Merge branch 'main' of https://github.com/kijai/ComfyUI-KJNodes

nodes.py

@@ -1872,8 +1872,13 @@ class BatchCropFromMaskAdvanced:
         
         def calculate_bbox(mask):
             non_zero_indices = np.nonzero(np.array(mask))
+
+            # handle empty masks
+            min_x, max_x, min_y, max_y = 0, 0, 0, 0
+            if len(non_zero_indices[1]) > 0 and len(non_zero_indices[0]) > 0:
                 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])
+
             width = max_x - min_x
             height = max_y - min_y
             bbox_size = max(width, height)
@@ -1903,10 +1908,17 @@ class BatchCropFromMaskAdvanced:
         # Make sure max_bbox_size is divisible by 16, if not, round it upwards so it is
         self.max_bbox_size = math.ceil(self.max_bbox_size / 16) * 16
 
+        if self.max_bbox_size > original_images[0].shape[0] or self.max_bbox_size > original_images[0].shape[1]:
+            # max_bbox_size can only be as big as our input's width or height, and it has to be even
+            self.max_bbox_size = math.floor(min(original_images[0].shape[0], original_images[0].shape[1]) / 2) * 2
+
         # Then, for each mask and corresponding image...
         for i, (mask, img) in enumerate(zip(masks, original_images)):
             _mask = tensor2pil(mask)[0]
             non_zero_indices = np.nonzero(np.array(_mask))
+
+            # check for empty masks
+            if len(non_zero_indices[0]) > 0 and len(non_zero_indices[1]) > 0:
                 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])
 
@@ -1930,7 +1942,6 @@ class BatchCropFromMaskAdvanced:
 
                 # Create bounding box using max_bbox_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)
@@ -1945,11 +1956,12 @@ class BatchCropFromMaskAdvanced:
 
                 # Resize the cropped image to a fixed size
                 new_size = max(cropped_img.shape[0], cropped_img.shape[1])
-            resize_transform = Resize(new_size, interpolation = InterpolationMode.NEAREST)
+                resize_transform = Resize(new_size, interpolation=InterpolationMode.NEAREST, max_size=max(img.shape[0], img.shape[1]))
                 resized_mask = resize_transform(cropped_mask.unsqueeze(0).unsqueeze(0)).squeeze(0).squeeze(0)
                 resized_img = resize_transform(cropped_img.permute(2, 0, 1))
                 # Perform the center crop to the desired size
-            crop_transform = CenterCrop((self.max_bbox_size, self.max_bbox_size))
+                # Constrain the crop to the smaller of our bbox or our image so we don't expand past the image dimensions.
+                crop_transform = CenterCrop((min(self.max_bbox_size, resized_img.shape[1]), min(self.max_bbox_size, resized_img.shape[2])))
 
                 cropped_resized_img = crop_transform(resized_img)
                 cropped_images.append(cropped_resized_img.permute(1, 2, 0))
@@ -1962,6 +1974,12 @@ class BatchCropFromMaskAdvanced:
 
                 combined_cropped_mask = masks[i][new_min_y:new_max_y, new_min_x:new_max_x]
                 combined_cropped_masks.append(combined_cropped_mask)
+            else:
+                bounding_boxes.append((0, 0, img.shape[1], img.shape[0]))
+                cropped_images.append(img)
+                cropped_masks.append(mask)
+                combined_cropped_images.append(img)
+                combined_cropped_masks.append(mask)
 
         cropped_out = torch.stack(cropped_images, dim=0)
         combined_crop_out = torch.stack(combined_cropped_images, dim=0)