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.
