caffe_batch_classifier.cpp

#include <caffe/caffe.hpp>

#ifdef USE_OPENCV
#include <opencv2/core/core.hpp>
#include <opencv2/dnn.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#endif  // USE_OPENCV

#include <algorithm>
#include <iosfwd>
#include <memory>
#include <string>
#include <utility>
#include <vector>

#include "caffe_batch_classifier.h"

BatchClassifier::BatchClassifier(
                       const string& model_file,
                       const string& trained_file,
                       const string& mean_file,
                       const string& label_file,
                       const bool use_GPU,
                       const int batch_size) {
   if (use_GPU) {
       Caffe::set_mode(Caffe::GPU);
   }
   else {
       Caffe::set_mode(Caffe::CPU);
   }

  /* Set batchsize */
  batch_size_ = batch_size;

  /* 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));
  num_classes_ = labels_.size();

  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;
}


std::vector<std::vector<Prediction>> BatchClassifier::Classify(const std::vector<cv::Mat> imgs, size_t top_n) {
    std::vector<float> output_batch = Predict(imgs);
    std::vector<std::vector<Prediction>> predictions;
    top_n = std::min<size_t>(num_classes_, top_n);
    for(size_t j = 0; j < imgs.size(); j++){
        std::vector<float> output(output_batch.begin()+j*num_classes_, output_batch.begin()+(j+1)*num_classes_);
        std::vector<int> maxN = Argmax(output, num_classes_);
        std::vector<Prediction> prediction_single;
        for (size_t i = 0; i < top_n; ++i) {
          int idx = maxN[i];
          prediction_single.push_back(std::make_pair(labels_[idx], output[idx]));
        }
        predictions.push_back(std::vector<Prediction>(prediction_single));
    }
    return predictions;
}

/* Load the mean file in binaryproto format. */
void BatchClassifier::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> BatchClassifier::Predict(const std::vector<cv::Mat> imgs) {
  Blob<float>* input_layer = net_->input_blobs()[0];

  input_layer->Reshape(batch_size_, num_channels_,
                       input_geometry_.height, input_geometry_.width);

  /* Forward dimension change to all layers. */
  net_->Reshape();

  std::vector<std::vector<cv::Mat>> input_batch;
  WrapInputLayer(&input_batch);

  Preprocess(imgs, &input_batch);

  net_->ForwardPrefilled();

  /* 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()*imgs.size();
  return std::vector<float>(begin, end);
}


void BatchClassifier::WrapInputLayer(std::vector<std::vector<cv::Mat>> *input_batch) {
    Blob<float>* input_layer = net_->input_blobs()[0];

    int width = input_layer->width();
    int height = input_layer->height();
    int num = input_layer->num();
    float* input_data = input_layer->mutable_cpu_data();
    for (int j = 0; j < num; j++) {
        vector<cv::Mat> input_channels;
        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;
        }
        input_batch -> push_back(vector<cv::Mat>(input_channels));
    }
    //cv::imshow("bla", input_batch->at(1).at(0));
    //cv::waitKey(1);
}


void BatchClassifier::Preprocess(const std::vector<cv::Mat> imgs,
                                 std::vector<std::vector<cv::Mat>>* input_batch) {
    for (int i = 0 ; i < imgs.size(); i++) {
        cv::Mat img = imgs[i];
        std::vector<cv::Mat> *input_channels = &(input_batch->at(i));

        /* 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_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;

        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.";
    }
}