Crop/Uncrop fixes

nodes.py

@@ -1272,6 +1272,7 @@ class ImageBatchTestPattern:
 #based on nodes from mtb https://github.com/melMass/comfy_mtb
 
 from .utility import tensor2pil, pil2tensor, tensor2np, np2tensor
+from torchvision.transforms import Resize
 
 class BatchCropFromMask:
 
@@ -1304,38 +1305,14 @@ class BatchCropFromMask:
     CATEGORY = "KJNodes/masking"
 
     def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
-            """
-            Smooth the bounding box size using exponential smoothing.
+          return int(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)
 
-            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 = []
 
@@ -1402,9 +1379,15 @@ class BatchCropFromMask:
 
             # Crop the image from the bounding box
             cropped_img = img[min_y:max_y, min_x:max_x, :]
-            cropped_images.append(cropped_img)
+            
+            # Resize the cropped image to a fixed size
+            resize_transform = Resize((self.max_bbox_size, self.max_bbox_size))
+            resized_img = resize_transform(cropped_img.permute(2, 0, 1)).permute(1, 2, 0)
+            
+            cropped_images.append(resized_img)
 
         cropped_out = torch.stack(cropped_images, dim=0)
+        
 
         return (original_images, cropped_out, bounding_boxes, self.max_bbox_size, self.max_bbox_size, )
 
@@ -1434,10 +1417,8 @@ class BatchUncrop:
                 "original_images": ("IMAGE",),
                 "cropped_images": ("IMAGE",),
                 "bboxes": ("BBOX",),
-                "border_blending": (
-                    "FLOAT",
-                    {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01},
-                ),
+                "border_blending": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01}, ),
+                "crop_rescale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
             }
         }
 
@@ -1446,7 +1427,7 @@ class BatchUncrop:
 
     CATEGORY = "KJNodes/masking"
 
-    def uncrop(self, original_images, cropped_images, bboxes, border_blending):
+    def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale):
         def inset_border(image, border_width=20, border_color=(0)):
             width, height = image.size
             bordered_image = Image.new(image.mode, (width, height), border_color)
@@ -1462,6 +1443,7 @@ class BatchUncrop:
 
         input_images = tensor2pil(original_images)
         crop_imgs = tensor2pil(cropped_images)
+        
         out_images = []
         for i in range(len(input_images)):
             img = input_images[i]
@@ -1472,9 +1454,18 @@ class BatchUncrop:
             bb_x, bb_y, bb_width, bb_height = bbox
 
             paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
+            
+            # scale factors
+            scale_x = crop_rescale
+            scale_y = crop_rescale
 
+            # scaled paste_region
+            paste_region = (int(paste_region[0]*scale_x), int(paste_region[1]*scale_y), int(paste_region[2]*scale_x), int(paste_region[3]*scale_y))
+
+            # rescale the crop image to fit the paste_region
+            crop = crop.resize((int(paste_region[2]-paste_region[0]), int(paste_region[3]-paste_region[1])))
             crop_img = crop.convert("RGB")
-
+   
             if border_blending > 1.0:
                 border_blending = 1.0
             elif border_blending < 0.0:
@@ -1485,9 +1476,9 @@ class BatchUncrop:
             blend = img.convert("RGBA")
             mask = Image.new("L", img.size, 0)
 
-            mask_block = Image.new("L", (bb_width, bb_height), 255)
+            mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
             mask_block = inset_border(mask_block, int(blend_ratio / 2), (0))
-
+                      
             mask.paste(mask_block, paste_region)
             blend.paste(crop_img, paste_region)