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提交 bea93b0c 编写于 作者: R root
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update transforms.py

上级 84d8b3ae
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Showing with 70 addition and 48 deletion
dygraph/transforms/transforms.py
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# coding:utf8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
# #
# Licensed under the Apache License, Version 2.0 (the "License"); # Licensed under the Apache License, Version 2.0 (the "License");
...@@ -33,6 +34,7 @@ class Compose: ...@@ -33,6 +34,7 @@ class Compose:
ValueError: transforms元素个数小于1。 ValueError: transforms元素个数小于1。
""" """
def __init__(self, transforms, to_rgb=True): def __init__(self, transforms, to_rgb=True):
if not isinstance(transforms, list): if not isinstance(transforms, list):
raise TypeError('The transforms must be a list!') raise TypeError('The transforms must be a list!')
...@@ -86,6 +88,7 @@ class RandomHorizontalFlip: ...@@ -86,6 +88,7 @@ class RandomHorizontalFlip:
prob (float): 随机水平翻转的概率。默认值为0.5。 prob (float): 随机水平翻转的概率。默认值为0.5。
""" """
def __init__(self, prob=0.5): def __init__(self, prob=0.5):
self.prob = prob self.prob = prob
...@@ -117,6 +120,7 @@ class RandomVerticalFlip: ...@@ -117,6 +120,7 @@ class RandomVerticalFlip:
Args: Args:
prob (float): 随机垂直翻转的概率。默认值为0.1。 prob (float): 随机垂直翻转的概率。默认值为0.1。
""" """
def __init__(self, prob=0.1): def __init__(self, prob=0.1):
self.prob = prob self.prob = prob
...@@ -233,6 +237,7 @@ class ResizeByLong: ...@@ -233,6 +237,7 @@ class ResizeByLong:
Args: Args:
long_size (int): resize后图像的长边大小。 long_size (int): resize后图像的长边大小。
""" """
def __init__(self, long_size): def __init__(self, long_size):
self.long_size = long_size self.long_size = long_size
...@@ -274,6 +279,7 @@ class ResizeRangeScaling: ...@@ -274,6 +279,7 @@ class ResizeRangeScaling:
Raises: Raises:
ValueError: min_value大于max_value ValueError: min_value大于max_value
""" """
def __init__(self, min_value=400, max_value=600): def __init__(self, min_value=400, max_value=600):
if min_value > max_value: if min_value > max_value:
raise ValueError('min_value must be less than max_value, ' raise ValueError('min_value must be less than max_value, '
...@@ -321,6 +327,7 @@ class ResizeStepScaling: ...@@ -321,6 +327,7 @@ class ResizeStepScaling:
Raises: Raises:
ValueError: min_scale_factor大于max_scale_factor ValueError: min_scale_factor大于max_scale_factor
""" """
def __init__(self, def __init__(self,
min_scale_factor=0.75, min_scale_factor=0.75,
max_scale_factor=1.25, max_scale_factor=1.25,
...@@ -386,6 +393,7 @@ class Normalize: ...@@ -386,6 +393,7 @@ class Normalize:
Raises: Raises:
ValueError: mean或std不是list对象。std包含0。 ValueError: mean或std不是list对象。std包含0。
""" """
def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]): def __init__(self, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]):
self.mean = mean self.mean = mean
self.std = std self.std = std
...@@ -431,6 +439,7 @@ class Padding: ...@@ -431,6 +439,7 @@ class Padding:
TypeError: target_size不是int|list|tuple。 TypeError: target_size不是int|list|tuple。
ValueError: target_size为list|tuple时元素个数不等于2。 ValueError: target_size为list|tuple时元素个数不等于2。
""" """
def __init__(self, def __init__(self,
target_size, target_size,
im_padding_value=[127.5, 127.5, 127.5], im_padding_value=[127.5, 127.5, 127.5],
...@@ -483,21 +492,23 @@ class Padding: ...@@ -483,21 +492,23 @@ class Padding:
'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})' 'the size of image should be less than target_size, but the size of image ({}, {}), is larger than target_size ({}, {})'
.format(im_width, im_height, target_width, target_height)) .format(im_width, im_height, target_width, target_height))
else: else:
im = cv2.copyMakeBorder(im, im = cv2.copyMakeBorder(
0, im,
pad_height, 0,
0, pad_height,
pad_width, 0,
cv2.BORDER_CONSTANT, pad_width,
value=self.im_padding_value) cv2.BORDER_CONSTANT,
value=self.im_padding_value)
if label is not None: if label is not None:
label = cv2.copyMakeBorder(label, label = cv2.copyMakeBorder(
0, label,
pad_height, 0,
0, pad_height,
pad_width, 0,
cv2.BORDER_CONSTANT, pad_width,
value=self.label_padding_value) cv2.BORDER_CONSTANT,
value=self.label_padding_value)
if label is None: if label is None:
return (im, im_info) return (im, im_info)
else: else:
...@@ -516,6 +527,7 @@ class RandomPaddingCrop: ...@@ -516,6 +527,7 @@ class RandomPaddingCrop:
TypeError: crop_size不是int/list/tuple。 TypeError: crop_size不是int/list/tuple。
ValueError: target_size为list/tuple时元素个数不等于2。 ValueError: target_size为list/tuple时元素个数不等于2。
""" """
def __init__(self, def __init__(self,
crop_size=512, crop_size=512,
im_padding_value=[127.5, 127.5, 127.5], im_padding_value=[127.5, 127.5, 127.5],
...@@ -564,21 +576,23 @@ class RandomPaddingCrop: ...@@ -564,21 +576,23 @@ class RandomPaddingCrop:
pad_height = max(crop_height - img_height, 0) pad_height = max(crop_height - img_height, 0)
pad_width = max(crop_width - img_width, 0) pad_width = max(crop_width - img_width, 0)
if (pad_height > 0 or pad_width > 0): if (pad_height > 0 or pad_width > 0):
im = cv2.copyMakeBorder(im, im = cv2.copyMakeBorder(
0, im,
pad_height, 0,
0, pad_height,
pad_width, 0,
cv2.BORDER_CONSTANT, pad_width,
value=self.im_padding_value) cv2.BORDER_CONSTANT,
value=self.im_padding_value)
if label is not None: if label is not None:
label = cv2.copyMakeBorder(label, label = cv2.copyMakeBorder(
0, label,
pad_height, 0,
0, pad_height,
pad_width, 0,
cv2.BORDER_CONSTANT, pad_width,
value=self.label_padding_value) cv2.BORDER_CONSTANT,
value=self.label_padding_value)
img_height = im.shape[0] img_height = im.shape[0]
img_width = im.shape[1] img_width = im.shape[1]
...@@ -586,11 +600,11 @@ class RandomPaddingCrop: ...@@ -586,11 +600,11 @@ class RandomPaddingCrop:
h_off = np.random.randint(img_height - crop_height + 1) h_off = np.random.randint(img_height - crop_height + 1)
w_off = np.random.randint(img_width - crop_width + 1) w_off = np.random.randint(img_width - crop_width + 1)
im = im[h_off:(crop_height + h_off), w_off:(w_off + im = im[h_off:(crop_height + h_off), w_off:(
crop_width), :] w_off + crop_width), :]
if label is not None: if label is not None:
label = label[h_off:(crop_height + label = label[h_off:(crop_height + h_off), w_off:(
h_off), w_off:(w_off + crop_width)] w_off + crop_width)]
if label is None: if label is None:
return (im, im_info) return (im, im_info)
else: else:
...@@ -603,6 +617,7 @@ class RandomBlur: ...@@ -603,6 +617,7 @@ class RandomBlur:
Args： Args：
prob (float): 图像模糊概率。默认为0.1。 prob (float): 图像模糊概率。默认为0.1。
""" """
def __init__(self, prob=0.1): def __init__(self, prob=0.1):
self.prob = prob self.prob = prob
...@@ -650,6 +665,7 @@ class RandomRotation: ...@@ -650,6 +665,7 @@ class RandomRotation:
label_padding_value (int): 标注图像padding的值。默认为255。 label_padding_value (int): 标注图像padding的值。默认为255。
""" """
def __init__(self, def __init__(self,
max_rotation=15, max_rotation=15,
im_padding_value=[127.5, 127.5, 127.5], im_padding_value=[127.5, 127.5, 127.5],
...@@ -686,18 +702,20 @@ class RandomRotation: ...@@ -686,18 +702,20 @@ class RandomRotation:
r[0, 2] += (nw / 2) - cx r[0, 2] += (nw / 2) - cx
r[1, 2] += (nh / 2) - cy r[1, 2] += (nh / 2) - cy
dsize = (nw, nh) dsize = (nw, nh)
im = cv2.warpAffine(im, im = cv2.warpAffine(
r, im,
dsize=dsize, r,
flags=cv2.INTER_LINEAR, dsize=dsize,
borderMode=cv2.BORDER_CONSTANT, flags=cv2.INTER_LINEAR,
borderValue=self.im_padding_value) borderMode=cv2.BORDER_CONSTANT,
label = cv2.warpAffine(label, borderValue=self.im_padding_value)
r, label = cv2.warpAffine(
dsize=dsize, label,
flags=cv2.INTER_NEAREST, r,
borderMode=cv2.BORDER_CONSTANT, dsize=dsize,
borderValue=self.label_padding_value) flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.label_padding_value)
if label is None: if label is None:
return (im, im_info) return (im, im_info)
...@@ -713,6 +731,7 @@ class RandomScaleAspect: ...@@ -713,6 +731,7 @@ class RandomScaleAspect:
min_scale (float)：裁取图像占原始图像的面积比，取值[0，1]，为0时则返回原图。默认为0.5。 min_scale (float)：裁取图像占原始图像的面积比，取值[0，1]，为0时则返回原图。默认为0.5。
aspect_ratio (float): 裁取图像的宽高比范围，非负值，为0时返回原图。默认为0.33。 aspect_ratio (float): 裁取图像的宽高比范围，非负值，为0时返回原图。默认为0.33。
""" """
def __init__(self, min_scale=0.5, aspect_ratio=0.33): def __init__(self, min_scale=0.5, aspect_ratio=0.33):
self.min_scale = min_scale self.min_scale = min_scale
self.aspect_ratio = aspect_ratio self.aspect_ratio = aspect_ratio
...@@ -751,10 +770,12 @@ class RandomScaleAspect: ...@@ -751,10 +770,12 @@ class RandomScaleAspect:
im = im[h1:(h1 + dh), w1:(w1 + dw), :] im = im[h1:(h1 + dh), w1:(w1 + dw), :]
label = label[h1:(h1 + dh), w1:(w1 + dw)] label = label[h1:(h1 + dh), w1:(w1 + dw)]
im = cv2.resize(im, (img_width, img_height), im = cv2.resize(
interpolation=cv2.INTER_LINEAR) im, (img_width, img_height),
label = cv2.resize(label, (img_width, img_height), interpolation=cv2.INTER_LINEAR)
interpolation=cv2.INTER_NEAREST) label = cv2.resize(
label, (img_width, img_height),
interpolation=cv2.INTER_NEAREST)
break break
if label is None: if label is None:
return (im, im_info) return (im, im_info)
...@@ -778,6 +799,7 @@ class RandomDistort: ...@@ -778,6 +799,7 @@ class RandomDistort:
hue_range (int): 色调因子的范围。默认为18。 hue_range (int): 色调因子的范围。默认为18。
hue_prob (float): 随机调整色调的概率。默认为0.5。 hue_prob (float): 随机调整色调的概率。默认为0.5。
""" """
def __init__(self, def __init__(self,
brightness_range=0.5, brightness_range=0.5,
brightness_prob=0.5, brightness_prob=0.5,
......
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