175 lines
7.1 KiB
Python
175 lines
7.1 KiB
Python
import os
|
|
from PIL import Image, ImageOps
|
|
import math
|
|
import platform
|
|
import sys
|
|
import tqdm
|
|
import time
|
|
|
|
from modules import shared, images
|
|
from modules.paths import models_path
|
|
from modules.shared import opts, cmd_opts
|
|
from modules.textual_inversion import autocrop
|
|
if cmd_opts.deepdanbooru:
|
|
import modules.deepbooru as deepbooru
|
|
|
|
|
|
def preprocess(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):
|
|
try:
|
|
if process_caption:
|
|
shared.interrogator.load()
|
|
|
|
if process_caption_deepbooru:
|
|
db_opts = deepbooru.create_deepbooru_opts()
|
|
db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
|
|
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
|
|
|
|
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)
|
|
|
|
finally:
|
|
|
|
if process_caption:
|
|
shared.interrogator.send_blip_to_ram()
|
|
|
|
if process_caption_deepbooru:
|
|
deepbooru.release_process()
|
|
|
|
|
|
|
|
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):
|
|
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 = os.listdir(src)
|
|
|
|
shared.state.textinfo = "Preprocessing..."
|
|
shared.state.job_count = len(files)
|
|
|
|
def save_pic_with_caption(image, index, existing_caption=None):
|
|
caption = ""
|
|
|
|
if process_caption:
|
|
caption += shared.interrogator.generate_caption(image)
|
|
|
|
if process_caption_deepbooru:
|
|
if len(caption) > 0:
|
|
caption += ", "
|
|
caption += deepbooru.get_tags_from_process(image)
|
|
|
|
filename_part = filename
|
|
filename_part = os.path.splitext(filename_part)[0]
|
|
filename_part = os.path.basename(filename_part)
|
|
|
|
basename = f"{index:05}-{subindex[0]}-{filename_part}"
|
|
image.save(os.path.join(dst, f"{basename}.png"))
|
|
|
|
if preprocess_txt_action == 'prepend' and existing_caption:
|
|
caption = existing_caption + ' ' + caption
|
|
elif preprocess_txt_action == 'append' and existing_caption:
|
|
caption = caption + ' ' + existing_caption
|
|
elif preprocess_txt_action == 'copy' and existing_caption:
|
|
caption = existing_caption
|
|
|
|
caption = caption.strip()
|
|
|
|
if len(caption) > 0:
|
|
with open(os.path.join(dst, f"{basename}.txt"), "w", encoding="utf8") as file:
|
|
file.write(caption)
|
|
|
|
subindex[0] += 1
|
|
|
|
def save_pic(image, index, existing_caption=None):
|
|
save_pic_with_caption(image, index, existing_caption=existing_caption)
|
|
|
|
if process_flip:
|
|
save_pic_with_caption(ImageOps.mirror(image), index, existing_caption=existing_caption)
|
|
|
|
def split_pic(image, inverse_xy):
|
|
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
|
|
|
|
|
|
for index, imagefile in enumerate(tqdm.tqdm(files)):
|
|
subindex = [0]
|
|
filename = os.path.join(src, imagefile)
|
|
try:
|
|
img = Image.open(filename).convert("RGB")
|
|
except Exception:
|
|
continue
|
|
|
|
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):
|
|
save_pic(splitted, index, 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, existing_caption=existing_caption)
|
|
process_default_resize = False
|
|
|
|
if process_default_resize:
|
|
img = images.resize_image(1, img, width, height)
|
|
save_pic(img, index, existing_caption=existing_caption)
|
|
|
|
shared.state.nextjob() |