/* Return the top N predictions. */
std::vector<Prediction> Deep_Classifier::Classify(const cv::Mat& img, int N) {
std::vector<float> output = Predict(img);
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;
}
/* Return the top N predictions. */
std::vector<Prediction> Classifier::Classify(const cv::Mat& img, int N) {
std::vector<float> output = Predict(img);
std::vector<int> maxN = Argmax(output, std::min((float)N, (float)output.size()));
std::vector<Prediction> predictions;
for (int i = 0; i < std::min((float)N, (float)output.size()); ++i) {
int idx = maxN[i];
//predictions.push_back(std::make_pair(labels_[idx], output[idx]));
predictions.push_back(std::make_pair(idx, output[idx]));
}
return predictions;
}
std::vector<Prediction> Classifier::classify( const cv::Mat &_img,
int N ) {
std::vector<float> output = this->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;
}
