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
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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

  1. 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
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  1. 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:
此代码由Java架构师必看网-架构君整理
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

此代码由Java架构师必看网-架构君整理
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.

本文来源木易修,由架构君转载发布,观点不代表Java架构师必看的立场,转载请标明来源出处:https://javajgs.com/archives/42214
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