pytorch图像增强

阳光穿透心脏的1/2处 2023-06-12 06:27 13阅读 0赞

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pytorch图像增强，主要是借用torchvision程序包。还有如imgaug等工具

[https://pypi.org/project/torchvision/][https_pypi.org_project_torchvision]

torchvision 微信介绍

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本文主要介绍Pytorch中torchvision.transforms 几个数据增强函数的使用。

>     from torchvision import transformsfrom PIL import Imagefrom torchvision.transforms import functional as TFimport torch# 读取一张测试图片path = "F:/jupyter/OpenCV-Python-Tutorial/Tutorial/sample_img/lena.jpg"img = Image.open(path)img

# **对 Torch 数据操作的变换** #

*  ### **ToPILImage** ###

Convert a tensor or an ndarray to PIL Image.

>     # 将 ``PIL Image`` or ``numpy.ndarray`` 转换成 tensor 再转成 PIL Image.transform = transforms.Compose([    transforms.ToTensor(),     # Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.    transforms.ToPILImage()     # Convert a tensor or an ndarray to PIL Image.])
>     new_img = transform(img)new_img

*  ### **Normalize** ###

提供一个所有通道的均值（mean） 和方差（std），会将原始数据进行归一化，操作的数据格式是 Tensor

> mean = \[0.5, 0.5, 0.5\]
> 
>     std = [0.5, 0.5, 0.5]transform = transforms.Compose([    transforms.ToTensor(),    transforms.Normalize(mean, std),     transforms.ToPILImage() # 这里是为了可视化，故将其再转为 PIL，以下同理])
>     new_img = transform(img)new_img

# **对 PIL 数据操作的变换** #

###  ###

*  ### **ToTensor** ###

将 PIL Image 或者 numpy.ndarray 格式的数据转换成 tensor

>     transform = transforms.Compose([    transforms.ToTensor(), # Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.])
>     new_img = transform(img)

*  ### **Resize** ###

Resize the input PIL Image to the given size.

参数

*  size: 一个值的话，高和宽共享，否则对应是 (h, w)
 *  interpolation: 插值方式 默认 PIL.Image.BILINEAR

>     size = (100, 100)transform = transforms.Compose([    transforms.Resize(size),])
>     new_img = transform(img)new_img

*  ### **CenterCrop** ###

Crops the given PIL Image at the center.  
裁剪一定 size 的图片，以图片的中心往外

参数

*  size: 一个值的话，高和宽共享，否则对应是 (h, w)，若是该值超过原始图片尺寸，则外围用 0 填充

>     size = (200, 500)transform = transforms.Compose([    transforms.CenterCrop(size),])
>     new_img = transform(img)new_img

*  ### **Pad** ###

Pad the given PIL Image on all sides with the given "pad" value.

参数

*  padding：填充的宽度，可以是一个 值、或者元组，分别对应 4 个边
 *  fill：填充的值，可以是一个值（所有通道都用该值填充），或者一个 3 元组（RGB 三通道） 当 padding\_mode=constant 起作用
 *  padding\_mode：填充的模式：constant, edge（填充值为边缘）, reflect (从边缘往内一个像素开始做镜像) or symmetric（从边缘做镜像）.

>     padding = (10, 20, 30, 40)transform = transforms.Compose([    transforms.Pad(padding, padding_mode="symmetric"), ])
>     new_img = transform(img)new_img

*  ### **Lambda** ###

根据用户自定义的方式进行变换

> lambd = lambda x: TF.rotate(x, 100)
> 
>     transform = transforms.Compose([    transforms.Lambda(lambd)])
>     new_img = transform(img)new_img

*  ### **RandomApply** ###

给定一定概率从一组 transformations 应用

>     transform = [transforms.Pad(100, fill=(0, 255, 255)), transforms.CenterCrop(100), transforms.RandomRotation(20)]transform = transforms.Compose([    transforms.RandomApply(transform, p=0.5)])
>     new_img = transform(img)new_img

*  ### **RandomChoice** ###

Apply single transformation randomly picked from a list

>     transform = [transforms.Pad(100, fill=(0, 255, 255)), transforms.CenterCrop((100, 300))]transform = transforms.Compose([    transforms.RandomChoice(transform)])
>     new_img = transform(img)new_img

*  ### **RandomOrder** ###

Apply a list of transformations in a random order

>     transform = [transforms.Pad(100, fill=(0, 255, 255)), transforms.CenterCrop((50, 50))]transform = transforms.Compose([    transforms.RandomOrder(transform)])
>     new_img = transform(img)new_img

*  ### **RandomCrop** ###

Crop the given PIL Image at a random location.

参数：

*  size
 *  padding=None
 *  pad\_if\_needed=False
 *  fill=0
 *  padding\_mode='constant'

>     transform = transforms.Compose([    transforms.RandomCrop((100, 300))])
>     new_img = transform(img)new_img

*  ### **RandomHorizontalFlip & RandomVerticalFlip** ###

Horizontally/Vertically flip the given PIL Image randomly with a given probability.

>     transform = transforms.Compose([    transforms.RandomHorizontalFlip(p=0.5),    transforms.RandomVerticalFlip(p=0.5)])
>     new_img = transform(img)new_img

*  ### **RandomResizedCrop** ###

Crop the given PIL Image to random size and aspect ratio.

参数：

*  size: expected output size of each edge
 *  scale: range of size of the origin size cropped
 *  ratio: range of aspect ratio of the origin aspect ratio cropped
 *  interpolation: Default: PIL.Image.BILINEAR

>     transform = transforms.Compose([    transforms.RandomResizedCrop((200, 300))])
>     new_img = transform(img)new_img

*  ### **RandomSizedCrop** ###

已经废弃，由 RandomResizedCrop. 取代了

*  ### **FiveCrop** ###

将给定的 PIL 图像裁剪成四个角和中间的裁剪

>     UNIT_SIZE = 200 # 每张图片的宽度是固定的size = (100, UNIT_SIZE)transform = transforms.Compose([    transforms.FiveCrop(size)])
>     new_img = transform(img)delta = 20  # 偏移量，几个图片间隔看起来比较明显new_img_2 = Image.new("RGB", (UNIT_SIZE*5+delta, 100))top_right = 0for im in new_img:    new_img_2.paste(im, (top_right, 0)) # 将image复制到target的指定位置中    top_right += UNIT_SIZE + int(delta/5) # 左上角的坐标，因为是横向的图片，所以只需要 x 轴的值变化就行
>     new_img_2

*  ### **TenCrop** ###

裁剪一张图片的 4 个角以及中间得到指定大小的图片，并且进行水平翻转 / 竖直翻转 共 10 张

参数：

*  size
 *  vertical\_flip=False (默认是水平翻转)

>     UNIT_SIZE = 200 # 每张图片的宽度是固定的size = (100, UNIT_SIZE)
>     transform = transforms.Compose([    transforms.TenCrop(size, vertical_flip=True)])
>     new_img = transform(img)
>     delta = 50  # 偏移量，几个图片间隔看起来比较明显new_img_2 = Image.new("RGB", (UNIT_SIZE*10+delta, 100))top_right = 0for im in new_img:    new_img_2.paste(im, (top_right, 0)) # 将image复制到target的指定位置中    top_right += UNIT_SIZE + int(delta/10) # 左上角的坐标，因为是横向的图片，所以只需要 x 轴的值变化就行
>     new_img_2

*  ### **LinearTransformation** ###

白化变换，笔者不是很理解，但是好像消耗的内存应该比较大

*  ### **ColorJitter** ###

Randomly change the brightness, contrast and saturation of an image. 随机改变图像的亮度、对比度和饱和度

参数：

*  brightness：亮度
 *  contrast：对比度
 *  saturation：饱和度
 *  hue：色调 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.

>     transform = transforms.Compose([    transforms.ColorJitter(brightness=(0, 36), contrast=(        0, 10), saturation=(0, 25), hue=(-0.5, 0.5))])
>     new_img = transform(img)new_img

*  ### **RandomRotation** ###

一定角度旋转图像

参数：

*  degrees：旋转的角度
 *  resample=False：重采样过滤器 可选 \{PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC\}
 *  expand=False：如果为 True ，则展开输出，使其足够大以容纳整个旋转后的图像。如果为 Fales 或省略，使输出图像的大小与输入图像相同。
 *  center=None 旋转中心

> transform = transforms.Compose(\[
> 
>     transforms.RandomRotation(30, resample=Image.BICUBIC, expand=False, center=(100, 300))])
>     new_img = transform(img)new_img

*  ### **RandomAffine** ###

保持图像中心不变的随机仿射变换，可以进行随心所欲的变化

参数：

*  degrees：旋转角度
 *  translate：水平偏移
 *  scale:
 *  shear: 裁剪
 *  resample (\{PIL.Image.NEAREST, PIL.Image.BILINEAR, PIL.Image.BICUBIC\}, optional)
 *  fillcolor: 图像外部填充颜色 int

> transform = transforms.Compose(\[
> 
>     transforms.RandomAffine(degrees=30, translate=(0, 0.2), scale=(0.9, 1), shear=(6, 9), fillcolor=66)])
>     new_img = transform(img)new_img

*  ### **Grayscale** ###

转换图像灰度。

参数：

*  num\_output\_channels：1 或者 3 输出图像所需的通道数 (若是 3 的话，则代表三个通道的值是一样的)

>     ransform = transforms.Compose([    transforms.Grayscale(num_output_channels=3)])
>     new_img = transform(img)new_img_array = np.array(new_img)r, g, b = new_img_array[:, :, 0], new_img_array[:, :, 1], new_img_array[:, :, 2]print(r == b)print("shape:", new_img_array.shape)new_img[[ True  True  True ...  True  True  True] [ True  True  True ...  True  True  True] [ True  True  True ...  True  True  True] ... [ True  True  True ...  True  True  True] [ True  True  True ...  True  True  True] [ True  True  True ...  True  True  True]]shape: (400, 400, 3)

*  ### **RandomGrayscale** ###

Randomly convert image to grayscale with a probability of p (default 0.1). 以一定的概率对图像进行灰度化，转换后的图片还是 3 通道的

>     transform = transforms.Compose([    transforms.RandomGrayscale(p=0.6)])
>     new_img = transform(img)print(np.array(new_img).shape)new_img(400, 400, 3)

*  ### **RandomPerspective** ###

对给定的 PIL 图像以给定的概率随机进行透视变换。

>     transform = transforms.Compose([    transforms.RandomPerspective(distortion_scale=1, p=1, interpolation=3)])
>     new_img = transform(img)new_img

# **其他** #

transforms.Compose 函数是将几个变化整合在一起的，变换是有顺序的，需要注意是变换函数是对 PIL 数据格式进行还是 Torch 数据格式进行变换

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[https_pypi.org_project_torchvision]: https://pypi.org/project/torchvision/
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