BatchUncrop¶
Documentation¶
- Class name:
BatchUncrop - Category:
KJNodes/masking - Output node:
False
The BatchUncrop node is designed to reverse the cropping process on a batch of images. It takes original images and their cropped versions along with cropping metadata to reconstruct the original images with the cropped areas seamlessly integrated, adjusting for borders and scaling as necessary.
Input types¶
Required¶
original_images- Original images before cropping, used as a base for the uncropping process.
- Comfy dtype:
IMAGE - Python dtype:
List[torch.Tensor]
cropped_images- Cropped images that need to be integrated back into the original images.
- Comfy dtype:
IMAGE - Python dtype:
List[torch.Tensor]
bboxes- Bounding boxes specifying the cropped areas in the original images.
- Comfy dtype:
BBOX - Python dtype:
List[Tuple[int, int, int, int]]
border_blending- Controls the blending of the borders during the uncropping process to ensure a seamless integration.
- Comfy dtype:
FLOAT - Python dtype:
float
crop_rescale- Indicates whether the cropped images should be rescaled to their original size before uncropping.
- Comfy dtype:
FLOAT - Python dtype:
float
border_top- Specifies whether to apply a top border during the uncropping process.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
border_bottom- Specifies whether to apply a bottom border during the uncropping process.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
border_left- Specifies whether to apply a left border during the uncropping process.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
border_right- Specifies whether to apply a right border during the uncropping process.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
Output types¶
image- Comfy dtype:
IMAGE - The original images with the cropped areas seamlessly integrated.
- Python dtype:
List[torch.Tensor]
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
Source code¶
class BatchUncrop:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"original_images": ("IMAGE",),
"cropped_images": ("IMAGE",),
"bboxes": ("BBOX",),
"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}),
"border_top": ("BOOLEAN", {"default": True}),
"border_bottom": ("BOOLEAN", {"default": True}),
"border_left": ("BOOLEAN", {"default": True}),
"border_right": ("BOOLEAN", {"default": True}),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "uncrop"
CATEGORY = "KJNodes/masking"
def uncrop(self, original_images, cropped_images, bboxes, border_blending, crop_rescale, border_top, border_bottom, border_left, border_right):
def inset_border(image, border_width, border_color, border_top, border_bottom, border_left, border_right):
draw = ImageDraw.Draw(image)
width, height = image.size
if border_top:
draw.rectangle((0, 0, width, border_width), fill=border_color)
if border_bottom:
draw.rectangle((0, height - border_width, width, height), fill=border_color)
if border_left:
draw.rectangle((0, 0, border_width, height), fill=border_color)
if border_right:
draw.rectangle((width - border_width, 0, width, height), fill=border_color)
return image
if len(original_images) != len(cropped_images):
raise ValueError(f"The number of original_images ({len(original_images)}) and cropped_images ({len(cropped_images)}) should be the same")
# Ensure there are enough bboxes, but drop the excess if there are more bboxes than images
if len(bboxes) > len(original_images):
print(f"Warning: Dropping excess bounding boxes. Expected {len(original_images)}, but got {len(bboxes)}")
bboxes = bboxes[:len(original_images)]
elif len(bboxes) < len(original_images):
raise ValueError("There should be at least as many bboxes as there are original and cropped images")
input_images = tensor2pil(original_images)
crop_imgs = tensor2pil(cropped_images)
out_images = []
for i in range(len(input_images)):
img = input_images[i]
crop = crop_imgs[i]
bbox = bboxes[i]
# uncrop the image based on the bounding box
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 = (round(paste_region[0]*scale_x), round(paste_region[1]*scale_y), round(paste_region[2]*scale_x), round(paste_region[3]*scale_y))
# rescale the crop image to fit the paste_region
crop = crop.resize((round(paste_region[2]-paste_region[0]), round(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:
border_blending = 0.0
blend_ratio = (max(crop_img.size) / 2) * float(border_blending)
blend = img.convert("RGBA")
mask = Image.new("L", img.size, 0)
mask_block = Image.new("L", (paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]), 255)
mask_block = inset_border(mask_block, round(blend_ratio / 2), (0), border_top, border_bottom, border_left, border_right)
mask.paste(mask_block, paste_region)
blend.paste(crop_img, paste_region)
mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))
blend.putalpha(mask)
img = Image.alpha_composite(img.convert("RGBA"), blend)
out_images.append(img.convert("RGB"))
return (pil2tensor(out_images),)