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| import os | |
| from PIL import Image, ImageOps | |
| import math | |
| import tqdm | |
| from modules import paths, shared, images, deepbooru | |
| from modules.textual_inversion import autocrop | |
| def preprocess(id_task, process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, 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=0.15, 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_keep_original_size, 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 caption: | |
| 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 = f"{existing_caption} {caption}" | |
| elif params.preprocess_txt_action == 'append' and existing_caption: | |
| caption = f"{caption} {existing_caption}" | |
| elif params.preprocess_txt_action == 'copy' and existing_caption: | |
| caption = existing_caption | |
| caption = caption.strip() | |
| if caption: | |
| 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)) | |
| wh = 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: (wh[0]*wh[1], -err(*wh))[::1 if objective=='Maximize area' else -1], | |
| default=None | |
| ) | |
| return wh and center_crop(image, *wh) | |
| def preprocess_work(process_src, process_dst, process_width, process_height, preprocess_txt_action, process_keep_original_size, 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) | |
| img = ImageOps.exif_transpose(img) | |
| img = img.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 = f"{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(paths.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_keep_original_size: | |
| save_pic(img, index, params, existing_caption=existing_caption) | |
| 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() | |