您好,登錄后才能下訂單哦!
這篇文章主要為大家展示了“python opencv、scikit-image和PIL圖像處理庫(kù)有什么區(qū)別”,內(nèi)容簡(jiǎn)而易懂,條理清晰,希望能夠幫助大家解決疑惑,下面讓小編帶領(lǐng)大家一起研究并學(xué)習(xí)一下“python opencv、scikit-image和PIL圖像處理庫(kù)有什么區(qū)別”這篇文章吧。
進(jìn)行深度學(xué)習(xí)時(shí),對(duì)圖像進(jìn)行預(yù)處理的過(guò)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(lái)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(lái)python中幾個(gè)基本的圖像處理庫(kù)進(jìn)行縱向?qū)Ρ取?/p>
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(shuō)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 <class 'numpy.ndarray'> img_opencv is <class 'numpy.ndarray'> img_plt is <class 'numpy.ndarray'> img_PIL if <class 'PIL.JpegImagePlugin.JpegImageFile'>
# 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(wú)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(lái)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(guò)少無(wú)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(lái)對(duì)圖像進(jìn)行剪裁 # transform_pad = transforms.Compose([transforms.Lambda()])
以上是“python opencv、scikit-image和PIL圖像處理庫(kù)有什么區(qū)別”這篇文章的所有內(nèi)容,感謝各位的閱讀!相信大家都有了一定的了解,希望分享的內(nèi)容對(duì)大家有所幫助,如果還想學(xué)習(xí)更多知識(shí),歡迎關(guān)注億速云行業(yè)資訊頻道!
免責(zé)聲明:本站發(fā)布的內(nèi)容(圖片、視頻和文字)以原創(chuàng)、轉(zhuǎn)載和分享為主,文章觀點(diǎn)不代表本網(wǎng)站立場(chǎng),如果涉及侵權(quán)請(qǐng)聯(lián)系站長(zhǎng)郵箱:is@yisu.com進(jìn)行舉報(bào),并提供相關(guān)證據(jù),一經(jīng)查實(shí),將立刻刪除涉嫌侵權(quán)內(nèi)容。