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import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFilter
from ..log import log
from ..utils import np2tensor, pil2tensor, tensor2np, tensor2pil
class MTB_Bbox:
"""The bounding box (BBOX) custom type used by other nodes"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
# "bbox": ("BBOX",),
"x": (
"INT",
{"default": 0, "max": 10000000, "min": 0, "step": 1},
),
"y": (
"INT",
{"default": 0, "max": 10000000, "min": 0, "step": 1},
),
"width": (
"INT",
{"default": 256, "max": 10000000, "min": 0, "step": 1},
),
"height": (
"INT",
{"default": 256, "max": 10000000, "min": 0, "step": 1},
),
}
}
RETURN_TYPES = ("BBOX",)
FUNCTION = "do_crop"
CATEGORY = "mtb/crop"
def do_crop(self, x: int, y: int, width: int, height: int): # bbox
return ((x, y, width, height),)
class MTB_SplitBbox:
"""Split the components of a bbox"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {"bbox": ("BBOX",)},
}
CATEGORY = "mtb/crop"
FUNCTION = "split_bbox"
RETURN_TYPES = ("INT", "INT", "INT", "INT")
RETURN_NAMES = ("x", "y", "width", "height")
def split_bbox(self, bbox):
return (bbox[0], bbox[1], bbox[2], bbox[3])
class MTB_UpscaleBboxBy:
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"bbox": ("BBOX",),
"scale": ("FLOAT", {"default": 1.0}),
},
}
CATEGORY = "mtb/crop"
RETURN_TYPES = ("BBOX",)
FUNCTION = "upscale"
def upscale(
self, bbox: tuple[int, int, int, int], scale: float
) -> tuple[tuple[int, int, int, int]]:
x, y, width, height = bbox
# scaled = (x * scale, y * scale, width * scale, height * scale)
scaled = (
int(x * scale),
int(y * scale),
int(width * scale),
int(height * scale),
)
return (scaled,)
class MTB_BboxFromMask:
"""From a mask extract the bounding box"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"mask": ("MASK",),
"invert": ("BOOLEAN", {"default": False}),
},
"optional": {
"image": ("IMAGE",),
},
}
RETURN_TYPES = (
"BBOX",
"IMAGE",
)
RETURN_NAMES = (
"bbox",
"image (optional)",
)
FUNCTION = "extract_bounding_box"
CATEGORY = "mtb/crop"
def extract_bounding_box(
self, mask: torch.Tensor, invert: bool, image=None
):
# if image != None:
# if mask.size(0) != image.size(0):
# if mask.size(0) != 1:
# log.error(
# f"Batch count mismatch for mask and image, it can either be 1 mask for X images, or X masks for X images (mask: {mask.shape} | image: {image.shape})"
# )
# raise Exception(
# f"Batch count mismatch for mask and image, it can either be 1 mask for X images, or X masks for X images (mask: {mask.shape} | image: {image.shape})"
# )
# we invert it
_mask = tensor2pil(1.0 - mask)[0] if invert else tensor2pil(mask)[0]
alpha_channel = np.array(_mask)
non_zero_indices = np.nonzero(alpha_channel)
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])
# Create a bounding box tuple
if image != None:
# Convert the image to a NumPy array
imgs = tensor2np(image)
out = []
for img in imgs:
# Crop the image from the bounding box
img = img[min_y:max_y, min_x:max_x, :]
log.debug(f"Cropped image to shape {img.shape}")
out.append(img)
image = np2tensor(out)
log.debug(f"Cropped images shape: {image.shape}")
bounding_box = (min_x, min_y, max_x - min_x, max_y - min_y)
return (
bounding_box,
image,
)
class MTB_Crop:
"""Crops an image and an optional mask to a given bounding box
The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type
The BBOX input takes precedence over the tuple input
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
},
"optional": {
"mask": ("MASK",),
"x": (
"INT",
{"default": 0, "max": 10000000, "min": 0, "step": 1},
),
"y": (
"INT",
{"default": 0, "max": 10000000, "min": 0, "step": 1},
),
"width": (
"INT",
{"default": 256, "max": 10000000, "min": 0, "step": 1},
),
"height": (
"INT",
{"default": 256, "max": 10000000, "min": 0, "step": 1},
),
"bbox": ("BBOX",),
},
}
RETURN_TYPES = ("IMAGE", "MASK", "BBOX")
FUNCTION = "do_crop"
CATEGORY = "mtb/crop"
def do_crop(
self,
image: torch.Tensor,
mask=None,
x=0,
y=0,
width=256,
height=256,
bbox=None,
):
image = image.numpy()
if mask is not None:
mask = mask.numpy()
if bbox is not None:
x, y, width, height = bbox
cropped_image = image[:, y : y + height, x : x + width, :]
cropped_mask = None
if mask is not None:
cropped_mask = (
mask[:, y : y + height, x : x + width]
if mask is not None
else None
)
crop_data = (x, y, width, height)
return (
torch.from_numpy(cropped_image),
torch.from_numpy(cropped_mask)
if cropped_mask is not None
else None,
crop_data,
)
# def calculate_intersection(rect1, rect2):
# x_left = max(rect1[0], rect2[0])
# y_top = max(rect1[1], rect2[1])
# x_right = min(rect1[2], rect2[2])
# y_bottom = min(rect1[3], rect2[3])
# return (x_left, y_top, x_right, y_bottom)
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]),
)
if new_bbox != bbox:
log.warn(f"BBox too big, constrained to {new_bbox}")
return new_bbox
def bbox_to_region(bbox, target_size=None):
bbox = bbox_check(bbox, target_size)
# to region
return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
class MTB_Uncrop:
"""Uncrops an image to a given bounding box
The bounding box can be given as a tuple of (x, y, width, height) or as a BBOX type
The BBOX input takes precedence over the tuple input
"""
@classmethod
def INPUT_TYPES(cls):
return {
"required": {
"image": ("IMAGE",),
"crop_image": ("IMAGE",),
"bbox": ("BBOX",),
"border_blending": (
"FLOAT",
{"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.01},
),
}
}
RETURN_TYPES = ("IMAGE",)
FUNCTION = "do_crop"
CATEGORY = "mtb/crop"
def do_crop(self, image, crop_image, bbox, border_blending):
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
single = image.size(0) == 1
if image.size(0) != crop_image.size(0):
if not single:
raise ValueError(
"The Image batch count is greater than 1, but doesn't match the crop_image batch count. If using batches they should either match or only crop_image must be greater than 1"
)
images = tensor2pil(image)
crop_imgs = tensor2pil(crop_image)
out_images = []
for i, crop in enumerate(crop_imgs):
if single:
img = images[0]
else:
img = images[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
)
# log.debug(f"Paste region: {paste_region}")
# new_region = adjust_paste_region(img.size, paste_region)
# log.debug(f"Adjusted paste region: {new_region}")
# # Check if the adjusted paste region is different from the original
crop_img = crop.convert("RGB")
log.debug(f"Crop image size: {crop_img.size}")
log.debug(f"Image size: {img.size}")
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", (bb_width, bb_height), 255)
mask_block = inset_border(mask_block, int(blend_ratio / 2), (0))
mask.paste(mask_block, paste_region)
log.debug(f"Blend size: {blend.size} | kind {blend.mode}")
log.debug(
f"Crop image size: {crop_img.size} | kind {crop_img.mode}"
)
log.debug(f"BBox: {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),)
__nodes__ = [
MTB_BboxFromMask,
MTB_Bbox,
MTB_Crop,
MTB_Uncrop,
MTB_SplitBbox,
MTB_UpscaleBboxBy,
]
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