├── README.md ├── faster_cxx_lib ├── CMakeFiles │ ├── 3.4.1 │ │ ├── CMakeCCompiler.cmake │ │ ├── CMakeCXXCompiler.cmake │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ ├── CMakeSystem.cmake │ │ ├── CompilerIdC │ │ │ ├── CMakeCCompilerId.c │ │ │ └── a.out │ │ └── CompilerIdCXX │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ └── a.out │ ├── CMakeDirectoryInformation.cmake │ ├── Makefile.cmake │ ├── Makefile2 │ ├── Progress │ │ ├── 1 │ │ └── count.txt │ ├── TargetDirectories.txt │ ├── cmake.check_cache │ ├── feature_tests.bin │ ├── feature_tests.c │ ├── feature_tests.cxx │ ├── main.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ └── progress.marks ├── CMakeLists.txt ├── Makefile ├── NODEFILE ├── cxx_faster_cnn.o3563 ├── lib │ ├── CMakeCache.txt │ ├── CMakeFiles │ │ ├── 3.4.1 │ │ │ ├── CMakeCCompiler.cmake │ │ │ ├── CMakeCXXCompiler.cmake │ │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ │ ├── CMakeSystem.cmake │ │ │ ├── CompilerIdC │ │ │ │ ├── CMakeCCompilerId.c │ │ │ │ └── a.out │ │ │ └── CompilerIdCXX │ │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ │ └── a.out │ │ ├── CMakeDirectoryInformation.cmake │ │ ├── Makefile.cmake │ │ ├── Makefile2 │ │ ├── Progress │ │ │ ├── 1 │ │ │ └── count.txt │ │ ├── TargetDirectories.txt │ │ ├── cmake.check_cache │ │ ├── faster_rcnn.dir │ │ │ ├── CXX.includecache │ │ │ ├── DependInfo.cmake │ │ │ ├── build.make │ │ │ ├── cmake_clean.cmake │ │ │ ├── depend.internal │ │ │ ├── depend.make │ │ │ ├── flags.make │ │ │ ├── link.txt │ │ │ └── progress.make │ │ ├── feature_tests.bin │ │ ├── feature_tests.c │ │ ├── feature_tests.cxx │ │ └── progress.marks │ ├── CMakeLists.txt │ ├── Makefile │ ├── cmake_install.cmake │ ├── faster_rcnn.cpp │ ├── faster_rcnn.hpp │ ├── faster_rcnn.so │ └── libfaster_rcnn.so ├── main ├── main.cpp ├── pbs_cxx_faster_rcnn_demo.job └── vis.jpg └── faster_cxx_lib_ev2641 ├── CMakeCache.txt ├── CMakeFiles ├── 3.4.1 │ ├── CMakeCCompiler.cmake │ ├── CMakeCXXCompiler.cmake │ ├── CMakeDetermineCompilerABI_C.bin │ ├── CMakeDetermineCompilerABI_CXX.bin │ ├── CMakeSystem.cmake │ ├── CompilerIdC │ │ ├── CMakeCCompilerId.c │ │ └── a.out │ └── CompilerIdCXX │ │ ├── CMakeCXXCompilerId.cpp │ │ └── a.out ├── CMakeDirectoryInformation.cmake ├── Makefile.cmake ├── Makefile2 ├── TargetDirectories.txt ├── cmake.check_cache ├── feature_tests.bin ├── feature_tests.c ├── feature_tests.cxx ├── main.dir │ ├── CXX.includecache │ ├── DependInfo.cmake │ ├── build.make │ ├── cmake_clean.cmake │ ├── depend.internal │ ├── depend.make │ ├── flags.make │ ├── link.txt │ ├── main.cpp.o │ └── progress.make └── progress.marks ├── CMakeLists.txt ├── Makefile ├── NODEFILE ├── cmake_install.cmake ├── cxx_faster_cnn.o2377 ├── cxx_faster_cnn.o2378 ├── cxx_faster_cnn.o2379 ├── cxx_faster_cnn.o2380 ├── cxx_faster_cnn.o3561 ├── cxx_faster_cnn.o3562 ├── lib ├── CMakeCache.txt ├── CMakeFiles │ ├── 3.4.1 │ │ ├── CMakeCCompiler.cmake │ │ ├── CMakeCXXCompiler.cmake │ │ ├── CMakeDetermineCompilerABI_C.bin │ │ ├── CMakeDetermineCompilerABI_CXX.bin │ │ ├── CMakeSystem.cmake │ │ ├── CompilerIdC │ │ │ ├── CMakeCCompilerId.c │ │ │ └── a.out │ │ └── CompilerIdCXX │ │ │ ├── CMakeCXXCompilerId.cpp │ │ │ └── a.out │ ├── CMakeDirectoryInformation.cmake │ ├── Makefile.cmake │ ├── Makefile2 │ ├── TargetDirectories.txt │ ├── cmake.check_cache │ ├── faster_rcnn.dir │ │ ├── CXX.includecache │ │ ├── DependInfo.cmake │ │ ├── build.make │ │ ├── cmake_clean.cmake │ │ ├── depend.internal │ │ ├── depend.make │ │ ├── faster_rcnn.cpp.o │ │ ├── flags.make │ │ ├── link.txt │ │ └── progress.make │ ├── feature_tests.bin │ ├── feature_tests.c │ ├── feature_tests.cxx │ └── progress.marks ├── CMakeLists.txt ├── Makefile ├── cmake_install.cmake ├── faster_rcnn.cpp ├── faster_rcnn.hpp ├── faster_rcnn.so └── libfaster_rcnn.so ├── main ├── main.cpp ├── pbs_cxx_faster_rcnn_demo.job └── test.jpg /README.md: -------------------------------------------------------------------------------- 1 | # FasterRCNN-Encapsulation-Cplusplus 2 | Encapsulation C++ version of FasterRCNN 3 | 4 | Here is C++ Implementation of FasterRCNN Interface. As you can see, there are two folders and you can compile the self-defined cpp files 5 | with caffe, or compile your files as a dynamic lib. 6 | How to compile it ? I have written the CMakeList.txt that can help you to comiple on 7 | your own environment. What you need to do is only change some necessary paths. 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"Install component: \"${COMPONENT}\"") 24 | set(CMAKE_INSTALL_COMPONENT "${COMPONENT}") 25 | else() 26 | set(CMAKE_INSTALL_COMPONENT) 27 | endif() 28 | endif() 29 | 30 | # Install shared libraries without execute permission? 31 | if(NOT DEFINED CMAKE_INSTALL_SO_NO_EXE) 32 | set(CMAKE_INSTALL_SO_NO_EXE "0") 33 | endif() 34 | 35 | if(CMAKE_INSTALL_COMPONENT) 36 | set(CMAKE_INSTALL_MANIFEST "install_manifest_${CMAKE_INSTALL_COMPONENT}.txt") 37 | else() 38 | set(CMAKE_INSTALL_MANIFEST "install_manifest.txt") 39 | endif() 40 | 41 | string(REPLACE ";" "\n" CMAKE_INSTALL_MANIFEST_CONTENT 42 | "${CMAKE_INSTALL_MANIFEST_FILES}") 43 | file(WRITE "/home/lyh1/workspace/py-faster-rcnn/faster_cxx_lib/lib/${CMAKE_INSTALL_MANIFEST}" 44 | "${CMAKE_INSTALL_MANIFEST_CONTENT}") 45 | -------------------------------------------------------------------------------- /faster_cxx_lib/lib/faster_rcnn.cpp: -------------------------------------------------------------------------------- 1 | #include // for snprintf 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include "caffe/caffe.hpp" 8 | #include "gpu_nms.hpp" 9 | #include 10 | #include 11 | #include 12 | #include "faster_rcnn.hpp" 13 | using namespace caffe; 14 | using namespace std; 15 | 16 | /* 17 | * === FUNCTION ====================================================================== 18 | * Name: Detector 19 | * Description: Load the model file and weights file 20 | * ===================================================================================== 21 | */ 22 | //load modelfile and weights 23 | Detector::Detector(const string& model_file, const string& weights_file) 24 | { 25 | net_ = shared_ptr >(new Net(model_file, caffe::TEST)); 26 | net_->CopyTrainedLayersFrom(weights_file); 27 | } 28 | 29 | /* 30 | * === FUNCTION ====================================================================== 31 | * Name: Detect 32 | * Description: Perform detection operation 33 | * Warning the max input size should less than 1000*600 34 | * ===================================================================================== 35 | */ 36 | //perform detection operation 37 | //input image max size 1000*600 38 | void Detector::Detect(const string& im_name) 39 | { 40 | float CONF_THRESH = 0.8; 41 | float NMS_THRESH = 0.3; 42 | const int max_input_side=1000; 43 | const int min_input_side=600; 44 | 45 | cv::Mat cv_img = cv::imread(im_name); 46 | cv::Mat cv_new(cv_img.rows, cv_img.cols, CV_32FC3, cv::Scalar(0,0,0)); 47 | if(cv_img.empty()) 48 | { 49 | std::cout<<"Can not get the image file !"< 1) 62 | { 63 | img_scale = float(1) / max_scale; 64 | } 65 | 66 | int height = int(cv_img.rows * img_scale); 67 | int width = int(cv_img.cols * img_scale); 68 | int num_out; 69 | cv::Mat cv_resized; 70 | 71 | std::cout<<"imagename "<(cv::Point(w, h))[0] = float(cv_img.at(cv::Point(w, h))[0])-float(102.9801); 89 | cv_new.at(cv::Point(w, h))[1] = float(cv_img.at(cv::Point(w, h))[1])-float(115.9465); 90 | cv_new.at(cv::Point(w, h))[2] = float(cv_img.at(cv::Point(w, h))[2])-float(122.7717); 91 | 92 | } 93 | } 94 | 95 | cv::resize(cv_new, cv_resized, cv::Size(width, height)); 96 | im_info[0] = cv_resized.rows; 97 | im_info[1] = cv_resized.cols; 98 | im_info[2] = img_scale; 99 | 100 | for (int h = 0; h < height; ++h ) 101 | { 102 | for (int w = 0; w < width; ++w) 103 | { 104 | data_buf[(0*height+h)*width+w] = float(cv_resized.at(cv::Point(w, h))[0]); 105 | data_buf[(1*height+h)*width+w] = float(cv_resized.at(cv::Point(w, h))[1]); 106 | data_buf[(2*height+h)*width+w] = float(cv_resized.at(cv::Point(w, h))[2]); 107 | } 108 | } 109 | 110 | net_->blob_by_name("data")->Reshape(1, 3, height, width); 111 | Blob * input_blobs= net_->input_blobs()[0]; 112 | switch(Caffe::mode()){ 113 | case Caffe::CPU: 114 | memcpy(input_blobs->mutable_cpu_data(), data_buf, sizeof(float) * input_blobs->count()); 115 | break; 116 | case Caffe::GPU: 117 | caffe_gpu_memcpy(sizeof(float)* input_blobs->count(), data_buf, input_blobs->mutable_gpu_data()); 118 | break; 119 | default: 120 | LOG(FATAL)<<"Unknow Caffe mode"; 121 | } 122 | 123 | //net_->blob_by_name("data")->set_cpu_data(data_buf); 124 | net_->blob_by_name("im_info")->set_cpu_data(im_info); 125 | net_->ForwardFrom(0); 126 | bbox_delt = net_->blob_by_name("bbox_pred")->cpu_data(); 127 | num = net_->blob_by_name("rois")->num(); 128 | 129 | 130 | rois = net_->blob_by_name("rois")->cpu_data(); 131 | pred_cls = net_->blob_by_name("cls_prob")->cpu_data(); 132 | boxes = new float[num*4]; 133 | pred = new float[num*5*class_num]; 134 | pred_per_class = new float[num*5]; 135 | sorted_pred_cls = new float[num*5]; 136 | keep = new int[num]; 137 | 138 | for (int n = 0; n < num; n++) 139 | { 140 | for (int c = 0; c < 4; c++) 141 | { 142 | boxes[n*4+c] = rois[n*5+c+1] / img_scale; 143 | } 144 | } 145 | 146 | bbox_transform_inv(num, bbox_delt, pred_cls, boxes, pred, cv_img.rows, cv_img.cols); 147 | for (int i = 1; i < class_num; i ++) 148 | { 149 | for (int j = 0; j< num; j++) 150 | { 151 | for (int k=0; k<5; k++) 152 | pred_per_class[j*5+k] = pred[(i*num+j)*5+k]; 153 | } 154 | boxes_sort(num, pred_per_class, sorted_pred_cls); 155 | _nms(keep, &num_out, sorted_pred_cls, num, 5, NMS_THRESH, 0); 156 | //for visualize only 157 | vis_detections(cv_img, keep, num_out, sorted_pred_cls, CONF_THRESH); 158 | } 159 | 160 | cv::imwrite("vis.jpg",cv_img); 161 | delete []boxes; 162 | delete []pred; 163 | delete []pred_per_class; 164 | delete []keep; 165 | delete []sorted_pred_cls; 166 | 167 | } 168 | 169 | /* 170 | * === FUNCTION ====================================================================== 171 | * Name: vis_detections 172 | * Description: Visuallize the detection result 173 | * ===================================================================================== 174 | */ 175 | void Detector::vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH) 176 | { 177 | int i=0; 178 | while(sorted_pred_cls[keep[i]*5+4]>CONF_THRESH && i < num_out) 179 | { 180 | if(i>=num_out) 181 | return; 182 | cv::rectangle(image,cv::Point(sorted_pred_cls[keep[i]*5+0], sorted_pred_cls[keep[i]*5+1]),cv::Point(sorted_pred_cls[keep[i]*5+2], sorted_pred_cls[keep[i]*5+3]),cv::Scalar(255,0,0)); 183 | i++; 184 | } 185 | } 186 | 187 | /* 188 | * === FUNCTION ====================================================================== 189 | * Name: boxes_sort 190 | * Description: Sort the bounding box according score 191 | * ===================================================================================== 192 | */ 193 | void Detector::boxes_sort(const int num, const float* pred, float* sorted_pred) 194 | { 195 | vector my; 196 | Info tmp; 197 | for (int i = 0; i< num; i++) 198 | { 199 | tmp.score = pred[i*5 + 4]; 200 | tmp.head = pred + i*5; 201 | my.push_back(tmp); 202 | } 203 | std::sort(my.begin(), my.end(), compare); 204 | for (int i=0; i // for snprintf 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include "caffe/caffe.hpp" 10 | #include "gpu_nms.hpp" 11 | #include 12 | #include 13 | #include 14 | using namespace caffe; 15 | using namespace std; 16 | 17 | #define max(a, b) (((a)>(b)) ? (a) :(b)) 18 | #define min(a, b) (((a)<(b)) ? (a) :(b)) 19 | #define EV2641_ERR_SUCCESS 0 20 | #define EV2641_ERR_IMAGE -13 21 | #define EV2641_ERR_MODEL -14 22 | 23 | int EV2641_InitCarDetector(char * cardetect_model, int ObjSize ,hcd_t* phcd); 24 | int EV2641_ReleaseCarDetector(hcd_t hcd); 25 | int EV2641_A_GetCarRect(hcd_t hcd, const EV2641Image * image, int &max_ret_num, EV2641Rect * rect); 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | //background and car 37 | const int class_num=2; 38 | 39 | /* 40 | * === Class ====================================================================== 41 | * Name: Detector 42 | * Description: FasterRCNN CXX Detector 43 | * ===================================================================================== 44 | */ 45 | class Detector { 46 | public: 47 | Detector(const string& model_file, const string& weights_file); 48 | void Detect(const string& im_name); 49 | void bbox_transform_inv(const int num, const float* box_deltas, const float* pred_cls, float* boxes, float* pred, int img_height, int img_width); 50 | void vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH); 51 | void boxes_sort(int num, const float* pred, float* sorted_pred); 52 | 53 | private: 54 | shared_ptr > net_; 55 | Detector(){} 56 | }; 57 | 58 | //Using for box sort 59 | struct Info 60 | { 61 | float score; 62 | const float* head; 63 | }; 64 | bool compare(const Info& Info1, const Info& Info2) 65 | { 66 | return Info1.score > Info2.score; 67 | } 68 | 69 | #endif 70 | -------------------------------------------------------------------------------- /faster_cxx_lib/lib/faster_rcnn.so: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus/ed3bf4c51ae1a1862988a110c00559632ae74004/faster_cxx_lib/lib/faster_rcnn.so -------------------------------------------------------------------------------- /faster_cxx_lib/lib/libfaster_rcnn.so: 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"/home/lyh1/workspace/py-faster-rcnn/output/default/yuanzhang_car/vgg_cnn_m_1024_fast_rcnn_stage2_iter_40000.caffemodel"; 6 | int GPUID=0; 7 | Caffe::SetDevice(GPUID); 8 | Caffe::set_mode(Caffe::GPU); 9 | Detector det = Detector(model_file, weights_file); 10 | det.Detect("/home/lyh1/workspace/py-faster-rcnn/data/demo/car.jpg"); 11 | return 0; 12 | } 13 | -------------------------------------------------------------------------------- /faster_cxx_lib/pbs_cxx_faster_rcnn_demo.job: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #PBS -N cxx_faster_cnn 3 | #PBS -l nodes=1:compute-0-3:ppn=1,walltime=100:00:00 4 | #PBS -j oe 5 | cd $PBS_O_WORKDIR 6 | nprocs=`cat $PBS_NODEFILE | wc -l` 7 | cat $PBS_NODEFILE > NODEFILE 8 | 9 | echo $PYTHONPATH 10 | ./main 11 | -------------------------------------------------------------------------------- /faster_cxx_lib/vis.jpg: 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-------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/lib/faster_rcnn.cpp: -------------------------------------------------------------------------------- 1 | #include // for snprintf 2 | #include 3 | #include 4 | #include 5 | #include 6 | #include 7 | #include "caffe/caffe.hpp" 8 | #include "gpu_nms.hpp" 9 | #include 10 | #include 11 | #include 12 | #include "faster_rcnn.hpp" 13 | using namespace caffe; 14 | using namespace std; 15 | 16 | 17 | /* 18 | * === FUNCTION ====================================================================== 19 | * Name: EV2641_InitCarDetector 20 | * Description: Load the model file and weights file ,set GPUID 21 | * ===================================================================================== 22 | */ 23 | int EV2641_InitCarDetector(const char * model_file, const char * weights_file, const int GPUID , Detector * &handle){ 24 | handle = new Detector(model_file, weights_file, GPUID); 25 | return EV2641_ERR_SUCCESS; 26 | } 27 | /* 28 | * === FUNCTION ====================================================================== 29 | * Name: EV2641_ReleaseCarDetector 30 | * Description: Release required resource 31 | * ===================================================================================== 32 | */ 33 | int EV2641_ReleaseCarDetector(){ 34 | return EV2641_ERR_SUCCESS; 35 | } 36 | /* 37 | * === FUNCTION ====================================================================== 38 | * Name: Detector 39 | * Description: Detect Car and return detection result 40 | * ===================================================================================== 41 | */ 42 | int EV2641_A_GetCarRect(const EV2641Image * image, int &max_ret_num, EV2641Rect * rect, Detector * &handle){ 43 | vector detection_result; 44 | 45 | IplImage * img = cvCreateImage(cvSize(image->width, image->height), 8, 3); 46 | memcpy(img->imageData, image->imagedata, img->imageSize); 47 | 48 | cv::Mat inputImg = cv::cvarrToMat(img, true); 49 | handle->Detect(inputImg, detection_result); 50 | 51 | for (int j = 0; j < detection_result.size(); j++) 52 | { 53 | if(j >= max_ret_num) 54 | { 55 | max_ret_num=j; 56 | break; 57 | } 58 | rect[j].x = detection_result[j].x ; 59 | rect[j].y = detection_result[j].y ; 60 | rect[j].w = detection_result[j].width ; 61 | rect[j].h = detection_result[j].height ; 62 | } 63 | cvReleaseImage(&img); 64 | inputImg.release(); 65 | return EV2641_ERR_SUCCESS; 66 | } 67 | 68 | 69 | /* 70 | * === FUNCTION ====================================================================== 71 | * Name: Detector 72 | * Description: Load the model file and weights file 73 | * ===================================================================================== 74 | */ 75 | //load modelfile and weights 76 | Detector::Detector(const string& model_file, const string& weights_file, const int GPUID) 77 | { 78 | Caffe::SetDevice(GPUID); 79 | Caffe::set_mode(Caffe::GPU); 80 | net_ = shared_ptr >(new Net(model_file, caffe::TEST)); 81 | net_->CopyTrainedLayersFrom(weights_file); 82 | } 83 | 84 | /* 85 | * === FUNCTION ====================================================================== 86 | * Name: Detect 87 | * Description: Perform detection operation 88 | * Warning the max input size should less than 1000*600 89 | * ===================================================================================== 90 | */ 91 | //perform detection operation 92 | //input image max size 1000*600 93 | void Detector::Detect(cv::Mat & cv_img, vector & detection_result ) 94 | { 95 | float CONF_THRESH = 0.8; 96 | float NMS_THRESH = 0.3; 97 | const int max_input_side=1000; 98 | const int min_input_side=600; 99 | 100 | cv::Mat cv_new(cv_img.rows, cv_img.cols, CV_32FC3, cv::Scalar(0,0,0)); 101 | if(cv_img.empty()) 102 | { 103 | std::cout<<"Can not get the image"< 1) 116 | { 117 | img_scale = float(1) / max_scale; 118 | } 119 | 120 | int height = int(cv_img.rows * img_scale); 121 | int width = int(cv_img.cols * img_scale); 122 | int num_out; 123 | cv::Mat cv_resized; 124 | 125 | float im_info[3]; 126 | float data_buf[height*width*3]; 127 | float *boxes = NULL; 128 | float *pred = NULL; 129 | float *pred_per_class = NULL; 130 | float *sorted_pred_cls = NULL; 131 | int *keep = NULL; 132 | const float* bbox_delt; 133 | const float* rois; 134 | const float* pred_cls; 135 | int num; 136 | 137 | for (int h = 0; h < cv_img.rows; ++h ) 138 | { 139 | for (int w = 0; w < cv_img.cols; ++w) 140 | { 141 | cv_new.at(cv::Point(w, h))[0] = float(cv_img.at(cv::Point(w, h))[0])-float(102.9801); 142 | cv_new.at(cv::Point(w, h))[1] = float(cv_img.at(cv::Point(w, h))[1])-float(115.9465); 143 | cv_new.at(cv::Point(w, h))[2] = float(cv_img.at(cv::Point(w, h))[2])-float(122.7717); 144 | 145 | } 146 | } 147 | 148 | cv::resize(cv_new, cv_resized, cv::Size(width, height)); 149 | im_info[0] = cv_resized.rows; 150 | im_info[1] = cv_resized.cols; 151 | im_info[2] = img_scale; 152 | 153 | for (int h = 0; h < height; ++h ) 154 | { 155 | for (int w = 0; w < width; ++w) 156 | { 157 | data_buf[(0*height+h)*width+w] = float(cv_resized.at(cv::Point(w, h))[0]); 158 | data_buf[(1*height+h)*width+w] = float(cv_resized.at(cv::Point(w, h))[1]); 159 | data_buf[(2*height+h)*width+w] = float(cv_resized.at(cv::Point(w, h))[2]); 160 | } 161 | } 162 | 163 | net_->blob_by_name("data")->Reshape(1, 3, height, width); 164 | //net_->blob_by_name("data")->set_cpu_data(data_buf); 165 | Blob * input_blobs= net_->input_blobs()[0]; 166 | switch(Caffe::mode()){ 167 | case Caffe::CPU: 168 | memcpy(input_blobs->mutable_cpu_data(), data_buf, sizeof(float) * input_blobs->count()); 169 | break; 170 | case Caffe::GPU: 171 | caffe_gpu_memcpy(sizeof(float)* input_blobs->count(), data_buf, input_blobs->mutable_gpu_data()); 172 | break; 173 | default: 174 | LOG(FATAL)<<"Unknow Caffe mode"; 175 | } 176 | net_->blob_by_name("im_info")->set_cpu_data(im_info); 177 | net_->ForwardFrom(0); 178 | bbox_delt = net_->blob_by_name("bbox_pred")->cpu_data(); 179 | num = net_->blob_by_name("rois")->num(); 180 | 181 | rois = net_->blob_by_name("rois")->cpu_data(); 182 | pred_cls = net_->blob_by_name("cls_prob")->cpu_data(); 183 | boxes = new float[num*4]; 184 | pred = new float[num*5*class_num]; 185 | pred_per_class = new float[num*5]; 186 | sorted_pred_cls = new float[num*5]; 187 | keep = new int[num]; 188 | 189 | for (int n = 0; n < num; n++) 190 | { 191 | for (int c = 0; c < 4; c++) 192 | { 193 | boxes[n*4+c] = rois[n*5+c+1] / img_scale; 194 | } 195 | } 196 | 197 | bbox_transform_inv(num, bbox_delt, pred_cls, boxes, pred, cv_img.rows, cv_img.cols); 198 | for (int i = 1; i < class_num; i ++) 199 | { 200 | for (int j = 0; j< num; j++) 201 | { 202 | for (int k=0; k<5; k++) 203 | pred_per_class[j*5+k] = pred[(i*num+j)*5+k]; 204 | } 205 | boxes_sort(num, pred_per_class, sorted_pred_cls); 206 | _nms(keep, &num_out, sorted_pred_cls, num, 5, NMS_THRESH, 0); 207 | //for visualize only 208 | //vis_detections(cv_img, keep, num_out, sorted_pred_cls, CONF_THRESH); 209 | } 210 | 211 | int k=0; 212 | while(sorted_pred_cls[keep[k]*5+4]>CONF_THRESH && k < num_out) 213 | { 214 | if(k>=num_out) 215 | break; 216 | //detection format x1 y1 width height 217 | detection_result.push_back(cv::Rect(sorted_pred_cls[keep[k]*5+0], 218 | sorted_pred_cls[keep[k]*5+1], 219 | sorted_pred_cls[keep[k]*5+2]-sorted_pred_cls[keep[k]*5+0], 220 | sorted_pred_cls[keep[k]*5+3]-sorted_pred_cls[keep[k]*5+1])); 221 | k++; 222 | } 223 | 224 | //cv::imwrite("vis.jpg",cv_img); 225 | delete []boxes; 226 | delete []pred; 227 | delete []pred_per_class; 228 | delete []keep; 229 | delete []sorted_pred_cls; 230 | 231 | } 232 | 233 | /* 234 | * === FUNCTION ====================================================================== 235 | * Name: vis_detections 236 | * Description: Visuallize the detection result 237 | * ===================================================================================== 238 | */ 239 | void Detector::vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH) 240 | { 241 | int i=0; 242 | while(sorted_pred_cls[keep[i]*5+4]>CONF_THRESH && i < num_out) 243 | { 244 | if(i>=num_out) 245 | return; 246 | cv::rectangle(image,cv::Point(sorted_pred_cls[keep[i]*5+0], sorted_pred_cls[keep[i]*5+1]),cv::Point(sorted_pred_cls[keep[i]*5+2], sorted_pred_cls[keep[i]*5+3]),cv::Scalar(255,0,0)); 247 | i++; 248 | } 249 | } 250 | 251 | /* 252 | * === FUNCTION ====================================================================== 253 | * Name: boxes_sort 254 | * Description: Sort the bounding box according score 255 | * ===================================================================================== 256 | */ 257 | void Detector::boxes_sort(const int num, const float* pred, float* sorted_pred) 258 | { 259 | vector my; 260 | Info tmp; 261 | for (int i = 0; i< num; i++) 262 | { 263 | tmp.score = pred[i*5 + 4]; 264 | tmp.head = pred + i*5; 265 | my.push_back(tmp); 266 | } 267 | std::sort(my.begin(), my.end(), compare); 268 | for (int i=0; i // for snprintf 4 | #include 5 | #include 6 | #include 7 | #include 8 | #include 9 | #include "caffe/caffe.hpp" 10 | #include "gpu_nms.hpp" 11 | #include 12 | #include 13 | #include 14 | using namespace caffe; 15 | using namespace std; 16 | 17 | #define max(a, b) (((a)>(b)) ? (a) :(b)) 18 | #define min(a, b) (((a)<(b)) ? (a) :(b)) 19 | 20 | 21 | #define EV2641_ERR_SUCCESS 0 22 | //矩形定义 23 | typedef struct { 24 | int x; 25 | int y; 26 | int w; 27 | int h; 28 | }EV2641Rect; 29 | 30 | typedef struct 31 | { 32 | unsigned char * imagedata; //图像矩阵指针 33 | int width; //图像宽度 34 | int height; //图像高度 35 | int widthStep; //每行像素的字节数 36 | int type; //图像类型 37 | char useROI; //感兴趣区域表示 38 | EV2641Rect mROI; //感兴趣区域 39 | }EV2641Image; 40 | class Detector; 41 | 42 | /* 43 | * === FUNCTION ====================================================================== 44 | * Name: EV2641_InitCarDetector 45 | * Description: Load the model file and weights file ,set GPUID 46 | * ===================================================================================== 47 | */ 48 | int EV2641_InitCarDetector(const char * model_file,const char * weights_file, const int GPUID ,Detector * &handle); 49 | /* 50 | * === FUNCTION ====================================================================== 51 | * Name: EV2641_ReleaseCarDetector 52 | * Description: Release required resource 53 | * ===================================================================================== 54 | */ 55 | int EV2641_ReleaseCarDetector(); 56 | /* 57 | * === FUNCTION ====================================================================== 58 | * Name: Detector 59 | * Description: Detect Car and return detection result 60 | * ===================================================================================== 61 | */ 62 | 63 | int EV2641_A_GetCarRect(const EV2641Image * image, int &max_ret_num, EV2641Rect * rect, Detector * &handle); 64 | 65 | //background and car 66 | const int class_num=2; 67 | 68 | /* 69 | * === Class ====================================================================== 70 | * Name: Detector 71 | * Description: FasterRCNN CXX Detector 72 | * ===================================================================================== 73 | */ 74 | class Detector { 75 | public: 76 | Detector(const string& model_file, const string& weights_file, const int GPUID); 77 | void Detect(cv::Mat & cv_image, vector & detection_result ); 78 | void bbox_transform_inv(const int num, const float* box_deltas, const float* pred_cls, float* boxes, float* pred, int img_height, int img_width); 79 | void vis_detections(cv::Mat image, int* keep, int num_out, float* sorted_pred_cls, float CONF_THRESH); 80 | void boxes_sort(int num, const float* pred, float* sorted_pred); 81 | 82 | private: 83 | shared_ptr > net_; 84 | Detector(){} 85 | }; 86 | 87 | //Using for box sort 88 | struct Info 89 | { 90 | float score; 91 | const float* head; 92 | }; 93 | bool compare(const Info& Info1, const Info& Info2) 94 | { 95 | return Info1.score > Info2.score; 96 | } 97 | 98 | #endif 99 | -------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/lib/faster_rcnn.so: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus/ed3bf4c51ae1a1862988a110c00559632ae74004/faster_cxx_lib_ev2641/lib/faster_rcnn.so -------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/lib/libfaster_rcnn.so: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus/ed3bf4c51ae1a1862988a110c00559632ae74004/faster_cxx_lib_ev2641/lib/libfaster_rcnn.so -------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/main: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus/ed3bf4c51ae1a1862988a110c00559632ae74004/faster_cxx_lib_ev2641/main -------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/main.cpp: -------------------------------------------------------------------------------- 1 | #include "faster_rcnn.hpp" 2 | int main() 3 | { 4 | string model_file = "/home/lyh1/workspace/py-faster-rcnn/models/pascal_voc/VGG_CNN_M_1024/faster_rcnn_alt_opt/faster_rcnn_test.pt"; 5 | string weights_file = "/home/lyh1/workspace/py-faster-rcnn/output/default/yuanzhang_car/vgg_cnn_m_1024_fast_rcnn_stage2_iter_40000.caffemodel"; 6 | int GPUID=0; 7 | int max_ret_num=30; 8 | 9 | Detector * handle = NULL; 10 | EV2641_InitCarDetector(model_file.c_str(), weights_file.c_str(), GPUID , handle); 11 | vector detection_result; 12 | cv::Mat inputimage = cv::imread("/home/lyh1/workspace/py-faster-rcnn/data/demo/car.jpg"); 13 | handle->Detect(inputimage, detection_result); 14 | for(int i=0;i < detection_result.size(); i++){ 15 | cv::rectangle(inputimage,cv::Point(detection_result[i].x,detection_result[i].y), 16 | cv::Point(detection_result[i].x + detection_result[i].width,detection_result[i].y + detection_result[i].height), 17 | cv::Scalar(0,255,0)); 18 | 19 | } 20 | cv::imwrite("test.jpg",inputimage); 21 | return 0; 22 | } 23 | -------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/pbs_cxx_faster_rcnn_demo.job: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #PBS -N cxx_faster_cnn 3 | #PBS -l nodes=1:compute-0-3:ppn=1,walltime=100:00:00 4 | #PBS -j oe 5 | cd $PBS_O_WORKDIR 6 | nprocs=`cat $PBS_NODEFILE | wc -l` 7 | cat $PBS_NODEFILE > NODEFILE 8 | 9 | echo $PYTHONPATH 10 | ./main 11 | -------------------------------------------------------------------------------- /faster_cxx_lib_ev2641/test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/YihangLou/FasterRCNN-Encapsulation-Cplusplus/ed3bf4c51ae1a1862988a110c00559632ae74004/faster_cxx_lib_ev2641/test.jpg --------------------------------------------------------------------------------