model_analysis.markdown



Training data generation using Icosphere {#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
You can download the code from here .
@include cnn_3dobj/samples/demo_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