OpenCV/opencv_contrib/modules/cnn_3dobj/tutorials/feature_classification/classify.markdown

Classify {#tutorial_feature_classification}

Goal

In this tutorial you will learn how to

• How to extract feature from an image
• How to extract features from images under a given root path
• How to make a prediction using reference images and target image

Code

@include cnn_3dobj/samples/classify.cpp

Explanation

Here is the general structure of the program:

• Initialize a net work with Device. @code{.cpp} cv::cnn_3dobj::descriptorExtractor descriptor(device); @endcode

• Load net with the caffe trained net work parameter and structure. @code{.cpp} if (strcmp(mean_file.c_str(), "no") == 0)

  descriptor.loadNet(network_forIMG, caffemodel);
  

  else

  descriptor.loadNet(network_forIMG, caffemodel, mean_file);
  

  @endcode

• List the file names under a given path. @code{.cpp} listDir(src_dir.c_str(), name_gallery, false); for (unsigned int i = 0; i < name_gallery.size(); i++) {

  name_gallery[i] = src_dir + name_gallery[i];
  

  } @endcode

• Extract feature from a set of images. @code{.cpp} descriptor.extract(img_gallery, feature_reference, feature_blob); @endcode

• Initialize a matcher which using L2 distance. @code{.cpp} cv::BFMatcher matcher(NORM_L2); std::vectorstd::vector<cv::DMatch > matches; @endcode

• Have a KNN match on the target and reference images. @code{.cpp} matcher.knnMatch(feature_test, feature_reference, matches, num_candidate); @endcode

• Print features of the reference images. @code{.cpp}std::cout << std::endl << "---------- Features of target image: " << target_img << "----------" << endl << feature_test << std::endl; @endcode

  Results