1.7 KiB
Classify
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:
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Initialize a net work with Device. @code{.cpp} cv::cnn_3dobj::descriptorExtractor descriptor(device); @endcode
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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
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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
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Extract feature from a set of images. @code{.cpp} descriptor.extract(img_gallery, feature_reference, feature_blob); @endcode
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Initialize a matcher which using L2 distance. @code{.cpp} cv::BFMatcher matcher(NORM_L2); std::vector<std::vectorcv::DMatch > matches; @endcode
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Have a KNN match on the target and reference images. @code{.cpp} matcher.knnMatch(feature_test, feature_reference, matches, num_candidate); @endcode
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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