wangyx-tkz / windows-caffe-faster-rcnn Goto Github PK

The windows C ++ version of faster-rcnn is based on two versions:  

This version is Microsoft's windows caffe version    

This is the D-X-Y's Linux c ++ version of faster-rcnn

• After combining the above two, this is the C ++ version faster-rcnn base on windows, no python, support training and testing.
• It takes about 60ms to test a image, under the GeForce GTX TITAN X , using the VGG16 model.

• Same as Microsoft Caffe, or refer to Blog
• Here for convenience, I have put the cudnn files under the project, the cuda folder under the main directory.
• If you want to configure cudnn, remember write the right cuda folder path in the "cuDnnPath" in the ".\Windows\CommonSettings.props" .

• The steps to train the model are exactly the same as D-X-Y's caffe-faster-rcnn version.
• You can read the script "caffe-master\examples\FRCNN\vgg16\train_frcnn.bat" for detail.
• Of course, you can train your model under python version.

Run the script "caffe-master\examples\FRCNN\vgg16\test_frcnn.bat" for detail.

• After compiling, all dependencies generated with faster-rcnn are under "caffe-master\Build\x64\Release".
• Third-party dependencies, such as OpenCV, Glog, protobuf, etc. are in the "NugetPackages".
• Just as VS2013 configures OpenCV, configure faster-rcnn as long as the include-files for faster-rcnn and third-party libraries are placed into the VC ++ directory of VS2013, the lib files are placed into the library directory, and linker-> input-> additional dependencies, input:

libboost_date_time-vc120-mt-1_59.lib
libboost_filesystem-vc120-mt-1_59.lib
libboost_system-vc120-mt-1_59.lib
libglog.lib
libcaffe.lib
gflags.lib
gflags_nothreads.lib
hdf5.lib
hdf5_hl.lib
libprotobuf.lib
libopenblas.dll.a
Shlwapi.lib
opencv_core2410.lib
opencv_highgui2410.lib
opencv_imgproc2410.lib
LevelDb.lib
lmdb.lib
opencv_video2410.lib
opencv_objdetect2410.lib
cublas.lib
cuda.lib
cublas_device.lib
cudart.lib
cudart_static.lib
curand.lib
kernel32.lib

• Refer to the blog: http://blog.csdn.net/auto1993/article/details/70198435 for detail.
• Because the third-party libraries are configured more trouble, for convenience, I packaged the NugetPackages files in the third-party library on Baidu network disk, the password : sj7f
• There are three folders in the packaged folder: bin, include, lib, respectively, corresponding to the executable file, header files, library files.

After VS2013 configuration successful, you can write code in VS2013 to test the pictures. The detector was defined in file caffe-master\include\caffe\api\FRCNN\frcnn_api.hpp:

#include <caffe\api\FRCNN\frcnn_api.hpp>  //Detect head file  
#include "Register.h"           //This file is necessary used to register the relevant caffe layer 
using namespace std;  
using namespace cv;  
using namespace caffe::Frcnn;  
int main(){    
        Mat frame = imread("1.jpg");  //image  
	/* Initiaze the detector, the four parameters were:  
	1. network file   
	2. trained model file  
	3. config file  
	4. whether to open the GPU mode, default true  
	5. whether to ignore print log, default true
	*/
	FRCNN_API::Detector detect("VGG16.prototxt", "VGG16.caffemodel", "config_file.json", true, true);  
	vector<BBox<float> > boxes = detect.predict(frame);    // forward, detect results saved here 
	for (int i = 0; i < boxes.size(); i++)   //draw rects  
	     rectangle(frame, cv::Point(boxes[i][0], boxes[i][1]), cv::Point(boxes[i][2], boxes[i][3]), Scalar(0, 0, 255)); 
	imshow("demo", frame);  
        waitKey(0);  
	return 0;  
}

• Need to add Register.h to your code, otherwise it will complain that no registration related layer, I had put this file in the main project directory.
• "VGG16.prototxt" -- a network description file under path "caffe-master\models\FRCNN\vgg16"
• "VGG16.caffemodel" -- trained model file
• "config_file.json" -- training and testing need to use the configuration file, it involves the number of target categories, NMS threshold, etc. under "caffe-master\examples\FRCNN\config"

• My csdn blog address: http://blog.csdn.net/zxj942405301/article/details/78602671
• If this version is helpful to you, give me a star, thank you ~