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feature_solution.py

@@ -616,13 +616,13 @@ def predict(entry, visualize=False) -> Tuple[np.ndarray, List[int]]:
         mkpts_filtered_1 = mkpts_filtered_1[filtered_index]
 
 
-        save_image_with_keypoints(f'keypoints_{i}.png', np.array(good_entry['gestalt'][i]), mkpts_filtered_0, (255, 0, 0))
-        save_image_with_keypoints(f'keypoints_{j}.png', np.array(good_entry['gestalt'][j]), mkpts_filtered_1, (255, 0, 0))
+        # save_image_with_keypoints(f'keypoints_{i}.png', np.array(good_entry['gestalt'][i]), mkpts_filtered_0, (255, 0, 0))
+        # save_image_with_keypoints(f'keypoints_{j}.png', np.array(good_entry['gestalt'][j]), mkpts_filtered_1, (255, 0, 0))
 
         # Line matching
         line_0, line_1 = line_matcher(good_entry['gestalt'][i], good_entry['gestalt'][j], good_entry['depthcm'][i]) 
-        save_image_with_lines(f'line_{i}.png', np.array(good_entry['gestalt'][i]), line_0, (255, 0, 0))
-        save_image_with_lines(f'line_{j}.png', np.array(good_entry['gestalt'][j]), line_1, (255, 0, 0))
+        # save_image_with_lines(f'line_{i}.png', np.array(good_entry['gestalt'][i]), line_0, (255, 0, 0))
+        # save_image_with_lines(f'line_{j}.png', np.array(good_entry['gestalt'][j]), line_1, (255, 0, 0))
 
 
         # Triangulation with matched keypoints