193 lines
7.8 KiB
Python
193 lines
7.8 KiB
Python
# layerstyle advance
|
|
|
|
import torch
|
|
import numpy as np
|
|
import math
|
|
from PIL import Image
|
|
from .imagefunc import log, tensor2pil, pil2tensor, num_round_up_to_multiple, draw_rect, gaussian_blur, mask_area
|
|
|
|
|
|
|
|
class ImageAutoCropV3:
|
|
|
|
def __init__(self):
|
|
self.NODE_NAME = 'ImageAutoCropV3'
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(self):
|
|
ratio_list = ['1:1', '3:2', '4:3', '16:9', '2:3', '3:4', '9:16', 'custom', 'original']
|
|
scale_to_side_list = ['None', 'longest', 'shortest', 'width', 'height', 'total_pixel(kilo pixel)']
|
|
multiple_list = ['8', '16', '32', '64', '128', '256', '512', 'None']
|
|
method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
|
|
return {
|
|
"required": {
|
|
"image": ("IMAGE", ),
|
|
"aspect_ratio": (ratio_list,),
|
|
"proportional_width": ("INT", {"default": 1, "min": 1, "max": 99999999, "step": 1}),
|
|
"proportional_height": ("INT", {"default": 1, "min": 1, "max": 99999999, "step": 1}),
|
|
"method": (method_mode,),
|
|
"scale_to_side": (scale_to_side_list,),
|
|
"scale_to_length": ("INT", {"default": 1024, "min": 4, "max": 999999, "step": 1}),
|
|
"round_to_multiple": (multiple_list,),
|
|
},
|
|
"optional": {
|
|
"mask": ("MASK",),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("IMAGE", "IMAGE",)
|
|
RETURN_NAMES = ("cropped_image", "box_preview",)
|
|
FUNCTION = 'image_auto_crop_v3'
|
|
CATEGORY = '😺dzNodes/LayerUtility'
|
|
|
|
def image_auto_crop_v3(self, image, aspect_ratio,
|
|
proportional_width, proportional_height, method,
|
|
scale_to_side, scale_to_length, round_to_multiple,
|
|
mask=None,
|
|
):
|
|
|
|
ret_images = []
|
|
ret_box_previews = []
|
|
ret_masks = []
|
|
input_images = []
|
|
input_masks = []
|
|
crop_boxs = []
|
|
|
|
for l in image:
|
|
input_images.append(torch.unsqueeze(l, 0))
|
|
m = tensor2pil(l)
|
|
if m.mode == 'RGBA':
|
|
input_masks.append(m.split()[-1])
|
|
if mask is not None:
|
|
if mask.dim() == 2:
|
|
mask = torch.unsqueeze(mask, 0)
|
|
input_masks = []
|
|
for m in mask:
|
|
input_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
|
|
|
|
if len(input_masks) > 0 and len(input_masks) != len(input_images):
|
|
input_masks = []
|
|
log(f"Warning, {self.NODE_NAME} unable align alpha to image, drop it.", message_type='warning')
|
|
|
|
fit = 'crop'
|
|
_image = tensor2pil(input_images[0])
|
|
(orig_width, orig_height) = _image.size
|
|
if aspect_ratio == 'custom':
|
|
ratio = proportional_width / proportional_height
|
|
elif aspect_ratio == 'original':
|
|
ratio = orig_width / orig_height
|
|
else:
|
|
s = aspect_ratio.split(":")
|
|
ratio = int(s[0]) / int(s[1])
|
|
|
|
resize_sampler = Image.LANCZOS
|
|
if method == "bicubic":
|
|
resize_sampler = Image.BICUBIC
|
|
elif method == "hamming":
|
|
resize_sampler = Image.HAMMING
|
|
elif method == "bilinear":
|
|
resize_sampler = Image.BILINEAR
|
|
elif method == "box":
|
|
resize_sampler = Image.BOX
|
|
elif method == "nearest":
|
|
resize_sampler = Image.NEAREST
|
|
|
|
# calculate target width and height
|
|
if ratio > 1:
|
|
if scale_to_side == 'longest':
|
|
target_width = scale_to_length
|
|
target_height = int(target_width / ratio)
|
|
elif scale_to_side == 'shortest':
|
|
target_height = scale_to_length
|
|
target_width = int(target_height * ratio)
|
|
elif scale_to_side == 'width':
|
|
target_width = scale_to_length
|
|
target_height = int(target_width / ratio)
|
|
elif scale_to_side == 'height':
|
|
target_height = scale_to_length
|
|
target_width = int(target_height * ratio)
|
|
elif scale_to_side == 'total_pixel(kilo pixel)':
|
|
target_width = math.sqrt(ratio * scale_to_length * 1000)
|
|
target_height = target_width / ratio
|
|
target_width = int(target_width)
|
|
target_height = int(target_height)
|
|
else:
|
|
target_width = orig_width
|
|
target_height = int(target_width / ratio)
|
|
else:
|
|
if scale_to_side == 'longest':
|
|
target_height = scale_to_length
|
|
target_width = int(target_height * ratio)
|
|
elif scale_to_side == 'shortest':
|
|
target_width = scale_to_length
|
|
target_height = int(target_width / ratio)
|
|
elif scale_to_side == 'width':
|
|
target_width = scale_to_length
|
|
target_height = int(target_width / ratio)
|
|
elif scale_to_side == 'height':
|
|
target_height = scale_to_length
|
|
target_width = int(target_height * ratio)
|
|
elif scale_to_side == 'total_pixel(kilo pixel)':
|
|
target_width = math.sqrt(ratio * scale_to_length * 1000)
|
|
target_height = target_width / ratio
|
|
target_width = int(target_width)
|
|
target_height = int(target_height)
|
|
else:
|
|
target_height = orig_height
|
|
target_width = int(target_height * ratio)
|
|
|
|
if round_to_multiple != 'None':
|
|
multiple = int(round_to_multiple)
|
|
target_width = num_round_up_to_multiple(target_width, multiple)
|
|
target_height = num_round_up_to_multiple(target_height, multiple)
|
|
|
|
for i in range(len(input_images)):
|
|
_image = tensor2pil(input_images[i]).convert('RGB')
|
|
|
|
if len(input_masks) > 0:
|
|
_mask = input_masks[i]
|
|
else:
|
|
_mask = Image.new('L', _image.size, color='black')
|
|
|
|
bluredmask = gaussian_blur(_mask, 20).convert('L')
|
|
(mask_x, mask_y, mask_w, mask_h) = mask_area(bluredmask)
|
|
orig_ratio = _image.width / _image.height
|
|
target_ratio = target_width / target_height
|
|
# crop image to target ratio
|
|
if orig_ratio > target_ratio: # crop LiftRight side
|
|
crop_w = int(_image.height * target_ratio)
|
|
crop_h = _image.height
|
|
else: # crop TopBottom side
|
|
crop_w = _image.width
|
|
crop_h = int(_image.width / target_ratio)
|
|
crop_x = mask_w // 2 + mask_x - crop_w // 2
|
|
if crop_x < 0:
|
|
crop_x = 0
|
|
if crop_x + crop_w > _image.width:
|
|
crop_x = _image.width - crop_w
|
|
crop_y = mask_h // 2 + mask_y - crop_h // 2
|
|
if crop_y < 0:
|
|
crop_y = 0
|
|
if crop_y + crop_h > _image.height:
|
|
crop_y = _image.height - crop_h
|
|
crop_image = _image.crop((crop_x, crop_y, crop_x + crop_w, crop_y + crop_h))
|
|
line_width = (_image.width + _image.height) // 200
|
|
preview_image = draw_rect(_image, crop_x, crop_y,
|
|
crop_w, crop_h,
|
|
line_color="#F00000", line_width=line_width)
|
|
ret_image = crop_image.resize((target_width, target_height), resize_sampler)
|
|
ret_images.append(pil2tensor(ret_image))
|
|
ret_box_previews.append(pil2tensor(preview_image))
|
|
|
|
log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
|
|
return (torch.cat(ret_images, dim=0),
|
|
torch.cat(ret_box_previews, dim=0),
|
|
)
|
|
|
|
NODE_CLASS_MAPPINGS = {
|
|
"LayerUtility: ImageAutoCrop V3": ImageAutoCropV3
|
|
}
|
|
|
|
NODE_DISPLAY_NAME_MAPPINGS = {
|
|
"LayerUtility: ImageAutoCrop V3": "LayerUtility: ImageAutoCrop V3(Advance)"
|
|
} |