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| # Copyright (c) 2023-2024, Zexin He | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # https://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import numpy as np | |
| import rembg | |
| import cv2 | |
| class Preprocessor: | |
| """ | |
| Preprocessing under cv2 conventions. | |
| """ | |
| def __init__(self): | |
| self.rembg_session = rembg.new_session( | |
| providers=["CUDAExecutionProvider", "CPUExecutionProvider"], | |
| ) | |
| def preprocess(self, image_path: str, save_path: str, rmbg: bool = True, recenter: bool = True, size: int = 512, border_ratio: float = 0.2): | |
| image = self.step_load_to_size(image_path=image_path, size=size*2) | |
| if rmbg: | |
| image = self.step_rembg(image_in=image) | |
| else: | |
| image = cv2.cvtColor(image, cv2.COLOR_BGR2BGRA) | |
| if recenter: | |
| image = self.step_recenter(image_in=image, border_ratio=border_ratio, square_size=size) | |
| else: | |
| image = cv2.resize( | |
| src=image, | |
| dsize=(size, size), | |
| interpolation=cv2.INTER_AREA, | |
| ) | |
| return cv2.imwrite(save_path, image) | |
| def step_rembg(self, image_in: np.ndarray) -> np.ndarray: | |
| image_out = rembg.remove( | |
| data=image_in, | |
| session=self.rembg_session, | |
| ) | |
| return image_out | |
| def step_recenter(self, image_in: np.ndarray, border_ratio: float, square_size: int) -> np.ndarray: | |
| assert image_in.shape[-1] == 4, "Image to recenter must be RGBA" | |
| mask = image_in[..., -1] > 0 | |
| ijs = np.nonzero(mask) | |
| # find bbox | |
| i_min, i_max = ijs[0].min(), ijs[0].max() | |
| j_min, j_max = ijs[1].min(), ijs[1].max() | |
| bbox_height, bbox_width = i_max - i_min, j_max - j_min | |
| # recenter and resize | |
| desired_size = int(square_size * (1 - border_ratio)) | |
| scale = desired_size / max(bbox_height, bbox_width) | |
| desired_height, desired_width = int(bbox_height * scale), int(bbox_width * scale) | |
| desired_i_min, desired_j_min = (square_size - desired_height) // 2, (square_size - desired_width) // 2 | |
| desired_i_max, desired_j_max = desired_i_min + desired_height, desired_j_min + desired_width | |
| # create new image | |
| image_out = np.zeros((square_size, square_size, 4), dtype=np.uint8) | |
| image_out[desired_i_min:desired_i_max, desired_j_min:desired_j_max] = cv2.resize( | |
| src=image_in[i_min:i_max, j_min:j_max], | |
| dsize=(desired_width, desired_height), | |
| interpolation=cv2.INTER_AREA, | |
| ) | |
| return image_out | |
| def step_load_to_size(self, image_path: str, size: int) -> np.ndarray: | |
| image = cv2.imread(image_path, cv2.IMREAD_UNCHANGED) | |
| height, width = image.shape[:2] | |
| scale = size / max(height, width) | |
| height, width = int(height * scale), int(width * scale) | |
| image_out = cv2.resize( | |
| src=image, | |
| dsize=(width, height), | |
| interpolation=cv2.INTER_AREA, | |
| ) | |
| return image_out | |