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