Update calculate_center_bounding_box.py

changed this node to canny image cropper node

calculate_center_bounding_box.py

@@ -1,72 +1,53 @@
-import comfy.utils
-import numpy as np
 import torch
 
-class MaskBoundingBox:
+class CannyImageCropper:
+    def __init__(self):
+        pass
+
     @classmethod
-    def INPUT_TYPES(s):
+    def INPUT_TYPES(cls):
         return {
             "required": {
-                "mask": ("MASK",),
-                "padding": ("INT", { "default": 0, "min": 0, "max": 4096, "step": 1, }),
-                "blur": ("INT", { "default": 0, "min": 0, "max": 256, "step": 1, }),
+                "canny_image": ("IMAGE",),  # Canny image input
+                "x": ("INT",),  # x-coordinate of the bounding box
+                "y": ("INT",),  # y-coordinate of the bounding box
+                "width": ("INT",),  # Width of the bounding box
+                "height": ("INT",),  # Height of the bounding box
+                "padding": ("INT", { "default": 5, "min": 0, "max": 100, "step": 1 }),  # Padding to add to the bounding box
             },
-            "optional": {
-                "image_optional": ("IMAGE",),
-            }
         }
 
-    RETURN_TYPES = ("MASK", "IMAGE", "INT", "INT", "INT", "INT", "INT", "INT")
-    RETURN_NAMES = ("MASK", "IMAGE", "x", "y", "width", "height", "center_x", "center_y")
-    FUNCTION = "execute"
-    CATEGORY = "REMADE/mask"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("cropped_canny_image",)
+    FUNCTION = "crop_canny_image"
 
-    def execute(self, mask, padding, blur, image_optional=None):
-        if mask.dim() == 2:
-            mask = mask.unsqueeze(0)
+    CATEGORY = "REMADE/Image"
 
-        if image_optional is None:
-            image_optional = mask.unsqueeze(3).repeat(1, 1, 1, 3)
+    def crop_canny_image(self, canny_image, x, y, width, height, padding):
+        # Apply padding to the bounding box to expand the cropping area
+        x -= padding
+        y -= padding
+        width += 2 * padding
+        height += 2 * padding
 
-        # Resize the image if it's not the same size as the mask
-        if image_optional.shape[1:] != mask.shape[1:]:
-            image_optional = comfy.utils.common_upscale(image_optional.permute([0, 3, 1, 2]), mask.shape[2], mask.shape[1], upscale_method='bicubic', crop='center').permute([0, 2, 3, 1])
+        # Ensure that the crop area doesn't exceed the image boundaries
+        x = max(0, x)
+        y = max(0, y)
+        x2 = min(canny_image.shape[2], x + width)
+        y2 = min(canny_image.shape[1], y + height)
 
-        # Match batch size
-        if image_optional.shape[0] < mask.shape[0]:
-            image_optional = torch.cat((image_optional, image_optional[-1].unsqueeze(0).repeat(mask.shape[0] - image_optional.shape[0], 1, 1, 1)), dim=0)
-        elif image_optional.shape[0] > mask.shape[0]:
-            image_optional = image_optional[:mask.shape[0]]
+        # Crop the canny image based on the expanded bounding box coordinates
+        cropped_canny_image = canny_image[:, y:y2, x:x2, :]
 
-        # Blur the mask
-        if blur > 0:
-            if blur % 2 == 0:
-                blur += 1
-            mask = T.functional.gaussian_blur(mask.unsqueeze(1), blur).squeeze(1)
-
-        # Calculate bounding box coordinates
-        _, y, x = torch.where(mask)
-        x1 = max(0, x.min().item() - padding)
-        x2 = min(mask.shape[2], x.max().item() + 1 + padding)
-        y1 = max(0, y.min().item() - padding)
-        y2 = min(mask.shape[1], y.max().item() + 1 + padding)
-
-        # Crop the mask and image to the bounding box
-        mask = mask[:, y1:y2, x1:x2]
-        image_optional = image_optional[:, y1:y2, x1:x2, :]
-
-        # Calculate the center coordinates
-        center_x = x1 + (x2 - x1) // 2
-        center_y = y1 + (y2 - y1) // 2
-
-        return (mask, image_optional, x1, y1, x2 - x1, y2 - y1, center_x, center_y)
+        # Return the cropped canny image
+        return (cropped_canny_image,)
 
 # A dictionary that contains all nodes you want to export with their names
 NODE_CLASS_MAPPINGS = {
-    "MaskBoundingBox": MaskBoundingBox
+    "CannyImageCropper": CannyImageCropper
 }
 
 # A dictionary that contains the friendly/human-readable titles for the nodes
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "MaskBoundingBox": "Mask Bounding Box with Center"
+    "CannyImageCropper": "Canny Image Cropper"
 }