【深度学习模型⼯程化2】C++基于opencv和基于caffe内部
C++接⼝对训练好的Ca。。。
最近两天，因为项⽬原因必须要将深度学习模型⼯程化。在MFC框架内实现分类功能。所以⽤了两天时间⼜深⼊研究了⼀下。
⼀、基于opencv的dnn模块的调⽤。
笔者在1年多前的上⼀篇博客中已经详细讲过这部分。当时觉得opencv越来越强⼤了，但实际情况opencv也有它开发的局限性。后⾯我们会详细提到。opencv⾃从进去3.X的时代，新增了dnn模块，实现了对部分深度学习框架的⽀持。直到⼀周之前刚发布的最新版本OpenCV4.0，官⽅明确⽀持以下5种深度学习框架的调⽤。
Models in  format
当然，我们发现4.0版本的opencv增加了许多新特点。其中有⼀点就是新版本需要c++ 11新特性的⽀持，这可能意味着我们经典的
VS2010极其以下的vs版本将彻底和新版opencv⽆缘。当然，不排除⼀些技术⼤⽜过两天就搞了⼀个可⽀持多种编译器的opencv改进版出来。
⼆、基于caffe内部c++接⼝的调⽤
笔者已经默认⼤家都编译过Caffe并且能够训练⾃⼰的模型了。⾄于编译caffe的时候会遇到很多巨坑，你要相信，⼤家都在陪你。所以这也是为什么笔者强烈建议已经2018年马上2019年了。希望⼤家不要再⽤caffe了。这种实现⽅法是需要⾃⼰编译caffe的（建议在release 和debug模式下都编译，⽅便后续程序调试）。所以这⽅法⼀听就没有⽤opencv⽅便。不过没办法，进⼊正题：
在caffe⼯程内部：
有⼀个classification.cpp。caffe已经帮你写好了分类测试的代码。你只需要改改路径什么的，就可以直接运⾏测试了。⽹上有很多博客都讲了怎么运⾏这个，所以跑通这个，绝⼤多数⼈都没问题。笔者重点
讲的是把这个cpp拿出来单独新建⼀个⼯程项⽬，绝⼤多数坑都是因为笔者的模型有⾃⼰新建的⽹络层，所以，没办法，⼀步步填坑。⾸先。附上最开始的测试代码：
// WheatTest.cpp : 定义控制台应⽤程序的⼊⼝点。
#include "stdafx.h"
#include "Head.h"
#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>
using namespace caffe;  // NOLINT(build/namespaces)
using std::string;
/* Pair (label, confidence) representing a prediction. */
typedef std::pair<string, float> Prediction;
class Classifier {
public:
Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file);
std::vector<Prediction> Classify(const cv::Mat& img, int N = 6);
private:
void SetMean(const string& mean_file);
std::vector<float> Predict(const cv::Mat& img);
void WrapInputLayer(std::vector<cv::Mat>* input_channels);
void Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels);
private:
shared_ptr<Net<float> > net_;
cv::Size input_geometry_;
int num_channels_;
cv::Mat mean_;
std::vector<string> labels_;
};
Classifier::Classifier(const string& model_file,
const string& trained_file,
const string& mean_file,
const string& label_file) {
Caffe::set_mode(Caffe::CPU);
/* Load the network. */
net_.reset(new Net<float>(model_file, TEST));
net_->CopyTrainedLayersFrom(trained_file);
CHECK_EQ(net_->num_inputs(), 1) << "Network should have exactly one input.";    CHECK_EQ(net_->num_outputs(), 1) << "Network should have exactly one output.";
Blob<float>* input_layer = net_->input_blobs()[0];
num_channels_ = input_layer->channels();
CHECK(num_channels_ == 3 || num_channels_ == 1)
<< "Input layer should have 1 or 3 channels.";
input_geometry_ = cv::Size(input_layer->width(), input_layer->height());
/* Load the binaryproto mean file. */
SetMean(mean_file);
/* Load labels. */
std::ifstream labels(label_file.c_str());
CHECK(labels) << "Unable to open labels file " << label_file;
string line;
while (std::getline(labels, line))
labels_.push_back(string(line));
Blob<float>* output_layer = net_->output_blobs()[0];
CHECK_EQ(labels_.size(), output_layer->channels())
<< "Number of labels is different from the output layer dimension.";
<< "Number of labels is different from the output layer dimension.";
}
static bool PairCompare(const std::pair<float, int>& lhs,
const std::pair<float, int>& rhs) {
return lhs.first > rhs.first;
}
/* Return the indices of the top N values of vector v. */
static std::vector<int> Argmax(const std::vector<float>& v, int N) {
std::vector<std::pair<float, int> > pairs;
for (size_t i = 0; i < v.size(); ++i)
pairs.push_back(std::make_pair(v[i], static_cast<int>(i)));
std::partial_sort(pairs.begin(), pairs.begin() + N, d(), PairCompare);
std::vector<int> result;
for (int i = 0; i < N; ++i)
result.push_back(pairs[i].second);
return result;
}
/* Return the top N predictions. */
std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
std::vector<float> output = Predict(img);
N = std::min<int>(labels_.size(), N);
std::vector<int> maxN = Argmax(output, N);
std::vector<Prediction> predictions;
for (int i = 0; i < N; ++i) {
int idx = maxN[i];
predictions.push_back(std::make_pair(labels_[idx], output[idx]));
}
return predictions;
}
/* Load the mean file in binaryproto format. */
void Classifier::SetMean(const string& mean_file) {
BlobProto blob_proto;
ReadProtoFromBinaryFileOrDie(mean_file.c_str(), &blob_proto);
/* Convert from BlobProto to Blob<float> */
Blob<float> mean_blob;
mean_blob.FromProto(blob_proto);
CHECK_EQ(mean_blob.channels(), num_channels_)
<< "Number of channels of mean file doesn't match input layer.";
/* The format of the mean file is planar 32-bit float BGR or grayscale. */
std::vector<cv::Mat> channels;
float* data = mean_blob.mutable_cpu_data();
for (int i = 0; i < num_channels_; ++i) {
/* Extract an individual channel. */
cv::Mat channel(mean_blob.height(), mean_blob.width(), CV_32FC1, data);        channels.push_back(channel);
data += mean_blob.height() * mean_blob.width();
}
/* Merge the separate channels into a single image. */
cv::Mat mean;
cv::merge(channels, mean);
/* Compute the global mean pixel value and create a mean image
* filled with this value. */
cv::Scalar channel_mean = cv::mean(mean);
mean_ = cv::Mat(input_geometry_, pe(), channel_mean);
}
}
std::vector<float> Classifier::Predict(const cv::Mat& img) {
Blob<float>* input_layer = net_->input_blobs()[0];
input_layer->Reshape(1, num_channels_,
input_geometry_.height, input_geometry_.width);
/* Forward dimension change to all layers. */
net_->Reshape();
std::vector<cv::Mat> input_channels;
WrapInputLayer(&input_channels);
Preprocess(img, &input_channels);
net_->Forward();
/* Copy the output layer to a std::vector */
Blob<float>* output_layer = net_->output_blobs()[0];
const float* begin = output_layer->cpu_data();
const float* end = begin + output_layer->channels();
return std::vector<float>(begin, end);
}
/* Wrap the input layer of the network in separate cv::Mat objects
* (one per channel). This way we save one memcpy operation and we * don't need to rely on cudaMemcpy2D. The last preprocessing
* operation will write the separate channels directly to the input
* layer. */
void Classifier::WrapInputLayer(std::vector<cv::Mat>* input_channels) {    Blob<float>* input_layer = net_->input_blobs()[0];
int width = input_layer->width();
int height = input_layer->height();
float* input_data = input_layer->mutable_cpu_data();
for (int i = 0; i < input_layer->channels(); ++i) {
cv::Mat channel(height, width, CV_32FC1, input_data);
input_channels->push_back(channel);
input_data += width * height;
}
}
void Classifier::Preprocess(const cv::Mat& img,
std::vector<cv::Mat>* input_channels) {
/* Convert the input image to the input image format of the network. */    cv::Mat sample;
if (img.channels() == 3 && num_channels_ == 1)
cv::cvtColor(img, sample, cv::COLOR_BGR2GRAY);
else if (img.channels() == 4 && num_channels_ == 1)
cv::cvtColor(img, sample, cv::COLOR_BGRA2GRAY);
else if (img.channels() == 4 && num_channels_ == 3)
cv::cvtColor(img, sample, cv::COLOR_BGRA2BGR);
else if (img.channels() == 1 && num_channels_ == 3)
cv::cvtColor(img, sample, cv::COLOR_GRAY2BGR);
else
sample = img;
cv::Mat sample_resized;
if (sample.size() != input_geometry_)
cv::resize(sample, sample_resized, input_geometry_);
else
sample_resized = sample;
cv::Mat sample_float;
if (num_channels_ == 3)
vertTo(sample_float, CV_32FC3);
else
else
vertTo(sample_float, CV_32FC1);
cv::Mat sample_normalized;
cv::subtract(sample_float, mean_, sample_normalized);
/* This operation will write the separate BGR planes directly to the
resized
* input layer of the network because it is wrapped by the cv::Mat
* objects in input_channels. */
cv::split(sample_normalized, *input_channels);
CHECK(reinterpret_cast<float*>(input_channels->at(0).data)
== net_->input_blobs()[0]->cpu_data())
<< "Input channels are not wrapping the input layer of the network.";
}
int main(int argc, char** argv)
{
::google::InitGoogleLogging(argv[0]);
string model_file = "C:\\Users\\lill\\Desktop\\WheatTest\\model\\mynet.prototxt";
string trained_file = "C:\\Users\\lill\\Desktop\\WheatTest\\model\\mynet.caffemodel";
string mean_file = "C:\\Users\\lill\\Desktop\\WheatTest\\model\\trainimgmean.binaryproto";
string label_file = "C:\\Users\\lill\\Desktop\\WheatTest\\model\\";
Classifier classifier(model_file, trained_file, mean_file, label_file);
string file = "C:\\Users\\lill\\Desktop\\WheatTest\\model\\1.bmp";
std::cout << "---------- Prediction for "<< file << " ----------" << std::endl;
cv::Mat img = cv::imread(file, -1);
CHECK(!pty()) << "Unable to decode image " << file;
std::vector<Prediction> predictions = classifier.Classify(img);
/* Print the top N predictions. */
for (size_t i = 0; i < predictions.size(); ++i)
{
Prediction p = predictions[i];
std::cout << std::fixed << std::setprecision(4) << p.second << " - \"" << p.first << "\"" << std::endl;    }
std::system("pause");
}
Release模式下：
包含⽬录：
D:\caffe-master\include
D:\caffe-master\include\caffe
D:\caffe-master\include\caffe\proto
D:\NugetPackages\boost.1.59.0.0\lib\native\include
D:\NugetPackages\gflags.2.1.2.1\build\native\include
D:\NugetPackages\glog.0.3.3.0\build\native\include
D:\NugetPackages\protobuf-v120.2.6.1\build\native\include
D:\NugetPackages\OpenBLAS.0.2.14.1\lib\native\include
D:\NugetPackages\OpenCV.2.4.10\build\native\include
D:\NugetPackages\LevelDB-vc120.1.2.0.0\build\native\include
D:\NugetPackages\lmdb-v120-clean.0.9.14.0\lib\native\include
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\include(GPU模式下需配置)
库⽬录：

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