408 lines
14 KiB
Python
408 lines
14 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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"""
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This code is refer from:
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https://github.com/FangShancheng/ABINet/blob/main/transforms.py
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"""
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import math
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import numbers
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import random
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import cv2
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import numpy as np
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from paddle.vision.transforms import Compose, ColorJitter
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def sample_asym(magnitude, size=None):
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return np.random.beta(1, 4, size) * magnitude
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def sample_sym(magnitude, size=None):
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return (np.random.beta(4, 4, size=size) - 0.5) * 2 * magnitude
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def sample_uniform(low, high, size=None):
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return np.random.uniform(low, high, size=size)
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def get_interpolation(type='random'):
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if type == 'random':
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choice = [
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cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA
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]
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interpolation = choice[random.randint(0, len(choice) - 1)]
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elif type == 'nearest':
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interpolation = cv2.INTER_NEAREST
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elif type == 'linear':
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interpolation = cv2.INTER_LINEAR
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elif type == 'cubic':
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interpolation = cv2.INTER_CUBIC
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elif type == 'area':
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interpolation = cv2.INTER_AREA
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else:
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raise TypeError(
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'Interpolation types only nearest, linear, cubic, area are supported!'
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)
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return interpolation
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class CVRandomRotation(object):
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def __init__(self, degrees=15):
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assert isinstance(degrees,
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numbers.Number), "degree should be a single number."
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assert degrees >= 0, "degree must be positive."
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self.degrees = degrees
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@staticmethod
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def get_params(degrees):
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return sample_sym(degrees)
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def __call__(self, img):
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angle = self.get_params(self.degrees)
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src_h, src_w = img.shape[:2]
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M = cv2.getRotationMatrix2D(
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center=(src_w / 2, src_h / 2), angle=angle, scale=1.0)
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abs_cos, abs_sin = abs(M[0, 0]), abs(M[0, 1])
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dst_w = int(src_h * abs_sin + src_w * abs_cos)
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dst_h = int(src_h * abs_cos + src_w * abs_sin)
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M[0, 2] += (dst_w - src_w) / 2
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M[1, 2] += (dst_h - src_h) / 2
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flags = get_interpolation()
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return cv2.warpAffine(
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img,
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M, (dst_w, dst_h),
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flags=flags,
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borderMode=cv2.BORDER_REPLICATE)
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class CVRandomAffine(object):
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def __init__(self, degrees, translate=None, scale=None, shear=None):
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assert isinstance(degrees,
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numbers.Number), "degree should be a single number."
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assert degrees >= 0, "degree must be positive."
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self.degrees = degrees
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if translate is not None:
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assert isinstance(translate, (tuple, list)) and len(translate) == 2, \
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"translate should be a list or tuple and it must be of length 2."
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for t in translate:
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if not (0.0 <= t <= 1.0):
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raise ValueError(
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"translation values should be between 0 and 1")
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self.translate = translate
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if scale is not None:
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assert isinstance(scale, (tuple, list)) and len(scale) == 2, \
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"scale should be a list or tuple and it must be of length 2."
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for s in scale:
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if s <= 0:
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raise ValueError("scale values should be positive")
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self.scale = scale
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if shear is not None:
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if isinstance(shear, numbers.Number):
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if shear < 0:
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raise ValueError(
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"If shear is a single number, it must be positive.")
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self.shear = [shear]
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else:
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assert isinstance(shear, (tuple, list)) and (len(shear) == 2), \
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"shear should be a list or tuple and it must be of length 2."
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self.shear = shear
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else:
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self.shear = shear
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def _get_inverse_affine_matrix(self, center, angle, translate, scale,
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shear):
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# https://github.com/pytorch/vision/blob/v0.4.0/torchvision/transforms/functional.py#L717
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from numpy import sin, cos, tan
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if isinstance(shear, numbers.Number):
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shear = [shear, 0]
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if not isinstance(shear, (tuple, list)) and len(shear) == 2:
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raise ValueError(
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"Shear should be a single value or a tuple/list containing " +
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"two values. Got {}".format(shear))
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rot = math.radians(angle)
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sx, sy = [math.radians(s) for s in shear]
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cx, cy = center
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tx, ty = translate
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# RSS without scaling
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a = cos(rot - sy) / cos(sy)
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b = -cos(rot - sy) * tan(sx) / cos(sy) - sin(rot)
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c = sin(rot - sy) / cos(sy)
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d = -sin(rot - sy) * tan(sx) / cos(sy) + cos(rot)
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# Inverted rotation matrix with scale and shear
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# det([[a, b], [c, d]]) == 1, since det(rotation) = 1 and det(shear) = 1
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M = [d, -b, 0, -c, a, 0]
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M = [x / scale for x in M]
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# Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
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M[2] += M[0] * (-cx - tx) + M[1] * (-cy - ty)
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M[5] += M[3] * (-cx - tx) + M[4] * (-cy - ty)
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# Apply center translation: C * RSS^-1 * C^-1 * T^-1
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M[2] += cx
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M[5] += cy
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return M
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@staticmethod
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def get_params(degrees, translate, scale_ranges, shears, height):
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angle = sample_sym(degrees)
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if translate is not None:
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max_dx = translate[0] * height
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max_dy = translate[1] * height
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translations = (np.round(sample_sym(max_dx)),
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np.round(sample_sym(max_dy)))
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else:
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translations = (0, 0)
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if scale_ranges is not None:
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scale = sample_uniform(scale_ranges[0], scale_ranges[1])
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else:
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scale = 1.0
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if shears is not None:
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if len(shears) == 1:
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shear = [sample_sym(shears[0]), 0.]
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elif len(shears) == 2:
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shear = [sample_sym(shears[0]), sample_sym(shears[1])]
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else:
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shear = 0.0
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return angle, translations, scale, shear
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def __call__(self, img):
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src_h, src_w = img.shape[:2]
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angle, translate, scale, shear = self.get_params(
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self.degrees, self.translate, self.scale, self.shear, src_h)
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M = self._get_inverse_affine_matrix((src_w / 2, src_h / 2), angle,
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(0, 0), scale, shear)
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M = np.array(M).reshape(2, 3)
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startpoints = [(0, 0), (src_w - 1, 0), (src_w - 1, src_h - 1),
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(0, src_h - 1)]
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project = lambda x, y, a, b, c: int(a * x + b * y + c)
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endpoints = [(project(x, y, *M[0]), project(x, y, *M[1]))
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for x, y in startpoints]
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rect = cv2.minAreaRect(np.array(endpoints))
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bbox = cv2.boxPoints(rect).astype(dtype=np.int)
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max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
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min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
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dst_w = int(max_x - min_x)
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dst_h = int(max_y - min_y)
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M[0, 2] += (dst_w - src_w) / 2
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M[1, 2] += (dst_h - src_h) / 2
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# add translate
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dst_w += int(abs(translate[0]))
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dst_h += int(abs(translate[1]))
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if translate[0] < 0: M[0, 2] += abs(translate[0])
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if translate[1] < 0: M[1, 2] += abs(translate[1])
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flags = get_interpolation()
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return cv2.warpAffine(
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img,
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M, (dst_w, dst_h),
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flags=flags,
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borderMode=cv2.BORDER_REPLICATE)
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class CVRandomPerspective(object):
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def __init__(self, distortion=0.5):
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self.distortion = distortion
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def get_params(self, width, height, distortion):
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offset_h = sample_asym(
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distortion * height / 2, size=4).astype(dtype=np.int)
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offset_w = sample_asym(
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distortion * width / 2, size=4).astype(dtype=np.int)
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topleft = (offset_w[0], offset_h[0])
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topright = (width - 1 - offset_w[1], offset_h[1])
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botright = (width - 1 - offset_w[2], height - 1 - offset_h[2])
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botleft = (offset_w[3], height - 1 - offset_h[3])
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startpoints = [(0, 0), (width - 1, 0), (width - 1, height - 1),
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(0, height - 1)]
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endpoints = [topleft, topright, botright, botleft]
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return np.array(
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startpoints, dtype=np.float32), np.array(
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endpoints, dtype=np.float32)
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def __call__(self, img):
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height, width = img.shape[:2]
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startpoints, endpoints = self.get_params(width, height, self.distortion)
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M = cv2.getPerspectiveTransform(startpoints, endpoints)
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# TODO: more robust way to crop image
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rect = cv2.minAreaRect(endpoints)
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bbox = cv2.boxPoints(rect).astype(dtype=np.int)
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max_x, max_y = bbox[:, 0].max(), bbox[:, 1].max()
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min_x, min_y = bbox[:, 0].min(), bbox[:, 1].min()
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min_x, min_y = max(min_x, 0), max(min_y, 0)
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flags = get_interpolation()
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img = cv2.warpPerspective(
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img,
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M, (max_x, max_y),
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flags=flags,
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borderMode=cv2.BORDER_REPLICATE)
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img = img[min_y:, min_x:]
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return img
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class CVRescale(object):
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def __init__(self, factor=4, base_size=(128, 512)):
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""" Define image scales using gaussian pyramid and rescale image to target scale.
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Args:
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factor: the decayed factor from base size, factor=4 keeps target scale by default.
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base_size: base size the build the bottom layer of pyramid
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"""
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if isinstance(factor, numbers.Number):
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self.factor = round(sample_uniform(0, factor))
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elif isinstance(factor, (tuple, list)) and len(factor) == 2:
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self.factor = round(sample_uniform(factor[0], factor[1]))
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else:
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raise Exception('factor must be number or list with length 2')
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# assert factor is valid
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self.base_h, self.base_w = base_size[:2]
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def __call__(self, img):
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if self.factor == 0: return img
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src_h, src_w = img.shape[:2]
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cur_w, cur_h = self.base_w, self.base_h
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scale_img = cv2.resize(
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img, (cur_w, cur_h), interpolation=get_interpolation())
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for _ in range(self.factor):
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scale_img = cv2.pyrDown(scale_img)
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scale_img = cv2.resize(
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scale_img, (src_w, src_h), interpolation=get_interpolation())
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return scale_img
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class CVGaussianNoise(object):
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def __init__(self, mean=0, var=20):
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self.mean = mean
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if isinstance(var, numbers.Number):
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self.var = max(int(sample_asym(var)), 1)
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elif isinstance(var, (tuple, list)) and len(var) == 2:
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self.var = int(sample_uniform(var[0], var[1]))
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else:
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raise Exception('degree must be number or list with length 2')
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def __call__(self, img):
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noise = np.random.normal(self.mean, self.var**0.5, img.shape)
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img = np.clip(img + noise, 0, 255).astype(np.uint8)
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return img
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class CVMotionBlur(object):
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def __init__(self, degrees=12, angle=90):
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if isinstance(degrees, numbers.Number):
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self.degree = max(int(sample_asym(degrees)), 1)
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elif isinstance(degrees, (tuple, list)) and len(degrees) == 2:
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self.degree = int(sample_uniform(degrees[0], degrees[1]))
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else:
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raise Exception('degree must be number or list with length 2')
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self.angle = sample_uniform(-angle, angle)
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def __call__(self, img):
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M = cv2.getRotationMatrix2D((self.degree // 2, self.degree // 2),
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self.angle, 1)
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motion_blur_kernel = np.zeros((self.degree, self.degree))
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motion_blur_kernel[self.degree // 2, :] = 1
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motion_blur_kernel = cv2.warpAffine(motion_blur_kernel, M,
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(self.degree, self.degree))
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motion_blur_kernel = motion_blur_kernel / self.degree
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img = cv2.filter2D(img, -1, motion_blur_kernel)
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img = np.clip(img, 0, 255).astype(np.uint8)
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return img
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class CVGeometry(object):
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def __init__(self,
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degrees=15,
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translate=(0.3, 0.3),
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scale=(0.5, 2.),
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shear=(45, 15),
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distortion=0.5,
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p=0.5):
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self.p = p
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type_p = random.random()
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if type_p < 0.33:
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self.transforms = CVRandomRotation(degrees=degrees)
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elif type_p < 0.66:
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self.transforms = CVRandomAffine(
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degrees=degrees, translate=translate, scale=scale, shear=shear)
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else:
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self.transforms = CVRandomPerspective(distortion=distortion)
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def __call__(self, img):
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if random.random() < self.p:
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return self.transforms(img)
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else:
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return img
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class CVDeterioration(object):
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def __init__(self, var, degrees, factor, p=0.5):
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self.p = p
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transforms = []
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if var is not None:
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transforms.append(CVGaussianNoise(var=var))
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if degrees is not None:
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transforms.append(CVMotionBlur(degrees=degrees))
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if factor is not None:
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transforms.append(CVRescale(factor=factor))
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random.shuffle(transforms)
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transforms = Compose(transforms)
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self.transforms = transforms
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def __call__(self, img):
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if random.random() < self.p:
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return self.transforms(img)
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else:
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return img
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class CVColorJitter(object):
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def __init__(self,
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brightness=0.5,
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contrast=0.5,
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saturation=0.5,
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hue=0.1,
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p=0.5):
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self.p = p
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self.transforms = ColorJitter(
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brightness=brightness,
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contrast=contrast,
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saturation=saturation,
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hue=hue)
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def __call__(self, img):
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if random.random() < self.p: return self.transforms(img)
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else: return img
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