/**
* Evaluate the given image to see its probability in the given category.
*/
float _getProbability(const cv::Mat& image, const int category)
{
this->initCaffeNet(); //Initialize caffe
// Initialize test network
shared_ptr<Net<float> > caffe_test_net = shared_ptr<Net<float> >( new Net<float>(Params::image::model_definition));
// Get the trained model
caffe_test_net->CopyTrainedLayersFrom(Params::image::pretrained_model);
// Run ForwardPrefilled
float loss;
// Add images and labels manually to the ImageDataLayer
vector<int> labels(10, 0);
vector<cv::Mat> images;
// Add images to the list
if (Params::image::use_crops)
{
// Ten crops have been stored in the vector
_createTenCrops(image, images);
}
else
{
images.push_back(image);
}
// Classify images
const shared_ptr<ImageDataLayer<float> > image_data_layer =
boost::static_pointer_cast<ImageDataLayer<float> >(
caffe_test_net->layer_by_name("data"));
image_data_layer->AddImagesAndLabels(images, labels);
const vector<Blob<float>*>& result = caffe_test_net->ForwardPrefilled(&loss);
// Get the highest layer of Softmax
const float* softmax = result[1]->cpu_data();
// If use 10 crops, we have to average the predictions of 10 crops
if (Params::image::use_crops)
{
vector<double> values;
// Average the predictions of evaluating 10 crops
for(int i = 0; i < Params::image::num_categories; ++i)
{
boost::accumulators::accumulator_set<double, boost::accumulators::stats<boost::accumulators::tag::mean> > avg;
for(int j = 0; j < 10 * Params::image::num_categories; j += Params::image::num_categories)
{
avg(softmax[i + j]);
}
double mean = boost::accumulators::mean(avg);
values.push_back(mean);
}
return values[category];
}
// If use only 1 crop
else
{
return softmax[category];
}
}
