進(jìn)行深度學(xué)習(xí)時(shí)，對(duì)圖像進(jìn)行預(yù)處理的過程是非常重要的，使用pytorch或者TensorFlow時(shí)需要對(duì)圖像進(jìn)行預(yù)處理以及展示來觀看處理效果，因此對(duì)python中的圖像處理框架進(jìn)行圖像的讀取和基本變換的掌握是必要的，接下來python中幾個(gè)基本的圖像處理庫進(jìn)行縱向?qū)Ρ取?/p>

項(xiàng)目地址：https://github.com/Oldpan/Pytorch-Learn/tree/master/Image-Processing

比較的圖像處理框架：

PIL

scikit-image

opencv-python

PIL：

由于PIL僅支持到Python 2.7，加上年久失修，于是一群志愿者在PIL的基礎(chǔ)上創(chuàng)建了兼容的版本，名字叫Pillow，支持最新Python 3.x，又加入了許多新特性，因此，我們可以直接安裝使用Pillow。

摘自廖雪峰的官方網(wǎng)站

scikit-image

scikit-image is a collection of algorithms for image processing. It is available free of charge and free of restriction. We pride ourselves on high-quality, peer-reviewed code, written by an active community of volunteers.

摘自官網(wǎng)的介紹，scikit-image的更新還是比較頻繁的，代碼質(zhì)量也很好。

opencv-python

opencv的大名就不要多說了，這個(gè)是opencv的python版

# Compare Image-Processing Modules

# Use Transforms Module of torchvision

# &&&

# 對(duì)比python中不同的圖像處理模塊

# 并且使用torchvision中的transforms模塊進(jìn)行圖像處理

# packages

from PIL import Image

from skimage import io, transform

import cv2

import torchvision.transforms as transforms

import matplotlib.pyplot as plt

%matplotlib inline

img_PIL = Image.open('./images/dancing.jpg')

img_skimage = io.imread('./images/dancing.jpg')

img_opencv = cv2.imread('./images/dancing.jpg')

img_plt = plt.imread('./images/dancing.jpg')

loader = transforms.Compose([

transforms.ToTensor()]) # 轉(zhuǎn)換為torch.tensor格式

print('The shape of \n img_skimage is {}\n img_opencv is {}\n img_plt is {}\n'.format(img_skimage.shape, img_opencv.shape, img_plt.shape))

print('The type of \n img_skimage is {}\n img_opencv is {}\n img_plt is {}\n'.format(type(img_skimage), type(img_opencv), type(img_plt)))

The shape of

img_skimage is (444, 444, 3)

img_opencv is (444, 444, 3)

img_plt is (444, 444, 3)

The size of

img_PIL is (444, 444)

The mode of

img_PIL is RGB

The type of

img_skimage is

img_opencv is

img_plt is

img_PIL if

# 定義一個(gè)圖像顯示函數(shù)

def my_imshow(image, title=None):

plt.imshow(image)

if title is not None:

plt.title(title)

plt.pause(0.001) # 這里延時(shí)一下，否則圖像無法加載

plt.figure()

my_imshow(img_skimage, title='img_skimage')

# 可以看到opencv讀取的圖像打印出來的顏色明顯與其他不同

plt.figure()

my_imshow(img_opencv, title='img_opencv')

plt.figure()

my_imshow(img_plt, title='img_plt')

# opencv讀出的圖像顏色通道為BGR，需要對(duì)此進(jìn)行轉(zhuǎn)換

img_opencv = cv2.cvtColor(img_opencv, cv2.COLOR_BGR2RGB)

plt.figure()

my_imshow(img_opencv, title='img_opencv_new')

toTensor = transforms.Compose([transforms.ToTensor()])

# 尺寸變化、縮放

transform_scale = transforms.Compose([transforms.Scale(128)])

temp = transform_scale(img_PIL)

plt.figure()

my_imshow(temp, title='after_scale')

# 隨機(jī)裁剪

transform_randomCrop = transforms.Compose([transforms.RandomCrop(32, padding=4)])

temp = transform_scale(img_PIL)

plt.figure()

my_imshow(temp, title='after_randomcrop')

# 隨機(jī)進(jìn)行水平翻轉(zhuǎn)(0.5幾率)

transform_ranHorFlip = transforms.Compose([transforms.RandomHorizontalFlip()])

temp = transform_scale(img_PIL)

plt.figure()

my_imshow(temp, title='after_ranhorflip')

# 隨機(jī)裁剪到特定大小

transform_ranSizeCrop = transforms.Compose([transforms.RandomSizedCrop(128)])

temp = transform_ranSizeCrop(img_PIL)

plt.figure()

my_imshow(temp, title='after_ranSizeCrop')

# 中心裁剪

transform_centerCrop = transforms.Compose([transforms.CenterCrop(128)])

temp = transform_centerCrop(img_PIL)

plt.figure()

my_imshow(temp, title='after_centerCrop')

# 空白填充

transform_pad = transforms.Compose([transforms.Pad(4)])

temp = transform_pad(img_PIL)

plt.figure()

my_imshow(temp, title='after_padding')

# 標(biāo)準(zhǔn)化是在整個(gè)數(shù)據(jù)集中對(duì)所有圖像進(jìn)行取平均和均方差，演示圖像數(shù)量過少無法進(jìn)行此操作

# print(train_data.mean(axis=(0,1,2))/255)

# print(train_data.std(axis=(0,1,2))/255)

# transform_normal = transforms.Compose([transforms.Normalize()])

# Lamdba使用用戶自定義函數(shù)來對(duì)圖像進(jìn)行剪裁

# transform_pad = transforms.Compose([transforms.Lambda()])

以上就是本文的全部內(nèi)容，希望對(duì)大家的學(xué)習(xí)有所幫助，也希望大家多多支持我們。

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