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102 lines
2.9 KiB
102 lines
2.9 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 sys
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import collections
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import random
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import cv2
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import numpy as np
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if sys.version_info < (3, 3):
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Sequence = collections.Sequence
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Iterable = collections.Iterable
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else:
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Sequence = collections.abc.Sequence
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Iterable = collections.abc.Iterable
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__all__ = ['flip', 'resize']
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def flip(image, code):
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"""
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Accordding to the code (the type of flip), flip the input image
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Args:
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image: Input image, with (H, W, C) shape
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code: Code that indicates the type of flip.
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-1 : Flip horizontally and vertically
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0 : Flip vertically
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1 : Flip horizontally
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Examples:
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.. code-block:: python
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import numpy as np
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from paddle.incubate.hapi.vision.transforms import functional as F
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fake_img = np.random.rand(224, 224, 3)
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# flip horizontally and vertically
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F.flip(fake_img, -1)
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# flip vertically
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F.flip(fake_img, 0)
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# flip horizontally
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F.flip(fake_img, 1)
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"""
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return cv2.flip(image, flipCode=code)
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def resize(img, size, interpolation=cv2.INTER_LINEAR):
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"""
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resize the input data to given size
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Args:
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input: Input data, could be image or masks, with (H, W, C) shape
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size: Target size of input data, with (height, width) shape.
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interpolation: Interpolation method.
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Examples:
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.. code-block:: python
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import numpy as np
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from paddle.incubate.hapi.vision.transforms import functional as F
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fake_img = np.random.rand(256, 256, 3)
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F.resize(fake_img, 224)
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F.resize(fake_img, (200, 150))
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"""
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if isinstance(interpolation, Sequence):
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interpolation = random.choice(interpolation)
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if isinstance(size, int):
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h, w = img.shape[:2]
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if (w <= h and w == size) or (h <= w and h == size):
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return img
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if w < h:
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ow = size
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oh = int(size * h / w)
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return cv2.resize(img, (ow, oh), interpolation=interpolation)
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else:
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oh = size
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ow = int(size * w / h)
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return cv2.resize(img, (ow, oh), interpolation=interpolation)
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else:
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return cv2.resize(img, size[::-1], interpolation=interpolation)
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