mask to png

notebooks/foo_points_prev.py

@@ -42,8 +42,10 @@ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
 # Variables for input and output directories
 SAM2_CHECKPOINT = "../checkpoints/sam2.1_hiera_large.pt"
 MODEL_CFG = "configs/sam2.1/sam2.1_hiera_l.yaml"
+GREEN = [0, 255, 0]
+BLUE = [255, 0, 0]
 
-INFERENCE_SCALE = 0.35
+INFERENCE_SCALE = 0.25
 FULL_SCALE = 1.0
 
 def open_video(video_path):
@@ -68,12 +70,21 @@ def open_video(video_path):
     cap.release()
 
 def load_previous_segment_mask(prev_segment_dir):
-    mask_path = os.path.join(prev_segment_dir,  "mask.jpg")
+    mask_path = os.path.join(prev_segment_dir,  "mask.png")
     mask_image = cv2.imread(mask_path)
 
+    if mask_image is None:
+        raise FileNotFoundError(f"Mask image not found at {mask_path}")
+
+    # Ensure the mask_image has three color channels
+    if len(mask_image.shape) != 3 or mask_image.shape[2] != 3:
+        raise ValueError("Mask image does not have three color channels.")
+
+    mask_image = mask_image.astype(np.uint8)
+    
     # Extract Object A and Object B masks
-    mask_a = (mask_image[:, :, 1] == 255)  # Green channel
+    mask_a = np.all(mask_image == GREEN, axis=2)
-    mask_b = (mask_image[:, :, 0] == 254)  # Blue channel
+    mask_b = np.all(mask_image == BLUE, axis=2)
 
     per_obj_input_mask = {1: mask_a, 2: mask_b}
     input_palette = None  # No palette needed for binary mask
@@ -110,6 +121,8 @@ def apply_green_mask(frame, masks):
                 interpolation=cv2.INTER_LINEAR
             )
             # Threshold the resized mask to obtain a boolean mask
+            # add a small gausian blur to the mask to smooth out the edges
+            resized_mask = cv2.GaussianBlur(resized_mask, (50, 50), 0)
 
             mask = resized_mask > 0.5
         else:
@@ -193,7 +206,7 @@ def select_points(first_frame):
                 points_a.append((x, y))
                 point_count += 1
                 print(f"Selected point {point_count} for Object A: ({x}, {y})")
-                if len(points_a) == 4:  # Collect 4 points for Object A
+                if len(points_a) == 5:  # Collect 4 points for Object A
                     current_object = 'B'
                     point_count = 0
                     print("Select point 1 for Object B")
@@ -201,7 +214,7 @@ def select_points(first_frame):
                 points_b.append((x, y))
                 point_count += 1
                 print(f"Selected point {point_count} for Object B: ({x}, {y})")
-                if len(points_b) == 4:  # Collect 4 points for Object B
+                if len(points_b) == 5:  # Collect 4 points for Object B
                     selection_complete = True
 
     print("Select point 1 for Object A")
@@ -217,7 +230,7 @@ def select_points(first_frame):
     return np.array(points_a, dtype=np.float32), np.array(points_b, dtype=np.float32)
 
 def add_points_to_predictor(predictor, inference_state, points, obj_id):
-    labels = np.array([1, 1, 1, 1], np.int32)  # Update labels to match 4 points
+    labels = np.array([1, 1, 1, 1, 1], np.int32)  # Update labels to match 4 points
     points = np.array(points, dtype=np.float32)  # Ensure points have shape (4, 2)
     try:
         print(f"Adding points for Object {obj_id}: {points}")
@@ -376,11 +389,11 @@ def save_final_masks(video_segments, mask_output_path):
     # mask a
     mask_a = mask_a.squeeze()
     indices = np.where(mask_a)
-    black_frame[mask_a] = [0, 255, 0]  # Green for Object A
+    black_frame[mask_a] = GREEN
     # mask b
     mask_b = mask_b.squeeze()
     indices = np.where(mask_b)
-    black_frame[mask_b] = [255, 0, 0]  # Green for Object B
+    black_frame[mask_b] = BLUE
 
     # Save the mask image
     cv2.imwrite(mask_output_path, black_frame)
@@ -410,11 +423,12 @@ def create_low_res_video(input_video_path, output_video_path, scale):
 
 def main():
     parser = argparse.ArgumentParser(description="Process video segments.")
+    # arg for setting base_dir
+    parser.add_argument("--base-dir", type=str, help="Base directory for video segments.")
     parser.add_argument("--segments-collect-points", nargs='+', type=int, help="Segments for which to collect points.")
     args = parser.parse_args()
 
-    base_dir = "./freya_short_segments"
+    base_dir = args.base_dir
-    #base_dir = "./606-short_segments"
     segments = [d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d)) and d.startswith("segment_")]
     segments.sort(key=lambda x: int(x.split("_")[1]))
     scaled_frames_dir_name = "frames_scaled"
@@ -455,8 +469,8 @@ def main():
         if os.path.exists(points_file):
             logger.info(f"Using segment_points for segment {segment}")
             points = np.loadtxt(points_file, comments="#")
-            points_a = points[:4]
+            points_a = points[:5]
-            points_b = points[4:]
+            points_b = points[5:]
         else:
             points_a = points_b = None
 
@@ -466,7 +480,7 @@ def main():
             # Try to load previous segment mask
             logger.info(f"Using previous segment mask for segment {segment}")
             prev_segment_dir = os.path.join(base_dir, segments[i - 1])
-            prev_mask_path = os.path.join(prev_segment_dir, "mask.jpg")
+            prev_mask_path = os.path.join(prev_segment_dir, "mask.png")
             if os.path.exists(prev_mask_path):
                 per_obj_input_mask, input_palette = load_previous_segment_mask(prev_segment_dir)
                 # Add previous masks to predictor
@@ -499,7 +513,7 @@ def main():
         )
 
         # Save final masks
-        mask_output_path = os.path.join(segment_dir, "mask.jpg")
+        mask_output_path = os.path.join(segment_dir, "mask.png")
         save_final_masks(video_segments, mask_output_path)
 
         # Clean up