利用Python和OpenCV库将URL转换为OpenCV格式的方法
今天的博客是直接来源于我自己的个人工具函数库。
过去几个月，有些PyImageSearch读者电邮问我：“如何获取URL指向的图片并将其转换成OpenCV格式（不用将其写入磁盘再读回）”。这篇文章我将展示一下怎么实现这个功能。
额外的，我们也会看到如何利用scikit-image从URL下载一幅图像。当然前行之路也会有一个常见的错误，它可能让你跌个跟头。
继续往下阅读，学习如何利用利用Python和OpenCV将URL转换为图像
方法1：OpenCV、NumPy、urllib
第一个方法：我们使用OpenCV、NumPy、urllib库从URL获取图像，并将其转换为图像。打开并新建一个文件，取名url_to_image.py，我们开始吧：
# import the necessary packages import numpy as np import urllib import cv2 # METHOD #1: OpenCV, NumPy, and urllib def url_to_image(url): # download the image, convert it to a NumPy array, and then read # it into OpenCV format resp = urllib.urlopen(url) image = np.asarray(bytearray(resp.read()), dtype="uint8") image = cv2.imdecode(image, cv2.IMREAD_COLOR) # return the image return image
首先要做的就是导入我们必需的包。我们将使用NumPy转换下载的字节序为NumPy数组，使用urllib来执行实际的网络请求，使用cv2来绑定OpenCV接口。
在第7行，我们定义了我们的url_to_image函数。这个函数带一个url参数，也就是我们想要下载的图像地址。
接下来，在第10行，我们使用urllib库来打开这个图像链接。11行则将这个下载下来的字节序转换为NumPy数组。
至此，NumPy数组还是一个1维数组（也就是一个长长的像素链表）。为了将其转换为2维格式，假设每个像素3个通道（意即分别为红，绿，蓝通道），在12行我们使用cv.imdecode函数。最后，在15行我们返回解码出来的图像给调用函数。
一切就绪，该到让它工作的时候了：
# initialize the list of image URLs to download
urls = [
"http://www.pyimagesearch.com/wp-content/uploads/2015/01/opencv_logo.png",
"http://www.pyimagesearch.com/wp-content/uploads/2015/01/google_logo.png",
"http://www.pyimagesearch.com/wp-content/uploads/2014/12/adrian_face_detection_sidebar.png",
]
# loop over the image URLs
for url in urls:
# download the image URL and display it
print "downloading %s" % (url)
image = url_to_image(url)
cv2.imshow("Image", image)
cv2.waitKey(0)
3-5行定义了我们将要下载和转换为OpenCV格式的图像地址列表。
第9行我们遍历这个列表，13行则调用url_to_image函数，然后在14行和15行将获取的图像显示到屏幕上。到此呢，我们就可以像正常情况下一样，使用OpenCV来操作和处理这些图像了。
眼见为实，打开终端，执行如下指令：
如果一切顺利的话，你会看到OpenCV的logo：
图1：从URL下载OpenCV logo并转换为OpenCV格式
接下来是Google的logo：
图2：从URL下载Gooogle并转换为OpenCV格式
这里也有在我书中验证人脸检测的例子，《Practical Python and OpenCV》:
图3：转换一个URL图像为OpenCV格式
现在，我们来看另一种获取图像并转换为OpenCV格式的方法。
方法2：使用scikit-image
第二种方法假定你已经在你计算机上安装好了scikit-image库。让我们看看怎样采用scikit-image从URL获取图像并将其转换为OpenCV格式：
# METHOD #2: scikit-image
from skimage import io
# loop over the image URLs
for url in urls:
# download the image using scikit-image
print "downloading %s" % (url)
image = io.imread(url)
cv2.imshow("Incorrect", image)
cv2.imshow("Correct", cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
cv2.waitKey(0)
scikit-image库中做得很漂亮的一点是：io子库中的imread函数能够区分图像路径到底在磁盘上还是一个URL（第9行)。
尽管这样，这里有一个很严重的错误可能让你跌一个跟头！
OpenCV以BGR顺序表达一幅图像，然而scikit-image则是RGB顺序。如果你使用scikit-iamge的imread函数，而且还想在下载完成后使用OpenCV的函数，那么你要小心了。如41行所述，你需要将图像从RBG转换为BGR。
如果你没有这一步，那么你可能得到错误的结果：
图4：在用scikit-image时，需要特别注意将RGB转换为BGR。左边的图像就是不正确的RGB顺序，右边的则是将RGB转换为BGR，所以能正常显示。
看看Google的logo就更明显了
图5：顺序很重要。确保将RGB转换为BGR，否则就留下了一个很难发现的bug。
到此为止，你明白了吧！这两种方法分别使用Python、OpenCV、urllib,和scikit-image来将URL指向的图片转换为图像。
总结
本文中，我们学会了如何从URL获取图像，且使用Python和OpenCV将其转换为OpenCV格式。
第一种方法使用urllib包获取图像，使用Numpy转换为数组，最后使用OpenCV重新构建数组产生我们的图像。
第二种方式使用scikit-image中的io.imread函数。
所以，哪种更好呢？
这完全取决于你的安装。
如果你已经安装scikit-image，那么我可能就用io.imread（只是不要忘记如果要用OpenCV函数的话，要将RGB转换为BGR）。
如果你没有安装scikit-image，那么url_to_image就是手边现成的工具。具体细节参考本文开始处。
我很快会在Github上将这个函数添加到imutils库中。
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