Caffe:C++呼び出しのCMakeLists.txt

C++でCaffeを呼び出す場合は、C++ファイルをコンパイルできるCMAKEプロファイルが用意されています.Caffeの例imageNet分類ではpython呼び出しの方法は以下の通りである.このコードはモデルをロードし、画像予測が属するカテゴリを読み込みます.[参考]

import numpy as np
import sys, os

caffe_root = '/home/XXX/software/caffe/' #  Caffe  
sys.path.insert(0, caffe_root + 'python')
import caffe

thisDir, _ = os.path.split(os.path.abspath(sys.argv[0]))
net_file=thisDir+'/data/deploy.prototxt'
caffe_model=thisDir+'/data/bvlc_reference_caffenet.caffemodel'
mean_file=thisDir+'/data/ilsvrc_2012_mean.npy'

net = caffe.Net(net_file,caffe_model,caffe.TEST)
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))
transformer.set_raw_scale('data', 255) 
transformer.set_channel_swap('data', (2,1,0))

im=caffe.io.load_image(thisDir+'/data/cat.jpg')
net.blobs['data'].data[...] = transformer.preprocess('data',im)
out = net.forward()


imagenet_labels_filename =thisDir+'/data/caffe_ilsvrc12/synset_words.txt'
labels = np.loadtxt(imagenet_labels_filename, str, delimiter='\t')

top_k = net.blobs['prob'].data[0].flatten().argsort()[-1:-6:-1]
for i in np.arange(top_k.size):
    print top_k[i], labels[top_k[i]]

以下、C++で呼び出します.
main.cppはモデルパスなどのパラメータで分類器を作成し,読み出した画像を分類器に渡して分類結果を取得する:[参照]

#include 

#ifdef USE_OPENCV
int main(int argc, char** argv) {
	if (argc != 6) {
		std::cerr << "Usage: " << argv[0]
		<< " deploy.prototxt network.caffemodel"
		<< " mean.binaryproto labels.txt img.jpg" << std::endl;
		return 1;
	}

	::google::InitGoogleLogging(argv[0]);

	string model_file   = argv[1];
	string trained_file = argv[2];
	string mean_file    = argv[3];
	string label_file   = argv[4];

	Classifier classifier(model_file, trained_file, mean_file, label_file);

	string file = argv[5];

	std::cout << "---------- Prediction for " << file << " ----------" << std::endl;

	cv::Mat img = cv::imread(file, -1);
	CHECK(!img.empty()) << "Unable to decode image " << file;
	std::vector 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;
	}
}

#else
int main(int argc, char** argv) {
	LOG(FATAL) << "This example requires OpenCV; compile with USE_OPENCV.";
}
#endif  // USE_OPENCV

分類器classifier.h:

#ifndef _CLASSIFIER_H_
#define _CLASSIFIER_H_

#include 
#ifdef USE_OPENCV
#include 
#include 
#endif  // USE_OPENCV

using namespace caffe;  // NOLINT(build/namespaces)

/* Pair (label, confidence) representing a prediction. */
typedef std::pair Prediction;

static bool PairCompare(const std::pair& lhs,
                        const std::pair& rhs) {
	return lhs.first > rhs.first;
}

/* Return the indices of the top N values of vector v. */
static std::vector Argmax(const std::vector& v, int N) {
	std::vector<:pair int=""> > pairs;
	for (size_t i = 0; i < v.size(); ++i)
		pairs.push_back(std::make_pair(v[i], i));
		std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), PairCompare);

	std::vector result;
	for (int i = 0; i < N; ++i)
		result.push_back(pairs[i].second);
	return result;
}

class Classifier {
  public:
	Classifier(const string& model_file,
		   const string& trained_file,
		   const string& mean_file,
		   const string& label_file);
	std::vector Classify(const cv::Mat& img, int N = 5);

  private:
	void SetMean(const string& mean_file);
	std::vector Predict(const cv::Mat& img);
	void WrapInputLayer(std::vector<:mat>* input_channels);
	void Preprocess(const cv::Mat& img, std::vector<:mat>* input_channels);

  private:
	shared_ptr > net_;
	cv::Size input_geometry_;
	int num_channels_;
	cv::Mat mean_;
	std::vector labels_;
};

#endif

classifier.cpp:

#include 
#ifdef USE_OPENCV
#include 
#endif  // USE_OPENCV

Classifier::Classifier(const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file) {
#ifdef CPU_ONLY
	Caffe::set_mode(Caffe::CPU);
#else
	Caffe::set_mode(Caffe::GPU);
#endif

	/* Load the network. */
	net_.reset(new Net(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* 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* output_layer = net_->output_blobs()[0];
	CHECK_EQ(labels_.size(), output_layer->channels())
		<< "Number of labels is different from the output layer dimension.";
}

/* Return the top N predictions. */
std::vector Classifier::Classify(const cv::Mat& img, int N) {
	std::vector output = Predict(img);

	N = std::min(labels_.size(), N);
	std::vector maxN = Argmax(output, N);
	std::vector 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 */
	Blob 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<: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_, mean.type(), channel_mean);
}

std::vector Classifier::Predict(const cv::Mat& img) {
	Blob* 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<:mat> input_channels;
	WrapInputLayer(&input_channels);

	Preprocess(img, &input_channels);

	net_->Forward();

	/* Copy the output layer to a std::vector */
	Blob* output_layer = net_->output_blobs()[0];
	const float* begin = output_layer->cpu_data();
	const float* end = begin + output_layer->channels();
	return std::vector(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<:mat>* input_channels) {
	Blob* 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<: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)
		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);

	/* This operation will write the separate BGR planes directly to the
	 * 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(input_channels->at(0).data)
		== net_->input_blobs()[0]->cpu_data())
		<< "Input channels are not wrapping the input layer of the network.";
}

プロファイル:[参照]

cmake_minimum_required(VERSION 2.8)
project(classifier)

add_definitions("-DUSE_OPENCV")#     #ifdef USE_OPENCV     

set(Caffe_Root
	/path/caffe#caffe  ，        
)
set(Caffe_INCLUDE_DIRS 
	${Caffe_Root}/include
	${Caffe_Root}/src
	/usr/local/cuda/include
)
set(Caffe_LIBRARIES
	caffe
	boost_system
	glog
)

#add_compile_options(-std=c++11)

find_package(OpenCV REQUIRED)
include_directories(
	${Caffe_INCLUDE_DIRS}
	${OpenCV_INCLUDE_DIRS}
	${CMAKE_CURRENT_SOURCE_DIR}
)

link_directories(
	${Caffe_Root}/build/lib
)

add_library(${PROJECT_NAME} #STATIC | SHARED | MODULE
	${CMAKE_CURRENT_SOURCE_DIR}/classifier.cpp#          
)
#target_link_libraries(${PROJECT_NAME}
#	${OpenCV_LIBS}
#)

add_executable(run main.cpp)# main.cpp        run
target_link_libraries(run
	${PROJECT_NAME}
	${OpenCV_LIBS}
	${Caffe_LIBRARIES}
)

実行方法:

./run \
../data/deploy.prototxt \
../data/bvlc_reference_caffenet.caffemodel \
../data/ilsvrc_2012_mean.binaryproto \
../data/caffe_ilsvrc12/synset_words.txt \
../data/cat.jpg

dataデータ抽出コード:8 mcv.