RefineDet：（3）C++测试代码

浅浅的花香味﹌ 2022-02-03 07:09 175阅读 0赞

代码地址：[RefineDet][]

**相关链接：**

1.  [《RefineDet：Single-Shot Refinement Neural Network for Object Detection》论文笔记][RefineDet_Single-Shot Refinement Neural Network for Object Detection]
2.  [RefineDet：（1）训练脚本解析][RefineDet_1]
3.  [RefineDet：（2）检测部分网络解析][RefineDet_2]

# 1. 概述 #

Python版本的测试代码在相应的文件夹下已经写好了，这里根据对应的Python文件写了一个测试Demo。

# 2. 测试Demo #

#include <stdio.h>
    #include <stdlib.h>
    #include <iostream>
    #include <vector>
    #include <map>
    #include <memory>
    #include <string>
    #include <opencv2/opencv.hpp>
    #include <caffe/blob.hpp>
    #include <caffe/common.hpp>
    #include <caffe/layer.hpp>
    #include <caffe/net.hpp>
    
    int main(int argc, char* argv[])
    { 
    	//GPU设置
    	int device_id = 0;
    	caffe::Caffe::set_mode(caffe::Caffe::GPU);
    	caffe::Caffe::SetDevice(device_id);
    	std::cout << "Use GPU: " << device_id << std::endl;
    	
    	//网络均值
    	std::vector<float> pixel_mean={ 104.0, 117.0, 123.0};
    	//模型文件路径
    	std::string deploy_file = "./models/deploy.prototxt";
    	std::string model_file = "./models/refine_det.caffemodel";
    	//模型所需的宽高尺寸
    	int det_img_width = 512;
    	int	det_img_height = 704;
    	//测试图像
    	std::string test_file = "./test.jpg";
    	//检测的概率阈值
    	float det_obj_thresh = 0.3;
    	
    	//网络加载
    	std::shared_ptr<caffe::Net<float> > det_net;
    	det_net.reset(new caffe::Net<float>(deploy_file, caffe::TEST));
    	det_net->CopyTrainedLayersFrom(model_file);
    	
    	//加载测试图片
    	cv::Mat src_img = cv::imread(test_file, cv::IMREAD_ANYCOLOR|cv::IMREAD_ANYDEPTH);
    	if(!img.data)
    	{ 
    		std::cout << "no image data: " << test_file<< std::endl;
    		continue;
    	}
    
    	//输入数据准备
    	cv::Mat det_img = src_img.clone();
    	det_img.convertTo(det_img, CV_32FC3); 
    	cv::resize(det_img, det_img, cv::Size(det_img_width, det_img_height), CV_INTER_LINEAR);
    	caffe::Blob<float>* input_blob = det_net->input_blobs()[0]; //get input blob
    	input_blob->Reshape(1, det_img.channels(), det_img.rows, det_img.cols);
    	float *blob_data = input_blob->mutable_cpu_data();
    	const int cols = det_img.cols;
    	const int rows = det_img.rows;
    	
    	for (int i = 0; i < rows; ++i)
    	{ 
    		const cv::Vec3f* data = det_img.ptr<cv::Vec3f>(i);  //此处用指针读取Mat数据，并存到blob中
    		for (int j = 0; j < cols; ++j)
    		{ 
    			blob_data[(0 * rows + i)*cols + j] = data[j][0] - pixel_mean[0];
    			blob_data[(1 * rows + i)*cols + j] = data[j][1] - pixel_mean[1];
    			blob_data[(2 * rows + i)*cols + j] = data[j][2] - pixel_mean[2];
    		}
    	}
    	
    	
    	// 网络前向传播，并获取检测结果
    	float loss;
    	det_net->Forward(&loss);
    	boost::shared_ptr<caffe::Blob<float> > det_output = this->det_net->blob_by_name("detection_out");
    	
    	// 抽取网络的检测结果
    	std::cout << "model detect boxes num:" << det_output->height() << std::endl;
    	for(int i=0; i<det_output->height(); ++i)
    	{ 
    		int label = det_output->data_at(0, 0, i, 1);
    		if (label < 1) continue; //background type
    		float conf = det_output->data_at(0, 0, i, 2);
    		int x_min = static_cast<int>(det_output->data_at(0, 0, i, 3)*float(src_img.cols));
    		int y_min = static_cast<int>(det_output->data_at(0, 0, i, 4)*float(src_img.rows));
    		int x_max = static_cast<int>(det_output->data_at(0, 0, i, 5)*float(src_img.cols));
    		int y_max = static_cast<int>(det_output->data_at(0, 0, i, 6)*float(src_img.rows));
    		if (conf < this->det_obj_thresh) continue;
    		//格式化存储检测框
    		......
    	}
    	
    	return 0;
    }

[RefineDet]: https://github.com/sfzhang15/RefineDet
[RefineDet_Single-Shot Refinement Neural Network for Object Detection]: https://blog.csdn.net/m_buddy/article/details/89932030
[RefineDet_1]: https://blog.csdn.net/m_buddy/article/details/89938934
[RefineDet_2]: https://blog.csdn.net/m_buddy/article/details/89981053