import glob import os import cv2 import argparse import shutil import math from PIL import Image import numpy as np def resize_images(src_img_folder, dst_img_folder, max_resolution="512x512", divisible_by=2, interpolation=None, save_as_png=False, copy_associated_files=False): # Split the max_resolution string by "," and strip any whitespaces max_resolutions = [res.strip() for res in max_resolution.split(',')] # # Calculate max_pixels from max_resolution string # max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1]) # Create destination folder if it does not exist if not os.path.exists(dst_img_folder): os.makedirs(dst_img_folder) # Select interpolation method if interpolation == 'lanczos4': cv2_interpolation = cv2.INTER_LANCZOS4 elif interpolation == 'cubic': cv2_interpolation = cv2.INTER_CUBIC else: cv2_interpolation = cv2.INTER_AREA # Iterate through all files in src_img_folder img_exts = (".png", ".jpg", ".jpeg", ".webp", ".bmp") # copy from train_util.py for filename in os.listdir(src_img_folder): # Check if the image is png, jpg or webp etc... if not filename.endswith(img_exts): # Copy the file to the destination folder if not png, jpg or webp etc (.txt or .caption or etc.) shutil.copy(os.path.join(src_img_folder, filename), os.path.join(dst_img_folder, filename)) continue # Load image image = Image.open(os.path.join(src_img_folder, filename)) if not image.mode == "RGB": image = image.convert("RGB") img = np.array(image, np.uint8) base, _ = os.path.splitext(filename) for max_resolution in max_resolutions: # Calculate max_pixels from max_resolution string max_pixels = int(max_resolution.split("x")[0]) * int(max_resolution.split("x")[1]) # Calculate current number of pixels current_pixels = img.shape[0] * img.shape[1] # Calculate current resolution current_resolution = (img.shape[0], img.shape[1]) # Calculate target resolution target_resolution = (int(max_resolution.split("x")[0]), int(max_resolution.split("x")[1])) # Skip to the next image if the current resolution is less than the target resolution if current_resolution[0] < target_resolution[0] or current_resolution[1] < target_resolution[1]: print(f"Skipped image: {filename} as its resolution is smaller than target resolution") continue # Check if the image needs resizing if current_pixels > max_pixels: # Calculate scaling factor scale_factor = max_pixels / current_pixels # Calculate new dimensions new_height = int(img.shape[0] * math.sqrt(scale_factor)) new_width = int(img.shape[1] * math.sqrt(scale_factor)) # Resize image img = cv2.resize(img, (new_width, new_height), interpolation=cv2_interpolation) else: new_height, new_width = img.shape[0:2] # Calculate the new height and width that are divisible by divisible_by (with/without resizing) new_height = new_height if new_height % divisible_by == 0 else new_height - new_height % divisible_by new_width = new_width if new_width % divisible_by == 0 else new_width - new_width % divisible_by # Center crop the image to the calculated dimensions y = int((img.shape[0] - new_height) / 2) x = int((img.shape[1] - new_width) / 2) img = img[y:y + new_height, x:x + new_width] # Split filename into base and extension new_filename = base + '+' + max_resolution + ('.png' if save_as_png else '.jpg') # Save resized image in dst_img_folder # cv2.imwrite(os.path.join(dst_img_folder, new_filename), img, [cv2.IMWRITE_JPEG_QUALITY, 100]) image = Image.fromarray(img) image.save(os.path.join(dst_img_folder, new_filename), quality=100) proc = "Resized" if current_pixels > max_pixels else "Saved" print(f"{proc} image: {filename} with size {img.shape[0]}x{img.shape[1]} as {new_filename}") # If other files with same basename, copy them with resolution suffix if copy_associated_files: asoc_files = glob.glob(os.path.join(src_img_folder, base + ".*")) for asoc_file in asoc_files: ext = os.path.splitext(asoc_file)[1] if ext in img_exts: continue for max_resolution in max_resolutions: new_asoc_file = base + '+' + max_resolution + ext print(f"Copy {asoc_file} as {new_asoc_file}") shutil.copy(os.path.join(src_img_folder, asoc_file), os.path.join(dst_img_folder, new_asoc_file)) def setup_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser( description='Resize images in a folder to a specified max resolution(s) / 指定されたフォルダ内の画像を指定した最大画像サイズ（面積）以下にアスペクト比を維持したままリサイズします') parser.add_argument('src_img_folder', type=str, help='Source folder containing the images / 元画像のフォルダ') parser.add_argument('dst_img_folder', type=str, help='Destination folder to save the resized images / リサイズ後の画像を保存するフォルダ') parser.add_argument('--max_resolution', type=str, help='Maximum resolution(s) in the format "512x512,384x384, etc, etc" / 最大画像サイズをカンマ区切りで指定 ("512x512,384x384, etc, etc" など)', default="512x512,384x384,256x256,128x128") parser.add_argument('--divisible_by', type=int, help='Ensure new dimensions are divisible by this value / リサイズ後の画像のサイズをこの値で割り切れるようにします', default=1) parser.add_argument('--interpolation', type=str, choices=['area', 'cubic', 'lanczos4'], default='area', help='Interpolation method for resizing / リサイズ時の補完方法') parser.add_argument('--save_as_png', action='store_true', help='Save as png format / png形式で保存') parser.add_argument('--copy_associated_files', action='store_true', help='Copy files with same base name to images (captions etc) / 画像と同じファイル名（拡張子を除く）のファイルもコピーする') return parser def main(): parser = setup_parser() args = parser.parse_args() resize_images(args.src_img_folder, args.dst_img_folder, args.max_resolution, args.divisible_by, args.interpolation, args.save_as_png, args.copy_associated_files) if __name__ == '__main__': main()