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Training Model Analysis {#tutorial_model_analysis}
=============
Goal
----
In this tutorial you will learn how to
- Extract feature from particular image.
- Have a meaningful comparation on the extracted feature.
Code
----
@include cnn_3dobj/samples/model_analysis.cpp
Explanation
-----------
Here is the general structure of the program:
- Sample which is most closest in pose to reference image and also the same class.
@code{.cpp}
ref_img.push_back(ref_img1);
@endcode
- Sample which is less closest in pose to reference image and also the same class.
@code{.cpp}
ref_img.push_back(ref_img2);
@endcode
- Sample which is very close in pose to reference image but not the same class.
@code{.cpp}
ref_img.push_back(ref_img3);
@endcode
- Initialize a net work with Device.
@code{.cpp}
cv::cnn_3dobj::descriptorExtractor descriptor(device, dev_id);
@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
- Have comparations on the distance between reference image and 3 other images
distance between closest sample and reference image should be smallest and
distance between sample in another class and reference image should be largest.
@code{.cpp}
if (matches[0] < matches[1] && matches[0] < matches[2])
pose_pass = true;
if (matches[1] < matches[2])
class_pass = true;
@endcode
Results
-------
|
