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84 lines
2.6 KiB
84 lines
2.6 KiB
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. |
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# |
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# Licensed under the Apache License, Version 2.0 (the "License"); |
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# you may not use this file except in compliance with the License. |
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# You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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import cv2 |
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import numpy as np |
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class DecodeImage(object): |
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def __init__(self, to_rgb=True): |
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self.to_rgb = to_rgb |
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def __call__(self, img): |
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data = np.frombuffer(img, dtype='uint8') |
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img = cv2.imdecode(data, 1) |
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if self.to_rgb: |
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assert img.shape[2] == 3, 'invalid shape of image[%s]' % (img.shape) |
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img = img[:, :, ::-1] |
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return img |
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class ResizeImage(object): |
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def __init__(self, resize_short=None, interpolation=1): |
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self.resize_short = resize_short |
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self.interpolation = interpolation |
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def __call__(self, img): |
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img_h, img_w = img.shape[:2] |
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percent = float(self.resize_short) / min(img_w, img_h) |
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w = int(round(img_w * percent)) |
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h = int(round(img_h * percent)) |
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return cv2.resize(img, (w, h), interpolation=self.interpolation) |
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class CropImage(object): |
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def __init__(self, size): |
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if type(size) is int: |
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self.size = (size, size) |
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else: |
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self.size = size |
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def __call__(self, img): |
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w, h = self.size |
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img_h, img_w = img.shape[:2] |
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w_start = (img_w - w) // 2 |
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h_start = (img_h - h) // 2 |
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w_end = w_start + w |
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h_end = h_start + h |
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return img[h_start:h_end, w_start:w_end, :] |
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class NormalizeImage(object): |
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def __init__(self, scale=None, mean=None, std=None): |
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self.scale = np.float32(scale if scale is not None else 1.0 / 255.0) |
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mean = mean if mean is not None else [0.485, 0.456, 0.406] |
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std = std if std is not None else [0.229, 0.224, 0.225] |
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shape = (1, 1, 3) |
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self.mean = np.array(mean).reshape(shape).astype('float32') |
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self.std = np.array(std).reshape(shape).astype('float32') |
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def __call__(self, img): |
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return (img.astype('float32') * self.scale - self.mean) / self.std |
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class ToTensor(object): |
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def __init__(self): |
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pass |
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def __call__(self, img): |
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img = img.transpose((2, 0, 1)) |
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return img
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