First notebook fixes

utils_cv/tracking/dataset.py

@@ -172,7 +172,7 @@ def _write_fairMOT_format(self) -> None:
         self.fairmot_imlist_path = osp.join(
             self.root, "{}.train".format(self.name)
         )
-        with open(self.fairmot_imlist_path, "a") as f:
+        with open(self.fairmot_imlist_path, "w") as f:
             for im_filename in sorted(self.im_filenames):
                 f.write(osp.join(self.im_dir, im_filename) + "\n")
 

utils_cv/tracking/model.py

@@ -43,7 +43,7 @@ def _get_gpu_str():
 
 def _get_frame(input_video: str, frame_id: int):
     video = cv2.VideoCapture()
-    video.open(input_video)    
+    video.open(input_video)
     video.set(cv2.CAP_PROP_POS_FRAMES, frame_id)
     _, im = video.read()
     im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
@@ -178,7 +178,7 @@ def fit(
 
         Raise:
             Exception if dataset is undefined
-        
+
         Implementation inspired from code found here: https://github.com/ifzhang/FairMOT/blob/master/src/train.py
         """
         if not self.dataset:
@@ -227,7 +227,7 @@ def fit(
                     print(f"{k}: {v}")
             if epoch in opt_fit.lr_step:
                 lr = opt_fit.lr * (0.1 ** (opt_fit.lr_step.index(epoch) + 1))
-                for param_group in optimizer.param_groups:
+                for param_group in self.optimizer.param_groups:
                     param_group["lr"] = lr
 
             # store losses in each epoch
@@ -237,11 +237,11 @@ def fit(
 
     def plot_training_losses(self, figsize: Tuple[int, int] = (10, 5)) -> None:
         """
-        Plot training loss.  
-        
+        Plot training loss.
+
         Args:
             figsize (optional): width and height wanted for figure of training-loss plot
-        
+
         """
         fig = plt.figure(figsize=figsize)
         ax1 = fig.add_subplot(1, 1, 1)
@@ -274,15 +274,15 @@ def evaluate(
         self, results: Dict[int, List[TrackingBbox]], gt_root_path: str
     ) -> str:
 
-        """ 
+        """
         Evaluate performance wrt MOTA, MOTP, track quality measures, global ID measures, and more,
         as computed by py-motmetrics on a single experiment. By default, use 'single_vid' as exp_name.
 
         Args:
-            results: prediction results from predict() function, i.e. Dict[int, List[TrackingBbox]] 
+            results: prediction results from predict() function, i.e. Dict[int, List[TrackingBbox]]
             gt_root_path: path of dataset containing GT annotations in MOTchallenge format (xywh)
         Returns:
-            strsummary: str output by method in 'motmetrics' package, containing metrics scores        
+            strsummary: str output by method in 'motmetrics' package, containing metrics scores
         """
 
         # Implementation inspired from code found here: https://github.com/ifzhang/FairMOT/blob/master/src/track.py
@@ -371,7 +371,7 @@ def predict(
 
         Args:
             im_or_video_path: path to image(s) or video. Supports jpg, jpeg, png, tif formats for images.
-                Supports mp4, avi formats for video. 
+                Supports mp4, avi formats for video.
             conf_thres: confidence thresh for tracking
             det_thres: confidence thresh for detection
             nms_thres: iou thresh for nms

utils_cv/tracking/plot.py

@@ -17,24 +17,24 @@
 
 
 def plot_single_frame(
-    results: Dict[int, List[TrackingBbox]], input_video: str, frame_id: int
+    input_video: str,
+    frame_id: int,
+    results: Dict[int, List[TrackingBbox]] = None
 ) -> None:
-    """ 
-    Plot the bounding box and id on a wanted frame. Display as image to front end. 
+    """
+    Plot the bounding box and id on a wanted frame. Display as image to front end.
 
     Args:
-        results: dictionary mapping frame id to a list of predicted TrackingBboxes
         input_video: path to the input video
         frame_id: frame_id for frame to show tracking result
+        results: dictionary mapping frame id to a list of predicted TrackingBboxes
     """
 
-    if results is None:  # if no tracking bboxes, only plot image
-        # Get frame from video
-        im = Image.fromarray(_get_frame(input_video, frame_id))
-        # Display image
-        IPython.display.display(im)
+    # Extract frame
+    im = _get_frame(input_video, frame_id)
 
-    else:
+    # Overlay results
+    if results:
         results = OrderedDict(sorted(results.items()))
 
         # Assign bbox color per id
@@ -43,27 +43,26 @@ def plot_single_frame(
         )
         color_map = assign_colors(unique_ids)
 
-        # Get frame from video
-        im = _get_frame(input_video, frame_id)
-
         # Extract tracking results for wanted frame, and draw bboxes+tracking id, display frame
         cur_tracks = results[frame_id]
 
         if len(cur_tracks) > 0:
             im = draw_boxes(im, cur_tracks, color_map)
-        im = Image.fromarray(im)
-        IPython.display.display(im)
+
+    # Display image
+    im = Image.fromarray(im)
+    IPython.display.display(im)
 
 
 def play_video(
     results: Dict[int, List[TrackingBbox]], input_video: str
 ) -> None:
-    """ 
+    """
      Plot the predicted tracks on the input video. Displays to front-end as sequence of images stringed together in a video.
 
     Args:
         results: dictionary mapping frame id to a list of predicted TrackingBboxes
-        input_video: path to the input video        
+        input_video: path to the input video
     """
 
     results = OrderedDict(sorted(results.items()))
@@ -98,7 +97,7 @@ def play_video(
 def write_video(
     results: Dict[int, List[TrackingBbox]], input_video: str, output_video: str
 ) -> None:
-    """ 
+    """
     Plot the predicted tracks on the input video. Write the output to {output_path}.
 
     Args:
@@ -143,7 +142,7 @@ def draw_boxes(
     cur_tracks: List[TrackingBbox],
     color_map: Dict[int, Tuple[int, int, int]],
 ) -> np.ndarray:
-    """ 
+    """
     Overlay bbox and id labels onto the frame
 
     Args:
@@ -181,11 +180,11 @@ def draw_boxes(
 
 
 def assign_colors(id_list: List[int],) -> Dict[int, Tuple[int, int, int]]:
-    """ 
+    """
     Produce corresponding unique color palettes for unique ids
-    
+
     Args:
-        id_list: list of track ids 
+        id_list: list of track ids
     """
     palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1)