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
参考链接
https://www.aiuai.cn/aifarm759.html
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原文链接：https://blog.csdn.net/libo1004/article/details/113257168