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186 lines
6.5 KiB
Python
186 lines
6.5 KiB
Python
import io
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from typing import Optional
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from PIL import Image, ImageChops, ImageOps,ImageFilter
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import cv2
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import numpy as np
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from math import isqrt, ceil
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import torch
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from ifnude import detect
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from scripts.faceswaplab_globals import NSFW_SCORE
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from modules import processing
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import base64
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def check_against_nsfw(img):
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shapes = []
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chunks = detect(img)
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for chunk in chunks:
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shapes.append(chunk["score"] > NSFW_SCORE)
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return any(shapes)
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def pil_to_cv2(pil_img):
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return cv2.cvtColor(np.array(pil_img), cv2.COLOR_RGB2BGR)
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def cv2_to_pil(cv2_img):
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return Image.fromarray(cv2.cvtColor(cv2_img, cv2.COLOR_BGR2RGB))
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def torch_to_pil(images):
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"""
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Convert a numpy image or a batch of images to a PIL image.
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"""
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images = images.cpu().permute(0, 2, 3, 1).numpy()
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if images.ndim == 3:
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images = images[None, ...]
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images = (images * 255).round().astype("uint8")
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pil_images = [Image.fromarray(image) for image in images]
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return pil_images
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def pil_to_torch(pil_images):
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"""
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Convert a PIL image or a list of PIL images to a torch tensor or a batch of torch tensors.
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"""
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if isinstance(pil_images, list):
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numpy_images = [np.array(image) for image in pil_images]
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torch_images = torch.from_numpy(np.stack(numpy_images)).permute(0, 3, 1, 2)
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return torch_images
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numpy_image = np.array(pil_images)
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torch_image = torch.from_numpy(numpy_image).permute(2, 0, 1)
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return torch_image
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from collections import Counter
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def create_square_image(image_list):
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"""
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Creates a square image by combining multiple images in a grid pattern.
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Args:
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image_list (list): List of PIL Image objects to be combined.
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Returns:
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PIL Image object: The resulting square image.
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None: If the image_list is empty or contains only one image.
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"""
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# Count the occurrences of each image size in the image_list
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size_counter = Counter(image.size for image in image_list)
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# Get the most common image size (size with the highest count)
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common_size = size_counter.most_common(1)[0][0]
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# Filter the image_list to include only images with the common size
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image_list = [image for image in image_list if image.size == common_size]
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# Get the dimensions (width and height) of the common size
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size = common_size
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# If there are more than one image in the image_list
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if len(image_list) > 1:
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num_images = len(image_list)
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# Calculate the number of rows and columns for the grid
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rows = isqrt(num_images)
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cols = ceil(num_images / rows)
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# Calculate the size of the square image
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square_size = (cols * size[0], rows * size[1])
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# Create a new RGB image with the square size
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square_image = Image.new("RGB", square_size)
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# Paste each image onto the square image at the appropriate position
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for i, image in enumerate(image_list):
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row = i // cols
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col = i % cols
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square_image.paste(image, (col * size[0], row * size[1]))
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# Return the resulting square image
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return square_image
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# Return None if there are no images or only one image in the image_list
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return None
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def create_mask(image, box_coords):
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width, height = image.size
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mask = Image.new("L", (width, height), 255)
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x1, y1, x2, y2 = box_coords
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for x in range(width):
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for y in range(height):
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if x1 <= x <= x2 and y1 <= y <= y2:
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mask.putpixel((x, y), 255)
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else:
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mask.putpixel((x, y), 0)
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return mask
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def apply_mask(img : Image.Image,p : processing.StableDiffusionProcessing, batch_index : int) -> Image.Image :
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"""
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Apply mask overlay and color correction to an image if enabled
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Args:
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img: PIL Image objects.
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p : The processing object
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batch_index : the batch index
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Returns:
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PIL Image object
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"""
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if isinstance(p, processing.StableDiffusionProcessingImg2Img) :
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if p.inpaint_full_res :
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overlays = p.overlay_images
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if overlays is None or batch_index >= len(overlays):
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return img
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overlay : Image.Image = overlays[batch_index]
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overlay = overlay.resize((img.size), resample= Image.Resampling.LANCZOS)
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img = img.copy()
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img.paste(overlay, (0, 0), overlay)
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return img
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img = processing.apply_overlay(img, p.paste_to, batch_index, p.overlay_images)
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if p.color_corrections is not None and batch_index < len(p.color_corrections):
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img = processing.apply_color_correction(p.color_corrections[batch_index], img)
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return img
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def prepare_mask(
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mask: Image.Image, p: processing.StableDiffusionProcessing
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) -> Image.Image:
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"""
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Prepare an image mask for the inpainting process. (This comes from controlnet)
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This function takes as input a PIL Image object and an instance of the
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StableDiffusionProcessing class, and performs the following steps to prepare the mask:
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1. Convert the mask to grayscale (mode "L").
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2. If the 'inpainting_mask_invert' attribute of the processing instance is True,
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invert the mask colors.
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3. If the 'mask_blur' attribute of the processing instance is greater than 0,
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apply a Gaussian blur to the mask with a radius equal to 'mask_blur'.
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Args:
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mask (Image.Image): The input mask as a PIL Image object.
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p (processing.StableDiffusionProcessing): An instance of the StableDiffusionProcessing class
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containing the processing parameters.
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Returns:
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mask (Image.Image): The prepared mask as a PIL Image object.
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"""
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mask = mask.convert("L")
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#FIXME : Properly fix blur
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# if getattr(p, "mask_blur", 0) > 0:
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# mask = mask.filter(ImageFilter.GaussianBlur(p.mask_blur))
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return mask
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def base64_to_pil(base64str : Optional[str]) -> Optional[Image.Image] :
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if base64str is None :
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return None
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if 'base64,' in base64str: # check if the base64 string has a data URL scheme
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base64_data = base64str.split('base64,')[-1]
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img_bytes = base64.b64decode(base64_data)
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else:
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# if no data URL scheme, just decode
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img_bytes = base64.b64decode(base64str)
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return Image.open(io.BytesIO(img_bytes))
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