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- # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
- #
- # 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
- #
- # http://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 cv2
- import numpy as np
- class DecodeImage(object):
- def __init__(self, to_rgb=True):
- self.to_rgb = to_rgb
- def __call__(self, img):
- data = np.frombuffer(img, dtype='uint8')
- img = cv2.imdecode(data, 1)
- if self.to_rgb:
- assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
- img.shape)
- img = img[:, :, ::-1]
- return img
- class ResizeImage(object):
- def __init__(self, resize_short=None, interpolation=1):
- self.resize_short = resize_short
- self.interpolation = interpolation
- def __call__(self, img):
- img_h, img_w = img.shape[:2]
- percent = float(self.resize_short) / min(img_w, img_h)
- w = int(round(img_w * percent))
- h = int(round(img_h * percent))
- return cv2.resize(img, (w, h), interpolation=self.interpolation)
- class CropImage(object):
- def __init__(self, size):
- if type(size) is int:
- self.size = (size, size)
- else:
- self.size = size
- def __call__(self, img):
- w, h = self.size
- img_h, img_w = img.shape[:2]
- w_start = (img_w - w) // 2
- h_start = (img_h - h) // 2
- w_end = w_start + w
- h_end = h_start + h
- return img[h_start:h_end, w_start:w_end, :]
- class NormalizeImage(object):
- def __init__(self, scale=None, mean=None, std=None):
- self.scale = np.float32(scale if scale is not None else 1.0 / 255.0)
- mean = mean if mean is not None else [0.485, 0.456, 0.406]
- std = std if std is not None else [0.229, 0.224, 0.225]
- shape = (1, 1, 3)
- self.mean = np.array(mean).reshape(shape).astype('float32')
- self.std = np.array(std).reshape(shape).astype('float32')
- def __call__(self, img):
- return (img.astype('float32') * self.scale - self.mean) / self.std
- class ToTensor(object):
- def __init__(self):
- pass
- def __call__(self, img):
- img = img.transpose((2, 0, 1))
- return img
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