title: OpenCV SVM Classifier not working across platform
body: <p>Using Python / OpenCV SVM + Local Binary Pattern (uniform into 59 bins) to train images into classes of -1 (class #1) with ~600 dataset and 1 (class #2) with ~250 dataset. There is also a testing/prediction function to test images on saved model file.</p>
  <p>On the C++ side, I only have a testing interface which loads the above created model, calculates the LBP (uniform) of the image and runs prediction.</p>
  <p>The above code was completed and tested to work uniformly across both languages/platforms. Then I re-trained the model with more data to improve model accuracy and re-ran tests in Python, which worked as expected. With the (updated) model in C++, all test data is being predicted to one class (class #1) while the same test work as expected in Python. I have cross checked that the LBP function is still returning same values in Python and C++.</p>
  <p>This is how the SVM file is available <a href="https://drive.google.com/file/d/1-Y-yF2Ht_XAnhuUD8oXeQYHasNfyEzEz/view?usp=sharing" rel="nofollow noreferrer">here</a></p>
  <p><strong>Data passed to predict</strong></p>
  <p>Python ==&gt; 1 is returned</p>
  <pre><code>hist32=[[145. 0. 3. 4. 1. 1. 7. 3. 3. 14. 26. 0. 4. 12. 16. 9. 2. 3. 8. 24. 14. 3. 1. 5. 11. 13. 2. 1. 0. 3. 0. 9. 39. 9. 0. 1. 4. 14. 17. 3. 0. 0. 9. 32. 7. 1. 1. 25. 3. 0. 0. 10. 0. 0. 0. 1. 0. 0. 47.]] 
  </code></pre>
  <p>C++ ==&gt; -1 is returned (should be +1)</p>
  <pre><code>[2.0318828e-43, 0, 4.2038954e-45, 5.6051939e-45, 1.4012985e-45, 1.4012985e-45, 9.8090893e-45, 4.2038954e-45, 4.2038954e-45, 1.9618179e-44, 3.643376e-44, 0, 5.6051939e-45, 1.6815582e-44, 2.2420775e-44, 1.2611686e-44, 2.8025969e-45, 4.2038954e-45, 1.1210388e-44, 3.3631163e-44, 1.9618179e-44, 4.2038954e-45, 1.4012985e-45, 7.0064923e-45, 1.5414283e-44, 1.821688e-44, 2.8025969e-45, 1.4012985e-45, 0, 4.2038954e-45, 0, 1.2611686e-44, 5.465064e-44, 1.2611686e-44, 0, 1.4012985e-45, 5.6051939e-45, 1.9618179e-44, 2.3822074e-44, 4.2038954e-45, 0, 0, 1.2611686e-44, 4.4841551e-44, 9.8090893e-45, 1.4012985e-45, 1.4012985e-45, 3.5032462e-44, 4.2038954e-45, 0, 0, 1.4012985e-44, 0, 0, 0, 1.4012985e-45, 0, 0, 6.5861028e-44]
  </code></pre>