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import abc
from typing import Optional
import cv2
import torch
from loguru import logger
from lama_cleaner.helper import boxes_from_mask, resize_max_size, pad_img_to_modulo
from lama_cleaner.schema import Config, HDStrategy
class InpaintModel:
min_size: Optional[int] = None
pad_mod = 8
pad_to_square = False
def __init__(self, device, **kwargs):
"""
Args:
device:
"""
self.device = device
self.init_model(device, **kwargs)
@abc.abstractmethod
def init_model(self, device, **kwargs):
...
@staticmethod
@abc.abstractmethod
def is_downloaded() -> bool:
...
@abc.abstractmethod
def forward(self, image, mask, config: Config):
"""Input images and output images have same size
images: [H, W, C] RGB
masks: [H, W, 1] 255 为 masks 区域
return: BGR IMAGE
"""
...
def _pad_forward(self, image, mask, config: Config):
origin_height, origin_width = image.shape[:2]
pad_image = pad_img_to_modulo(
image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
)
pad_mask = pad_img_to_modulo(
mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
)
logger.info(f"final forward pad size: {pad_image.shape}")
result = self.forward(pad_image, pad_mask, config)
result = result[0:origin_height, 0:origin_width, :]
original_pixel_indices = mask < 127
result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
return result
@torch.no_grad()
def __call__(self, image, mask, config: Config):
"""
images: [H, W, C] RGB, not normalized
masks: [H, W]
return: BGR IMAGE
"""
inpaint_result = None
logger.info(f"hd_strategy: {config.hd_strategy}")
if config.hd_strategy == HDStrategy.CROP:
if max(image.shape) > config.hd_strategy_crop_trigger_size:
logger.info(f"Run crop strategy")
boxes = boxes_from_mask(mask)
crop_result = []
for box in boxes:
crop_image, crop_box = self._run_box(image, mask, box, config)
crop_result.append((crop_image, crop_box))
inpaint_result = image[:, :, ::-1]
for crop_image, crop_box in crop_result:
x1, y1, x2, y2 = crop_box
inpaint_result[y1:y2, x1:x2, :] = crop_image
elif config.hd_strategy == HDStrategy.RESIZE:
if max(image.shape) > config.hd_strategy_resize_limit:
origin_size = image.shape[:2]
downsize_image = resize_max_size(
image, size_limit=config.hd_strategy_resize_limit
)
downsize_mask = resize_max_size(
mask, size_limit=config.hd_strategy_resize_limit
)
logger.info(
f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}"
)
inpaint_result = self._pad_forward(
downsize_image, downsize_mask, config
)
# only paste masked area result
inpaint_result = cv2.resize(
inpaint_result,
(origin_size[1], origin_size[0]),
interpolation=cv2.INTER_CUBIC,
)
original_pixel_indices = mask < 127
inpaint_result[original_pixel_indices] = image[:, :, ::-1][
original_pixel_indices
]
if inpaint_result is None:
inpaint_result = self._pad_forward(image, mask, config)
return inpaint_result
def _crop_box(self, image, mask, box, config: Config):
"""
Args:
image: [H, W, C] RGB
mask: [H, W, 1]
box: [left,top,right,bottom]
Returns:
BGR IMAGE, (l, r, r, b)
"""
box_h = box[3] - box[1]
box_w = box[2] - box[0]
cx = (box[0] + box[2]) // 2
cy = (box[1] + box[3]) // 2
img_h, img_w = image.shape[:2]
w = box_w + config.hd_strategy_crop_margin * 2
h = box_h + config.hd_strategy_crop_margin * 2
_l = cx - w // 2
_r = cx + w // 2
_t = cy - h // 2
_b = cy + h // 2
l = max(_l, 0)
r = min(_r, img_w)
t = max(_t, 0)
b = min(_b, img_h)
# try to get more context when crop around image edge
if _l < 0:
r += abs(_l)
if _r > img_w:
l -= _r - img_w
if _t < 0:
b += abs(_t)
if _b > img_h:
t -= _b - img_h
l = max(l, 0)
r = min(r, img_w)
t = max(t, 0)
b = min(b, img_h)
crop_img = image[t:b, l:r, :]
crop_mask = mask[t:b, l:r]
logger.info(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
return crop_img, crop_mask, [l, t, r, b]
def _run_box(self, image, mask, box, config: Config):
"""
Args:
image: [H, W, C] RGB
mask: [H, W, 1]
box: [left,top,right,bottom]
Returns:
BGR IMAGE
"""
crop_img, crop_mask, [l, t, r, b] = self._crop_box(image, mask, box, config)
return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]
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