test

app.py

@@ -134,7 +134,7 @@ def crop_animal_detections(yolo_results,
             pred_label = det_array[j,5]
 
             # keep animal crops above threshold
-            pdb.set_trace()
+            #pdb.set_trace()
             if (pred_label == list_labels_as_str.index('animal')) and \
                 (pred_llk >= likelihood_th):
                 area = (xmin_rd, ymin_rd, xmax_rd, ymax_rd)
@@ -164,7 +164,7 @@ def predict_dlc(list_np_crops,
         keypts_xyp = dlc_live.get_pose(crop) # third column is llk!
         # set kpts below threhsold to nan
         print(keypts_xyp)
-        pdb.set_trace()
+        #pdb.set_trace()
         keypts_xyp[keypts_xyp[:,-1] < kpts_likelihood_th,:] = np_aux.fill(np.nan)
         # add kpts of this crop to list 
         list_kpts_per_crop.append(keypts_xyp)
@@ -205,7 +205,7 @@ def predict_pipeline(img_input,
     md_results = predict_md(img_input, 
                             mega_model_input,
                             size=640) #Image.fromarray(results.imgs[0])
-
+    pdb.set_trace()
     ################################################################
     # Obtain animal crops for bboxes with confidence above th