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import numpy as np
import cv2
from typing import Dict, List
from collections import OrderedDict
import sys
from utils.registry import Registry
from utils.textblock_mask import extract_ballon_mask
from utils.imgproc_utils import enlarge_window
from ..base import BaseModule, DEFAULT_DEVICE, soft_empty_cache, DEVICE_SELECTOR, GPUINTENSIVE_SET, TORCH_DTYPE_MAP, BF16_SUPPORTED
from ..textdetector import TextBlock
INPAINTERS = Registry('inpainters')
register_inpainter = INPAINTERS.register_module
def inpaint_handle_alpha_channel(original_alpha, mask):
'''
perhaps a better idea is to feed the alpha into inpainting model, but it'll double the cost
for now it just return the original alpha
'''
result_alpha = original_alpha.copy()
# Analyze the alpha values around the original mask to determine appropriate transparency
mask_dilated = cv2.dilate((mask > 127).astype(np.uint8), np.ones((15, 15), np.uint8), iterations=1)
surrounding_mask = mask_dilated - (mask > 127).astype(np.uint8)
if np.any(surrounding_mask > 0):
surrounding_alpha = original_alpha[surrounding_mask > 0]
if len(surrounding_alpha) > 0:
median_surrounding_alpha = np.median(surrounding_alpha)
# If surrounding area is mostly transparent (median alpha < 128),
# make inpainted areas transparent too
if median_surrounding_alpha < 128:
inpainted_mask = (mask > 127)
result_alpha[inpainted_mask] = median_surrounding_alpha
return result_alpha
class InpainterBase(BaseModule):
inpaint_by_block = True
check_need_inpaint = True
_postprocess_hooks = OrderedDict()
_preprocess_hooks = OrderedDict()
def __init__(self, **params) -> None:
super().__init__(**params)
self.name = ''
for key in INPAINTERS.module_dict:
if INPAINTERS.module_dict[key] == self.__class__:
self.name = key
break
def memory_safe_inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
'''
handle cuda out of memory
'''
try:
return self._inpaint(img, mask, textblock_list)
except Exception as e:
if DEFAULT_DEVICE == 'cuda' and isinstance(e, torch.cuda.OutOfMemoryError):
soft_empty_cache()
try:
return self._inpaint(img, mask, textblock_list)
except Exception as ee:
if isinstance(ee, torch.cuda.OutOfMemoryError):
self.logger.warning(f'CUDA out of memory while calling {self.name}, fall back to cpu...\n\
if running into it frequently, consider lowering the inpaint_size')
self.moveToDevice('cpu')
inpainted = self._inpaint(img, mask, textblock_list)
precision = None
if hasattr(self, 'precision'):
precision = self.precision
self.moveToDevice('cuda', precision)
return inpainted
else:
raise e
def inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None, check_need_inpaint: bool = False) -> np.ndarray:
if not self.all_model_loaded():
self.load_model()
# Handle RGBA images by preserving alpha channel
original_alpha = None
if len(img.shape) == 3 and img.shape[2] == 4:
original_alpha = img[:, :, 3:4] # Keep alpha channel
img_rgb = img[:, :, :3] # Use only RGB for inpainting
else:
img_rgb = img
if not self.inpaint_by_block or textblock_list is None:
if check_need_inpaint:
ballon_msk, non_text_msk = extract_ballon_mask(img_rgb, mask)
if ballon_msk is not None:
non_text_region = np.where(non_text_msk > 0)
non_text_px = img_rgb[non_text_region]
average_bg_color = np.median(non_text_px, axis=0)
std_rgb = np.std(non_text_px - average_bg_color, axis=0)
std_max = np.max(std_rgb)
inpaint_thresh = 7 if np.std(std_rgb) > 1 else 10
if std_max < inpaint_thresh:
result_rgb = img_rgb.copy()
result_rgb[np.where(ballon_msk > 0)] = average_bg_color
# Recombine with alpha if original was RGBA
if original_alpha is not None:
return np.concatenate([result_rgb, original_alpha], axis=2)
return result_rgb
result_rgb = self.memory_safe_inpaint(img_rgb, mask, textblock_list)
# Recombine with alpha if original was RGBA
if original_alpha is not None:
result_alpha = inpaint_handle_alpha_channel(original_alpha, mask)
return np.concatenate([result_rgb, result_alpha], axis=2)
return result_rgb
else:
im_h, im_w = img_rgb.shape[:2]
inpainted = np.copy(img_rgb)
# Preserve original mask for transparency analysis
original_mask = mask.copy()
for blk in textblock_list:
xyxy = blk.xyxy
xyxy_e = enlarge_window(xyxy, im_w, im_h, ratio=1.7)
im = inpainted[xyxy_e[1]:xyxy_e[3], xyxy_e[0]:xyxy_e[2]]
msk = mask[xyxy_e[1]:xyxy_e[3], xyxy_e[0]:xyxy_e[2]]
need_inpaint = True
if self.check_need_inpaint or check_need_inpaint:
ballon_msk, non_text_msk = extract_ballon_mask(im, msk)
if ballon_msk is not None:
non_text_region = np.where(non_text_msk > 0)
non_text_px = im[non_text_region]
average_bg_color = np.median(non_text_px, axis=0)
std_rgb = np.std(non_text_px - average_bg_color, axis=0)
std_max = np.max(std_rgb)
inpaint_thresh = 7 if np.std(std_rgb) > 1 else 10
if std_max < inpaint_thresh:
need_inpaint = False
im[np.where(ballon_msk > 0)] = average_bg_color
# cv2.imshow('im', im)
# cv2.imshow('ballon', ballon_msk)
# cv2.imshow('non_text', non_text_msk)
# cv2.waitKey(0)
if need_inpaint:
inpainted[xyxy_e[1]:xyxy_e[3], xyxy_e[0]:xyxy_e[2]] = self.memory_safe_inpaint(im, msk)
mask[xyxy[1]:xyxy[3], xyxy[0]:xyxy[2]] = 0
# Recombine with alpha if original was RGBA
if original_alpha is not None:
result_alpha = inpaint_handle_alpha_channel(original_alpha, original_mask)
return np.concatenate([inpainted, result_alpha], axis=2)
return inpainted
def _inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
raise NotImplementedError
def moveToDevice(self, device: str, precision: str = None):
raise not NotImplementedError
@register_inpainter('opencv-tela')
class OpenCVInpainter(InpainterBase):
def __init__(self, **params) -> None:
super().__init__(**params)
self.inpaint_method = lambda img, mask, *args, **kwargs: cv2.inpaint(img, mask, 3, cv2.INPAINT_NS)
def _inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
return self.inpaint_method(img, mask)
def is_computational_intensive(self) -> bool:
return True
def is_cpu_intensive(self) -> bool:
return True
@register_inpainter('patchmatch')
class PatchmatchInpainter(InpainterBase):
if sys.platform == 'darwin':
download_file_list = [{
'url': 'https://github.com/dmMaze/PyPatchMatchInpaint/releases/download/v1.0/macos_arm64_patchmatch_libs.7z',
'sha256_pre_calculated': ['843704ab096d3afd8709abe2a2c525ce3a836bb0a629ed1ee9b8f5cee9938310', '849ca84759385d410c9587d69690e668822a3fc376ce2219e583e7e0be5b5e9a'],
'files': ['macos_libopencv_world.4.8.0.dylib', 'macos_libpatchmatch_inpaint.dylib'],
'save_dir': 'data/libs',
'archived_files': 'macos_patchmatch_libs.7z',
'archive_sha256_pre_calculated': '9f332c888be0f160dbe9f6d6887eb698a302e62f4c102a0f24359c540d5858ea'
}]
elif sys.platform == 'win32':
download_file_list = [{
'url': 'https://github.com/dmMaze/PyPatchMatchInpaint/releases/download/v1.0/windows_patchmatch_libs.7z',
'sha256_pre_calculated': ['3b7619caa29dc3352b939de4e9981217a9585a13a756e1101a50c90c100acd8d', '0ba60cfe664c97629daa7e4d05c0888ebfe3edcb3feaf1ed5a14544079c6d7af'],
'files': ['opencv_world455.dll', 'patchmatch_inpaint.dll'],
'save_dir': 'data/libs',
'archived_files': 'windows_patchmatch_libs.7z',
'archive_sha256_pre_calculated': 'c991ff61f7cb3efaf8e75d957e62d56ba646083bc25535f913ac65775c16ca65'
}]
def __init__(self, **params) -> None:
super().__init__(**params)
from . import patch_match
self.inpaint_method = lambda img, mask, *args, **kwargs: patch_match.inpaint(img, mask, patch_size=3)
def _inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
return self.inpaint_method(img, mask)
def is_computational_intensive(self) -> bool:
return True
def is_cpu_intensive(self) -> bool:
return True
import torch
from utils.imgproc_utils import resize_keepasp
from .aot import AOTGenerator, load_aot_model
@register_inpainter('aot')
class AOTInpainter(InpainterBase):
params = {
'inpaint_size': {
'type': 'selector',
'options': [
1024,
2048
],
'value': 2048
},
'device': DEVICE_SELECTOR(),
'description': 'manga-image-translator inpainter'
}
device = DEFAULT_DEVICE
inpaint_size = 2048
model: AOTGenerator = None
_load_model_keys = {'model'}
download_file_list = [{
'url': 'https://github.com/zyddnys/manga-image-translator/releases/download/beta-0.3/inpainting.ckpt',
'sha256_pre_calculated': '878d541c68648969bc1b042a6e997f3a58e49b6c07c5636ad55130736977149f',
'files': 'data/models/aot_inpainter.ckpt',
}]
def __init__(self, **params) -> None:
super().__init__(**params)
self.device = self.params['device']['value']
self.inpaint_size = int(self.params['inpaint_size']['value'])
self.model: AOTGenerator = None
def _load_model(self):
AOTMODEL_PATH = 'data/models/aot_inpainter.ckpt'
self.model = load_aot_model(AOTMODEL_PATH, self.device)
def moveToDevice(self, device: str, precision: str = None):
self.model.to(device)
self.device = device
def inpaint_preprocess(self, img: np.ndarray, mask: np.ndarray) -> np.ndarray:
img_original = np.copy(img)
mask_original = np.copy(mask)
mask_original[mask_original < 127] = 0
mask_original[mask_original >= 127] = 1
mask_original = mask_original[:, :, None]
new_shape = self.inpaint_size if max(img.shape[0: 2]) > self.inpaint_size else None
img = resize_keepasp(img, new_shape, stride=None)
mask = resize_keepasp(mask, new_shape, stride=None)
im_h, im_w = img.shape[:2]
pad_bottom = 128 - im_h if im_h < 128 else 0
pad_right = 128 - im_w if im_w < 128 else 0
mask = cv2.copyMakeBorder(mask, 0, pad_bottom, 0, pad_right, cv2.BORDER_REFLECT)
img = cv2.copyMakeBorder(img, 0, pad_bottom, 0, pad_right, cv2.BORDER_REFLECT)
img_torch = torch.from_numpy(img).permute(2, 0, 1).unsqueeze_(0).float() / 127.5 - 1.0
mask_torch = torch.from_numpy(mask).unsqueeze_(0).unsqueeze_(0).float() / 255.0
mask_torch[mask_torch < 0.5] = 0
mask_torch[mask_torch >= 0.5] = 1
if self.device != 'cpu':
img_torch = img_torch.to(self.device)
mask_torch = mask_torch.to(self.device)
img_torch *= (1 - mask_torch)
return img_torch, mask_torch, img_original, mask_original, pad_bottom, pad_right
@torch.no_grad()
def _inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
im_h, im_w = img.shape[:2]
img_torch, mask_torch, img_original, mask_original, pad_bottom, pad_right = self.inpaint_preprocess(img, mask)
img_inpainted_torch = self.model(img_torch, mask_torch)
img_inpainted = ((img_inpainted_torch.cpu().squeeze_(0).permute(1, 2, 0).numpy() + 1.0) * 127.5)
img_inpainted = (np.clip(np.round(img_inpainted), 0, 255)).astype(np.uint8)
if pad_bottom > 0:
img_inpainted = img_inpainted[:-pad_bottom]
if pad_right > 0:
img_inpainted = img_inpainted[:, :-pad_right]
new_shape = img_inpainted.shape[:2]
if new_shape[0] != im_h or new_shape[1] != im_w :
img_inpainted = cv2.resize(img_inpainted, (im_w, im_h), interpolation = cv2.INTER_LINEAR)
img_inpainted = img_inpainted * mask_original + img_original * (1 - mask_original)
return img_inpainted
def updateParam(self, param_key: str, param_content):
super().updateParam(param_key, param_content)
if param_key == 'device':
param_device = self.params['device']['value']
if self.model is not None:
self.model.to(param_device)
self.device = param_device
elif param_key == 'inpaint_size':
self.inpaint_size = int(self.params['inpaint_size']['value'])
from .lama import LamaFourier, load_lama_mpe
@register_inpainter('lama_mpe')
class LamaInpainterMPE(InpainterBase):
params = {
'inpaint_size': {
'type': 'selector',
'options': [
1024,
2048
],
'value': 2048
},
'device': DEVICE_SELECTOR(not_supported=['privateuseone'])
}
download_file_list = [{
'url': 'https://github.com/zyddnys/manga-image-translator/releases/download/beta-0.3/inpainting_lama_mpe.ckpt',
'sha256_pre_calculated': 'd625aa1b3e0d0408acfd6928aa84f005867aa8dbb9162480346a4e20660786cc',
'files': 'data/models/lama_mpe.ckpt',
}]
_load_model_keys = {'model'}
def __init__(self, **params) -> None:
super().__init__(**params)
self.device = self.params['device']['value']
self.inpaint_size = int(self.params['inpaint_size']['value'])
self.precision = 'fp32'
self.model: LamaFourier = None
def _load_model(self):
self.model = load_lama_mpe(r'data/models/lama_mpe.ckpt', self.device)
def inpaint_preprocess(self, img: np.ndarray, mask: np.ndarray) -> np.ndarray:
img_original = np.copy(img)
mask_original = np.copy(mask)
mask_original[mask_original < 127] = 0
mask_original[mask_original >= 127] = 1
mask_original = mask_original[:, :, None]
new_shape = self.inpaint_size if max(img.shape[0: 2]) > self.inpaint_size else None
# high resolution input could produce cloudy artifacts
img = resize_keepasp(img, new_shape, stride=64)
mask = resize_keepasp(mask, new_shape, stride=64)
im_h, im_w = img.shape[:2]
longer = max(im_h, im_w)
pad_bottom = longer - im_h if im_h < longer else 0
pad_right = longer - im_w if im_w < longer else 0
mask = cv2.copyMakeBorder(mask, 0, pad_bottom, 0, pad_right, cv2.BORDER_REFLECT)
img = cv2.copyMakeBorder(img, 0, pad_bottom, 0, pad_right, cv2.BORDER_REFLECT)
img_torch = torch.from_numpy(img).permute(2, 0, 1).unsqueeze_(0).float() / 255.0
mask_torch = torch.from_numpy(mask).unsqueeze_(0).unsqueeze_(0).float() / 255.0
mask_torch[mask_torch < 0.5] = 0
mask_torch[mask_torch >= 0.5] = 1
rel_pos, _, direct = self.model.load_masked_position_encoding(mask_torch[0][0].numpy())
rel_pos = torch.LongTensor(rel_pos).unsqueeze_(0)
direct = torch.LongTensor(direct).unsqueeze_(0)
if self.device != 'cpu':
img_torch = img_torch.to(self.device)
mask_torch = mask_torch.to(self.device)
rel_pos = rel_pos.to(self.device)
direct = direct.to(self.device)
img_torch *= (1 - mask_torch)
return img_torch, mask_torch, rel_pos, direct, img_original, mask_original, pad_bottom, pad_right
@torch.no_grad()
def _inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
im_h, im_w = img.shape[:2]
img_torch, mask_torch, rel_pos, direct, img_original, mask_original, pad_bottom, pad_right = self.inpaint_preprocess(img, mask)
precision = TORCH_DTYPE_MAP[self.precision]
if self.device in {'cuda'}:
try:
with torch.autocast(device_type=self.device, dtype=precision):
img_inpainted_torch = self.model(img_torch, mask_torch, rel_pos, direct)
except Exception as e:
self.logger.error(e)
self.logger.error(f'{precision} inference is not supported for this device, use fp32 instead.')
img_inpainted_torch = self.model(img_torch, mask_torch, rel_pos, direct)
else:
img_inpainted_torch = self.model(img_torch, mask_torch, rel_pos, direct)
img_inpainted = (img_inpainted_torch.to(device='cpu', dtype=torch.float32).squeeze_(0).permute(1, 2, 0).numpy() * 255)
img_inpainted = (np.clip(np.round(img_inpainted), 0, 255)).astype(np.uint8)
if pad_bottom > 0:
img_inpainted = img_inpainted[:-pad_bottom]
if pad_right > 0:
img_inpainted = img_inpainted[:, :-pad_right]
new_shape = img_inpainted.shape[:2]
if new_shape[0] != im_h or new_shape[1] != im_w :
img_inpainted = cv2.resize(img_inpainted, (im_w, im_h), interpolation = cv2.INTER_LINEAR)
img_inpainted = img_inpainted * mask_original + img_original * (1 - mask_original)
return img_inpainted
def updateParam(self, param_key: str, param_content):
super().updateParam(param_key, param_content)
if param_key == 'device':
param_device = self.params['device']['value']
if self.model is not None:
self.model.to(param_device)
self.device = param_device
elif param_key == 'inpaint_size':
self.inpaint_size = int(self.params['inpaint_size']['value'])
elif param_key == 'precision':
precision = self.params['precision']['value']
self.precision = precision
def moveToDevice(self, device: str, precision: str = None):
self.model.to(device)
self.device = device
if precision is not None:
self.precision = precision
@register_inpainter('lama_large_512px')
class LamaLarge(LamaInpainterMPE):
params = {
'inpaint_size': {
'type': 'selector',
'options': [
512,
768,
1024,
1536,
2048
],
'value': 1536,
},
'device': DEVICE_SELECTOR(not_supported=['privateuseone']),
'precision': {
'type': 'selector',
'options': [
'fp32',
'bf16'
],
'value': 'bf16' if BF16_SUPPORTED == 'cuda' else 'fp32'
},
}
download_file_list = [{
'url': 'https://huggingface.co/dreMaz/AnimeMangaInpainting/resolve/main/lama_large_512px.ckpt',
'sha256_pre_calculated': '11d30fbb3000fb2eceae318b75d9ced9229d99ae990a7f8b3ac35c8d31f2c935',
'files': 'data/models/lama_large_512px.ckpt',
}]
def __init__(self, **params) -> None:
super().__init__(**params)
self.precision = self.params['precision']['value']
def _load_model(self):
device = self.params['device']['value']
precision = self.params['precision']['value']
self.model = load_lama_mpe(r'data/models/lama_large_512px.ckpt', device='cpu', use_mpe=False, large_arch=True)
self.moveToDevice(device, precision=precision)
# LAMA_ORI: LamaFourier = None
# @register_inpainter('lama_ori')
# class LamaInpainterORI(InpainterBase):
# params = {
# 'inpaint_size': {
# 'type': 'selector',
# 'options': [
# 1024,
# 2048
# ],
# 'value': 2048
# },
# 'device': {
# 'type': 'selector',
# 'options': [
# 'cpu',
# 'cuda'
# ],
# 'value': DEFAULT_DEVICE
# }
# }
# device = DEFAULT_DEVICE
# inpaint_size = 2048
# def setup_inpainter(self):
# global LAMA_ORI
# self.device = self.params['device']['value']
# if LAMA_ORI is None:
# self.model = LAMA_ORI = load_lama_mpe(r'data/models/lama_org.ckpt', self.device, False)
# else:
# self.model = LAMA_ORI
# self.model.to(self.device)
# self.inpaint_by_block = True if self.device == 'cuda' else False
# self.inpaint_size = int(self.params['inpaint_size']['value'])
# def inpaint_preprocess(self, img: np.ndarray, mask: np.ndarray) -> np.ndarray:
# img_original = np.copy(img)
# mask_original = np.copy(mask)
# mask_original[mask_original < 127] = 0
# mask_original[mask_original >= 127] = 1
# mask_original = mask_original[:, :, None]
# new_shape = self.inpaint_size if max(img.shape[0: 2]) > self.inpaint_size else None
# # high resolution input could produce cloudy artifacts
# img = resize_keepasp(img, new_shape, stride=64)
# mask = resize_keepasp(mask, new_shape, stride=64)
# im_h, im_w = img.shape[:2]
# longer = max(im_h, im_w)
# pad_bottom = longer - im_h if im_h < longer else 0
# pad_right = longer - im_w if im_w < longer else 0
# mask = cv2.copyMakeBorder(mask, 0, pad_bottom, 0, pad_right, cv2.BORDER_REFLECT)
# img = cv2.copyMakeBorder(img, 0, pad_bottom, 0, pad_right, cv2.BORDER_REFLECT)
# img_torch = torch.from_numpy(img).permute(2, 0, 1).unsqueeze_(0).float() / 255.0
# mask_torch = torch.from_numpy(mask).unsqueeze_(0).unsqueeze_(0).float() / 255.0
# mask_torch[mask_torch < 0.5] = 0
# mask_torch[mask_torch >= 0.5] = 1
# rel_pos, _, direct = self.model.load_masked_position_encoding(mask_torch[0][0].numpy())
# rel_pos = torch.LongTensor(rel_pos).unsqueeze_(0)
# direct = torch.LongTensor(direct).unsqueeze_(0)
# if self.device == 'cuda':
# img_torch = img_torch.cuda()
# mask_torch = mask_torch.cuda()
# rel_pos = rel_pos.cuda()
# direct = direct.cuda()
# img_torch *= (1 - mask_torch)
# return img_torch, mask_torch, rel_pos, direct, img_original, mask_original, pad_bottom, pad_right
# @torch.no_grad()
# def _inpaint(self, img: np.ndarray, mask: np.ndarray, textblock_list: List[TextBlock] = None) -> np.ndarray:
# im_h, im_w = img.shape[:2]
# img_torch, mask_torch, rel_pos, direct, img_original, mask_original, pad_bottom, pad_right = self.inpaint_preprocess(img, mask)
# img_inpainted_torch = self.model(img_torch, mask_torch, rel_pos, direct)
# img_inpainted = (img_inpainted_torch.cpu().squeeze_(0).permute(1, 2, 0).numpy() * 255).astype(np.uint8)
# if pad_bottom > 0:
# img_inpainted = img_inpainted[:-pad_bottom]
# if pad_right > 0:
# img_inpainted = img_inpainted[:, :-pad_right]
# new_shape = img_inpainted.shape[:2]
# if new_shape[0] != im_h or new_shape[1] != im_w :
# img_inpainted = cv2.resize(img_inpainted, (im_w, im_h), interpolation = cv2.INTER_LINEAR)
# img_inpainted = img_inpainted * mask_original + img_original * (1 - mask_original)
# return img_inpainted
# def updateParam(self, param_key: str, param_content):
# super().updateParam(param_key, param_content)
# if param_key == 'device':
# param_device = self.params['device']['value']
# self.model.to(param_device)
# self.device = param_device
# if param_device == 'cuda':
# self.inpaint_by_block = False
# else:
# self.inpaint_by_block = True
# elif param_key == 'inpaint_size':
# self.inpaint_size = int(self.params['inpaint_size']['value']) |