import os from PIL import Image, ImageOps import math import platform import sys import tqdm import time from modules import shared, images, deepbooru from modules.paths import models_path from modules.shared import opts, cmd_opts from modules.textual_inversion import autocrop def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None): try: if process_caption: shared.interrogator.load() if process_caption_deepbooru: deepbooru.model.start() preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru, split_threshold, overlap_ratio, process_focal_crop, process_focal_crop_face_weight, process_focal_crop_entropy_weight, process_focal_crop_edges_weight, process_focal_crop_debug, process_multicrop, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold) finally: if process_caption: shared.interrogator.send_blip_to_ram() if process_caption_deepbooru: deepbooru.model.stop() def listfiles(dirname): return os.listdir(dirname) class PreprocessParams: src = None dstdir = None subindex = 0 flip = False process_caption = False process_caption_deepbooru = False preprocess_txt_action = None def save_pic_with_caption(image, index, params: PreprocessParams, existing_caption=None): caption = "" if params.process_caption: caption += shared.interrogator.generate_caption(image) if params.process_caption_deepbooru: if len(caption) > 0: caption += ", " caption += deepbooru.model.tag_multi(image) filename_part = params.src filename_part = os.path.splitext(filename_part)[0] filename_part = os.path.basename(filename_part) basename = f"{index:05}-{params.subindex}-{filename_part}" image.save(os.path.join(params.dstdir, f"{basename}.png")) if params.preprocess_txt_action == 'prepend' and existing_caption: caption = existing_caption + ' ' + caption elif params.preprocess_txt_action == 'append' and existing_caption: caption = caption + ' ' + existing_caption elif params.preprocess_txt_action == 'copy' and existing_caption: caption = existing_caption caption = caption.strip() if len(caption) > 0: with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file: file.write(caption) params.subindex += 1 def save_pic(image, index, params, existing_caption=None): save_pic_with_caption(image, index, params, existing_caption=existing_caption) if params.flip: save_pic_with_caption(ImageOps.mirror(image), index, params, existing_caption=existing_caption) def split_pic(image, inverse_xy, width, height, overlap_ratio): if inverse_xy: from_w, from_h = image.height, image.width to_w, to_h = height, width else: from_w, from_h = image.width, image.height to_w, to_h = width, height h = from_h * to_w // from_w if inverse_xy: image = image.resize((h, to_w)) else: image = image.resize((to_w, h)) split_count = math.ceil((h - to_h * overlap_ratio) / (to_h * (1.0 - overlap_ratio))) y_step = (h - to_h) / (split_count - 1) for i in range(split_count): y = int(y_step * i) if inverse_xy: splitted = image.crop((y, 0, y + to_h, to_w)) else: splitted = image.crop((0, y, to_w, y + to_h)) yield splitted # not using torchvision.transforms.CenterCrop because it doesn't allow float regions def center_crop(image: Image, w: int, h: int): iw, ih = image.size if ih / h < iw / w: sw = w * ih / h box = (iw - sw) / 2, 0, iw - (iw - sw) / 2, ih else: sh = h * iw / w box = 0, (ih - sh) / 2, iw, ih - (ih - sh) / 2 return image.resize((w, h), Image.Resampling.LANCZOS, box) def multicrop_pic(image: Image, mindim, maxdim, minarea, maxarea, objective, threshold): iw, ih = image.size err = lambda w, h: 1-(lambda x: x if x < 1 else 1/x)(iw/ih/(w/h)) try: w, h = max(((w, h) for w in range(mindim, maxdim+1, 64) for h in range(mindim, maxdim+1, 64) if minarea <= w * h <= maxarea and err(w, h) <= threshold), key= lambda wh: ((objective=='Maximize area')*wh[0]*wh[1], -err(*wh)) ) except ValueError: return return center_crop(image, w, h) def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_flip, process_split, process_caption, process_caption_deepbooru=False, split_threshold=0.5, overlap_ratio=0.2, process_focal_crop=False, process_focal_crop_face_weight=0.9, process_focal_crop_entropy_weight=0.3, process_focal_crop_edges_weight=0.5, process_focal_crop_debug=False, process_multicrop=None, process_multicrop_mindim=None, process_multicrop_maxdim=None, process_multicrop_minarea=None, process_multicrop_maxarea=None, process_multicrop_objective=None, process_multicrop_threshold=None): width = process_width height = process_height src = os.path.abspath(process_src) dst = os.path.abspath(process_dst) split_threshold = max(0.0, min(1.0, split_threshold)) overlap_ratio = max(0.0, min(0.9, overlap_ratio)) assert src != dst, 'same directory specified as source and destination' os.makedirs(dst, exist_ok=True) files = listfiles(src) shared.state.job = "preprocess" shared.state.textinfo = "Preprocessing..." shared.state.job_count = len(files) params = PreprocessParams() params.dstdir = dst params.flip = process_flip params.process_caption = process_caption params.process_caption_deepbooru = process_caption_deepbooru params.preprocess_txt_action = preprocess_txt_action pbar = tqdm.tqdm(files) for index, imagefile in enumerate(pbar): params.subindex = 0 filename = os.path.join(src, imagefile) try: img = Image.open(filename).convert("RGB") except Exception: continue description = f"Preprocessing [Image {index}/{len(files)}]" pbar.set_description(description) shared.state.textinfo = description params.src = filename existing_caption = None existing_caption_filename = os.path.splitext(filename)[0] + '.txt' if os.path.exists(existing_caption_filename): with open(existing_caption_filename, 'r', encoding="utf8") as file: existing_caption = file.read() if shared.state.interrupted: break if img.height > img.width: ratio = (img.width * height) / (img.height * width) inverse_xy = False else: ratio = (img.height * width) / (img.width * height) inverse_xy = True process_default_resize = True if process_split and ratio < 1.0 and ratio <= split_threshold: for splitted in split_pic(img, inverse_xy, width, height, overlap_ratio): save_pic(splitted, index, params, existing_caption=existing_caption) process_default_resize = False if process_focal_crop and img.height != img.width: dnn_model_path = None try: dnn_model_path = autocrop.download_and_cache_models(os.path.join(models_path, "opencv")) except Exception as e: print("Unable to load face detection model for auto crop selection. Falling back to lower quality haar method.", e) autocrop_settings = autocrop.Settings( crop_width = width, crop_height = height, face_points_weight = process_focal_crop_face_weight, entropy_points_weight = process_focal_crop_entropy_weight, corner_points_weight = process_focal_crop_edges_weight, annotate_image = process_focal_crop_debug, dnn_model_path = dnn_model_path, ) for focal in autocrop.crop_image(img, autocrop_settings): save_pic(focal, index, params, existing_caption=existing_caption) process_default_resize = False if process_multicrop: cropped = multicrop_pic(img, process_multicrop_mindim, process_multicrop_maxdim, process_multicrop_minarea, process_multicrop_maxarea, process_multicrop_objective, process_multicrop_threshold) if cropped is not None: save_pic(cropped, index, params, existing_caption=existing_caption) else: print(f"skipped {img.width}x{img.height} image {filename} (can't find suitable size within error threshold)") process_default_resize = False if process_default_resize: img = images.resize_image(1, img, width, height) save_pic(img, index, params, existing_caption=existing_caption) shared.state.nextjob()