std::vector<cv::Mat>> nChannels; | ||
60 | std::vector<cv::Mat> rgbChannels(3); | ||
61 | cv::split(input_data, rgbChannels); | ||
62 | nChannels.push_back(rgbChannels); // NHWC 转NCHW | ||
63 | auto *pvData = malloc(1 * 3 * 112 * 112 *sizeof(float)); | ||
64 | int nPlaneSize = 112 * 112; | ||
65 | for (int c = 0; c < 3; ++c) | ||
66 | { | ||
67 | cv::Mat matPlane = nChannels[0][c]; | ||
68 | memcpy((float *)(pvData) + c * nPlaneSize,\ | ||
69 | matPlane.data, nPlaneSize * sizeof(float)); | ||
70 | } | ||
71 | auto inTensor = net->getSessionInput(session, NULL); | ||
72 | net->resizeTensor(inTensor, {1, 3, 112,112}); | ||
73 | net->resizeSession(session); | ||
74 | auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE); | ||
75 | ::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float)); | ||
76 | inTensor->copyFromHostTensor(nchwTensor); | ||
77 | // //推理 | ||
78 | net->runSession(session); | ||
79 | auto output= net->getSessionOutput(session, NULL); | ||
80 | |||
81 | MNN::Tensor feat_tensor(output, output->getDimensionType()); | ||
82 | output->copyToHostTensor(&feat_tensor); | ||
83 | |||
84 | vector<vector<float>> landmarks; | ||
85 | for(int idx =0;idx<106;++idx){ | ||
86 | float x_= *(feat_tensor.host<float>()+2*idx)*w_r; | ||
87 | float y_= *(feat_tensor.host<float>()+2*idx+1)*h_r; | ||
88 | vector<float> tmp={x_,y_}; | ||
89 | landmarks.push_back(tmp); | ||
90 | } | ||
91 | return landmarks; | ||
92 | } | ||
... | \ No newline at end of file | ... | \ No newline at end of file |
facecomparison.cpp
0 → 100644
1 | #include "facecomparison.h" | ||
2 | |||
3 | |||
4 | bool FaceComparison::face_compare(string image1_path,string image2_path){ | ||
5 | bool result=false; | ||
6 | cv::Mat image1=cv::imread(image1_path); | ||
7 | cv::Mat image2=cv::imread(image2_path); | ||
8 | if(image1.empty()||image2.empty()){ | ||
9 | return false; | ||
10 | } | ||
11 | vector<Bbox> box1=face_det.detect_image(image1); | ||
12 | vector<Bbox> box2=face_det.detect_image(image2); | ||
13 | if(box1.empty()||box2.empty()){ | ||
14 | return false; | ||
15 | } | ||
16 | int max_box1=0; | ||
17 | double max_area1=0,max_area2=0; | ||
18 | for(int i=0;i<box1.size();++i){ | ||
19 | double tmp_area1=(box1[i].ymax-box1[i].ymin)*(box1[i].xmax-box1[i].xmin); | ||
20 | if(tmp_area1>max_area1){ | ||
21 | max_box1=i; | ||
22 | max_area1=tmp_area1; | ||
23 | } | ||
24 | } | ||
25 | Rect rect1=Rect(box1[max_box1].xmin,box1[max_box1].ymin,box1[max_box1].xmax-box1[max_box1].xmin,box1[max_box1].ymax-box1[max_box1].ymin); | ||
26 | Mat face_area1=image1(rect1); | ||
27 | vector<vector<float>> landms1=face_landm.detect_landmarks_image(face_area1); | ||
28 | vector<vector<float>> land1={ | ||
29 | {float(landms1[104][0]),float(landms1[104][1])}, | ||
30 | {float(landms1[105][0]),float(landms1[105][1])}, | ||
31 | {float(landms1[46][0]),float(landms1[46][1])}, | ||
32 | {float(landms1[84][0]),float(landms1[84][1])}, | ||
33 | {float(landms1[90][0]),float(landms1[90][1])} | ||
34 | }; | ||
35 | Mat align_resize_image1=face_rec.preprocess_face(face_area1,land1); | ||
36 | for(int j=0;j<box2.size();++j){ | ||
37 | Rect rect2=Rect(box2[j].xmin,box2[j].ymin,box2[j].xmax-box2[j].xmin,box2[j].ymax-box2[j].ymin); | ||
38 | Mat face_area2=image2(rect2); | ||
39 | vector<vector<float>> landms2=face_landm.detect_landmarks_image(face_area2); | ||
40 | vector<vector<float>> land2={ | ||
41 | {float(landms2[104][0]),float(landms2[104][1])}, | ||
42 | {float(landms2[105][0]),float(landms2[105][1])}, | ||
43 | {float(landms2[46][0]),float(landms2[46][1])}, | ||
44 | {float(landms2[84][0]),float(landms2[84][1])}, | ||
45 | {float(landms2[90][0]),float(landms2[90][1])} | ||
46 | }; | ||
47 | Mat align_resize_image2=face_rec.preprocess_face(face_area2,land2); | ||
48 | double samilar_score=face_rec.get_samilar_image(align_resize_image1,align_resize_image2); | ||
49 | if(samilar_score>face_recongnize_thr){ | ||
50 | result=true; | ||
51 | } | ||
52 | } | ||
53 | return result; | ||
54 | } | ||
55 | |||
56 | bool FaceComparison::face_compare_image(Mat image1,Mat image2){ | ||
57 | bool result=false; | ||
58 | vector<Bbox> box1=face_det.detect_image(image1); | ||
59 | vector<Bbox> box2=face_det.detect_image(image2); | ||
60 | if(box1.empty()||box2.empty()){ | ||
61 | return false; | ||
62 | } | ||
63 | int max_box1=0; | ||
64 | double max_area1=0,max_area2=0; | ||
65 | for(int i=0;i<box1.size();++i){ | ||
66 | double tmp_area1=(box1[i].ymax-box1[i].ymin)*(box1[i].xmax-box1[i].xmin); | ||
67 | if(tmp_area1>max_area1){ | ||
68 | max_box1=i; | ||
69 | max_area1=tmp_area1; | ||
70 | } | ||
71 | } | ||
72 | Rect rect1=Rect(box1[max_box1].xmin,box1[max_box1].ymin,box1[max_box1].xmax-box1[max_box1].xmin,box1[max_box1].ymax-box1[max_box1].ymin); | ||
73 | Mat face_area1=image1(rect1); | ||
74 | vector<vector<float>> landms1=face_landm.detect_landmarks_image(face_area1); | ||
75 | vector<vector<float>> land1={ | ||
76 | {float(landms1[104][0]),float(landms1[104][1])}, | ||
77 | {float(landms1[105][0]),float(landms1[105][1])}, | ||
78 | {float(landms1[46][0]),float(landms1[46][1])}, | ||
79 | {float(landms1[84][0]),float(landms1[84][1])}, | ||
80 | {float(landms1[90][0]),float(landms1[90][1])} | ||
81 | }; | ||
82 | Mat align_resize_image1=face_rec.preprocess_face(face_area1,land1); | ||
83 | for(int j=0;j<box2.size();++j){ | ||
84 | Rect rect2=Rect(box2[j].xmin,box2[j].ymin,box2[j].xmax-box2[j].xmin,box2[j].ymax-box2[j].ymin); | ||
85 | Mat face_area2=image2(rect2); | ||
86 | vector<vector<float>> landms2=face_landm.detect_landmarks_image(face_area2); | ||
87 | vector<vector<float>> land2={ | ||
88 | {float(landms2[104][0]),float(landms2[104][1])}, | ||
89 | {float(landms2[105][0]),float(landms2[105][1])}, | ||
90 | {float(landms2[46][0]),float(landms2[46][1])}, | ||
91 | {float(landms2[84][0]),float(landms2[84][1])}, | ||
92 | {float(landms2[90][0]),float(landms2[90][1])} | ||
93 | }; | ||
94 | Mat align_resize_image2=face_rec.preprocess_face(face_area2,land2); | ||
95 | double samilar_score=face_rec.get_samilar_image(align_resize_image1,align_resize_image2); | ||
96 | if(samilar_score>face_recongnize_thr){ | ||
97 | result=true; | ||
98 | } | ||
99 | } | ||
100 | return result; | ||
101 | } | ||
... | \ No newline at end of file | ... | \ No newline at end of file |
facelandmarks.cpp
0 → 100644
1 | #include "facelandmarks.h" | ||
2 | |||
3 | FaceLandmarks::FaceLandmarks(){ | ||
4 | model_init=false; | ||
5 | } | ||
6 | // FaceLandmarks::~FaceLandmarks(){ | ||
7 | // pfld_interpreter->releaseModel(); | ||
8 | // pfld_interpreter->releaseSession(session); | ||
9 | // } | ||
10 | |||
11 | bool FaceLandmarks::init_model(string model_path){ | ||
12 | pfld_interpreter = unique_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str())); | ||
13 | if(nullptr==pfld_interpreter){ | ||
14 | return false; | ||
15 | } | ||
16 | //创建session | ||
17 | MNN::ScheduleConfig schedule_config; | ||
18 | schedule_config.type = MNN_FORWARD_CPU; | ||
19 | schedule_config.numThread = num_thread; | ||
20 | MNN::BackendConfig backend_config; | ||
21 | backend_config.memory = MNN::BackendConfig::Memory_Normal; | ||
22 | backend_config.power = MNN::BackendConfig::Power_Normal; | ||
23 | backend_config.precision = MNN::BackendConfig::Precision_Normal; | ||
24 | schedule_config.backendConfig = &backend_config; | ||
25 | session = pfld_interpreter->createSession(schedule_config); | ||
26 | input_tensor = pfld_interpreter->getSessionInput(session,NULL); | ||
27 | pfld_interpreter->resizeTensor(input_tensor,{1,3,112,112}); | ||
28 | pfld_interpreter->resizeSession(session); | ||
29 | |||
30 | //数据预处理 | ||
31 | MNN::CV::ImageProcess::Config image_config; | ||
32 | ::memcpy(image_config.normal,normal,sizeof(normal)); | ||
33 | image_config.sourceFormat = MNN::CV::BGR; | ||
34 | image_config.destFormat = MNN::CV::BGR; | ||
35 | |||
36 | pretreat = shared_ptr<MNN::CV::ImageProcess>(MNN::CV::ImageProcess::create(image_config)); | ||
37 | // pretreat->setMatrix(transforms); | ||
38 | |||
39 | model_init = true; | ||
40 | } | ||
41 | |||
42 | vector<vector<float>> FaceLandmarks::detect_landmarks(string image_path){ | ||
43 | Mat image = cv::imread(image_path); | ||
44 | vector<vector<float>> landmarks; | ||
45 | int width = image.cols; | ||
46 | int height = image.rows; | ||
47 | Mat resize_image; | ||
48 | cv::resize(image,resize_image,Size(112,112)); | ||
49 | float ws = float(width)/float(112.0); | ||
50 | float hs = float(height)/float(112.0); | ||
51 | |||
52 | pretreat->convert(resize_image.data,112,112,0,input_tensor); | ||
53 | |||
54 | pfld_interpreter->runSession(session); | ||
55 | |||
56 | auto output_landmark = pfld_interpreter->getSessionOutput(session, NULL); | ||
57 | MNN::Tensor landmark_tensor(output_landmark, output_landmark->getDimensionType()); | ||
58 | output_landmark->copyToHostTensor(&landmark_tensor); | ||
59 | float* result = landmark_tensor.host<float>(); | ||
60 | for (int i = 0; i < 106; ++i) { | ||
61 | vector<float> curr_pt={result[2 * i + 0] * ws,result[2 * i + 1] * hs}; | ||
62 | landmarks.push_back(curr_pt); | ||
63 | } | ||
64 | return landmarks; | ||
65 | } | ||
66 | |||
67 | vector<vector<float>> FaceLandmarks::detect_landmarks_image(Mat image){ | ||
68 | vector<vector<float>> landmarks; | ||
69 | int width = image.cols; | ||
70 | int height = image.rows; | ||
71 | Mat resize_image; | ||
72 | cv::resize(image,resize_image,Size(112,112)); | ||
73 | float ws = float(width)/float(112.0); | ||
74 | float hs = float(height)/float(112.0); | ||
75 | |||
76 | pretreat->convert(resize_image.data,112,112,0,input_tensor); | ||
77 | |||
78 | pfld_interpreter->runSession(session); | ||
79 | |||
80 | auto output_landmark = pfld_interpreter->getSessionOutput(session, NULL); | ||
81 | MNN::Tensor landmark_tensor(output_landmark, output_landmark->getDimensionType()); | ||
82 | output_landmark->copyToHostTensor(&landmark_tensor); | ||
83 | float* result = landmark_tensor.host<float>(); | ||
84 | for (int i = 0; i < 106; ++i) { | ||
85 | vector<float> curr_pt={result[2 * i + 0] * ws,result[2 * i + 1] * hs}; | ||
86 | landmarks.push_back(curr_pt); | ||
87 | } | ||
88 | return landmarks; | ||
89 | } | ||
... | \ No newline at end of file | ... | \ No newline at end of file |
1 | #include "facerecognize.h" | 1 | #include "facerecognize.h" |
2 | 2 | ||
3 | FaceRecognize::FaceRecognize(){} | ||
4 | |||
5 | // FaceRecognize::~FaceRecognize(){ | ||
6 | // net->releaseModel(); | ||
7 | // net->releaseSession(session1); | ||
8 | // net->releaseSession(session2); | ||
9 | // } | ||
10 | |||
11 | bool FaceRecognize::init_model(string model_path){ | ||
12 | net=shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str())); | ||
13 | |||
14 | ScheduleConfig config; | ||
15 | config.numThread=num_thread; | ||
16 | config.type=MNN_FORWARD_CPU; | ||
17 | |||
18 | session1 = net->createSession(config); | ||
19 | session2 = net->createSession(config); | ||
20 | |||
21 | input_tensor1 = net->getSessionInput(session1,NULL); | ||
22 | input_tensor2 = net->getSessionInput(session2,NULL); | ||
23 | net->resizeTensor(input_tensor1,{1,3,input_size[1],input_size[0]}); | ||
24 | net->resizeSession(session1); | ||
25 | net->resizeTensor(input_tensor2,{1,3,input_size[1],input_size[0]}); | ||
26 | net->resizeSession(session2); | ||
27 | |||
28 | //数据预处理 | ||
29 | MNN::CV::ImageProcess::Config image_config; | ||
30 | image_config.sourceFormat = MNN::CV::BGR; | ||
31 | image_config.destFormat = MNN::CV::BGR; | ||
32 | ::memcpy(image_config.mean,mean,sizeof(mean)); | ||
33 | ::memcpy(image_config.normal,normal,sizeof(normal)); | ||
34 | pretreat = shared_ptr<MNN::CV::ImageProcess>(MNN::CV::ImageProcess::create(image_config)); | ||
35 | model_init = true; | ||
36 | return model_init; | ||
37 | } | ||
38 | |||
39 | |||
3 | cv::Mat FaceRecognize::meanAxis0(const cv::Mat &src) | 40 | cv::Mat FaceRecognize::meanAxis0(const cv::Mat &src) |
4 | { | 41 | { |
5 | int num = src.rows; | 42 | int num = src.rows; |
... | @@ -168,61 +205,52 @@ double FaceRecognize::cos_distance(const vector<double>& base, const vector<doub | ... | @@ -168,61 +205,52 @@ double FaceRecognize::cos_distance(const vector<double>& base, const vector<doub |
168 | return simility; | 205 | return simility; |
169 | } | 206 | } |
170 | 207 | ||
171 | double FaceRecognize::get_samilar(Mat image1,Mat image2){ | 208 | double FaceRecognize::get_samilar_image(Mat image1,Mat image2){ |
172 | cv::resize(image1,image1,Size2d(input_size[0],input_size[1])); | 209 | cv::resize(image1,image1,Size2d(input_size[0],input_size[1])); |
173 | cv::resize(image2,image2,Size2d(input_size[0],input_size[1])); | 210 | cv::resize(image2,image2,Size2d(input_size[0],input_size[1])); |
174 | image1.convertTo(image1, CV_32F); | 211 | pretreat->convert(image1.data,input_size[0],input_size[1],0,input_tensor1); |
175 | image2.convertTo(image2, CV_32F); | 212 | pretreat->convert(image2.data,input_size[0],input_size[1],0,input_tensor2); |
176 | image1 = (image1-mean)*scale; | 213 | //推理 |
177 | image2 = (image2-mean)*scale; | ||
178 | |||
179 | std::vector<std::vector<cv::Mat>> nChannels1; | ||
180 | std::vector<cv::Mat> rgbChannels1(3); | ||
181 | cv::split(image1, rgbChannels1); | ||
182 | nChannels1.push_back(rgbChannels1); // NHWC 转NCHW | ||
183 | auto *pvData1 = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float)); | ||
184 | int nPlaneSize = input_size[0] * input_size[1]; | ||
185 | for (int c = 0; c < 3; ++c) | ||
186 | { | ||
187 | cv::Mat matPlane1 = nChannels1[0][c]; | ||
188 | memcpy((float *)(pvData1) + c * nPlaneSize,\ | ||
189 | matPlane1.data, nPlaneSize * sizeof(float)); | ||
190 | } | ||
191 | auto inTensor1 = net->getSessionInput(session1, NULL); | ||
192 | net->resizeTensor(inTensor1, {1, 3, input_size[1],input_size[0]}); | ||
193 | net->resizeSession(session1); | ||
194 | |||
195 | auto nchwTensor1 = new Tensor(inTensor1, Tensor::CAFFE); | ||
196 | ::memcpy(nchwTensor1->host<float>(), pvData1, nPlaneSize * 3 * sizeof(float)); | ||
197 | inTensor1->copyFromHostTensor(nchwTensor1); | ||
198 | // //推理 | ||
199 | net->runSession(session1); | 214 | net->runSession(session1); |
200 | auto output1= net->getSessionOutput(session1, NULL); | 215 | auto output1= net->getSessionOutput(session1, NULL); |
216 | //推理 | ||
217 | net->runSession(session2); | ||
218 | auto output2= net->getSessionOutput(session2, NULL); | ||
201 | 219 | ||
202 | std::vector<std::vector<cv::Mat>> nChannels2; | 220 | |
203 | std::vector<cv::Mat> rgbChannels2(3); | 221 | MNN::Tensor feat_tensor1(output1, output1->getDimensionType()); |
204 | cv::split(image2, rgbChannels2); | 222 | MNN::Tensor feat_tensor2(output2, output2->getDimensionType()); |
205 | nChannels2.push_back(rgbChannels2); // NHWC 转NCHW | 223 | output1->copyToHostTensor(&feat_tensor1); |
206 | auto *pvData2 = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float)); | 224 | output2->copyToHostTensor(&feat_tensor2); |
207 | for (int c = 0; c < 3; ++c) | 225 | auto feature1 = feat_tensor1.host<float>(); |
208 | { | 226 | auto feature2 = feat_tensor2.host<float>(); |
209 | cv::Mat matPlane2 = nChannels2[0][c]; | 227 | |
210 | memcpy((float *)(pvData2) + c * nPlaneSize,\ | 228 | vector<double> v1,v2; |
211 | matPlane2.data, nPlaneSize * sizeof(float)); | 229 | for(int i=0;i<int(feat_tensor1.size()/4);i++){ |
230 | v1.push_back((double)feature1[i]); | ||
231 | v2.push_back((double)feature2[i]); | ||
212 | } | 232 | } |
213 | auto inTensor2 = net->getSessionInput(session2, NULL); | 233 | double cos_score=cos_distance(v1,v2); |
214 | net->resizeTensor(inTensor2, {1, 3, input_size[1],input_size[0]}); | 234 | return cos_score; |
215 | net->resizeSession(session2); | 235 | } |
216 | auto nchwTensor2 = new Tensor(inTensor2, Tensor::CAFFE); | 236 | |
217 | ::memcpy(nchwTensor2->host<float>(), pvData2, nPlaneSize * 3 * sizeof(float)); | 237 | double FaceRecognize::get_samilar(string image_path1,string image_path2){ |
218 | inTensor2->copyFromHostTensor(nchwTensor2); | 238 | Mat image1 = cv::imread(image_path1); |
219 | // //推理 | 239 | Mat image2 = cv::imread(image_path2); |
240 | cv::resize(image1,image1,Size2d(input_size[0],input_size[1])); | ||
241 | cv::resize(image2,image2,Size2d(input_size[0],input_size[1])); | ||
242 | pretreat->convert(image1.data,input_size[0],input_size[1],0,input_tensor1); | ||
243 | pretreat->convert(image2.data,input_size[0],input_size[1],0,input_tensor2); | ||
244 | //推理 | ||
245 | net->runSession(session1); | ||
246 | auto output1= net->getSessionOutput(session1, NULL); | ||
247 | //推理 | ||
220 | net->runSession(session2); | 248 | net->runSession(session2); |
221 | auto output2= net->getSessionOutput(session2, NULL); | 249 | auto output2= net->getSessionOutput(session2, NULL); |
222 | 250 | ||
223 | 251 | ||
224 | MNN::Tensor feat_tensor1(output1, MNN::Tensor::CAFFE); | 252 | MNN::Tensor feat_tensor1(output1, output1->getDimensionType()); |
225 | MNN::Tensor feat_tensor2(output2, MNN::Tensor::CAFFE); | 253 | MNN::Tensor feat_tensor2(output2, output2->getDimensionType()); |
226 | output1->copyToHostTensor(&feat_tensor1); | 254 | output1->copyToHostTensor(&feat_tensor1); |
227 | output2->copyToHostTensor(&feat_tensor2); | 255 | output2->copyToHostTensor(&feat_tensor2); |
228 | auto feature1 = feat_tensor1.host<float>(); | 256 | auto feature1 = feat_tensor1.host<float>(); |
... | @@ -236,4 +264,3 @@ double FaceRecognize::get_samilar(Mat image1,Mat image2){ | ... | @@ -236,4 +264,3 @@ double FaceRecognize::get_samilar(Mat image1,Mat image2){ |
236 | double cos_score=cos_distance(v1,v2); | 264 | double cos_score=cos_distance(v1,v2); |
237 | return cos_score; | 265 | return cos_score; |
238 | } | 266 | } |
239 | ... | ... |
include/facecomparison.h
0 → 100644
1 | #ifndef FACECOMPARISON_H | ||
2 | #define FACECOMPARISON_H | ||
3 | |||
4 | |||
5 | #include "facerecognize.h" | ||
6 | #include "retinaface.h" | ||
7 | #include "facelandmarks.h" | ||
8 | |||
9 | |||
10 | class FaceComparison{ | ||
11 | public: | ||
12 | // 人脸检测阈值 | ||
13 | float confidence_threshold = 0.2; | ||
14 | // 是否进行人脸外廓 | ||
15 | bool is_bbox_process = true; | ||
16 | |||
17 | // 人脸比对相似度阈值 | ||
18 | float face_recongnize_thr = 0.5; | ||
19 | |||
20 | //线程个数 | ||
21 | int num_thread = 2; | ||
22 | //接口 | ||
23 | bool face_compare(string image_path1,string image_path2); | ||
24 | bool face_compare_image(Mat image1,Mat image2); | ||
25 | |||
26 | private: | ||
27 | RetinaFace face_det; | ||
28 | FaceLandmarks face_landm; | ||
29 | FaceRecognize face_rec; | ||
30 | |||
31 | public: | ||
32 | FaceComparison(){}; | ||
33 | FaceComparison(string face_det_model,string face_landm_model,string face_rec_model){ | ||
34 | face_det = RetinaFace(face_det_model); | ||
35 | face_det.confidence_threshold = confidence_threshold; | ||
36 | face_det.is_bbox_process = is_bbox_process; | ||
37 | face_det.num_thread=num_thread; | ||
38 | face_landm =FaceLandmarks(face_landm_model); | ||
39 | face_landm.num_thread=num_thread; | ||
40 | face_rec =FaceRecognize(face_rec_model); | ||
41 | face_rec.num_thread=num_thread; | ||
42 | } | ||
43 | |||
44 | |||
45 | |||
46 | |||
47 | }; | ||
48 | #endif | ||
... | \ No newline at end of file | ... | \ No newline at end of file |
1 | #ifndef FACELANDMARKS_H | 1 | #ifndef FACELANDMARKS_H |
2 | #define FACELANDMARKS_H | 2 | #define FACELANDMARKS_H |
3 | |||
4 | #include <opencv2/opencv.hpp> | 3 | #include <opencv2/opencv.hpp> |
5 | #include<MNN/Interpreter.hpp> | 4 | #include<MNN/Interpreter.hpp> |
6 | #include<MNN/ImageProcess.hpp> | 5 | #include<MNN/ImageProcess.hpp> |
7 | #include<iostream> | 6 | #include<iostream> |
7 | #include<memory> | ||
8 | 8 | ||
9 | using namespace std; | 9 | using namespace std; |
10 | using namespace cv; | 10 | using namespace cv; |
11 | using namespace MNN; | 11 | using namespace MNN; |
12 | 12 | ||
13 | class FaceLandmarks{ | 13 | class FaceLandmarks{ |
14 | private: | 14 | public: |
15 | vector<float> input_size={112,112}; | 15 | int num_thread = 2; |
16 | std::shared_ptr<MNN::Interpreter> net; | ||
17 | Session *session = nullptr; | ||
18 | ScheduleConfig config; | ||
19 | 16 | ||
20 | public: | 17 | public: |
21 | FaceLandmarks(){}; | 18 | FaceLandmarks(); |
19 | // ~FaceLandmarks(); | ||
22 | FaceLandmarks(string model_path){ | 20 | FaceLandmarks(string model_path){ |
23 | net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器 | 21 | init_model(model_path); |
24 | config.numThread = 8; | 22 | }; |
25 | config.type = MNN_FORWARD_CPU; | 23 | bool init_model(string model_path); |
26 | session = net->createSession(config);//创建session | ||
27 | } | ||
28 | |||
29 | vector<vector<float>> detect_landmarks(string image_path); | 24 | vector<vector<float>> detect_landmarks(string image_path); |
30 | vector<vector<float>> detect_image_landmarks(cv::Mat image); | 25 | vector<vector<float>> detect_landmarks_image(Mat image); |
31 | 26 | ||
27 | private: | ||
28 | bool model_init; | ||
29 | float normal[3]={1.0f/256.f,1.0f/256.f,1.0f/256.f}; | ||
30 | std::shared_ptr<MNN::Interpreter> pfld_interpreter = nullptr; | ||
31 | MNN::Session* session = nullptr; | ||
32 | MNN::Tensor* input_tensor = nullptr; | ||
33 | shared_ptr<MNN::CV::ImageProcess> pretreat; | ||
32 | }; | 34 | }; |
33 | |||
34 | |||
35 | #endif | 35 | #endif | ... | ... |
... | @@ -4,29 +4,40 @@ | ... | @@ -4,29 +4,40 @@ |
4 | #include<MNN/Interpreter.hpp> | 4 | #include<MNN/Interpreter.hpp> |
5 | #include<MNN/ImageProcess.hpp> | 5 | #include<MNN/ImageProcess.hpp> |
6 | #include<iostream> | 6 | #include<iostream> |
7 | #include<memory> | ||
7 | 8 | ||
8 | using namespace MNN; | 9 | using namespace MNN; |
9 | using namespace std; | 10 | using namespace std; |
10 | using namespace cv; | 11 | using namespace cv; |
11 | class FaceRecognize{ | 12 | class FaceRecognize{ |
13 | public: | ||
14 | int num_thread = 2; | ||
15 | // 推理 | ||
16 | double get_samilar(string image_path1,string image_path2); | ||
17 | double get_samilar_image(Mat image1,Mat image2); | ||
18 | |||
19 | |||
20 | |||
21 | |||
12 | private: | 22 | private: |
13 | vector<float> input_size={112,112}; | 23 | vector<int> input_size={112,112}; |
24 | bool model_init=false; | ||
14 | std::shared_ptr<MNN::Interpreter> net; | 25 | std::shared_ptr<MNN::Interpreter> net; |
15 | Session *session1 = nullptr; | 26 | Session *session1 = nullptr; |
16 | Session *session2 = nullptr; | 27 | Session *session2 = nullptr; |
17 | ScheduleConfig config; | 28 | MNN::Tensor* input_tensor1=nullptr; |
18 | Scalar mean=Scalar(127.5f,127.5f,127.5f); | 29 | MNN::Tensor* input_tensor2=nullptr; |
19 | float scale = 1.0f/127.5f; | 30 | shared_ptr<MNN::CV::ImageProcess> pretreat; |
31 | float mean[3]={127.5f,127.5f,127.5f}; | ||
32 | float normal[3] = {1.0f/127.5f,1.0f/127.5f,1.0f/127.5f}; | ||
20 | 33 | ||
21 | public: | 34 | public: |
22 | FaceRecognize(){}; | 35 | FaceRecognize(); |
36 | // ~FaceRecognize(); | ||
23 | FaceRecognize(string model_path){ | 37 | FaceRecognize(string model_path){ |
24 | net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器 | 38 | init_model(model_path); |
25 | config.numThread = 8; | ||
26 | config.type = MNN_FORWARD_CPU; | ||
27 | session1 = net->createSession(config);//创建session | ||
28 | session2 = net->createSession(config);//创建session | ||
29 | } | 39 | } |
40 | bool init_model(string model_path); | ||
30 | //预处理 | 41 | //预处理 |
31 | cv::Mat meanAxis0(const cv::Mat &src); | 42 | cv::Mat meanAxis0(const cv::Mat &src); |
32 | cv::Mat elementwiseMinus(const cv::Mat &A,const cv::Mat &B); | 43 | cv::Mat elementwiseMinus(const cv::Mat &A,const cv::Mat &B); |
... | @@ -36,7 +47,5 @@ class FaceRecognize{ | ... | @@ -36,7 +47,5 @@ class FaceRecognize{ |
36 | Mat preprocess_face(Mat image,vector<vector<float>> land); | 47 | Mat preprocess_face(Mat image,vector<vector<float>> land); |
37 | double getMold(const vector<double>& vec); | 48 | double getMold(const vector<double>& vec); |
38 | double cos_distance(const vector<double>& base, const vector<double>& target); | 49 | double cos_distance(const vector<double>& base, const vector<double>& target); |
39 | // 推理 | ||
40 | double get_samilar(Mat image1,Mat image2); | ||
41 | }; | 50 | }; |
42 | #endif | 51 | #endif |
... | \ No newline at end of file | ... | \ No newline at end of file | ... | ... |
... | @@ -4,6 +4,7 @@ | ... | @@ -4,6 +4,7 @@ |
4 | #include<MNN/Interpreter.hpp> | 4 | #include<MNN/Interpreter.hpp> |
5 | #include<MNN/ImageProcess.hpp> | 5 | #include<MNN/ImageProcess.hpp> |
6 | #include<iostream> | 6 | #include<iostream> |
7 | #include<memory> | ||
7 | 8 | ||
8 | using namespace MNN; | 9 | using namespace MNN; |
9 | using namespace std; | 10 | using namespace std; |
... | @@ -29,24 +30,26 @@ class RetinaFace{ | ... | @@ -29,24 +30,26 @@ class RetinaFace{ |
29 | public: | 30 | public: |
30 | float confidence_threshold = 0.5; | 31 | float confidence_threshold = 0.5; |
31 | bool is_bbox_process=true; | 32 | bool is_bbox_process=true; |
33 | int num_thread = 2; | ||
32 | 34 | ||
33 | private: | 35 | public: |
34 | bool use_gpu=true; | 36 | bool model_init=false; |
35 | vector<float> input_size={640,640}; | 37 | vector<int> input_size={640,640}; |
36 | vector<float> variances={0.1,0.2}; | 38 | vector<float> variances={0.1,0.2}; |
37 | Scalar mean = Scalar(104.0f, 117.0f, 123.0f); | 39 | float mean[3] = {104.0f, 117.0f, 123.0f}; |
38 | float keep_top_k = 100; | 40 | float keep_top_k = 100; |
39 | float nms_threshold = 0.4; | 41 | float nms_threshold = 0.4; |
40 | float resize_scale = 1.0; | 42 | float resize_scale = 1.0; |
41 | 43 | ||
42 | std::shared_ptr<MNN::Interpreter> net; | 44 | std::shared_ptr<MNN::Interpreter> net; |
43 | Session *session = nullptr; | 45 | Session *session = nullptr; |
44 | ScheduleConfig config; | 46 | MNN::Tensor* input_tensor=nullptr; |
47 | shared_ptr<MNN::CV::ImageProcess> pretreat; | ||
45 | vector<vector<float>> anchors; | 48 | vector<vector<float>> anchors; |
46 | 49 | ||
47 | private: | 50 | private: |
48 | // 生成anchors | 51 | // 生成anchors |
49 | vector<vector<float>> priorBox(vector<float> image_size); | 52 | vector<vector<float>> priorBox(vector<int> image_size); |
50 | // 解析bounding box landmarks 包含置信度 | 53 | // 解析bounding box landmarks 包含置信度 |
51 | vector<Bbox> decode(float *loc,float *score,float *pre,vector<vector<float>> priors,vector<float> variances); | 54 | vector<Bbox> decode(float *loc,float *score,float *pre,vector<vector<float>> priors,vector<float> variances); |
52 | // 解析landmarks | 55 | // 解析landmarks |
... | @@ -60,14 +63,12 @@ class RetinaFace{ | ... | @@ -60,14 +63,12 @@ class RetinaFace{ |
60 | 63 | ||
61 | public: | 64 | public: |
62 | 65 | ||
63 | RetinaFace(){}; | 66 | RetinaFace(); |
67 | // ~RetinaFace(); | ||
64 | RetinaFace(string model_path){ | 68 | RetinaFace(string model_path){ |
65 | net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器 | 69 | init_model(model_path); |
66 | config.numThread = 8; | ||
67 | config.type = MNN_FORWARD_CPU; | ||
68 | session = net->createSession(config);//创建session | ||
69 | anchors=priorBox(input_size); | ||
70 | } | 70 | } |
71 | bool init_model(string model_path); | ||
71 | 72 | ||
72 | // 推理 | 73 | // 推理 |
73 | vector<Bbox> detect(string image_path); | 74 | vector<Bbox> detect(string image_path); | ... | ... |
lib/libfacecomparison.so
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1 | #include "faceLandmarks.h" | 1 | #include "facecomparison.h" |
2 | int main(){ | 2 | int main(){ |
3 | FaceLandmarks face_landmarks1 = FaceLandmarks("/home/situ/qfs/sdk_project/gitlab_demo/face_recognize_mnn/model/det_landmarks_106_v0.0.1.mnn"); | 3 | // FaceLandmarks face_landmarks1 = FaceLandmarks("/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn2/model/det_landmarks_106_v0.0.1.mnn"); |
4 | vector<string> filenames; | 4 | // vector<string> filenames; |
5 | cv::glob("/home/situ/图片/img3", filenames, false); | 5 | // cv::glob("/home/situ/图片/img3", filenames, false); |
6 | for(auto path:filenames){ | 6 | // for(auto path:filenames){ |
7 | 7 | ||
8 | // cout<<path<<endl; | 8 | // // cout<<path<<endl; |
9 | Mat img1 =cv::imread(path); | 9 | // Mat img1 =cv::imread(path); |
10 | auto landmarks1 = face_landmarks1.detect_landmarks(path); | 10 | // auto landmarks1 = face_landmarks1.detect_landmarks(path); |
11 | for(auto landm:landmarks1){ | 11 | // for(auto landm:landmarks1){ |
12 | cv::circle(img1,Point2d(landm[0],landm[1]),2,Scalar(255,0,0)); | 12 | // cv::circle(img1,Point2d(landm[0],landm[1]),2,Scalar(255,0,0)); |
13 | } | 13 | // } |
14 | cv::imshow("img",img1); | 14 | // cv::imshow("img",img1); |
15 | cv::waitKey(0); | 15 | // cv::waitKey(0); |
16 | } | 16 | // } |
17 | 17 | ||
18 | // Mat image1 = cv::imread("/home/situ/图片/4.jpg"); | 18 | Mat image1 = cv::imread("/data/face_recognize/pipeline_test/35326a760af111ec9f2300163e772630/310cardImageContent163047910320427.jpg"); |
19 | // Mat image2 = cv::imread("/home/situ/图片/img3/1.jpg"); | 19 | Mat image2 = cv::imread("/data/face_recognize/pipeline_test/35326a760af111ec9f2300163e772630/310faceImageContent163047910353714.jpg"); |
20 | // string face_det_model = "/home/situ/qfs/sdk_project/face_recognize_mnn/model/mnn/det_face_retina_mnn_1.0.0_v0.1.1.mnn"; | 20 | string face_det_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/det_face_retina_mnn_1.0.0_v0.1.1.mnn"; |
21 | // string face_landm_model = "/home/situ/qfs/sdk_project/face_recognize_mnn/model/mnn/det_landmarks_106_v0.0.1.mnn"; | 21 | string face_landm_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/det_landmarks_106_v0.0.1.mnn"; |
22 | // string face_rec_model = "/home/situ/qfs/mobile_face_recognize/models/cls_face_mnn_1.0.0_v0.1.0.mnn"; | 22 | // string face_rec_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/cls_face_mnn_1.0.0_v0.0.2.mnn"; |
23 | string face_rec_model = "/home/situ/qfs/sdk_project/mnn_projects/face_recognize_mnn/model/cls_face_mnn_1.0.0_v0.1.1.mnn"; | ||
23 | 24 | ||
24 | // FaceComparison face_rec = FaceComparison(face_det_model,face_landm_model,face_rec_model); | 25 | FaceComparison face_rec = FaceComparison(face_det_model,face_landm_model,face_rec_model); |
25 | // bool result = face_rec.face_compare("/home/situ/图片/2.png","/home/situ/图片/2.png"); | 26 | bool result = face_rec.face_compare_image(image1,image2); |
26 | // cout<<result<<endl; | 27 | cout<<result<<endl; |
28 | |||
29 | |||
30 | // string model_path = "/home/situ/qfs/sdk_project/mnn_projects/face_retinaface_mnn/det_face_retina_mnn_1.0.0_v0.1.1.mnn"; | ||
31 | // RetinaFace face_det = RetinaFace(model_path.c_str()); | ||
32 | // Mat image = cv::imread("/home/situ/图片/2.png"); | ||
33 | |||
34 | // vector<Bbox> result = face_det.detect_image(image); | ||
35 | // for(auto res:result){ | ||
36 | // cv::rectangle(image,cv::Rect(res.xmin,res.ymin,res.xmax-res.xmin,res.ymax-res.ymin),Scalar(0,255,0),2); | ||
37 | // } | ||
38 | // cv::imshow("image",image); | ||
39 | // cv::waitKey(0); | ||
40 | return 0; | ||
27 | } | 41 | } | ... | ... |
model/cls_face_mnn_1.0.0_v0.0.2.mnn
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model/cls_face_mnn_1.0.0_v0.1.1.mnn
0 → 100644
No preview for this file type
1 | #include "retinaface.h" | 1 | #include "retinaface.h" |
2 | |||
3 | RetinaFace::RetinaFace(){} | ||
4 | |||
5 | // RetinaFace::~RetinaFace(){ | ||
6 | // net->releaseModel(); | ||
7 | // net->releaseSession(session); | ||
8 | // } | ||
9 | |||
10 | bool RetinaFace::init_model(string model_path){ | ||
11 | net = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_path.c_str()));//创建解释器 | ||
12 | if(nullptr==net){ | ||
13 | return false; | ||
14 | } | ||
15 | ScheduleConfig config; | ||
16 | config.numThread = num_thread; | ||
17 | config.type = MNN_FORWARD_CPU; | ||
18 | session = net->createSession(config);//创建session | ||
19 | anchors=priorBox(input_size); //生成建议框 | ||
20 | input_tensor = net->getSessionInput(session,NULL); | ||
21 | net->resizeTensor(input_tensor,{1,3,input_size[1],input_size[0]}); | ||
22 | net->resizeSession(session); | ||
23 | |||
24 | //数据预处理 | ||
25 | MNN::CV::ImageProcess::Config image_config; | ||
26 | image_config.sourceFormat = MNN::CV::BGR; | ||
27 | image_config.destFormat = MNN::CV::BGR; | ||
28 | ::memcpy(image_config.mean,mean,sizeof(mean)); | ||
29 | pretreat = shared_ptr<MNN::CV::ImageProcess>(CV::ImageProcess::create(image_config)); | ||
30 | |||
31 | model_init = true; | ||
32 | } | ||
33 | |||
34 | |||
2 | // 生成anchors | 35 | // 生成anchors |
3 | vector<vector<float>> RetinaFace::priorBox(vector<float> image_size){ | 36 | vector<vector<float>> RetinaFace::priorBox(vector<int> image_size){ |
4 | vector<int> tmp1={16,32}; | 37 | vector<int> tmp1={16,32}; |
5 | vector<int> tmp2={64,128}; | 38 | vector<int> tmp2={64,128}; |
6 | vector<int> tmp3={256,512}; | 39 | vector<int> tmp3={256,512}; |
... | @@ -171,27 +204,8 @@ vector<Bbox> RetinaFace::detect(string image_path){ | ... | @@ -171,27 +204,8 @@ vector<Bbox> RetinaFace::detect(string image_path){ |
171 | float w_r=float(input_size[0])/float(image.cols); | 204 | float w_r=float(input_size[0])/float(image.cols); |
172 | float h_r=float(input_size[1])/float(image.rows); | 205 | float h_r=float(input_size[1])/float(image.rows); |
173 | Mat input_data; | 206 | Mat input_data; |
174 | cv::resize(image,input_data,Size(input_size[0],input_size[1])); | 207 | cv::resize(image,input_data,Size(input_size[1],input_size[0])); |
175 | input_data = input_data-mean; | 208 | pretreat->convert(input_data.data,input_size[0],input_size[1],0,input_tensor); |
176 | input_data.convertTo(input_data, CV_32F); | ||
177 | std::vector<std::vector<cv::Mat>> nChannels; | ||
178 | std::vector<cv::Mat> rgbChannels(3); | ||
179 | cv::split(input_data, rgbChannels); | ||
180 | nChannels.push_back(rgbChannels); // NHWC 转NCHW | ||
181 | auto *pvData = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float)); | ||
182 | int nPlaneSize = input_size[0] * input_size[1]; | ||
183 | for (int c = 0; c < 3; ++c) | ||
184 | { | ||
185 | cv::Mat matPlane = nChannels[0][c]; | ||
186 | memcpy((float *)(pvData) + c * nPlaneSize,\ | ||
187 | matPlane.data, nPlaneSize * sizeof(float)); | ||
188 | } | ||
189 | auto inTensor = net->getSessionInput(session, NULL); | ||
190 | net->resizeTensor(inTensor, {1, 3, input_size[1],input_size[0]}); | ||
191 | net->resizeSession(session); | ||
192 | auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE); | ||
193 | ::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float)); | ||
194 | inTensor->copyFromHostTensor(nchwTensor); | ||
195 | // //推理 | 209 | // //推理 |
196 | net->runSession(session); | 210 | net->runSession(session); |
197 | auto output0= net->getSessionOutput(session, "output0"); | 211 | auto output0= net->getSessionOutput(session, "output0"); |
... | @@ -222,36 +236,20 @@ vector<Bbox> RetinaFace::detect(string image_path){ | ... | @@ -222,36 +236,20 @@ vector<Bbox> RetinaFace::detect(string image_path){ |
222 | vector<Bbox> RetinaFace::detect_image(Mat image){ | 236 | vector<Bbox> RetinaFace::detect_image(Mat image){ |
223 | float w_r=float(input_size[0])/float(image.cols); | 237 | float w_r=float(input_size[0])/float(image.cols); |
224 | float h_r=float(input_size[1])/float(image.rows); | 238 | float h_r=float(input_size[1])/float(image.rows); |
239 | |||
225 | Mat input_data; | 240 | Mat input_data; |
226 | cv::resize(image,input_data,Size(input_size[0],input_size[1])); | 241 | cv::resize(image,input_data,Size(input_size[1],input_size[0])); |
227 | input_data = input_data-mean; | 242 | pretreat->convert(input_data.data,input_size[0],input_size[1],0,input_tensor); |
228 | input_data.convertTo(input_data, CV_32F); | ||
229 | std::vector<std::vector<cv::Mat>> nChannels; | ||
230 | std::vector<cv::Mat> rgbChannels(3); | ||
231 | cv::split(input_data, rgbChannels); | ||
232 | nChannels.push_back(rgbChannels); // NHWC 转NCHW | ||
233 | auto *pvData = malloc(1 * 3 * input_size[1] * input_size[0] *sizeof(float)); | ||
234 | int nPlaneSize = input_size[0] * input_size[1]; | ||
235 | for (int c = 0; c < 3; ++c) | ||
236 | { | ||
237 | cv::Mat matPlane = nChannels[0][c]; | ||
238 | memcpy((float *)(pvData) + c * nPlaneSize,\ | ||
239 | matPlane.data, nPlaneSize * sizeof(float)); | ||
240 | } | ||
241 | auto inTensor = net->getSessionInput(session, NULL); | ||
242 | net->resizeTensor(inTensor, {1, 3, input_size[1],input_size[0]}); | ||
243 | net->resizeSession(session); | ||
244 | auto nchwTensor = new Tensor(inTensor, Tensor::CAFFE); | ||
245 | ::memcpy(nchwTensor->host<float>(), pvData, nPlaneSize * 3 * sizeof(float)); | ||
246 | inTensor->copyFromHostTensor(nchwTensor); | ||
247 | // //推理 | 243 | // //推理 |
248 | net->runSession(session); | 244 | net->runSession(session); |
249 | auto output0= net->getSessionOutput(session, "output0"); | 245 | auto output0= net->getSessionOutput(session, "output0"); |
250 | auto output1= net->getSessionOutput(session, "output1"); | 246 | auto output1= net->getSessionOutput(session, "output1"); |
251 | auto output2= net->getSessionOutput(session, "output2"); | 247 | auto output2= net->getSessionOutput(session, "output2"); |
252 | MNN::Tensor feat_tensor0(output0, MNN::Tensor::CAFFE); | 248 | |
253 | MNN::Tensor feat_tensor1(output1, MNN::Tensor::CAFFE); | 249 | |
254 | MNN::Tensor feat_tensor2(output2, MNN::Tensor::CAFFE); | 250 | MNN::Tensor feat_tensor0(output0, output0->getDimensionType()); |
251 | MNN::Tensor feat_tensor1(output1, output1->getDimensionType()); | ||
252 | MNN::Tensor feat_tensor2(output2, output2->getDimensionType()); | ||
255 | output0->copyToHostTensor(&feat_tensor0); | 253 | output0->copyToHostTensor(&feat_tensor0); |
256 | output1->copyToHostTensor(&feat_tensor1); | 254 | output1->copyToHostTensor(&feat_tensor1); |
257 | output2->copyToHostTensor(&feat_tensor2); | 255 | output2->copyToHostTensor(&feat_tensor2); |
... | @@ -261,7 +259,6 @@ vector<Bbox> RetinaFace::detect_image(Mat image){ | ... | @@ -261,7 +259,6 @@ vector<Bbox> RetinaFace::detect_image(Mat image){ |
261 | 259 | ||
262 | vector<Bbox> result_boxes = decode(loc,score,landm,anchors,variances); | 260 | vector<Bbox> result_boxes = decode(loc,score,landm,anchors,variances); |
263 | vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r); | 261 | vector<Bbox> results=select_score(result_boxes,confidence_threshold,w_r,h_r); |
264 | |||
265 | nms_cpu(results,nms_threshold); | 262 | nms_cpu(results,nms_threshold); |
266 | if(is_bbox_process){ | 263 | if(is_bbox_process){ |
267 | vector<Bbox> res_bboxes=bbox_process(results,image.cols,image.rows); | 264 | vector<Bbox> res_bboxes=bbox_process(results,image.cols,image.rows); | ... | ... |
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