利用Clion呼叫caffe的c++介面
阿新 • • 發佈:2018-11-28
之前簡單學習了一下CMake的知識,現在就應用到caffe裡.
CLION呼叫caffe關鍵是CMakeLists.txt檔案的編寫,涉及到一些CMake的知識.
首先用CLion建立一個caffeReadImgClassify工程,這時候發現工程目錄下有main.cpp和CmakeLists.txt兩個檔案
看 CMakeLists.txt檔案內容,注意,有的路徑需要自己改變一下
cmake_minimum_required(VERSION 3.12) # 最低的CMAKE版本 project(caffeReadImgClassify) # 可以用這個指令定義工程的名稱,並指定工程支援的語言,語言列表是可以忽略的,預設情況表示支援所有語言 # 這個指令還隱式定義了兩個cmake變數 projectname_BINARY_DIR 和 projectname_SOURCE_DIR,因為採用的是內部編 # 譯,兩個變數目前指的是工作所在路徑,如果是外部編譯,兩者所指代的內容會有所不同,但是如果改變工程名, # 那麼這兩個變數也需要修改,那會很麻煩,所有,建議直接使用PROJECT_BINARY_DIR 和 PROJECT_SOURCE_DIR # 外部編譯時,PROJECT_BINATY_DIR指的是編譯路徑, PROJECT_SOURCE_DIR指工程路徑 set(CMAKE_CXX_STANDARD 14) #用來定義顯式變數 include_directories(/home/hjxu/git/caffe/include /usr/local/cuda/include /usr/include/opencv /usr/include/boost /home/hjxu/git/caffe/.build_release/src) # INCLUDE_DIRECTORIES([AFTER|BEFORE] [SYSTEM] dir1 dir2 ...) # 這條指令可以用來向工程中新增多個特定的標頭檔案搜尋路徑, 路徑之間用空格分割,如果路徑中包含了空格, # 可以用雙引號將它括起來, 預設的行為是追加到當前標頭檔案搜尋路徑的後面,當然,也可以通過兩種方式來控制搜尋路徑新增的方式 # 一種方式:CMAKE_INCLUDE_DIRECORIES_BEFORE,通過SET這個cmake變數為on,可以將新增的標頭檔案搜尋路徑放在已有路徑的前面 # 第二種方式: 通過AFTER 或者 BEFORE 引數控制是追加還是置前 find_library(caffe /home/hjxu/git/caffe/build/lib) # find_library(<VAR> name1 path1 path2 ...) # VAR變量表示要找的庫的全路徑, 包含庫檔名 link_libraries("/home/hjxu/git/caffe/.build_release/lib/libcaffe.so" "/usr/lib/x86_64-linux-gnu/libglog.so" "/usr/lib/x86_64-linux-gnu/libboost_filesystem.so" "/usr/lib/x86_64-linux-gnu/libboost_system.so") # LINL_LIBRARIES 新增需要庫連結的庫檔案路徑,這裡是全路徑 find_package(OpenCV REQUIRED) # FIND_PACKAGE(<name> [major.minor] [QUIET] [NO_MODULE] [[REQUIRED|COMPONENTS] [componets...]]) # QUIET 會執行 caffeReadImgClassify_FIND_QUIETLY,如果不指定這個引數,就會執行 # MESSAGE(STATUS "Found caffeReadImgClassify: &{caffeReadImgClassify_LIBRARY}") # REQUIRED 引數, 其含義是這個共享庫是否是工程必須的,如果使用了這個引數,說明這個連結庫是必須庫, # 如果找不到這個連結庫,則工程不能編譯,對應於.cmake模組中的HELLO_FIND_REQUIRED 變數 link_directories("/usr/local/lib/") # 新增非標準共享庫的搜尋路徑, 如果,在工程內部同時存在共享庫和可執行二進位制, 在編譯時就需要指定一下這些共享庫的路徑. set(SOURCE_FILES main.cpp) add_executable(caffeReadImgClassify ${SOURCE_FILES}) # cmake會自動的在本目錄查詢main.c或者main.cpp等,但是最好不要投這個懶,萬一目錄中有 main. target_link_libraries(caffeReadImgClassify ${OpenCV_LIBS}) # TARGET_LINK_LIBRARIES(target library1 <debug | optimized> library2 ...) # 這個指令可以用來為target新增需要連結的共享庫,本例中是一個可執行檔案,但是同樣可以用於為自己編寫的共享庫 # 新增共享庫連結
再看main.cpp的內容,這部分內容和caffe目錄下的test中的cpp介面差不多
#include <iostream> #include "opencv2/opencv.hpp" #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> #include <boost/smart_ptr/shared_ptr.hpp> #include "openslide/openslide.h" using boost::shared_ptr; using namespace caffe; // NOLINT(build/namespaces) using std::string; using namespace std; using namespace cv; /* 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 = 5); 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) { #ifdef CPU_ONLY Caffe::set_mode(Caffe::CPU); #else Caffe::set_mode(Caffe::GPU); #endif /* 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."; } 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], i)); std::partial_sort(pairs.begin(), pairs.begin() + N, pairs.end(), 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_, mean.type(), 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) 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<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() { const char *fileName = "/home../tempC5.jpg"; string model_file = "/home/../profile/deploy_vgg16_places365.prototxt"; string trained_file = "/home/../vgg16-model/vgg_iter_100000.caffemodel"; string mean_file = "/home/../lmdb_5/train_mean.binaryproto"; string label_file = "/home/hjxu/CLionProjects/caffeClassification/labels.txt"; Classifier classifier(model_file, trained_file, mean_file, label_file); cv::Mat img = cv::imread(fileName, -1); // CHECK(!img.empty()) << "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; } // }
由於CSDN在ubuntu下程式碼不清潔的問題, 現將工程逐步更新到github上,本例子github地址如下: