Pytorch 图像变换函数集合小结-巨人网络通讯

Pytorch 图像变换函数集合小结

2.1 判断图像数据类型
2.2 to_tensor(pic)
2.3 to_pil_image(pic, mode=None)
2.4 normalize(tensor, mean, std)
2.5 resize(img, size, interpolation=Image.BILINEAR)
2.6 pad(img, padding, fill=0, padding_mode=‘constant')
2.7 crop(img, i, j, h, w)
2.8 center_crop(img, output_size)
2.9 resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR)
2.10 hflip(img)
2.11 vflip(img)
2.12 five_crop(img, size)
2.13 ten_crop(img, size, vertical_flip=False)
2.14 adjust_brightness(img, brightness_factor)
2.15 adjust_contrast(img, contrast_factor)
2.16 adjust_saturation(img, saturation_factor)
2.17 adjust_hue(img, hue_factor)
2.18 adjust_gamma(img, gamma, gain=1)
2.19 rotate(img, angle, resample=False, expand=False, center=None)
2.20 affine(img, angle, translate, scale, shear, resample=0, fillcolor=None)
2.21 to_grayscale(img, num_output_channels=1)

参考链接

一、必要的 python 模块

PyTorch 的 Vision 模块提供了图像变换的很多函数.

torchvision/transforms/functional.py

from __future__ import division
import torch
import sys
import math
from PIL import Image, ImageOps, ImageEnhance, PILLOW_VERSION
try:
 import accimage
except ImportError:
 accimage = None
import numpy as np
import numbers
import collections
import warnings
import matplotlib as plt

if sys.version_info  (3, 3):
 Sequence = collections.Sequence
 Iterable = collections.Iterable
else:
 Sequence = collections.abc.Sequence
 Iterable = collections.abc.Iterable

以下图为例：

img_file = "test.jpe"
img = Image.open(img_file)
width, height = img.size #(750, 815)
img.show()

二、PyTorch 图像变换函数

2.1 判断图像数据类型

# 图像格式检查，如，pil, tensor, numpy
def _is_pil_image(img):
 if accimage is not None:
  return isinstance(img, (Image.Image, accimage.Image))
 else:
  return isinstance(img, Image.Image)

def _is_tensor_image(img):
 return torch.is_tensor(img) and img.ndimension() == 3

def _is_numpy_image(img):
 return isinstance(img, np.ndarray) and (img.ndim in {2, 3})

# example:
_is_pil_image(img)
# True

_is_tensor_image(img)
# False

_is_numpy_image(img)
# False

_is_numpy_image(np.array(img))
# True

2.2 to_tensor(pic)

将 PIL Image 或 nupy.ndarray 转换为 tensor

def to_tensor(pic):
 """
 Args:
  pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

 Returns:
  Tensor: Converted image.
 """
 if not(_is_pil_image(pic) or _is_numpy_image(pic)):
  raise TypeError('pic should be PIL Image or ndarray. Got {}'.format(type(pic)))

 if isinstance(pic, np.ndarray):
  # handle numpy array
  img = torch.from_numpy(pic.transpose((2, 0, 1)))
  # backward compatibility
  if isinstance(img, torch.ByteTensor):
   return img.float().div(255)
  else:
   return img

 if accimage is not None and isinstance(pic, accimage.Image):
  nppic = np.zeros([pic.channels, pic.height, pic.width], dtype=np.float32)
  pic.copyto(nppic)
  return torch.from_numpy(nppic)

 # handle PIL Image
 if pic.mode == 'I':
  img = torch.from_numpy(np.array(pic, np.int32, copy=False))
 elif pic.mode == 'I;16':
  img = torch.from_numpy(np.array(pic, np.int16, copy=False))
 elif pic.mode == 'F':
  img = torch.from_numpy(np.array(pic, np.float32, copy=False))
 elif pic.mode == '1':
  img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
 else:
  img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
 # PIL image mode: L, P, I, F, RGB, YCbCr, RGBA, CMYK
 if pic.mode == 'YCbCr':
  nchannel = 3
 elif pic.mode == 'I;16':
  nchannel = 1
 else:
  nchannel = len(pic.mode)
 img = img.view(pic.size[1], pic.size[0], nchannel)
 # put it from HWC to CHW format
 # yikes, this transpose takes 80% of the loading time/CPU
 img = img.transpose(0, 1).transpose(0, 2).contiguous()
 if isinstance(img, torch.ByteTensor):
  return img.float().div(255)
 else:
  return img

2.3 to_pil_image(pic, mode=None)

将 tensor 或 ndarray 转换为 PIL Image

def to_pil_image(pic, mode=None):
 """
 Args:
  pic (Tensor or numpy.ndarray): Image to be converted to PIL Image.
  mode (`PIL.Image mode`_): color space and pixel depth of input data (optional).

 .. _PIL.Image mode: https://pillow.readthedocs.io/en/latest/handbook/concepts.html#concept-modes

 Returns:
  PIL Image: Image converted to PIL Image.
 """
 if not(isinstance(pic, torch.Tensor) or isinstance(pic, np.ndarray)):
  raise TypeError('pic should be Tensor or ndarray. Got {}.'.format(type(pic)))

 elif isinstance(pic, torch.Tensor):
  if pic.ndimension() not in {2, 3}:
   raise ValueError('pic should be 2/3 dimensional. Got {} '\

        'dimensions.'.format(pic.ndimension()))

  elif pic.ndimension() == 2:
   # if 2D image, add channel dimension (CHW)
   pic.unsqueeze_(0)

 elif isinstance(pic, np.ndarray):
  if pic.ndim not in {2, 3}:
   raise ValueError('pic should be 2/3 dimensional. Got {} '\

        'dimensions.'.format(pic.ndim))

  elif pic.ndim == 2:
   # if 2D image, add channel dimension (HWC)
   pic = np.expand_dims(pic, 2)

 npimg = pic
 if isinstance(pic, torch.FloatTensor):
  pic = pic.mul(255).byte()
 if isinstance(pic, torch.Tensor):
  npimg = np.transpose(pic.numpy(), (1, 2, 0))

 if not isinstance(npimg, np.ndarray):
  raise TypeError('Input pic must be a torch.Tensor or NumPy ndarray, ' +
      'not {}'.format(type(npimg)))

 if npimg.shape[2] == 1:
  expected_mode = None
  npimg = npimg[:, :, 0]
  if npimg.dtype == np.uint8:
   expected_mode = 'L'
  elif npimg.dtype == np.int16:
   expected_mode = 'I;16'
  elif npimg.dtype == np.int32:
   expected_mode = 'I'
  elif npimg.dtype == np.float32:
   expected_mode = 'F'
  if mode is not None and mode != expected_mode:
   raise ValueError("Incorrect mode ({}) supplied for input type {}. Should be {}"
        .format(mode, np.dtype, expected_mode))
  mode = expected_mode

 elif npimg.shape[2] == 4:
  permitted_4_channel_modes = ['RGBA', 'CMYK']
  if mode is not None and mode not in permitted_4_channel_modes:
   raise ValueError("Only modes {} are supported for 4D inputs".format(permitted_4_channel_modes))

  if mode is None and npimg.dtype == np.uint8:
   mode = 'RGBA'
 else:
  permitted_3_channel_modes = ['RGB', 'YCbCr', 'HSV']
  if mode is not None and mode not in permitted_3_channel_modes:
   raise ValueError("Only modes {} are supported for 3D inputs".format(permitted_3_channel_modes))
  if mode is None and npimg.dtype == np.uint8:
   mode = 'RGB'

 if mode is None:
  raise TypeError('Input type {} is not supported'.format(npimg.dtype))

 return Image.fromarray(npimg, mode=mode)

2.4 normalize(tensor, mean, std)

归一化 tensor 的图像. in-place 计算.

def normalize(tensor, mean, std):
 """
 Args:
  tensor (Tensor): Tensor image of size (C, H, W) to be normalized.
  mean (sequence): Sequence of means for each channel.
  std (sequence): Sequence of standard deviations for each channely.

 Returns:
  Tensor: Normalized Tensor image.
 """
 if not _is_tensor_image(tensor):
  raise TypeError('tensor is not a torch image.')

 # This is faster than using broadcasting, don't change without benchmarking
 for t, m, s in zip(tensor, mean, std):
  t.sub_(m).div_(s)
 return tensor

# example
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
img_normalize = normalize(img_tensor, mean, std)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(to_pil_image(img_normalize))
ax2.axis("off")
ax2.set_title("normalize img")
plt.show()

2.5 resize(img, size, interpolation=Image.BILINEAR)

对输入的 PIL Image 进行 resize 到给定尺寸.
参数 size 为调整后的尺寸.
如果 size 是数组(h, w)，则直接调整到该 (h, w) 尺寸.
如果 size 是一个 int 值，则调整后图像的最短边是该值，且保持固定的长宽比.

def resize(img, size, interpolation=Image.BILINEAR):
 """
 Args:
  img (PIL Image): Image to be resized.
  size (sequence or int): Desired output size. 
  interpolation (int, optional): Desired interpolation. Default is
   ``PIL.Image.BILINEAR``
 Returns:
  PIL Image: Resized image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))
 if not (isinstance(size, int) or (isinstance(size, Iterable) and len(size) == 2)):
  raise TypeError('Got inappropriate size arg: {}'.format(size))

 if isinstance(size, int):
  w, h = img.size
  if (w = h and w == size) or (h = w and h == size):
   return img
  if w  h:
   ow = size
   oh = int(size * h / w)
   return img.resize((ow, oh), interpolation)
  else:
   oh = size
   ow = int(size * w / h)
   return img.resize((ow, oh), interpolation)
 else:
  return img.resize(size[::-1], interpolation)

# example:
img_resize_256x256 = resize(img, (256, 256)) # (256, 256)
img_resize_256 = resize(img, 256) # (256, 278)

# vis
ax1 = plt.subplot(1, 3, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 3, 2)
ax2.imshow(img_resize_256x256)
ax2.axis("off")
ax2.set_title("resize_256x256 img")
ax3 = plt.subplot(1, 3, 3)
ax3.imshow(img_resize_256)
ax3.axis("off")
ax3.set_title("resize_256 img")
plt.show()

2.6 pad(img, padding, fill=0, padding_mode=‘constant')

根据指定的 padding 模式和填充值，对给定的 PIL Image 的所有边进行 pad 处理.
参数 padding - int 或 tuple 形式.

padding:

如果是 int 值，则对所有的边都 padding 该 int 值.
如果是长度为 2 的tuple，则对 left/right 和 top/bottom 分别进行 padding.
如果是长度为 4 的 tuple，则对 left，top，right， bottom 边分别进行 padding.

参数 fill - 像素填充值，默认为 0. 如果值是长度为 3 的 tuple，则分别对 R，G，B 通道进行填充. 仅用于当 padding_mode='constant' 的情况.

参数 padding_mode - 填充的类型，可选：constant，edge，reflect，symmetric. 默认为 constant. 填充常数值.

constant - padding 填充常数值 fill.

edge - padding 图像边缘的最后一个值.

reflect - padding 图像的反射(reflection)值，（不对图像边缘的最后一个像素值进行重复）
如，[1, 2, 3, 4] 在 reflect 模式下在两边 padding 2 个元素值，会得到：
[3, 2, 1, 2, 3, 4, 3, 2]

symmetric - padding 图像的反射(reflection)值，(对图像边缘的最后一个像素值进行重复).
如，[1, 2, 3, 4] 在 symmetric 模式下在两边 padding 2 个元素值，会得到：
[2, 1, 1, 2, 3, 4, 4, 3]

def pad(img, padding, fill=0, padding_mode='constant'):
 """
 Args:
  img (PIL Image): Image to be padded.
  padding (int or tuple): Padding on each border. 
  fill: Pixel fill value for constant fill. Default is 0. 
  padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. 
      Default is constant.
 Returns:
  PIL Image: Padded image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 if not isinstance(padding, (numbers.Number, tuple)):
  raise TypeError('Got inappropriate padding arg')
 if not isinstance(fill, (numbers.Number, str, tuple)):
  raise TypeError('Got inappropriate fill arg')
 if not isinstance(padding_mode, str):
  raise TypeError('Got inappropriate padding_mode arg')

 if isinstance(padding, Sequence) and len(padding) not in [2, 4]:
  raise ValueError("Padding must be an int or a 2, or 4 element tuple, not a " +
       "{} element tuple".format(len(padding)))

 assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'], \

  'Padding mode should be either constant, edge, reflect or symmetric'

 if padding_mode == 'constant':
  if img.mode == 'P':
   palette = img.getpalette()
   image = ImageOps.expand(img, border=padding, fill=fill)
   image.putpalette(palette)
   return image

  return ImageOps.expand(img, border=padding, fill=fill)
 else:
  if isinstance(padding, int):
   pad_left = pad_right = pad_top = pad_bottom = padding
  if isinstance(padding, Sequence) and len(padding) == 2:
   pad_left = pad_right = padding[0]
   pad_top = pad_bottom = padding[1]
  if isinstance(padding, Sequence) and len(padding) == 4:
   pad_left = padding[0]
   pad_top = padding[1]
   pad_right = padding[2]
   pad_bottom = padding[3]

  if img.mode == 'P':
   palette = img.getpalette()
   img = np.asarray(img)
   img = np.pad(img, 
       ((pad_top, pad_bottom), (pad_left, pad_right)), 
       padding_mode)
   img = Image.fromarray(img)
   img.putpalette(palette)
   return img

  img = np.asarray(img)
  # RGB image
  if len(img.shape) == 3:
   img = np.pad(img, 
       ((pad_top, pad_bottom), 
       (pad_left, pad_right), 
       (0, 0)), 
       padding_mode)
  # Grayscale image
  if len(img.shape) == 2:
   img = np.pad(img, 
       ((pad_top, pad_bottom), (pad_left, pad_right)), 
       padding_mode)

  return Image.fromarray(img)

# example:
img_padding = pad(img, (10, 20, 30 ,40), fill=128)	# (750, 815) -> (790, 875)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_padding)
ax2.axis("off")
ax2.set_title("padding img")
plt.show()

2.7 crop(img, i, j, h, w)

裁剪给定的 PIL Image.

def crop(img, i, j, h, w):
 """
 Args:
  img (PIL Image): Image to be cropped.
  i: Upper pixel coordinate.
  j: Left pixel coordinate.
  h: Height of the cropped image.
  w: Width of the cropped image.

 Returns:
  PIL Image: Cropped image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 return img.crop((j, i, j + w, i + h))

# example
img_crop = crop(img, 100, 100, 500, 500)	# (750, 815) -> (500, 500)

ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_crop)
ax2.axis("off")
ax2.set_title("crop img")
plt.show()

2.8 center_crop(img, output_size)

def center_crop(img, output_size):
 if isinstance(output_size, numbers.Number):
  output_size = (int(output_size), int(output_size))
 w, h = img.size
 th, tw = output_size
 i = int(round((h - th) / 2.))
 j = int(round((w - tw) / 2.))
 return crop(img, i, j, th, tw)

#example
img_centercrop = center_crop(img, (256, 256))	# (750, 815) -> (256, 256)

ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_centercrop)
ax2.axis("off")
ax2.set_title("centercrop img")
plt.show()

2.9 resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR)

对给定 PIL Image 进行裁剪，并 resize 到特定尺寸.

def resized_crop(img, i, j, h, w, size, interpolation=Image.BILINEAR):
 """
 Args:
  img (PIL Image): Image to be cropped.
  i: Upper pixel coordinate.
  j: Left pixel coordinate.
  h: Height of the cropped image.
  w: Width of the cropped image.
  size (sequence or int): Desired output size. Same semantics as ``resize``.
  interpolation (int, optional): Desired interpolation. Default is
   ``PIL.Image.BILINEAR``.
 Returns:
  PIL Image: Cropped image.
 """
 assert _is_pil_image(img), 'img should be PIL Image'
 img = crop(img, i, j, h, w)
 img = resize(img, size, interpolation)
 return img

# example
img_resizedcrop = resized_crop(img, 100, 100, 500, 500, (256, 256))	# (750, 815) -> (500, 500) -> (256, 256)

ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_resizedcrop)
ax2.axis("off")
ax2.set_title("resizedcrop img")
plt.show()

2.10 hflip(img)

水平翻转 (Horizontally flip) 给定的 PIL Image.

def hflip(img):
 """
 Args:
  img (PIL Image): Image to be flipped.

 Returns:
  PIL Image: Horizontall flipped image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 return img.transpose(Image.FLIP_LEFT_RIGHT)

2.11 vflip(img)

垂直翻转 (Vertically flip) 给定的 PIL Image.

def vflip(img):
 """
 Args:
  img (PIL Image): Image to be flipped.

 Returns:
  PIL Image: Vertically flipped image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 return img.transpose(Image.FLIP_TOP_BOTTOM)

# example:
img_hflip = hflip(img)
img_vflip = vflip(img)

ax1 = plt.subplot(1, 3, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 3, 2)
ax2.imshow(img_hflip)
ax2.axis("off")
ax2.set_title("hflip img")
ax3 = plt.subplot(1, 3, 3)
ax3.imshow(img_vflip)
ax3.axis("off")
ax3.set_title("vflip img")
plt.show()

2.12 five_crop(img, size)

Crop the given PIL Image into four corners and the central crop.
从给定 PIL Image 的四个角和中间裁剪出五个子图像.

def five_crop(img, size):
 """
 Args:
  size (sequence or int): Desired output size of the crop. If size is an
   int instead of sequence like (h, w), a square crop (size, size) is
   made.

 Returns:
  tuple: tuple (tl, tr, bl, br, center)
    Corresponding top left, top right, bottom left, 
    bottom right and center crop.
 """
 if isinstance(size, numbers.Number):
  size = (int(size), int(size))
 else:
  assert len(size) == 2, "Please provide only two dimensions (h, w) for size."

 w, h = img.size
 crop_h, crop_w = size
 if crop_w > w or crop_h > h:
  raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
                      (h, w)))
 tl = img.crop((0, 0, crop_w, crop_h))
 tr = img.crop((w - crop_w, 0, w, crop_h))
 bl = img.crop((0, h - crop_h, crop_w, h))
 br = img.crop((w - crop_w, h - crop_h, w, h))
 center = center_crop(img, (crop_h, crop_w))
 return (tl, tr, bl, br, center)

# example:
img_tl, img_tr, img_bl, img_br, img_center = five_crop(img, (400, 400))

ax1 = plt.subplot(2, 3, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(2, 3, 2)
ax2.imshow(img_tl)
ax2.axis("off")
ax2.set_title("tl img")
ax3 = plt.subplot(2, 3, 3)
ax3.imshow(img_tr)
ax3.axis("off")
ax3.set_title("tr img")
ax4 = plt.subplot(2, 3, 4)
ax4.imshow(img_bl)
ax4.axis("off")
ax4.set_title("bl img")
ax5 = plt.subplot(2, 3, 5)
ax5.imshow(img_br)
ax5.axis("off")
ax5.set_title("br img")
ax6 = plt.subplot(2, 3, 6)
ax6.imshow(img_center)
ax6.axis("off")
ax6.set_title("center img")
plt.show()

2.13 ten_crop(img, size, vertical_flip=False)

将给定 PIL Image 裁剪出的四个角和中间部分的五个子图像，每个子图像进行翻转处理. 默认时水平翻转.

def ten_crop(img, size, vertical_flip=False):
 """
 Args:
  size (sequence or int): Desired output size of the crop. If size is an
   int instead of sequence like (h, w), a square crop (size, size) is
   made.
  vertical_flip (bool): Use vertical flipping instead of horizontal

 Returns:
  tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
  Corresponding top left, top right, bottom left, bottom right and center crop
  and same for the flipped image.
 """
 if isinstance(size, numbers.Number):
  size = (int(size), int(size))
 else:
  assert len(size) == 2, "Please provide only two dimensions (h, w) for size."

 first_five = five_crop(img, size)

 if vertical_flip:
  img = vflip(img)
 else:
  img = hflip(img)

 second_five = five_crop(img, size)
 return first_five + second_five

2.14 adjust_brightness(img, brightness_factor)

def adjust_brightness(img, brightness_factor):
 """
 Args:
  img (PIL Image): PIL Image to be adjusted.
  brightness_factor (float): How much to adjust the brightness.
   Can be any non negative number. 
   0 gives a black image, 
   1 gives the original image,
   2 increases the brightness by a factor of 2.

 Returns:
  PIL Image: Brightness adjusted image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 enhancer = ImageEnhance.Brightness(img)
 img = enhancer.enhance(brightness_factor)
 return img

# example:
img_adjust_brightness = adjust_brightness(img, 2.5)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_adjust_brightness)
ax2.axis("off")
ax2.set_title("adjust_brightness img")
plt.show()

2.15 adjust_contrast(img, contrast_factor)

调整对比度.

def adjust_contrast(img, contrast_factor):
 """
 Args:
  img (PIL Image): PIL Image to be adjusted.
  contrast_factor (float): How much to adjust the contrast. 
   Can be any non negative number. 
   0 gives a solid gray image, 
   1 gives the original image, 
   2 increases the contrast by a factor of 2.

 Returns:
  PIL Image: Contrast adjusted image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 enhancer = ImageEnhance.Contrast(img)
 img = enhancer.enhance(contrast_factor)
 return img

# example:
img_adjust_contrast = adjust_contrast(img, 2.5)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_adjust_contrast)
ax2.axis("off")
ax2.set_title("adjust_contrast img")
plt.show()

2.16 adjust_saturation(img, saturation_factor)

调整颜色饱和度.

def adjust_saturation(img, saturation_factor):
 """
 Args:
  img (PIL Image): PIL Image to be adjusted.
  saturation_factor (float): How much to adjust the saturation. 
   0 will give a black and white image, 
   1 will give the original image while
   2 will enhance the saturation by a factor of 2.

 Returns:
  PIL Image: Saturation adjusted image.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 enhancer = ImageEnhance.Color(img)
 img = enhancer.enhance(saturation_factor)
 return img

# example
img_adjust_saturation = adjust_saturation(img, 2.5)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_adjust_saturation)
ax2.axis("off")
ax2.set_title("adjust_saturation img")
plt.show()

2.17 adjust_hue(img, hue_factor)

调整图像 HUE.

通过将图像转换为 HSV 空间，并周期地移动在 hue 通道(H) 的强度，以实现图像 hue 的调整.

最后，再将结果转换回原始的图像模式.参数 hue_factor - H 通道平移的因子，其值必须在区间 [-0.5, 0.5].

def adjust_hue(img, hue_factor):
 """
 Args:
  img (PIL Image): PIL Image to be adjusted.
  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.

 Returns:
  PIL Image: Hue adjusted image.
 """
 if not(-0.5 = hue_factor = 0.5):
  raise ValueError('hue_factor is not in [-0.5, 0.5].'.format(hue_factor))

 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 input_mode = img.mode
 if input_mode in {'L', '1', 'I', 'F'}:
  return img

 h, s, v = img.convert('HSV').split()

 np_h = np.array(h, dtype=np.uint8)
 # uint8 addition take cares of rotation across boundaries
 with np.errstate(over='ignore'):
  np_h += np.uint8(hue_factor * 255)
 h = Image.fromarray(np_h, 'L')

 img = Image.merge('HSV', (h, s, v)).convert(input_mode)
 return img

# example:
img_adjust_hue = adjust_hue(img, 0.5)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_adjust_hue)
ax2.axis("off")
ax2.set_title("adjust_hue img")
plt.show()

2.18 adjust_gamma(img, gamma, gain=1)

对图像进行伽马校正(gamma correction). 也被叫作 Power Law Transform.

def adjust_gamma(img, gamma, gain=1):
 """
 Args:
  img (PIL Image): PIL Image to be adjusted.
  gamma (float): Non negative real number, 如公式中的 \gamma 值.
   gamma larger than 1 make the shadows darker,
   while gamma smaller than 1 make dark regions lighter.
  gain (float): The constant multiplier.
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 if gamma  0:
  raise ValueError('Gamma should be a non-negative real number')

 input_mode = img.mode
 img = img.convert('RGB')

 gamma_map = [255 * gain * pow(ele / 255., gamma) for ele in range(256)] * 3
 img = img.point(gamma_map) # use PIL's point-function to accelerate this part

 img = img.convert(input_mode)
 return img

# example:
img_adjust_gamma = adjust_gamma(img, 0.5)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_adjust_gamma)
ax2.axis("off")
ax2.set_title("adjust_gamma img")
plt.show()

2.19 rotate(img, angle, resample=False, expand=False, center=None)

旋转图像.

参数 resample
可选值：PIL.Image.NEAREST， PIL.Image.BILINEAR， PIL.Image.BICUBIC.
如果参数 resample 被忽略，或图像的模式是 1 或 P，则resample=PIL.Image.NEAREST.

参数 expand
如果 expand=True，则延展输出图像，以能包含旋转后的全部图像.
如果 expand=False 或被忽略，则保持输出图像与输入图像的尺寸一致.
expand 假设旋转是以中心进行旋转，且没有平移.

def rotate(img, angle, resample=False, expand=False, center=None):
 """
 Args:
  img (PIL Image): PIL Image to be rotated.
  angle (float or int): In degrees degrees counter clockwise order.
  resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or 
     ``PIL.Image.BICUBIC``, optional):
  expand (bool, optional): Optional expansion flag.
  center (2-tuple, optional): Optional center of rotation.
   Origin is the upper left corner.
   Default is the center of the image.
 """

 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 return img.rotate(angle, resample, expand, center)

# example:
img_rotate = rotate(img, 60)

# vis
ax1 = plt.subplot(1, 2, 1)
ax1.imshow(img)
ax1.axis("off")
ax1.set_title("orig img")
ax2 = plt.subplot(1, 2, 2)
ax2.imshow(img_rotate)
ax2.axis("off")
ax2.set_title("rotate img")
plt.show()

2.20 affine(img, angle, translate, scale, shear, resample=0, fillcolor=None)

保持图像中心不变，进行仿射变换.

def _get_inverse_affine_matrix(center, angle, translate, scale, shear):
 # Helper method to compute inverse matrix for affine transformation

 # As it is explained in PIL.Image.rotate
 # We need compute INVERSE of affine transformation matrix: M = T * C * RSS * C^-1
 # where T is translation matrix: [1, 0, tx | 0, 1, ty | 0, 0, 1]
 #  C is translation matrix to keep center: [1, 0, cx | 0, 1, cy | 0, 0, 1]
 #  RSS is rotation with scale and shear matrix
 #  RSS(a, scale, shear) = [ cos(a)*scale -sin(a + shear)*scale  0]
 #        [ sin(a)*scale cos(a + shear)*scale  0]
 #        [  0     0   1]
 # Thus, the inverse is M^-1 = C * RSS^-1 * C^-1 * T^-1

 angle = math.radians(angle)
 shear = math.radians(shear)
 scale = 1.0 / scale

 # Inverted rotation matrix with scale and shear
 d = math.cos(angle + shear) * math.cos(angle) + math.sin(angle + shear) * math.sin(angle)
 matrix = [
  math.cos(angle + shear), math.sin(angle + shear), 0,
  -math.sin(angle), math.cos(angle), 0
 ]
 matrix = [scale / d * m for m in matrix]

 # Apply inverse of translation and of center translation: RSS^-1 * C^-1 * T^-1
 matrix[2] += matrix[0] * (-center[0] - translate[0]) + matrix[1] * (-center[1] - translate[1])
 matrix[5] += matrix[3] * (-center[0] - translate[0]) + matrix[4] * (-center[1] - translate[1])

 # Apply center translation: C * RSS^-1 * C^-1 * T^-1
 matrix[2] += center[0]
 matrix[5] += center[1]
 return matrix


def affine(img, angle, translate, scale, shear, resample=0, fillcolor=None):
 """
 Args:
  img (PIL Image): PIL Image to be rotated.
  angle (float or int): rotation angle in degrees between -180 and 180, 
        clockwise direction.
  translate (list or tuple of integers): horizontal and vertical translations 
        (post-rotation translation)
  scale (float): overall scale
  shear (float): shear angle value in degrees between -180 to 180, 
      clockwise direction.
  resample (``PIL.Image.NEAREST`` or ``PIL.Image.BILINEAR`` or 
     ``PIL.Image.BICUBIC``, optional):
  fillcolor (int): Optional fill color for the area outside the transform in the output image. (Pillow>=5.0.0)
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 assert isinstance(translate, (tuple, list)) and len(translate) == 2, \

  "Argument translate should be a list or tuple of length 2"

 assert scale > 0.0, "Argument scale should be positive"

 output_size = img.size
 center = (img.size[0] * 0.5 + 0.5, img.size[1] * 0.5 + 0.5)
 matrix = _get_inverse_affine_matrix(center, angle, translate, scale, shear)
 kwargs = {"fillcolor": fillcolor} if PILLOW_VERSION[0] == '5' else {}
 return img.transform(output_size, Image.AFFINE, matrix, resample, **kwargs)

2.21 to_grayscale(img, num_output_channels=1)

将图像转换为灰度图.

def to_grayscale(img, num_output_channels=1):
 """
 Args:
  img (PIL Image): Image to be converted to grayscale.

 Returns:
  PIL Image: Grayscale version of the image.
   if num_output_channels = 1 : 
    returned image is single channel
   if num_output_channels = 3 : 
    returned image is 3 channel with r = g = b
 """
 if not _is_pil_image(img):
  raise TypeError('img should be PIL Image. Got {}'.format(type(img)))

 if num_output_channels == 1:
  img = img.convert('L')
 elif num_output_channels == 3:
  img = img.convert('L')
  np_img = np.array(img, dtype=np.uint8)
  np_img = np.dstack([np_img, np_img, np_img])
  img = Image.fromarray(np_img, 'RGB')
 else:
  raise ValueError('num_output_channels should be either 1 or 3')

 return img