# utils/image_utils.py import os from io import BytesIO import base64 import numpy as np #from decimal import ROUND_CEILING from PIL import Image, ImageChops, ImageDraw, ImageEnhance, ImageFilter, ImageDraw, ImageOps, ImageMath from typing import List, Union #import numpy as np #import math from utils.constants import default_lut_example_img from utils.color_utils import ( detect_color_format, update_color_opacity ) from utils.misc import (pause) def open_image(image_path): """ Opens an image from a file path or URL, or decodes a DataURL string into an image. Parameters: image_path (str): The file path, URL, or DataURL string of the image to open. Returns: Image: A PIL Image object of the opened image. Raises: Exception: If there is an error opening the image. """ import requests try: # Strip leading and trailing double quotation marks, if present image_path = image_path.strip('"') if image_path.startswith('http'): # If the image path is a URL, download the image using requests response = requests.get(image_path) img = Image.open(BytesIO(response.content)) elif image_path.startswith('data'): # If the image path is a DataURL, decode the base64 string encoded_data = image_path.split(',')[1] decoded_data = base64.b64decode(encoded_data) img = Image.open(BytesIO(decoded_data)) else: # Assume that the image path is a file path img = Image.open(image_path) except Exception as e: raise Exception(f'Error opening image: {e}') return img def build_prerendered_images(images_list): """ Opens a list of images from file paths, URLs, or DataURL strings. Parameters: images_list (list): A list of file paths, URLs, or DataURL strings of the images to open. Returns: list: A list of PIL Image objects of the opened images. """ return [open_image(image) for image in images_list] def build_encoded_images(images_list): """ Encodes a list of images to base64 strings. Parameters: images_list (list): A list of file paths, URLs, DataURL strings, or PIL Image objects of the images to encode. Returns: list: A list of base64-encoded strings of the images. """ return [image_to_base64(image) for image in images_list] def image_to_base64(image): """ Encodes an image to a base64 string. Parameters: image (str or PIL.Image.Image): The file path, URL, DataURL string, or PIL Image object of the image to encode. Returns: str: A base64-encoded string of the image. """ buffered = BytesIO() if type(image) is str: image = open_image(image) image.save(buffered, format="PNG") return "data:image/png;base64," + base64.b64encode(buffered.getvalue()).decode() def change_color(image, color, opacity=0.75): """ Changes the color of an image by overlaying it with a specified color and opacity. Parameters: image (str or PIL.Image.Image): The file path, URL, DataURL string, or PIL Image object of the image to change. color (str or tuple): The color to overlay on the image. opacity (float): The opacity of the overlay color (0.0 to 1.0). Returns: PIL.Image.Image: The image with the color changed. """ if type(image) is str: image = open_image(image) try: # Convert the color to RGBA format rgba_color = detect_color_format(color) rgba_color = update_color_opacity(rgba_color, opacity) # Convert the image to RGBA mode image = image.convert("RGBA") # Create a new image with the same size and mode new_image = Image.new("RGBA", image.size, rgba_color) # Composite the new image with the original image result = Image.alpha_composite(image, new_image) except Exception as e: print(f"Error changing color: {e}") return image return result def convert_str_to_int_or_zero(value): """ Converts a string to an integer, or returns zero if the conversion fails. Parameters: value (str): The string to convert. Returns: int: The converted integer, or zero if the conversion fails. """ try: return int(value) except ValueError: return 0 def upscale_image(image, scale_factor): """ Upscales an image by a given scale factor using the LANCZOS filter. Parameters: image (PIL.Image.Image): The input image to be upscaled. scale_factor (float): The factor by which to upscale the image. Returns: PIL.Image.Image: The upscaled image. """ # Calculate the new size new_width = int(image.width * scale_factor) new_height = int(image.height * scale_factor) # Upscale the image using the LANCZOS filter upscaled_image = image.resize((new_width, new_height), Image.LANCZOS) return upscaled_image def crop_and_resize_image(image, width, height): """ Crops the image to a centered square and resizes it to the specified width and height. Parameters: image (PIL.Image.Image): The input image to be cropped and resized. width (int): The desired width of the output image. height (int): The desired height of the output image. Returns: PIL.Image.Image: The cropped and resized image. """ # Get original dimensions original_width, original_height = image.size # Determine the smaller dimension to make a square crop min_dim = min(original_width, original_height) # Calculate coordinates for cropping to a centered square left = (original_width - min_dim) // 2 top = (original_height - min_dim) // 2 right = left + min_dim bottom = top + min_dim # Crop the image cropped_image = image.crop((left, top, right, bottom)) # Resize the image to the desired dimensions resized_image = cropped_image.resize((width, height), Image.LANCZOS) return resized_image def resize_image_with_aspect_ratio(image, target_width, target_height): """ Resizes the image to fit within the target dimensions while maintaining aspect ratio. If the aspect ratio does not match, the image will be padded with black pixels. Parameters: image (PIL.Image.Image): The input image to be resized. target_width (int): The target width. target_height (int): The target height. Returns: PIL.Image.Image: The resized image. """ # Calculate aspect ratios original_width, original_height = image.size target_aspect = target_width / target_height original_aspect = original_width / original_height # Decide whether to fit width or height if original_aspect > target_aspect: # Image is wider than target aspect ratio new_width = target_width new_height = int(target_width / original_aspect) else: # Image is taller than target aspect ratio new_height = target_height new_width = int(target_height * original_aspect) # Resize the image resized_image = image.resize((new_width, new_height), Image.LANCZOS) # Create a new image with target dimensions and black background new_image = Image.new("RGB", (target_width, target_height), (0, 0, 0)) # Paste the resized image onto the center of the new image paste_x = (target_width - new_width) // 2 paste_y = (target_height - new_height) // 2 new_image.paste(resized_image, (paste_x, paste_y)) return new_image def lerp_imagemath(img1, img2, alpha_percent: int = 50): """ Performs linear interpolation (LERP) between two images based on the given alpha value. Parameters: img1 (str or PIL.Image.Image): The first image or its file path. img2 (str or PIL.Image.Image): The second image or its file path. alpha (int): The interpolation factor (0 to 100). Returns: PIL.Image.Image: The interpolated image. """ if isinstance(img1, str): img1 = open_image(img1) if isinstance(img2, str): img2 = open_image(img2) # Ensure both images are in the same mode (e.g., RGBA) img1 = img1.convert('RGBA') img2 = img2.convert('RGBA') # Convert images to NumPy arrays arr1 = np.array(img1, dtype=np.float32) arr2 = np.array(img2, dtype=np.float32) # Perform linear interpolation alpha = alpha_percent / 100.0 result_arr = (arr1 * (1 - alpha)) + (arr2 * alpha) # Convert the result back to a PIL image result_img = Image.fromarray(np.uint8(result_arr)) #result_img.show() return result_img def shrink_and_paste_on_blank(current_image, mask_width, mask_height, blank_color:tuple[int, int, int, int] = (0,0,0,0)): """ Decreases size of current_image by mask_width pixels from each side, then adds a mask_width width transparent frame, so that the image the function returns is the same size as the input. Parameters: current_image (PIL.Image.Image): The input image to transform. mask_width (int): Width in pixels to shrink from each side. mask_height (int): Height in pixels to shrink from each side. blank_color (tuple): The color of the blank frame (default is transparent). Returns: PIL.Image.Image: The transformed image. """ # calculate new dimensions width, height = current_image.size new_width = width - (2 * mask_width) new_height = height - (2 * mask_height) # resize and paste onto blank image prev_image = current_image.resize((new_width, new_height)) blank_image = Image.new("RGBA", (width, height), blank_color) blank_image.paste(prev_image, (mask_width, mask_height)) return blank_image def multiply_and_blend_images(base_image, image2, alpha_percent=50): """ Multiplies two images and blends the result with the original image. Parameters: image1 (PIL.Image.Image): The first input image. image2 (PIL.Image.Image): The second input image. alpha (float): The blend factor (0.0 to 100.0) for blending the multiplied result with the original image. Returns: PIL.Image.Image: The blended image. """ alpha = alpha_percent / 100.0 if isinstance(base_image, str): base_image = open_image(base_image) if isinstance(image2, str): image2 = open_image(image2) # Ensure both images are in the same mode and size base_image = base_image.convert('RGBA') image2 = image2.convert('RGBA') image2 = image2.resize(base_image.size) # Multiply the images multiplied_image = ImageChops.multiply(base_image, image2) # Blend the multiplied result with the original blended_image = Image.blend(base_image, multiplied_image, alpha) return blended_image def alpha_composite_with_control(base_image, image_with_alpha, alpha_percent=100): """ Overlays image_with_alpha onto base_image with controlled alpha transparency. Parameters: base_image (PIL.Image.Image): The base image. image_with_alpha (PIL.Image.Image): The image to overlay with an alpha channel. alpha_percent (float): The multiplier for the alpha channel (0.0 to 100.0). Returns: PIL.Image.Image: The resulting image after alpha compositing. """ alpha_multiplier = alpha_percent / 100.0 if isinstance(base_image, str): base_image = open_image(base_image) if isinstance(image_with_alpha, str): image_with_alpha = open_image(image_with_alpha) # Ensure both images are in RGBA mode base_image = base_image.convert('RGBA') image_with_alpha = image_with_alpha.convert('RGBA') # Extract the alpha channel and multiply by alpha_multiplier alpha_channel = image_with_alpha.split()[3] alpha_channel = alpha_channel.point(lambda p: p * alpha_multiplier) # Apply the modified alpha channel back to the image image_with_alpha.putalpha(alpha_channel) # Composite the images result = Image.alpha_composite(base_image, image_with_alpha) return result def apply_alpha_mask(image, mask_image, invert = False): """ Applies a mask image as the alpha channel of the input image. Parameters: image (PIL.Image.Image): The image to apply the mask to. mask_image (PIL.Image.Image): The alpha mask to apply. invert (bool): Whether to invert the mask (default is False). Returns: PIL.Image.Image: The image with the applied alpha mask. """ # Resize the mask to match the current image size mask_image = resize_and_crop_image(mask_image, image.width, image.height).convert('L') # convert to grayscale if invert: mask_image = ImageOps.invert(mask_image) # Apply the mask as the alpha layer of the current image result_image = image.copy() result_image.putalpha(mask_image) return result_image def resize_and_crop_image(image: Image, new_width: int = 512, new_height: int = 512) -> Image: """ Resizes and crops an image to a specified width and height. This ensures that the entire new_width and new_height dimensions are filled by the image, and the aspect ratio is maintained. Parameters: image (PIL.Image.Image): The image to be resized and cropped. new_width (int): The desired width of the new image (default is 512). new_height (int): The desired height of the new image (default is 512). Returns: PIL.Image.Image: The resized and cropped image. """ # Get the dimensions of the original image orig_width, orig_height = image.size # Calculate the aspect ratios of the original and new images orig_aspect_ratio = orig_width / float(orig_height) new_aspect_ratio = new_width / float(new_height) # Calculate the new size of the image while maintaining aspect ratio if orig_aspect_ratio > new_aspect_ratio: # The original image is wider than the new image, so we need to crop the sides resized_width = int(new_height * orig_aspect_ratio) resized_height = new_height left_offset = (resized_width - new_width) // 2 top_offset = 0 else: # The original image is taller than the new image, so we need to crop the top and bottom resized_width = new_width resized_height = int(new_width / orig_aspect_ratio) left_offset = 0 top_offset = (resized_height - new_height) // 2 # Resize the image with Lanczos resampling filter resized_image = image.resize((resized_width, resized_height), resample=Image.Resampling.LANCZOS) # Crop the image to fill the entire height and width of the new image cropped_image = resized_image.crop((left_offset, top_offset, left_offset + new_width, top_offset + new_height)) return cropped_image ##################################################### LUTs ############################################################ def is_3dlut_row(row: List[str]) -> bool: """ Check if one line in the file has exactly 3 numeric values. Parameters: row (list): A list of strings representing the values in a row. Returns: bool: True if the row has exactly 3 numeric values, False otherwise. """ try: row_values = [float(val) for val in row] return len(row_values) == 3 except ValueError: return False def read_lut(path_lut: Union[str, os.PathLike], num_channels: int = 3) -> ImageFilter.Color3DLUT: """ Read LUT from a raw file. Each line in the file is considered part of the LUT table. The function reads the file, parses the rows, and constructs a Color3DLUT object. Args: path_lut: A string or os.PathLike object representing the path to the LUT file. num_channels: An integer specifying the number of color channels in the LUT (default is 3). Returns: An instance of ImageFilter.Color3DLUT representing the LUT. Raises: FileNotFoundError: If the LUT file specified by path_lut does not exist. """ with open(path_lut) as f: lut_raw = f.read().splitlines() size = round(len(lut_raw) ** (1 / 3)) row2val = lambda row: tuple([float(val) for val in row.split(" ")]) lut_table = [row2val(row) for row in lut_raw if is_3dlut_row(row.split(" "))] return ImageFilter.Color3DLUT(size, lut_table, num_channels) def apply_lut(img: Image, lut_path: str = "", lut: ImageFilter.Color3DLUT = None) -> Image: """ Apply a LUT to an image and return a PIL Image with the LUT applied. The function applies the LUT to the input image using the filter() method of the PIL Image class. Args: img: A PIL Image object to which the LUT should be applied. lut_path: A string representing the path to the LUT file (optional if lut argument is provided). lut: An instance of ImageFilter.Color3DLUT representing the LUT (optional if lut_path is provided). Returns: A PIL Image object with the LUT applied. Raises: ValueError: If both lut_path and lut arguments are not provided. """ if lut is None: if lut_path == "": raise ValueError("Either lut_path or lut argument must be provided.") lut = read_lut(lut_path) return img.filter(lut) def show_lut(lut_filename: str, lut_example_image: Image = default_lut_example_img) -> Image: if lut_filename is not None: try: lut_example_image = apply_lut(lut_example_image, lut_filename) except Exception as e: print(f"BAD LUT: Error applying LUT {str(e)}.") else: lut_example_image = open_image(default_lut_example_img) return lut_example_image def convert_rgb_to_rgba_safe(image: Image) -> Image: """ Converts an RGB image to RGBA by adding an alpha channel. Ensures that the original image remains unaltered. Parameters: image (PIL.Image.Image): The RGB image to convert. Returns: PIL.Image.Image: The converted RGBA image. """ if image.mode != 'RGB': if image.mode == 'RGBA': return image elif image.mode == 'P': # Convert palette image to RGBA image = image.convert('RGB') else: raise ValueError("Unsupported image mode for conversion to RGBA.") # Create a copy of the image to avoid modifying the original rgba_image = image.copy() # Optionally, set a default alpha value (e.g., fully opaque) alpha = Image.new('L', rgba_image.size, 255) # 255 for full opacity rgba_image.putalpha(alpha) return rgba_image def apply_lut_to_image_path(lut_filename: str, image_path: str) -> Image: """ Apply a LUT to an image and return the result. Args: lut_filename: A string representing the path to the LUT file. image_path: A string representing the path to the input image. Returns: A PIL Image object with the LUT applied. """ img = open_image(image_path) # Handle specific file formats by converting to appropriate modes if image_path.lower().endswith(('.gif', '.webp')): # Convert to RGBA to preserve transparency img = img.convert('RGBA') elif image_path.lower().endswith(('.jpg', '.jpeg')): # Convert to RGB since JPEG doesn't support transparency img = convert_rgb_to_rgba_safe(img) # For other formats like PNG, retain the existing mode # Apply the LUT if provided if lut_filename is not None: try: img = apply_lut(img, lut_filename) except Exception as e: print(f"BAD LUT: Error applying LUT {str(e)}.") return img def convert_to_rgba_png(file_path: str) -> None: """ Converts an image to RGBA PNG format and saves it with the same base name and a .png extension. Args: file_path (str): The path to the input image file. Raises: ValueError: If the input file extension is not a supported image format. Exception: If there is an error during the conversion or saving process. """ try: # Open the original image img = open_image(file_path) # Convert the image to RGBA rgba_img = convert_rgb_to_rgba_safe(img) # Generate the new file name with .png extension base_name = os.path.splitext(file_path)[0] new_file_path = f"{base_name}.png" # Save the RGBA image as PNG rgba_img.save(new_file_path, format='PNG') print(f"Image saved as {new_file_path}") except ValueError as ve: print(f"ValueError: {ve}") except Exception as e: print(f"Error converting image: {e}")