IOPaint/lama_cleaner/model/base.py

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2022-04-15 18:11:51 +02:00
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):
...
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@staticmethod
@abc.abstractmethod
def is_downloaded() -> bool:
...
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@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]