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提交 90294595 编写于 作者: R root
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update transforms.py

上级 86bc060d
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Showing with 70 addition and 48 deletion
dygraph/transforms/transforms.py
浏览文件 @ 90294595
# coding: utf8
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
......@@ -33,6 +34,7 @@ class Compose:
ValueError: transforms元素个数小于1。
"""
def __init__(self, transforms, to_rgb=True):
if not isinstance(transforms, list):
raise TypeError('The transforms must be a list!')
......@@ -86,6 +88,7 @@ class RandomHorizontalFlip:
prob (float): 随机水平翻转的概率。默认值为0.5。
"""
def __init__(self, prob=0.5):
self.prob = prob
......@@ -117,6 +120,7 @@ class RandomVerticalFlip:
Args:
prob (float): 随机垂直翻转的概率。默认值为0.1。
"""
def __init__(self, prob=0.1):
self.prob = prob
......@@ -233,6 +237,7 @@ class ResizeByLong:
Args:
long_size (int): resize后图像的长边大小。
"""
def __init__(self, long_size):
self.long_size = long_size
......@@ -274,6 +279,7 @@ class ResizeRangeScaling:
Raises:
ValueError: min_value大于max_value
"""
def __init__(self, min_value=400, max_value=600):
if min_value > max_value:
raise ValueError('min_value must be less than max_value, '
......@@ -321,6 +327,7 @@ class ResizeStepScaling:
Raises:
ValueError: min_scale_factor大于max_scale_factor
"""
def __init__(self,
min_scale_factor=0.75,
max_scale_factor=1.25,
......@@ -386,6 +393,7 @@ class Normalize:
Raises:
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]):
self.mean = mean
self.std = std
......@@ -431,6 +439,7 @@ class Padding:
TypeError: target_size不是int|list|tuple。
ValueError: target_size为list|tuple时元素个数不等于2。
"""
def __init__(self,
target_size,
im_padding_value=[127.5, 127.5, 127.5],
......@@ -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 ({}, {})'
.format(im_width, im_height, target_width, target_height))
else:
im = cv2.copyMakeBorder(im,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.im_padding_value)
im = cv2.copyMakeBorder(
im,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.im_padding_value)
if label is not None:
label = cv2.copyMakeBorder(label,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.label_padding_value)
label = cv2.copyMakeBorder(
label,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.label_padding_value)
if label is None:
return (im, im_info)
else:
......@@ -516,6 +527,7 @@ class RandomPaddingCrop:
TypeError: crop_size不是int/list/tuple。
ValueError: target_size为list/tuple时元素个数不等于2。
"""
def __init__(self,
crop_size=512,
im_padding_value=[127.5, 127.5, 127.5],
......@@ -564,21 +576,23 @@ class RandomPaddingCrop:
pad_height = max(crop_height - img_height, 0)
pad_width = max(crop_width - img_width, 0)
if (pad_height > 0 or pad_width > 0):
im = cv2.copyMakeBorder(im,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.im_padding_value)
im = cv2.copyMakeBorder(
im,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.im_padding_value)
if label is not None:
label = cv2.copyMakeBorder(label,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.label_padding_value)
label = cv2.copyMakeBorder(
label,
0,
pad_height,
0,
pad_width,
cv2.BORDER_CONSTANT,
value=self.label_padding_value)
img_height = im.shape[0]
img_width = im.shape[1]
......@@ -586,11 +600,11 @@ class RandomPaddingCrop:
h_off = np.random.randint(img_height - crop_height + 1)
w_off = np.random.randint(img_width - crop_width + 1)
im = im[h_off:(crop_height + h_off), w_off:(w_off +
crop_width), :]
im = im[h_off:(crop_height + h_off), w_off:(
w_off + crop_width), :]
if label is not None:
label = label[h_off:(crop_height +
h_off), w_off:(w_off + crop_width)]
label = label[h_off:(crop_height + h_off), w_off:(
w_off + crop_width)]
if label is None:
return (im, im_info)
else:
......@@ -603,6 +617,7 @@ class RandomBlur:
Args：
prob (float): 图像模糊概率。默认为0.1。
"""
def __init__(self, prob=0.1):
self.prob = prob
......@@ -650,6 +665,7 @@ class RandomRotation:
label_padding_value (int): 标注图像padding的值。默认为255。
"""
def __init__(self,
max_rotation=15,
im_padding_value=[127.5, 127.5, 127.5],
......@@ -686,18 +702,20 @@ class RandomRotation:
r[0, 2] += (nw / 2) - cx
r[1, 2] += (nh / 2) - cy
dsize = (nw, nh)
im = cv2.warpAffine(im,
r,
dsize=dsize,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.im_padding_value)
label = cv2.warpAffine(label,
r,
dsize=dsize,
flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.label_padding_value)
im = cv2.warpAffine(
im,
r,
dsize=dsize,
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.im_padding_value)
label = cv2.warpAffine(
label,
r,
dsize=dsize,
flags=cv2.INTER_NEAREST,
borderMode=cv2.BORDER_CONSTANT,
borderValue=self.label_padding_value)
if label is None:
return (im, im_info)
......@@ -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。
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):
self.min_scale = min_scale
self.aspect_ratio = aspect_ratio
......@@ -751,10 +770,12 @@ class RandomScaleAspect:
im = im[h1:(h1 + dh), w1:(w1 + dw), :]
label = label[h1:(h1 + dh), w1:(w1 + dw)]
im = cv2.resize(im, (img_width, img_height),
interpolation=cv2.INTER_LINEAR)
label = cv2.resize(label, (img_width, img_height),
interpolation=cv2.INTER_NEAREST)
im = cv2.resize(
im, (img_width, img_height),
interpolation=cv2.INTER_LINEAR)
label = cv2.resize(
label, (img_width, img_height),
interpolation=cv2.INTER_NEAREST)
break
if label is None:
return (im, im_info)
......@@ -778,6 +799,7 @@ class RandomDistort:
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。
"""
def __init__(self,
brightness_range=0.5,
brightness_prob=0.5,
......
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