Travel distance

aris.py

@@ -341,6 +341,7 @@ def add_metadata_to_result(aris_fp, json_data, beam_width_dir=BEAM_WIDTH_DIR):
         }
 
     def get_entry(fish):
+        print(fish)
         if 'marking_frame' in fish:
             frame_num = fish['marking_frame']
             entry = None
@@ -364,10 +365,14 @@ def add_metadata_to_result(aris_fp, json_data, beam_width_dir=BEAM_WIDTH_DIR):
         return None # TODO better error handling
     
     entries = []
+    print(json_data['fish'])
     for fish in json_data['fish']:
         entry = get_entry(fish)
         entry['length'] = fish['length']*100
         entry['direction'] = fish['direction']
+        entry['travel_dist'] = fish['travel_dist']
+        entry['start_frame_index'] = fish['start_frame_index']
+        entry['end_frame_index'] = fish['end_frame_index']
         entries.append(entry)
     
     metadata["FISH"] = []
@@ -390,6 +395,9 @@ def add_metadata_to_result(aris_fp, json_data, beam_width_dir=BEAM_WIDTH_DIR):
         fish_entry['FILE'] = 1
         fish_entry['TOTAL'] = entry['fish_id']
         fish_entry['FRAME_NUM'] = entry['frame_num']
+        fish_entry['START_FRAME'] = entry['start_frame_index']
+        fish_entry['END_FRAME'] = entry['end_frame_index']
+        fish_entry['TRAVEL'] = entry['travel_dist']
         fish_entry['DIR'] = upstream_motion_map[entry['direction']]
         fish_entry['R'] = bin_num * pixel_meter_size + frame.windowstart
         fish_entry['THETA'] = beam_width_data['beam_center'][beam_num]

gradio_scripts/result_ui.py

@@ -14,9 +14,9 @@ js_update_tab_labels = """
     }
 """
 
-table_headers = ["TOTAL" , "FRAME_NUM", "DIR", "R", "THETA", "L", "TIME", "DATE", "SPECIES"]
+table_headers = ["TOTAL", "START_FRAME", "END_FRAME", "DIR", "R", "THETA", "L", "TRAVEL"]
 info_headers = [
-    "TOTAL_TIME", "DATE", "START", "END", "TOTAL_FRAMES", "FRAME_RATE", "",
+    "TOTAL_TIME", "DATE", "START", "END", "FRAME_RATE", "",
     "TOTAL_FISH", "UPSTREAM_FISH", "DOWNSTREAM_FISH", "NONDIRECTIONAL_FISH", "",
     "UPSTREAM_MOTION", "INTENSITY", "THRESHOLD", "WATER_TEMP", "",
 ]

inference.py

@@ -323,6 +323,7 @@ def do_tracking(all_preds, image_meter_width, image_meter_height, gp=None, max_a
             pbar.update(1)
 
     json_data = tracker.finalize(min_length=min_length)
+    print(json_data['fish'])
 
     return json_data
 

lib/fish_eye/tracker.py

@@ -20,6 +20,8 @@ class Tracker:
     def update(self, dets=np.empty((0, 5))):
         new_frame_entries = []
         for track in self.algorithm.update(dets):
+
+            # Match confidence with correct track
             conf = 0
             min_score = 1000000
             for det in dets:
@@ -27,6 +29,8 @@ class Tracker:
                 if (score < min_score):
                     min_score = score
                     conf = det[4]
+            
+            # Assign Track
             self.fish_ids[int(track[4])] += 1
             new_frame_entries.append({
                 'fish_id': int(track[4]),
@@ -96,6 +100,8 @@ class Tracker:
             start_bbox = boxes[0][0]
             end_bbox = boxes[-1][0]
             fish_entry['direction'] = Tracker.get_direction(start_bbox, end_bbox)
+
+            fish_entry['travel_dist'] = Tracker.get_travel_distance(start_bbox, end_bbox, json_data['image_meter_width'], json_data['image_meter_height'])
             
             fish_entry['start_frame_index'] = boxes[0][1]
             fish_entry['end_frame_index'] = boxes[-1][1]
@@ -103,7 +109,7 @@ class Tracker:
 
             json_data['fish'].append(fish_entry)
 
-        
+        print(json_data['fish'])
 
         # filter 'fish' field by fish length
         json_data = Fish_Length.add_lengths(json_data)
@@ -157,6 +163,14 @@ class Tracker:
         else:
             return 'none'
 
+    @staticmethod
+    def get_travel_distance(start_bbox, end_bbox, image_meter_width, image_meter_height):
+        dx = (start_bbox[2] + start_bbox[0])/2 - (end_bbox[2] + end_bbox[0])/2
+        dx *= image_meter_width
+        dy = (start_bbox[3] + start_bbox[1])/2 - (end_bbox[3] + end_bbox[1])/2
+        dy *= image_meter_height
+        return np.sqrt(dx*dx + dy*dy)
+
     @staticmethod
     def count_dirs(json_data):
         right = 0

main.py

@@ -47,8 +47,10 @@ def predict_task(filepath, hyperparams, gradio_progress=None):
     # run detection + tracking
     results = do_full_inference(dataloader, image_meter_width, image_meter_height, gp=gradio_progress, hyperparams=hyperparams)
 
+    print(results['fish'])
     # re-index results if desired - this should be done before writing the file
     results = prep_for_mm(results)
+    print(results['fish'])
     results = add_metadata_to_result(filepath, results)
     results['metadata']['hyperparameters'] = hyperparams