1257 lines
38 KiB
Python
1257 lines
38 KiB
Python
#! /usr/bin/python
|
||
# -*- coding: utf-8 -*-
|
||
|
||
import tensorlayer as tl
|
||
from . import load_vision_backend as F
|
||
import numbers
|
||
import numpy as np
|
||
__all__ = [
|
||
'Crop',
|
||
'CentralCrop',
|
||
'HsvToRgb',
|
||
'AdjustBrightness',
|
||
'AdjustContrast',
|
||
'AdjustHue',
|
||
'AdjustSaturation',
|
||
'FlipHorizontal',
|
||
'FlipVertical',
|
||
'RgbToGray',
|
||
'PadToBoundingbox',
|
||
'Pad',
|
||
'Normalize',
|
||
'StandardizePerImage',
|
||
'RandomBrightness',
|
||
'RandomContrast',
|
||
'RandomHue',
|
||
'RandomSaturation',
|
||
'RandomCrop',
|
||
'Resize',
|
||
'RgbToHsv',
|
||
'Transpose',
|
||
'RandomRotation',
|
||
'RandomShift',
|
||
'RandomShear',
|
||
'RandomZoom',
|
||
'RandomFlipVertical',
|
||
'RandomFlipHorizontal',
|
||
'HWC2CHW',
|
||
'CHW2HWC',
|
||
'ToTensor',
|
||
'Compose',
|
||
'RandomResizedCrop',
|
||
'RandomAffine',
|
||
'ColorJitter',
|
||
]
|
||
|
||
|
||
class ToTensor(object):
|
||
"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.
|
||
|
||
Parameters
|
||
----------
|
||
data_format : str
|
||
Data format of output tensor, should be 'HWC' or 'CHW'. Default: 'HWC'.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.ToTensor(data_format='HWC')
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, data_format='HWC'):
|
||
|
||
if not data_format in ['CHW', 'HWC']:
|
||
raise ValueError('data_format should be CHW or HWC. Got {}'.format(data_format))
|
||
|
||
self.data_format = data_format
|
||
|
||
def __call__(self, image):
|
||
|
||
F.to_tensor(image, self.data_format)
|
||
|
||
|
||
class CentralCrop(object):
|
||
"""Crops the given image at the center.If the size is given, image will be cropped as size.
|
||
If the central_fraction is given, image will cropped as (H * central_fraction, W * fraction).
|
||
Size has a higher priority.
|
||
|
||
Parameters
|
||
----------
|
||
size : int or sequence of int
|
||
The output size of the cropped image.
|
||
If size is an integer, a square crop of size (size, size) is returned.
|
||
If size is a sequence of length 2, it should be (height, width).
|
||
central_fraction : float
|
||
float (0, 1], fraction of size to crop
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.CentralCrop(size = (50, 50))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (50, 50, 3)
|
||
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.CentralCrop(central_fraction=0.5)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (112, 112, 3)
|
||
|
||
"""
|
||
|
||
def __init__(self, size=None, central_fraction=None):
|
||
|
||
self.central_fraction = central_fraction
|
||
self.size = size
|
||
|
||
def __call__(self, image):
|
||
|
||
F.central_crop(image, self.size, self.central_fraction)
|
||
|
||
|
||
class Compose(object):
|
||
"""Composes several transforms together.
|
||
|
||
Parameters
|
||
----------
|
||
transforms : list of 'transform' objects
|
||
list of transforms to compose.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.Compose([tl.vision.transforms.ToTensor(data_format='HWC'),tl.vision.transforms.CentralCrop(size = 100)])
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (100, 100, 3)
|
||
|
||
"""
|
||
|
||
def __init__(self, transforms):
|
||
|
||
self.transforms = transforms
|
||
|
||
def __call__(self, data):
|
||
|
||
for t in self.transforms:
|
||
|
||
data = t(data)
|
||
|
||
return data
|
||
|
||
|
||
class Crop(object):
|
||
"""Crops an image to a specified bounding box.
|
||
|
||
Parameters
|
||
----------
|
||
offset_height : int
|
||
Vertical coordinate of the top-left corner of the bounding box in image.
|
||
offset_width: int
|
||
Horizontal coordinate of the top-left corner of the bounding box in image.
|
||
target_height: int
|
||
Height of the bounding box.
|
||
target_width: int
|
||
Width of the bounding box.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.Crop(offset_height=10, offset_width=10, target_height=100, target_width=100)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (100, 100, 3)
|
||
|
||
"""
|
||
|
||
def __init__(self, offset_height, offset_width, target_height, target_width):
|
||
|
||
self.offset_height = offset_height
|
||
self.offset_width = offset_width
|
||
self.target_height = target_height
|
||
self.target_width = target_width
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.crop(image, self.offset_height, self.offset_width, self.target_height, self.target_width)
|
||
|
||
|
||
class Pad(object):
|
||
"""Pad the given image on all sides with the given "pad" value.
|
||
|
||
Parameters
|
||
----------
|
||
padding : int or sequenece
|
||
Padding on each border.
|
||
If a single int is provided， this is used to pad all borders.
|
||
If sequence of length 2 is provided， this is the padding on left/right and top/bottom respectively.
|
||
If a sequence of length 4 is provided， this is the padding for the left, top, right and bottom borders respectively.
|
||
padding_value : number or sequenece
|
||
Pixel fill value for constant fill. Default is 0.
|
||
If a tuple of length 3, it is used to fill R, G, B channels respectively.
|
||
This value is only used when the mode is constant.
|
||
mode : str
|
||
Type of padding. Default is constant.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.Pad(padding=10, padding_value=0, mode='constant')
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (244, 244, 3)
|
||
|
||
"""
|
||
|
||
def __init__(self, padding, padding_value=0, mode='constant'):
|
||
|
||
self.padding = padding
|
||
self.padding_value = padding_value
|
||
self.mode = mode
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.pad(image, self.padding, self.padding_value, self.mode)
|
||
|
||
|
||
class Resize(object):
|
||
"""Resize the input image to the given size.
|
||
|
||
Parameters
|
||
----------
|
||
size : int or sequenece
|
||
Desired output size.
|
||
If size is a sequence like (h, w), output size will be matched to this.
|
||
If size is an int, smaller edge of the image will be matched to this number.
|
||
i.e, if height > width, then image will be rescaled to (size * height / width, size).
|
||
interpolation : str
|
||
Interpolation method. Default: 'bilinear'.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.Resize(size = (100,100), interpolation='bilinear')
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (100, 100, 3)
|
||
|
||
"""
|
||
|
||
def __init__(self, size, interpolation='bilinear'):
|
||
|
||
self.size = size
|
||
self.interpolation = interpolation
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.resize(image, self.size, self.interpolation)
|
||
|
||
|
||
class Transpose(object):
|
||
"""Transpose image(s) by swapping dimension.
|
||
|
||
Parameters
|
||
----------
|
||
order : sequenece of int
|
||
Desired output dimension order.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.Transpose(order=(2, 0 ,1))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (3, 224, 224)
|
||
|
||
"""
|
||
|
||
def __init__(self, order):
|
||
|
||
self.order = order
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.transpose(image, self.order)
|
||
|
||
|
||
class HWC2CHW(object):
|
||
"""Transpose a image shape (H, W, C) to shape (C, H, W).
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.HWC2CHW()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (3, 224, 224)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
F.hwc_to_chw(image)
|
||
|
||
|
||
class CHW2HWC(object):
|
||
"""Transpose a image shape (C, H, W) to shape (H, W, C).
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(3, 224, 224) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.CHW2HWC()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (224, 224, 3)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
F.chw_to_hwc(image)
|
||
|
||
|
||
class RgbToHsv(object):
|
||
"""Converts a image from RGB to HSV.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RgbToHsv()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (224, 224, 3)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
F.rgb_to_hsv(image)
|
||
|
||
|
||
class HsvToRgb(object):
|
||
"""Converts a image from HSV to RGB.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.HsvToRgb()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (224, 224, 3)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
F.hsv_to_rgb(image)
|
||
|
||
|
||
class RgbToGray(object):
|
||
"""Converts a image from RGB to grayscale.
|
||
|
||
Parameters
|
||
----------
|
||
num_output_channels: int
|
||
(1 or 3) number of channels desired for output image. Default is 1.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RgbToGray(num_output_channels=1)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (224, 224, 1)
|
||
|
||
"""
|
||
|
||
def __init__(self, num_output_channels=1):
|
||
|
||
self.num_output_channels = num_output_channels
|
||
|
||
def __call__(self, image):
|
||
|
||
F.rgb_to_gray(image, self.num_output_channels)
|
||
|
||
|
||
class AdjustBrightness(object):
|
||
"""Adjust brightness of the image.
|
||
|
||
Parameters
|
||
----------
|
||
brightness_factor: float
|
||
How much to adjust the brightness. Can be any non negative number. 1 gives the original image.
|
||
Default is 1.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.AdjustBrightness(brightness_factor=1)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, brightness_factor=1):
|
||
self.brightness_factor = brightness_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.adjust_brightness(image, self.brightness_factor)
|
||
|
||
|
||
class AdjustContrast(object):
|
||
"""Adjust contrast of the image.
|
||
|
||
Parameters
|
||
----------
|
||
contrast_factor: float
|
||
How much to adjust the contrast. Can be any non negative number. 1 gives the original image.
|
||
Default is 1.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.AdjustContrast(contrast_factor=1)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, contrast_factor=1):
|
||
|
||
self.contrast_factor = contrast_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.adjust_contrast(image, self.contrast_factor)
|
||
|
||
|
||
class AdjustHue(object):
|
||
"""Adjust hue of the image.
|
||
|
||
Parameters
|
||
----------
|
||
hue_factor: float
|
||
How much to shift the hue channel. Should be in [-0.5, 0.5].
|
||
0.5 and -0.5 give complete reversal of hue channel in HSV space in positive and negative direction respectively.
|
||
0 means no shift. Therefore, both -0.5 and 0.5 will give an image with complementary colors while 0 gives the original image.
|
||
Default is 0.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.AdjustHue(hue_factor=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, hue_factor=0):
|
||
|
||
self.hue_factor = hue_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.adjust_hue(image, self.hue_factor)
|
||
|
||
|
||
class AdjustSaturation(object):
|
||
"""Adjust saturation of the image.
|
||
|
||
Parameters
|
||
----------
|
||
saturation_factor: float
|
||
How much to adjust the saturation. Can be any non negative number. 1 gives the original image.
|
||
Default is 1.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.AdjustSaturation(saturation_factor=1)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, saturation_factor=1):
|
||
|
||
self.saturation_factor = saturation_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.adjust_saturation(image, self.saturation_factor)
|
||
|
||
|
||
class FlipHorizontal(object):
|
||
"""Flip an image horizontally.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.FlipHorizontal()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.hflip(image)
|
||
|
||
|
||
class FlipVertical(object):
|
||
"""Flip an image vertically.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.FlipVertical()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.vflip(image)
|
||
|
||
|
||
class PadToBoundingbox(object):
|
||
"""Pad image with the specified height and width to target size.
|
||
|
||
Parameters
|
||
----------
|
||
offset_height: int
|
||
Number of rows to add on top.
|
||
offset_width: int
|
||
Number of columns to add on the left.
|
||
target_height: int
|
||
Height of output image.
|
||
target_width: int
|
||
Width of output image.
|
||
padding_value: int or sequence
|
||
value to pad.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand( 224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.PadToBoundingbox(offset_height=10, offset_width=10, target_height=300, target_width=300, padding_value=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (300, 300, 3)
|
||
"""
|
||
|
||
def __init__(self, offset_height, offset_width, target_height, target_width, padding_value=0):
|
||
self.offset_height = offset_height
|
||
self.offset_width = offset_width
|
||
self.target_height = target_height
|
||
self.target_width = target_width
|
||
self.padding_value = padding_value
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.padtoboundingbox(
|
||
image, self.offset_height, self.offset_width, self.target_height, self.target_width, self.padding_value
|
||
)
|
||
|
||
|
||
class Normalize(object):
|
||
"""Normalize a tensor image with mean and standard deviation.
|
||
|
||
Parameters
|
||
----------
|
||
mean: number or sequence
|
||
If mean is a number, mean will be applied for all channels. Sequence of means for each channel.
|
||
std: number or sequnece
|
||
If std is a number, std will be applied for all channels.Sequence of standard deviations for each channel.
|
||
data_format: str
|
||
Data format of input image, should be 'HWC' or 'CHW'. Default: 'HWC'.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand( 224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.Normalize(mean = (155.0, 155.0, 155.0), std = (75.0, 75.0, 75.0),data_format='HWC')
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, mean, std, data_format='HWC'):
|
||
|
||
self.mean = mean
|
||
self.std = std
|
||
self.data_format = data_format
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.normalize(image, self.mean, self.std, self.data_format)
|
||
|
||
|
||
class StandardizePerImage(object):
|
||
"""For each 3-D image x in image, computes (x - mean) / adjusted_stddev, where mean is the average of all values in x.
|
||
adjusted_stddev = max(stddev, 1.0/sqrt(N)) is capped away from 0 to protect against division by 0 when handling uniform images.
|
||
N is the number of elements in x. stddev is the standard deviation of all values in x
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand( 224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.StandardizePerImage()
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.standardize(image)
|
||
|
||
|
||
class RandomBrightness(object):
|
||
"""Random adjust brightness of the image.
|
||
|
||
Parameters
|
||
----------
|
||
brightness_factor: float or sequence
|
||
Brightness adjustment factor (default=(1, 1)).
|
||
If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness_factor), 1+brightness_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should be non negative numbers.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomBrightness(brightness_factor=(0.5, 2))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, brightness_factor=(1, 1)):
|
||
self.brighthness_factor = brightness_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_brightness(image, self.brighthness_factor)
|
||
|
||
|
||
class RandomContrast(object):
|
||
"""Random adjust contrast of the image.
|
||
|
||
Parameters
|
||
----------
|
||
contrast_factor: float or sequence
|
||
Contrast adjustment factor (default=(1, 1)).
|
||
If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast_factor), 1+contrast_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should be non negative numbers.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomContrast(contrast_factor=(0.5, 2))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, contrast_factor=(1, 1)):
|
||
|
||
self.contrast_factor = contrast_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_contrast(image, self.contrast_factor)
|
||
|
||
|
||
class RandomSaturation(object):
|
||
"""Random adjust saturation of the image.
|
||
|
||
Parameters
|
||
----------
|
||
saturation_factor: float or sequence
|
||
Saturation adjustment factor (default=(1, 1)).
|
||
If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation_factor), 1+saturation_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should be non negative numbers.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomSaturation(saturation_factor=(0.5, 2))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, saturation_factor=(1, 1)):
|
||
|
||
self.saturation_factor = saturation_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_saturation(image, self.saturation_factor)
|
||
|
||
|
||
class RandomHue(object):
|
||
"""Random adjust hue of the image.
|
||
|
||
Parameters
|
||
----------
|
||
hue_factor: float or sequence
|
||
Hue adjustment factor (default=(0, 0)).
|
||
If it is a float, the factor is uniformly chosen from the range [-hue_factor, hue_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomHue(hue_factor=(-0.5, 0.5))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, hue_factor=(0, 0)):
|
||
|
||
self.hue_factor = hue_factor
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_hue(image, self.hue_factor)
|
||
|
||
|
||
class RandomCrop(object):
|
||
"""Crop the given image at a random location.
|
||
|
||
Parameters
|
||
----------
|
||
size: int or sequence
|
||
Desired output size of the crop.
|
||
If size is an int instead of sequence like (h, w), a square crop (size, size) is made.
|
||
If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
|
||
padding: int or sequence, optional
|
||
Optional padding on each border of the image.
|
||
If a single int is provided this is used to pad all borders.
|
||
If sequence of length 2 is provided this is the padding on left/right and top/bottom respectively.
|
||
If a sequence of length 4 is provided, it is used to pad left, top, right, bottom borders respectively.
|
||
Default: 0.
|
||
pad_if_needed: boolean
|
||
It will pad the image if smaller than the desired size to avoid raising an exception.
|
||
Since cropping is done after padding, the padding seems to be done at a random offset.
|
||
fill: number or sequence
|
||
Pixel fill value for constant fill. Default is 0.
|
||
If a tuple of length 3, it is used to fill R, G, B channels respectively.
|
||
padding_mode: str
|
||
Type of padding. Default is constant.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomCrop(size=50, padding=10, pad_if_needed=False, fill=0, padding_mode='constant')
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (70,70,3)
|
||
|
||
"""
|
||
|
||
def __init__(self, size, padding=None, pad_if_needed=False, fill=0, padding_mode='constant'):
|
||
|
||
self.size = size
|
||
self.padding = padding
|
||
self.pad_if_needed = pad_if_needed
|
||
self.fill = fill
|
||
self.padding_mode = padding_mode
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_crop(
|
||
image,
|
||
size=self.size,
|
||
padding=self.padding,
|
||
pad_if_needed=self.pad_if_needed,
|
||
fill=self.fill,
|
||
padding_mode=self.padding_mode,
|
||
)
|
||
|
||
|
||
class RandomResizedCrop(object):
|
||
"""Crop the given image to random size and aspect ratio.
|
||
|
||
Parameters
|
||
----------
|
||
size: int or sequence
|
||
Desired output size of the crop.
|
||
If size is an int instead of sequence like (h, w), a square crop (size, size) is made.
|
||
If provided a sequence of length 1, it will be interpreted as (size[0], size[0]).
|
||
scale: tuple of float
|
||
scale range of the cropped image before resizing, relatively to the origin image.
|
||
ratio: tuple of float
|
||
aspect ratio range of the cropped image before resizing.
|
||
interpolation: str
|
||
Type of interpolation. Default is bilinear.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomResizedCrop(size = (100, 100), scale = (0.08, 1.0), ratio = (3./4.,4./3.), interpolation = 'bilinear')
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
>>> image shape : (100,100,3)
|
||
|
||
"""
|
||
|
||
def __init__(self, size, scale=(0.08, 1.0), ratio=(3. / 4., 4. / 3.), interpolation='bilinear'):
|
||
self.size = size
|
||
self.scale = scale
|
||
self.ratio = ratio
|
||
self.interpolation = interpolation
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_resized_crop(image, self.size, self.scale, self.ratio, self.interpolation)
|
||
|
||
|
||
class RandomFlipVertical(object):
|
||
"""Vertically flip the given image randomly with a given probability.
|
||
|
||
Parameters
|
||
----------
|
||
prob: float
|
||
probability of the image being flipped. Default value is 0.5
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomFlipVertical(prob = 0.5)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, prob=0.5):
|
||
|
||
self.prob = prob
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_vflip(image, self.prob)
|
||
|
||
|
||
class RandomFlipHorizontal(object):
|
||
"""Horizontally flip the given image randomly with a given probability.
|
||
|
||
Parameters
|
||
----------
|
||
prob: float
|
||
probability of the image being flipped. Default value is 0.5
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomFlipHorizontal(prob = 0.5)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, prob=0.5):
|
||
|
||
self.prob = prob
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_hflip(image, self.prob)
|
||
|
||
|
||
class RandomRotation(object):
|
||
"""Rotate the image by random angle.
|
||
|
||
Parameters
|
||
----------
|
||
degrees: number or sequnence
|
||
Range of degrees to select from.
|
||
If degrees is a number, the range of degrees will be (-degrees, +degrees).
|
||
If degrees is a sequence, the range of degrees will (degrees[0], degrees[1]).
|
||
interpolation: str
|
||
Interpolation method. Default is 'bilinear'.
|
||
expand: boolean
|
||
If true, expands the output to make it large enough to hold the entire rotated image.
|
||
If false or omitted, make the output image the same size as the input image.
|
||
Note that the expand flag assumes rotation around the center and no translation.
|
||
center: sequence or None
|
||
Optional center of rotation, (x, y). Origin is the upper left corner.
|
||
Default is the center of the image.
|
||
fill: number or sequence
|
||
Pixel fill value for the area outside the rotated image. Default is 0.
|
||
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomRotation(degrees=30, interpolation='bilinear', expand=False, center=None, fill=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, degrees, interpolation='bilinear', expand=False, center=None, fill=0):
|
||
|
||
self.degrees = degrees
|
||
self.interpolation = interpolation
|
||
self.expand = expand
|
||
self.center = center
|
||
self.fill = fill
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_rotation(image, self.degrees, self.interpolation, self.expand, self.center, self.fill)
|
||
|
||
|
||
class RandomShear(object):
|
||
"""Shear the image by random angle.
|
||
|
||
Parameters
|
||
----------
|
||
degrees: number or sequnence
|
||
Range of degrees to select from.
|
||
If degrees is a number, a shear parallel to the x axis in the range (-shear, +shear) will be applied.
|
||
If shear is a sequence of 2 values a shear parallel to the x axis in the range (shear[0], shear[1]) will be applied.
|
||
If shear is a sequence of 4 values, a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
|
||
interpolation: str
|
||
Interpolation method. Default is 'bilinear'.
|
||
fill: number or sequence
|
||
Pixel fill value for the area outside the sheared image. Default is 0.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomShear(degrees=30, interpolation='bilinear', fill=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, degrees, interpolation='bilinear', fill=0):
|
||
|
||
self.degrees = degrees
|
||
self.interpolation = interpolation
|
||
self.fill = fill
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_shear(image, self.degrees, self.interpolation, self.fill)
|
||
|
||
|
||
class RandomShift(object):
|
||
"""Shift the image by random translations.
|
||
|
||
Parameters
|
||
----------
|
||
shift: list or tuple
|
||
Maximum absolute fraction for horizontal and vertical translations.
|
||
shift=(a, b), then horizontal shift is randomly sampled in the range -img_width * a < dx < img_width * a.
|
||
vertical shift is randomly sampled in the range -img_height * b < dy < img_height * b.
|
||
interpolation: str
|
||
Interpolation method. Default is 'bilinear'.
|
||
fill: number or sequence
|
||
Pixel fill value for the area outside the sheared image. Default is 0.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomShift(shift=(0.2, 0.2), interpolation='bilinear', fill=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, shift, interpolation='bilinear', fill=0):
|
||
|
||
self.shift = shift
|
||
self.interpolation = interpolation
|
||
self.fill = fill
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_shift(image, self.shift, self.interpolation, self.fill)
|
||
|
||
|
||
class RandomZoom(object):
|
||
"""Zoom the image by random scale.
|
||
|
||
Parameters
|
||
----------
|
||
zoom: list or tuple
|
||
Scaling factor interval, e.g (a, b), then scale is randomly sampled from the range a <= scale <= b.
|
||
interpolation: str
|
||
Interpolation method. Default is 'bilinear'.
|
||
fill: number or sequence
|
||
Pixel fill value for the area outside the sheared image. Default is 0.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomZoom(zoom=(0.2, 0.5), interpolation='bilinear', fill=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, zoom, interpolation='bilinear', fill=0):
|
||
|
||
self.zoom = zoom
|
||
self.interpolation = interpolation
|
||
self.fill = fill
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_zoom(image, self.zoom, self.interpolation, self.fill)
|
||
|
||
|
||
class RandomAffine(object):
|
||
"""Random affine transformation of the image keeping center invariant.
|
||
|
||
Parameters
|
||
----------
|
||
degrees: number or sequnence
|
||
Range of degrees to select from.
|
||
If degrees is a number, the range of degrees will be (-degrees, +degrees).
|
||
If degrees is a sequence, the range of degrees will (degrees[0], degrees[1]).
|
||
Set to 0 to deactivate rotations.
|
||
shift: sequence or None
|
||
Maximum absolute fraction for horizontal and vertical translations.
|
||
shift=(a, b), then horizontal shift is randomly sampled in the range -img_width * a < dx < img_width * a.
|
||
vertical shift is randomly sampled in the range -img_height * b < dy < img_height * b.
|
||
Will not shift by default.
|
||
shear: number or sequnence or None
|
||
Range of degrees to select from.
|
||
If degrees is a number, a shear parallel to the x axis in the range (-shear, +shear) will be applied.
|
||
If shear is a sequence of 2 values a shear parallel to the x axis in the range (shear[0], shear[1]) will be applied.
|
||
If shear is a sequence of 4 values, a x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied.
|
||
Will not apply shear by default.
|
||
zoom: sequence or None
|
||
Scaling factor interval, e.g (a, b), then scale is randomly sampled from the range a <= scale <= b.
|
||
Will not zoom by default.
|
||
interpolation: str
|
||
Interpolation method. Default is 'bilinear'.
|
||
fill: number or sequence
|
||
Pixel fill value for the area outside the sheared image. Default is 0.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.RandomAffine(degrees=30, shift=(0.2,0.2), zoom=(0.2, 0.5), shear=30, interpolation='bilinear', fill=0)
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, degrees, shift=None, zoom=None, shear=None, interpolation='bilinear', fill=0):
|
||
|
||
if isinstance(degrees, numbers.Number):
|
||
if degrees < 0:
|
||
raise ValueError('If degrees is a single number, it must be positive.' 'But got {}.'.format(degrees))
|
||
degrees = [-degrees, degrees]
|
||
elif not (isinstance(degrees, (list, tuple)) and len(degrees) == 2):
|
||
raise TypeError('If degrees is a list or tuple, it should be length of 2.' 'But got {}'.format(degrees))
|
||
|
||
self.degrees = (float(x) for x in degrees)
|
||
|
||
if shift is not None:
|
||
if not (isinstance(shift, (list, tuple)) and len(shift) == 2):
|
||
raise TypeError("shift should be a list or tuple of length 2." "But got {}.".format(shift))
|
||
|
||
for s in shift:
|
||
if not (0.0 <= s <= 1.0):
|
||
raise ValueError('shift values should be between 0 and 1.' 'But got {}.'.format(shift))
|
||
self.shift = shift
|
||
|
||
if zoom is not None:
|
||
if not (isinstance(zoom, (list, tuple)) and len(zoom) == 2):
|
||
raise TypeError("zoom should be a list or tuple of length 2." "But got {}.".format(zoom))
|
||
|
||
if not (0 <= zoom[0] <= zoom[1]):
|
||
raise ValueError("zoom valuse should be positive, and zoom[1] should be less than zoom[0].")
|
||
|
||
self.zoom = zoom
|
||
|
||
if shear is not None:
|
||
if isinstance(shear, numbers.Number):
|
||
if shear < 0:
|
||
raise ValueError("If shear is a single number, it must be positive.")
|
||
shear = [-shear, shear]
|
||
elif not (isinstance(shear, (list, tuple)) and len(shear) in (2, 4)):
|
||
raise TypeError('shear should be a list or tuple of length (2, 4).')
|
||
|
||
self.shear = (float(x) for x in shear)
|
||
|
||
self.interpolation = interpolation
|
||
|
||
if fill is None:
|
||
fill = 0
|
||
elif not isinstance(fill, (list, tuple, numbers.Number)):
|
||
raise TypeError("Fill should be either a sequence or a number.")
|
||
|
||
self.fill = fill
|
||
|
||
def __call__(self, image):
|
||
|
||
return F.random_affine(image, self.degrees, self.shift, self.zoom, self.shear, self.interpolation, self.fill)
|
||
|
||
|
||
class ColorJitter(object):
|
||
"""Randomly change the brightness, contrast, saturation and hue of an image.
|
||
|
||
Parameters
|
||
----------
|
||
brightness: float or sequence
|
||
Brightness adjustment factor (default=(1, 1)).
|
||
If it is a float, the factor is uniformly chosen from the range [max(0, 1-brightness_factor), 1+brightness_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should be non negative numbers.
|
||
contrast: float or sequence
|
||
Contrast adjustment factor (default=(1, 1)).
|
||
If it is a float, the factor is uniformly chosen from the range [max(0, 1-contrast_factor), 1+contrast_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should be non negative numbers.
|
||
saturation: float or sequence
|
||
Saturation adjustment factor (default=(1, 1)).
|
||
If it is a float, the factor is uniformly chosen from the range [max(0, 1-saturation_factor), 1+saturation_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should be non negative numbers.
|
||
hue: float or sequence
|
||
Hue adjustment factor (default=(0, 0)).
|
||
If it is a float, the factor is uniformly chosen from the range [-hue_factor, hue_factor].
|
||
If it is a sequence, it should be [min, max] for the range.Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
|
||
|
||
Examples
|
||
----------
|
||
With TensorLayer
|
||
|
||
>>> image = (np.random.rand(224, 224, 3) * 255.).astype(np.uint8)
|
||
>>> transform = tl.vision.transforms.ColorJitter(brightness=(1,5), contrast=(1,5), saturation=(1,5), hue=(-0.2,0.2))
|
||
>>> image = transform(image)
|
||
>>> print(image)
|
||
|
||
"""
|
||
|
||
def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
|
||
|
||
self.brightness = self._check_input(brightness, 'brightness')
|
||
self.contrast = self._check_input(contrast, 'contrast')
|
||
self.saturation = self._check_input(saturation, 'saturation')
|
||
self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5), clip_first_on_zero=False)
|
||
|
||
def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
|
||
if isinstance(value, numbers.Number):
|
||
if value < 0:
|
||
raise ValueError("If {} is a single number, it must be non negative.".format(name))
|
||
value = [center - float(value), center + float(value)]
|
||
if clip_first_on_zero:
|
||
value[0] = max(value[0], 0.0)
|
||
elif isinstance(value, (tuple, list)) and len(value) == 2:
|
||
if not bound[0] <= value[0] <= value[1] <= bound[1]:
|
||
raise ValueError("{} values should be between {}".format(name, bound))
|
||
else:
|
||
raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name))
|
||
|
||
if value[0] == value[1] == center:
|
||
value = None
|
||
return value
|
||
|
||
@staticmethod
|
||
def get_params(brightness, contrast, saturation, hue):
|
||
fn_idx = np.random.permutation(np.arange(4))
|
||
|
||
b = None if brightness is None else float(np.random.uniform(brightness[0], brightness[1]))
|
||
c = None if contrast is None else float(np.random.uniform(contrast[0], contrast[1]))
|
||
s = None if saturation is None else float(np.random.uniform(saturation[0], saturation[1]))
|
||
h = None if hue is None else float(np.random.uniform(hue[0], hue[1]))
|
||
|
||
return fn_idx, b, c, s, h
|
||
|
||
def __call__(self, image):
|
||
|
||
fn_idx, brightness_factor, contrast_factor, saturation_factor, hue_factor = \
|
||
self.get_params(self.brightness, self.contrast, self.saturation, self.hue)
|
||
|
||
for fn_id in fn_idx:
|
||
if fn_id == 0 and brightness_factor is not None:
|
||
image = F.adjust_brightness(image, brightness_factor)
|
||
elif fn_id == 1 and contrast_factor is not None:
|
||
image = F.adjust_contrast(image, contrast_factor)
|
||
elif fn_id == 2 and saturation_factor is not None:
|
||
image = F.adjust_saturation(image, saturation_factor)
|
||
elif fn_id == 3 and hue_factor is not None:
|
||
image = F.adjust_hue(image, hue_factor)
|
||
|
||
return image
|