added frame transitions at 0.3 opacity

owl_core.py

@@ -76,21 +76,29 @@ def owl_full_video(
     frame_filenames = os.listdir(frames_dir)
 
     frame_paths = []  # list of frame paths to process based on fps_processed
+
+    # TESTING OUT FADING OUT THE ANNOTATED BOX BETWEEN FRAMES
+    annotation_guide = {}
+    last_frame_run = frame_filenames[0]
+
     # for every frame processed, add to frame_paths
     for i, frame in enumerate(frame_filenames):
+        path = os.path.join(frames_dir, frame)
         if i % fps_processed == 0:
-            frame_paths.append(os.path.join(frames_dir, frame))
+            last_frame_run = path
+            frame_paths.append(path)
+            annotation_guide[path] = [] # TESTING
+        else:
+            annotation_guide[last_frame_run].append(path) # TESTING
 
     # set up df for results
     df  = pd.DataFrame(columns=["frame", "boxes", "scores", "labels"])
 
-    # for positive detection frames whether the directory has been created
-    dir_created = False
-
     # run owl in batches
     for i in tqdm(range(0, len(frame_paths), batch_size), desc="Running batches"):
         frame_nums = [i*fps_processed for i in range(batch_size)]
         batch_paths = frame_paths[i:i+batch_size]  # paths for this batch
+        filenames = [os.path.basename(p) for p in batch_paths]
         images = [Image.open(image_path) for image_path  in batch_paths]
 
         # run owl on this batch of frames
@@ -125,13 +133,28 @@ def owl_full_video(
             if labels[j] is not None:
                 annotated_frame = plot_predictions(image, labels[j], scores, boxes)
                 cv2.imwrite(image, annotated_frame)
+
+    # annotate all other frames with no detections
+    for key in annotation_guide:
+        labels = df[df["frame"] == key]["labels"].tolist()[0]
+        boxes = df[df["frame"] == key]["boxes"].tolist()[0]
+        scores = df[df["frame"] == key]["scores"].tolist()[0]
+
+        print(labels)
+        # Flatten nested lists if necessary
+        if not labels: 
+            continue
+
+        for frame in annotation_guide[key]:
+            annotated_frame = plot_predictions(frame, labels, scores, boxes, opacity=0.3)
+            cv2.imwrite(frame, annotated_frame)
                 
     # save the df to a csv
     csv_path = f"{results_dir}/{filename}_{threshold}.csv"
     df.to_csv(csv_path, index=False)
 
     # stitch the frames into a video
-    save_path = vid_stitcher(frames_dir, output_path=os.path.join(results_dir, "output.mp4"))
+    save_path = vid_stitcher(frames_dir, output_path=os.path.join(results_dir, "output.mp4"), fps=fps)
 
     return csv_path, save_path
 

utils.py

@@ -14,6 +14,7 @@ def plot_predictions(
         labels: list[str],
         scores: list[float],
         boxes: list[float],
+        opacity: float = 1.0
     ) -> np.ndarray:
 
     image_source = cv2.imread(image)
@@ -34,9 +35,20 @@ def plot_predictions(
 
     bbox_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX, thickness=thickness)
     label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX, text_scale=text_scale, text_thickness=text_thickness)
-    annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
-    annotated_frame = bbox_annotator.annotate(scene=annotated_frame, detections=boxes)
-    annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=boxes, labels=labels)
+
+    # Create a semi-transparent overlay
+    overlay = image_source.copy()
+
+    # Apply bounding box annotations to the overlay
+    overlay = bbox_annotator.annotate(scene=overlay, detections=boxes)
+    overlay = label_annotator.annotate(scene=overlay, detections=boxes, labels=labels)
+
+    # annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
+    # annotated_frame = bbox_annotator.annotate(scene=annotated_frame, detections=boxes)
+    # annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=boxes, labels=labels)
+
+    # Blend overlay with original image using the specified opacity
+    annotated_frame = cv2.addWeighted(overlay, opacity, image_source, 1 - opacity, 0)
 
     return annotated_frame