tensor转换为图片_PyTorch载入图片后ToTensor解读(含PIL和OpenCV读取图片对比)

概述

PyTorch在做一般的深度学习图像处理任务时,先使用dataset类和dataloader类读入图片,在读入的时候需要做transform变换,其中transform一般都需要ToTensor()操作,将dataset类中__getitem__()方法内读入的PIL或CV的图像数据转换为torch.FloatTensor。详细过程如下:

PIL与CV数据格式

PIL(RGB)

PIL(Python Imaging Library)是Python中最基础的图像处理库,一般操作如下:

from PIL import Image

import numpy as np

image = Image.open('test.jpg') # 图片是400x300 宽x高

print type(image) # out: PIL.JpegImagePlugin.JpegImageFile

print image.size # out: (400,300)

print image.mode # out: 'RGB'

print image.getpixel((0,0)) # out: (143, 198, 201)

# resize w*h

image = image.resize((200,100),Image.NEAREST)

print image.size # out: (200,100)

'''

代码解释

**注意image是 class:`~PIL.Image.Image` object**,它有很多属性,比如它的size是(w,h),通道是RGB,,他也有很多方法,比如获取getpixel((x,y))某个位置的像素,得到三个通道的值,x最大可取w-1,y最大可取h-1

比如resize方法,可以实现图片的放缩,具体参数如下

resize(self, size, resample=0) method of PIL.Image.Image instance

Returns a resized copy of this image.

:param size: The requested size in pixels, as a 2-tuple:

(width, height).

注意size是 (w,h),和原本的(w,h)保持一致

:param resample: An optional resampling filter. This can be

one of :py:attr:`PIL.Image.NEAREST`, :py:attr:`PIL.Image.BOX`,

:py:attr:`PIL.Image.BILINEAR`, :py:attr:`PIL.Image.HAMMING`,

:py:attr:`PIL.Image.BICUBIC` or :py:attr:`PIL.Image.LANCZOS`.

If omitted, or if the image has mode "1" or "P", it is

set :py:attr:`PIL.Image.NEAREST`.

See: :ref:`concept-filters`.

注意这几种插值方法,默认NEAREST最近邻(分割常用),分类常用BILINEAR双线性,BICUBIC立方

:returns: An :py:class:`~PIL.Image.Image` object.

'''

image = np.array(image,dtype=np.float32) # image = np.array(image)默认是uint8

print image.shape # out: (100, 200, 3)

# 神奇的事情发生了,w和h换了,变成(h,w,c)了

# 注意ndarray中是 行row x 列col x 维度dim 所以行数是高,列数是宽

OpenCV(python版)(BGR)

OpenCV是一个很强大的图像处理库,适用面更广,可以在各种场合看到,性能也较好,相关代码也较多。常用操作如下:

import cv2

import numpy as np

image = cv2.imread('test.jpg')

print type(image) # out: numpy.ndarray

print image.dtype # out: dtype('uint8')

print image.shape # out: (300, 400, 3) (h,w,c) 和skimage类似

print image # BGR

'''

array([

[ [143, 198, 201 (dim=3)],[143, 198, 201],... (w=200)],

[ [143, 198, 201],[143, 198, 201],... ],

...(h=100)

], dtype=uint8)

'''

# w*h

image = cv2.resize(image,(100,200),interpolation=cv2.INTER_LINEAR)

print image.dtype # out: dtype('uint8')

print image.shape # out: (200, 100, 3)

'''

注意注意注意 和skimage不同

resize(src, dsize[, dst[, fx[, fy[, interpolation]]]])

关键字参数为dst,fx,fy,interpolation

dst为缩放后的图像

dsize为(w,h),但是image是(h,w,c)

fx,fy为图像x,y方向的缩放比例,

interplolation为缩放时的插值方式,有三种插值方式:

cv2.INTER_AREA:使用象素关系重采样。当图像缩小时候,该方法可以避免波纹出现。当图像放大时,类似于 CV_INTER_NN方法

cv2.INTER_CUBIC: 立方插值

cv2.INTER_LINEAR: 双线形插值

cv2.INTER_NN: 最近邻插值

[详细可查看该博客](http://www.tuicool.com/articles/rq6fIn)

'''

'''

cv2.imread(filename, flags=None):

flag:

cv2.IMREAD_COLOR 1: Loads a color image. Any transparency of image will be neglected. It is the default flag. 正常的3通道图

cv2.IMREAD_GRAYSCALE 0: Loads image in grayscale mode 单通道灰度图

cv2.IMREAD_UNCHANGED -1: Loads image as such including alpha channel 4通道图

注意: 默认应该是cv2.IMREAD_COLOR,如果你cv2.imread('gray.png'),虽然图片是灰度图,但是读入后会是3个通道值一样的3通道图片

'''

另外,PIL图像在转换为numpy.ndarray后,格式为(h,w,c),像素顺序为RGB;

OpenCV在cv2.imread()后数据类型为numpy.ndarray,格式为(h,w,c),像素顺序为BGR。

torchvision.transforms.ToTensor()

torchvision.transforms.transforms.py:61

class ToTensor(object):

"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.

Converts a PIL Image or numpy.ndarray (H x W x C) in the range

[0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].

"""

def __call__(self, pic):

"""

Args:

pic (PIL Image or numpy.ndarray): Image to be converted to tensor.

Returns:

Tensor: Converted image.

"""

return F.to_tensor(pic)

def __repr__(self):

return self.__class__.__name__ + '()'

torchvision.transforms.functional.py:32

def to_tensor(pic):

"""Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor.

See ``ToTensor`` for more details.

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

可以从to_tensor()函数看到,函数接受PIL Image或numpy.ndarray,将其先由HWC转置为CHW格式,再转为float后每个像素除以255.

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