Backend/custom_nodes/ComfyUI-KJNodes/nodes/batchcrop_nodes.py

from ..utility.utility import tensor2pil, pil2tensor
from PIL import Image, ImageDraw, ImageFilter
import numpy as np
import torch
from torchvision.transforms import Resize, CenterCrop, InterpolationMode
import math

#based on nodes from mtb https://github.com/melMass/comfy_mtb

def bbox_to_region(bbox, target_size=None):
    bbox = bbox_check(bbox, target_size)
    return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])

def bbox_check(bbox, target_size=None):
    if not target_size:
        return bbox

    new_bbox = (
        bbox[0],
        bbox[1],
        min(target_size[0] - bbox[0], bbox[2]),
        min(target_size[1] - bbox[1], bbox[3]),
    )
    return new_bbox

class BatchCropFromMask:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "original_images": ("IMAGE",),
                "masks": ("MASK",),
                "crop_size_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}),
                "bbox_smooth_alpha": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
            },
        }

    RETURN_TYPES = (
        "IMAGE",
        "IMAGE",
        "BBOX",
        "INT",
        "INT",
    )
    RETURN_NAMES = (
        "original_images",
        "cropped_images",
        "bboxes",
        "width",
        "height",
    )
    FUNCTION = "crop"
    CATEGORY = "KJNodes/masking"

    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
        if alpha == 0:
            return prev_bbox_size
        return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)

    def smooth_center(self, prev_center, curr_center, alpha=0.5):
        if alpha == 0:
            return prev_center
        return (
            round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
            round(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 = []

        self.max_bbox_width = 0
        self.max_bbox_height = 0

        # First, calculate the maximum bounding box size across all masks
        curr_max_bbox_width = 0
        curr_max_bbox_height = 0
        for mask in masks:
            _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])
            width = max_x - min_x
            height = max_y - min_y
            curr_max_bbox_width = max(curr_max_bbox_width, width)
            curr_max_bbox_height = max(curr_max_bbox_height, height)

        # Smooth the changes in the bounding box size
        self.max_bbox_width = self.smooth_bbox_size(self.max_bbox_width, curr_max_bbox_width, bbox_smooth_alpha)
        self.max_bbox_height = self.smooth_bbox_size(self.max_bbox_height, curr_max_bbox_height, bbox_smooth_alpha)

        # Apply the crop size multiplier
        self.max_bbox_width = round(self.max_bbox_width * crop_size_mult)
        self.max_bbox_height = round(self.max_bbox_height * crop_size_mult)
        bbox_aspect_ratio = self.max_bbox_width / self.max_bbox_height

        # 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))
            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 = np.mean(non_zero_indices[1])
            center_y = np.mean(non_zero_indices[0])
            curr_center = (round(center_x), round(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_width and max_bbox_height
            half_box_width = round(self.max_bbox_width / 2)
            half_box_height = round(self.max_bbox_height / 2)
            min_x = max(0, center[0] - half_box_width)
            max_x = min(img.shape[1], center[0] + half_box_width)
            min_y = max(0, center[1] - half_box_height)
            max_y = min(img.shape[0], center[1] + half_box_height)

            # Append bounding box coordinates
            bounding_boxes.append((min_x, min_y, max_x - min_x, max_y - min_y))

            # Crop the image from the bounding box
            cropped_img = img[min_y:max_y, min_x:max_x, :]
            
            # Calculate the new dimensions while maintaining the aspect ratio
            new_height = min(cropped_img.shape[0], self.max_bbox_height)
            new_width = round(new_height * bbox_aspect_ratio)

            # Resize the image
            resize_transform = Resize((new_height, new_width))
            resized_img = resize_transform(cropped_img.permute(2, 0, 1))

            # Perform the center crop to the desired size
            crop_transform = CenterCrop((self.max_bbox_height, self.max_bbox_width)) # swap the order here if necessary
            cropped_resized_img = crop_transform(resized_img)

            cropped_images.append(cropped_resized_img.permute(1, 2, 0))

        cropped_out = torch.stack(cropped_images, dim=0)
        
        return (original_images, cropped_out, bounding_boxes, self.max_bbox_width, self.max_bbox_height, )

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),)

class BatchCropFromMaskAdvanced:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "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}),
            },
        }

    RETURN_TYPES = (
        "IMAGE",
        "IMAGE",
        "MASK",
        "IMAGE",
        "MASK",
        "BBOX",
        "BBOX",
        "INT",
        "INT",
    )
    RETURN_NAMES = (
        "original_images",
        "cropped_images",
        "cropped_masks",
        "combined_crop_image",
        "combined_crop_masks",
        "bboxes",
        "combined_bounding_box",
        "bbox_width",
        "bbox_height",
    )
    FUNCTION = "crop"
    CATEGORY = "KJNodes/masking"

    def smooth_bbox_size(self, prev_bbox_size, curr_bbox_size, alpha):
          return round(alpha * curr_bbox_size + (1 - alpha) * prev_bbox_size)

    def smooth_center(self, prev_center, curr_center, alpha=0.5):
        return (round(alpha * curr_center[0] + (1 - alpha) * prev_center[0]),
                round(alpha * curr_center[1] + (1 - alpha) * prev_center[1]))

    def crop(self, masks, original_images, crop_size_mult, bbox_smooth_alpha):
        bounding_boxes = []
        combined_bounding_box = []
        cropped_images = []
        cropped_masks = []
        cropped_masks_out = []
        combined_crop_out = []
        combined_cropped_images = []
        combined_cropped_masks = []
        
        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)
            return min_x, max_x, min_y, max_y, bbox_size

        combined_mask = torch.max(masks, dim=0)[0]
        _mask = tensor2pil(combined_mask)[0]
        new_min_x, new_max_x, new_min_y, new_max_y, combined_bbox_size = calculate_bbox(_mask)
        center_x = (new_min_x + new_max_x) / 2
        center_y = (new_min_y + new_max_y) / 2
        half_box_size = round(combined_bbox_size // 2)
        new_min_x = max(0, round(center_x - half_box_size))
        new_max_x = min(original_images[0].shape[1], round(center_x + half_box_size))
        new_min_y = max(0, round(center_y - half_box_size))
        new_max_y = min(original_images[0].shape[0], round(center_y + half_box_size))
        
        combined_bounding_box.append((new_min_x, new_min_y, new_max_x - new_min_x, new_max_y - new_min_y))   
        
        self.max_bbox_size = 0
        
        # First, calculate the maximum bounding box size across all masks
        curr_max_bbox_size = max(calculate_bbox(tensor2pil(mask)[0])[-1] for mask in masks)
        # 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
        self.max_bbox_size = round(self.max_bbox_size * crop_size_mult)
        # 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])

                # Calculate center of bounding box
                center_x = np.mean(non_zero_indices[1])
                center_y = np.mean(non_zero_indices[0])
                curr_center = (round(center_x), round(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 = 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))

                # Crop the image from the bounding box
                cropped_img = img[min_y:max_y, min_x:max_x, :]
                cropped_mask = mask[min_y:max_y, min_x:max_x]

                # 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, 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
                # 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))

                cropped_resized_mask = crop_transform(resized_mask)
                cropped_masks.append(cropped_resized_mask)

                combined_cropped_img = original_images[i][new_min_y:new_max_y, new_min_x:new_max_x, :]
                combined_cropped_images.append(combined_cropped_img)

                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)
        cropped_masks_out = torch.stack(cropped_masks, dim=0)
        combined_crop_mask_out = torch.stack(combined_cropped_masks, dim=0)

        return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size)

class FilterZeroMasksAndCorrespondingImages:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "masks": ("MASK",),
            },
            "optional": {
                "original_images": ("IMAGE",), 
            },
        }

    RETURN_TYPES = ("MASK", "IMAGE", "IMAGE", "INDEXES",)
    RETURN_NAMES = ("non_zero_masks_out", "non_zero_mask_images_out", "zero_mask_images_out", "zero_mask_images_out_indexes",)
    FUNCTION = "filter"
    CATEGORY = "KJNodes/masking"
    DESCRIPTION = """

Filter out all the empty (i.e. all zero) mask in masks  

Also filter out all the corresponding images in original_images by indexes if provide  

  

original_images (optional): If provided, need have same length as masks.

"""
    
    def filter(self, masks, original_images=None):
        non_zero_masks = []
        non_zero_mask_images = []
        zero_mask_images = []
        zero_mask_images_indexes = []
        
        masks_num = len(masks)
        also_process_images = False
        if original_images is not None:
            imgs_num = len(original_images)
            if len(original_images) == masks_num:
                also_process_images = True
            else:
                print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})")
        
        for i in range(masks_num):
            non_zero_num = np.count_nonzero(np.array(masks[i]))
            if non_zero_num > 0:
                non_zero_masks.append(masks[i])
                if also_process_images:
                    non_zero_mask_images.append(original_images[i])
            else:
                zero_mask_images.append(original_images[i])
                zero_mask_images_indexes.append(i)

        non_zero_masks_out = torch.stack(non_zero_masks, dim=0)
        non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None
        
        if also_process_images:
            non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0)
            if len(zero_mask_images) > 0:
                zero_mask_images_out = torch.stack(zero_mask_images, dim=0)
                zero_mask_images_out_indexes = zero_mask_images_indexes

        return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes)

class InsertImageBatchByIndexes:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",), 
                "images_to_insert": ("IMAGE",), 
                "insert_indexes": ("INDEXES",),
            },
        }

    RETURN_TYPES = ("IMAGE", )
    RETURN_NAMES = ("images_after_insert", )
    FUNCTION = "insert"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """

This node is designed to be use with node FilterZeroMasksAndCorrespondingImages

It inserts the images_to_insert into images according to insert_indexes



Returns:

    images_after_insert: updated original images with origonal sequence order

"""
    
    def insert(self, images, images_to_insert, insert_indexes):        
        images_after_insert = images
        
        if images_to_insert is not None and insert_indexes is not None:
            images_to_insert_num = len(images_to_insert)
            insert_indexes_num = len(insert_indexes)
            if images_to_insert_num == insert_indexes_num:
                images_after_insert = []

                i_images = 0
                for i in range(len(images) + images_to_insert_num):
                    if i in insert_indexes:
                        images_after_insert.append(images_to_insert[insert_indexes.index(i)])
                    else:
                        images_after_insert.append(images[i_images])
                        i_images += 1
                        
                images_after_insert = torch.stack(images_after_insert, dim=0)
                
            else:
                print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})")


        return (images_after_insert, )

class BatchUncropAdvanced:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "original_images": ("IMAGE",),
                "cropped_images": ("IMAGE",), 
                "cropped_masks": ("MASK",),
                "combined_crop_mask": ("MASK",),
                "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}),
                "use_combined_mask": ("BOOLEAN", {"default": False}),
                "use_square_mask": ("BOOLEAN", {"default": True}),
            },
            "optional": {
                "combined_bounding_box": ("BBOX", {"default": None}),  
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "uncrop"
    CATEGORY = "KJNodes/masking"


    def uncrop(self, original_images, cropped_images, cropped_masks, combined_crop_mask, bboxes, border_blending, crop_rescale, use_combined_mask, use_square_mask, combined_bounding_box = None):
        
        def inset_border(image, border_width=20, border_color=(0)):
            width, height = image.size
            bordered_image = Image.new(image.mode, (width, height), border_color)
            bordered_image.paste(image, (0, 0))
            draw = ImageDraw.Draw(bordered_image)
            draw.rectangle((0, 0, width - 1, height - 1), outline=border_color, width=border_width)
            return bordered_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")

        crop_imgs = tensor2pil(cropped_images)
        input_images = tensor2pil(original_images)
        out_images = []

        for i in range(len(input_images)):
            img = input_images[i]
            crop = crop_imgs[i]
            bbox = bboxes[i]
            
            if use_combined_mask:
                bb_x, bb_y, bb_width, bb_height = combined_bounding_box[0]
                paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
                mask = combined_crop_mask[i]
            else:
                bb_x, bb_y, bb_width, bb_height = bbox
                paste_region = bbox_to_region((bb_x, bb_y, bb_width, bb_height), img.size)
                mask = cropped_masks[i]
            
            # scale paste_region
            scale_x = scale_y = crop_rescale
            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")

            #border blending
            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")

            if use_square_mask:
                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))
                mask.paste(mask_block, paste_region)
            else:
                original_mask = tensor2pil(mask)[0]
                original_mask = original_mask.resize((paste_region[2]-paste_region[0], paste_region[3]-paste_region[1]))
                mask = Image.new("L", img.size, 0)
                mask.paste(original_mask, paste_region)

            mask = mask.filter(ImageFilter.BoxBlur(radius=blend_ratio / 4))
            mask = mask.filter(ImageFilter.GaussianBlur(radius=blend_ratio / 4))

            blend.paste(crop_img, paste_region) 
            blend.putalpha(mask)
            
            img = Image.alpha_composite(img.convert("RGBA"), blend)
            out_images.append(img.convert("RGB"))

        return (pil2tensor(out_images),)

class SplitBboxes:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "bboxes": ("BBOX",),
                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
            },
        }

    RETURN_TYPES = ("BBOX","BBOX",)
    RETURN_NAMES = ("bboxes_a","bboxes_b",)
    FUNCTION = "splitbbox"
    CATEGORY = "KJNodes/masking"
    DESCRIPTION = """

Splits the specified bbox list at the given index into two lists.

"""

    def splitbbox(self, bboxes, index):
        bboxes_a = bboxes[:index]  # Sub-list from the start of bboxes up to (but not including) the index
        bboxes_b = bboxes[index:]  # Sub-list from the index to the end of bboxes

        return (bboxes_a, bboxes_b,)
    
class BboxToInt:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "bboxes": ("BBOX",),
                "index": ("INT", {"default": 0,"min": 0, "max": 99999999, "step": 1}),
            },
        }

    RETURN_TYPES = ("INT","INT","INT","INT","INT","INT",)
    RETURN_NAMES = ("x_min","y_min","width","height", "center_x","center_y",)
    FUNCTION = "bboxtoint"
    CATEGORY = "KJNodes/masking"
    DESCRIPTION = """

Returns selected index from bounding box list as integers.

"""
    def bboxtoint(self, bboxes, index):
        x_min, y_min, width, height = bboxes[index]
        center_x = int(x_min + width / 2)
        center_y = int(y_min + height / 2)
        
        return (x_min, y_min, width, height, center_x, center_y,)

class BboxVisualize:

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "bboxes": ("BBOX",),
                "line_width": ("INT", {"default": 1,"min": 1, "max": 10, "step": 1}),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("images",)
    FUNCTION = "visualizebbox"
    DESCRIPTION = """

Visualizes the specified bbox on the image.

"""

    CATEGORY = "KJNodes/masking"

    def visualizebbox(self, bboxes, images, line_width):
        image_list = []
        for image, bbox in zip(images, bboxes):
            x_min, y_min, width, height = bbox
            
            # Ensure bbox coordinates are integers
            x_min = int(x_min)
            y_min = int(y_min)
            width = int(width)
            height = int(height)
            
            # Permute the image dimensions
            image = image.permute(2, 0, 1)

            # Clone the image to draw bounding boxes
            img_with_bbox = image.clone()
            
            # Define the color for the bbox, e.g., red
            color = torch.tensor([1, 0, 0], dtype=torch.float32)
            
            # Ensure color tensor matches the image channels
            if color.shape[0] != img_with_bbox.shape[0]:
                color = color.unsqueeze(1).expand(-1, line_width)

            # Draw lines for each side of the bbox with the specified line width
            for lw in range(line_width):
                # Top horizontal line
                if y_min + lw < img_with_bbox.shape[1]:
                    img_with_bbox[:, y_min + lw, x_min:x_min + width] = color[:, None]
                
                # Bottom horizontal line
                if y_min + height - lw < img_with_bbox.shape[1]:
                    img_with_bbox[:, y_min + height - lw, x_min:x_min + width] = color[:, None]
                
                # Left vertical line
                if x_min + lw < img_with_bbox.shape[2]:
                    img_with_bbox[:, y_min:y_min + height, x_min + lw] = color[:, None]
                
                # Right vertical line
                if x_min + width - lw < img_with_bbox.shape[2]:
                    img_with_bbox[:, y_min:y_min + height, x_min + width - lw] = color[:, None]
        
            # Permute the image dimensions back
            img_with_bbox = img_with_bbox.permute(1, 2, 0).unsqueeze(0)
            image_list.append(img_with_bbox)

        return (torch.cat(image_list, dim=0),)

        return (torch.cat(image_list, dim=0),)