248 lines
7.6 KiB
Python
248 lines
7.6 KiB
Python
import abc
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from typing import Optional
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import cv2
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import torch
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import numpy as np
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from loguru import logger
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from lama_cleaner.helper import boxes_from_mask, resize_max_size, pad_img_to_modulo
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from lama_cleaner.schema import Config, HDStrategy
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class InpaintModel:
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min_size: Optional[int] = None
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pad_mod = 8
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pad_to_square = False
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def __init__(self, device, **kwargs):
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"""
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Args:
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device:
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"""
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self.device = device
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self.init_model(device, **kwargs)
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@abc.abstractmethod
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def init_model(self, device, **kwargs):
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...
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@staticmethod
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@abc.abstractmethod
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def is_downloaded() -> bool:
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...
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@abc.abstractmethod
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def forward(self, image, mask, config: Config):
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"""Input images and output images have same size
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images: [H, W, C] RGB
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masks: [H, W, 1] 255 为 masks 区域
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return: BGR IMAGE
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"""
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...
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def _pad_forward(self, image, mask, config: Config):
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origin_height, origin_width = image.shape[:2]
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pad_image = pad_img_to_modulo(
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image, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
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)
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pad_mask = pad_img_to_modulo(
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mask, mod=self.pad_mod, square=self.pad_to_square, min_size=self.min_size
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)
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logger.info(f"final forward pad size: {pad_image.shape}")
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result = self.forward(pad_image, pad_mask, config)
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result = result[0:origin_height, 0:origin_width, :]
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result, image, mask = self.forward_post_process(result, image, mask, config)
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mask = mask[:, :, np.newaxis]
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result = result * (mask / 255) + image[:, :, ::-1] * (1 - (mask / 255))
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return result
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def forward_post_process(self, result, image, mask, config):
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return result, image, mask
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@torch.no_grad()
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def __call__(self, image, mask, config: Config):
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"""
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images: [H, W, C] RGB, not normalized
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masks: [H, W]
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return: BGR IMAGE
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"""
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inpaint_result = None
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logger.info(f"hd_strategy: {config.hd_strategy}")
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if config.hd_strategy == HDStrategy.CROP:
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if max(image.shape) > config.hd_strategy_crop_trigger_size:
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logger.info(f"Run crop strategy")
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boxes = boxes_from_mask(mask)
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crop_result = []
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for box in boxes:
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crop_image, crop_box = self._run_box(image, mask, box, config)
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crop_result.append((crop_image, crop_box))
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inpaint_result = image[:, :, ::-1]
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for crop_image, crop_box in crop_result:
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x1, y1, x2, y2 = crop_box
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inpaint_result[y1:y2, x1:x2, :] = crop_image
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elif config.hd_strategy == HDStrategy.RESIZE:
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if max(image.shape) > config.hd_strategy_resize_limit:
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origin_size = image.shape[:2]
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downsize_image = resize_max_size(
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image, size_limit=config.hd_strategy_resize_limit
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)
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downsize_mask = resize_max_size(
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mask, size_limit=config.hd_strategy_resize_limit
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)
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logger.info(
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f"Run resize strategy, origin size: {image.shape} forward size: {downsize_image.shape}"
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)
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inpaint_result = self._pad_forward(
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downsize_image, downsize_mask, config
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)
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# only paste masked area result
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inpaint_result = cv2.resize(
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inpaint_result,
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(origin_size[1], origin_size[0]),
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interpolation=cv2.INTER_CUBIC,
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)
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original_pixel_indices = mask < 127
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inpaint_result[original_pixel_indices] = image[:, :, ::-1][
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original_pixel_indices
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]
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if inpaint_result is None:
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inpaint_result = self._pad_forward(image, mask, config)
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return inpaint_result
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def _crop_box(self, image, mask, box, config: Config):
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"""
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Args:
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image: [H, W, C] RGB
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mask: [H, W, 1]
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box: [left,top,right,bottom]
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Returns:
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BGR IMAGE, (l, r, r, b)
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"""
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box_h = box[3] - box[1]
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box_w = box[2] - box[0]
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cx = (box[0] + box[2]) // 2
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cy = (box[1] + box[3]) // 2
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img_h, img_w = image.shape[:2]
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w = box_w + config.hd_strategy_crop_margin * 2
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h = box_h + config.hd_strategy_crop_margin * 2
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_l = cx - w // 2
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_r = cx + w // 2
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_t = cy - h // 2
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_b = cy + h // 2
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l = max(_l, 0)
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r = min(_r, img_w)
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t = max(_t, 0)
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b = min(_b, img_h)
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# try to get more context when crop around image edge
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if _l < 0:
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r += abs(_l)
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if _r > img_w:
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l -= _r - img_w
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if _t < 0:
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b += abs(_t)
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if _b > img_h:
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t -= _b - img_h
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l = max(l, 0)
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r = min(r, img_w)
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t = max(t, 0)
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b = min(b, img_h)
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crop_img = image[t:b, l:r, :]
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crop_mask = mask[t:b, l:r]
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logger.info(f"box size: ({box_h},{box_w}) crop size: {crop_img.shape}")
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return crop_img, crop_mask, [l, t, r, b]
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def _calculate_cdf(self, histogram):
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cdf = histogram.cumsum()
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normalized_cdf = cdf / float(cdf.max())
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return normalized_cdf
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def _calculate_lookup(self, source_cdf, reference_cdf):
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lookup_table = np.zeros(256)
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lookup_val = 0
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for source_index, source_val in enumerate(source_cdf):
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for reference_index, reference_val in enumerate(reference_cdf):
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if reference_val >= source_val:
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lookup_val = reference_index
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break
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lookup_table[source_index] = lookup_val
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return lookup_table
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def _match_histograms(self, source, reference, mask):
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transformed_channels = []
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for channel in range(source.shape[-1]):
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source_channel = source[:, :, channel]
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reference_channel = reference[:, :, channel]
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# only calculate histograms for non-masked parts
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source_histogram, _ = np.histogram(source_channel[mask == 0], 256, [0, 256])
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reference_histogram, _ = np.histogram(reference_channel[mask == 0], 256, [0, 256])
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source_cdf = self._calculate_cdf(source_histogram)
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reference_cdf = self._calculate_cdf(reference_histogram)
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lookup = self._calculate_lookup(source_cdf, reference_cdf)
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transformed_channels.append(cv2.LUT(source_channel, lookup))
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result = cv2.merge(transformed_channels)
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result = cv2.convertScaleAbs(result)
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return result
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def _apply_cropper(self, image, mask, config: Config):
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img_h, img_w = image.shape[:2]
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l, t, w, h = (
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config.croper_x,
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config.croper_y,
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config.croper_width,
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config.croper_height,
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)
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r = l + w
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b = t + h
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l = max(l, 0)
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r = min(r, img_w)
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t = max(t, 0)
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b = min(b, img_h)
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crop_img = image[t:b, l:r, :]
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crop_mask = mask[t:b, l:r]
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return crop_img, crop_mask, (l, t, r, b)
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def _run_box(self, image, mask, box, config: Config):
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"""
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Args:
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image: [H, W, C] RGB
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mask: [H, W, 1]
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box: [left,top,right,bottom]
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Returns:
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BGR IMAGE
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"""
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crop_img, crop_mask, [l, t, r, b] = self._crop_box(image, mask, box, config)
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return self._pad_forward(crop_img, crop_mask, config), [l, t, r, b]
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