#include?
#include?
#include?
#include?
#include?
#include?“caffe/data_transformer.hpp“
#include?“caffe/util/io.hpp“
#include?“caffe/util/math_functions.hpp“
#include?“caffe/util/rng.hpp“

using?namespace?caffe;
using?namespace?cv;
using?namespace?std;


#define?PRINT_SHAPE1（x）?\
????std::cout?<#define?PRINT_SHAPE2（x）?\
????std::cout?<num（）?<channels（）?<height（）?<width（）?<#define?PRINT_DATA（x）?\
????std::cout?<


class?EdgeDetect
{
public:
????Mat?mean_;
????Size?input_geometry;
????int?num_channels;
????shared_ptr??>?net;

public:
????EdgeDetect（const?string&?model_file?const?string&?trained_fileconst?string&?mean_file）;
????void?WrapInputlayer（std::vector*?input_channels）;
????void?Preprocess（const?cv::Mat&?imgstd::vector*?input_channels）;
????void?SetMean（const?string&?mean_file）;
};



EdgeDetect::EdgeDetect（const?string&?model_fileconst?string&?trained_fileconst?string&?mean_file）
{
??Caffe::set_mode（Caffe::CPU）;



??net.reset（new?Net（model_file?TEST））;

??net->CopyTrainedlayersFrom（trained_file）;

??CHECK_EQ（net->num_inputs（）?1）?<??//CHECK_EQ（net->num_outputs（）?1）?<
??Blob*?input_layer?=?net->input_blobs（）[0];
??num_channels?=?input_layer->channels（）;

??CHECK（num_channels?==?3?||?num_channels?==?1）<yer?should?have?1?or?3?channels.“;

??input_geometry?=?cv::Size（input_layer->width（）?input_layer->height（））;


??SetMean（mean_file）;
}




void?EdgeDetect::Preprocess（const?cv::Mat&?imgstd::vector*?input_channels）
{

??cv::Mat?sample;

??if?（img.channels（）?==?3?&&?num_channels?==?1）
????cv::cvtColor（img?sample?CV_BGR2GRAY）;
??else?if?（img.channels（）?==?4?&&?num_channels?==?1）
????cv::cvtColor（img?sample?CV_BGRA2GRAY）;

??else?if?（img.channels（）?==?4?&&?num_channels?==?3）
????cv::cvtColor（img?sample?CV_BGRA2BGR）;
??else?if?（img.channels（）?==?1?&&?num_channels?==?3）
????cv::cvtColor（img?sample?CV_GRAY2BGR）;
??else
????sample?=?img;

??cv::Mat?sample_resized;
??if?（sample.size（）?!=?input_geometry）
????cv::resize（sample?sample_resized?input_geometry）;
??else
????sample_resized?=?sample;

??imshow（“resize“sample_resized）;

??cv::Mat?sample_float;
??if?（num_channels?==?3）
????sample_resized.convertTo（sample_float?CV_32FC3）;
??else
????sample_resized.convertTo（sample_float?CV_32FC1）;

??cv::Mat?sample_normalized;
??cv::subtract（sample_float?mean_?sample_normalized）;

??/*?3通道數(shù)據(jù)分開存儲?*/
??cv::split（sample_normalized?*input_channels）;

??CHECK（reinterpret_cast（input_channels->at（0）.data）?==?net->input_blobs（）[0]->cpu_data（））?<yer?of?the?network.“;
}

//加載均值文件
void

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件??????47609??2015-10-01?10:38??HEDC++\3063.jpg

?????文件???????8186??2017-02-28?11:50??HEDC++\deploy.prototxt

?????文件???58876104??2017-03-31?09:17??HEDC++\hed_pretrained_bsds.caffemodel

?????文件?????786446??2014-02-25?16:24??HEDC++\imagenet_mean.binaryproto

?????文件??????10130??2017-06-21?15:34??HEDC++\main.cpp

?????目錄??????????0??2017-06-21?15:34??HEDC++

-----------?---------??----------?-----??----

?????????????59728475????????????????????6