Bbox smoothing stuff

nodes.py

@@ -1282,6 +1282,7 @@ class BatchCropFromMask:
                 "original_images": ("IMAGE",),
                 "masks": ("MASK",),
                 "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
             },
         }
 
@@ -1302,12 +1303,48 @@ class BatchCropFromMask:
     FUNCTION = "crop"
     CATEGORY = "KJNodes/masking"
 
-    def crop(self, masks, original_images, crop_size_mult):
+    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
+            """
+            Smooth the bounding box size using exponential smoothing.
+
+            Args:
+                prev_bbox_size (int): The bounding box size of the previous frame.
+                curr_bbox_size (int): The bounding box size of the current frame.
+                alpha (float): The smoothing factor, between 0 and 1. 
+                            A larger alpha places more weight on the current frame's size.
+
+            Returns:
+                int: The smoothed bounding box size.
+            """
+            return int(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
+    def smooth_center(self, prev_center, curr_center, alpha=0.5):
+        """
+        Smooth the center coordinates using exponential smoothing.
+
+        Args:
+            prev_center (tuple): The center coordinates of the previous frame.
+            curr_center (tuple): The center coordinates of the current frame.
+            alpha (float): The smoothing factor, between 0 and 1. 
+                        A larger alpha places more weight on the current frame's center.
+
+        Returns:
+            tuple: The smoothed center coordinates.
+        """
+        return (int(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
+                int(alpha * curr_center[1] + (1 - alpha) * prev_center[1]))
+
+    def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
+
+        
         bounding_boxes = []
         cropped_images = []
 
+       # Initialize max_bbox_size for the first frame
+        if not hasattr(self, 'max_bbox_size'):
+            self.max_bbox_size = 0
+
         # First, calculate the maximum bounding box size across all masks
-        max_bbox_size = 0
+        curr_max_bbox_size = 0
         for mask in masks:
             _mask = tensor2pil(mask)[0]
             non_zero_indices = np.nonzero(np.array(_mask))
@@ -1316,31 +1353,49 @@ class BatchCropFromMask:
             width = max_x - min_x
             height = max_y - min_y
             bbox_size = max(width, height)
-            max_bbox_size = max(max_bbox_size, bbox_size)
+            curr_max_bbox_size = max(curr_max_bbox_size, bbox_size)
 
-        # Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is
-        max_bbox_size = math.ceil(max_bbox_size / 32) * 32
+        # Smooth the changes in the bounding box size
+        self.max_bbox_size = self.smooth_bbox_size(self.max_bbox_size, curr_max_bbox_size, bbox_smooth_alpha)
 
         # Apply the crop size multiplier
-        max_bbox_size = int(max_bbox_size * crop_size_mult)
+        self.max_bbox_size = int(self.max_bbox_size * crop_size_mult)
+
+        # Make sure max_bbox_size is divisible by 32, if not, round it upwards so it is
+        self.max_bbox_size = math.ceil(self.max_bbox_size / 32) * 32
 
         # Then, for each mask and corresponding image...
-        for mask, img in zip(masks, original_images):
+        for i, (mask, img) in enumerate(zip(masks, original_images)):
             _mask = tensor2pil(mask)[0]
             non_zero_indices = np.nonzero(np.array(_mask))
             min_x, max_x = np.min(non_zero_indices[1]), np.max(non_zero_indices[1])
             min_y, max_y = np.min(non_zero_indices[0]), np.max(non_zero_indices[0])
 
             # Calculate center of bounding box
-            center_x = (max_x + min_x) // 2
-            center_y = (max_y + min_y) // 2
+            center_x = np.mean(non_zero_indices[1])
+            center_y = np.mean(non_zero_indices[0])
+            curr_center = (int(center_x), int(center_y))
+
+            # If this is the first frame, initialize prev_center with curr_center
+            if not hasattr(self, 'prev_center'):
+                self.prev_center = curr_center
+
+            # Smooth the changes in the center coordinates from the second frame onwards
+            if i > 0:
+                center = self.smooth_center(self.prev_center, curr_center, bbox_smooth_alpha)
+            else:
+                center = curr_center
+
+            # Update prev_center for the next frame
+            self.prev_center = center
 
             # Create bounding box using max_bbox_size
-            half_box_size = max_bbox_size // 2
-            min_x = max(0, center_x - half_box_size)
-            max_x = min(img.shape[1], center_x + half_box_size)
-            min_y = max(0, center_y - half_box_size)
-            max_y = min(img.shape[0], center_y + half_box_size)
+            half_box_size = self.max_bbox_size // 2
+            half_box_size = self.max_bbox_size // 2
+            min_x = max(0, center[0] - half_box_size)
+            max_x = min(img.shape[1], center[0] + half_box_size)
+            min_y = max(0, center[1] - half_box_size)
+            max_y = min(img.shape[0], center[1] + half_box_size)
 
             # Append bounding box coordinates
             bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))
@@ -1351,7 +1406,7 @@ class BatchCropFromMask:
 
         cropped_out = torch.stack(cropped_images, dim=0)
 
-        return (original_images, cropped_out, bounding_boxes, max_bbox_size, max_bbox_size, )
+        return (original_images, cropped_out, bounding_boxes, self.max_bbox_size, self.max_bbox_size, )
 
 
 def bbox_to_region(bbox, target_size=None):