128 lines
4.1 KiB
Python
128 lines
4.1 KiB
Python
import abc
|
|
|
|
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:
|
|
pad_mod = 8
|
|
|
|
def __init__(self, device):
|
|
"""
|
|
|
|
Args:
|
|
device:
|
|
"""
|
|
self.device = device
|
|
self.init_model(device)
|
|
|
|
@abc.abstractmethod
|
|
def init_model(self, device):
|
|
...
|
|
|
|
@staticmethod
|
|
@abc.abstractmethod
|
|
def is_downloaded() -> bool:
|
|
...
|
|
|
|
@abc.abstractmethod
|
|
def forward(self, image, mask, config: Config):
|
|
"""Input image and output image have same size
|
|
image: [H, W, C] RGB
|
|
mask: [H, W]
|
|
return: BGR IMAGE
|
|
"""
|
|
...
|
|
|
|
def _pad_forward(self, image, mask, config: Config):
|
|
origin_height, origin_width = image.shape[:2]
|
|
padd_image = pad_img_to_modulo(image, mod=self.pad_mod)
|
|
padd_mask = pad_img_to_modulo(mask, mod=self.pad_mod)
|
|
result = self.forward(padd_image, padd_mask, config)
|
|
result = result[0:origin_height, 0:origin_width, :]
|
|
|
|
original_pixel_indices = mask != 255
|
|
result[original_pixel_indices] = image[:, :, ::-1][original_pixel_indices]
|
|
return result
|
|
|
|
@torch.no_grad()
|
|
def __call__(self, image, mask, config: Config):
|
|
"""
|
|
image: [H, W, C] RGB, not normalized
|
|
mask: [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 != 255
|
|
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 _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
|
|
"""
|
|
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 = max(cx - w // 2, 0)
|
|
t = max(cy - h // 2, 0)
|
|
r = min(cx + w // 2, img_w)
|
|
b = min(cy + h // 2, 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 self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]
|