And as a bonus we’ll also see how we can utilize scikit-image to download an image from a URL, along with a common “gotcha” that could trip you up along the way.
OpenCV and Python versions: In order to run this example, you’ll need Python 2.7 and OpenCV 2.4.X.
The first method we’ll explore is converting a URL to an image using the OpenCV, NumPy, and the urllib libraries. Open up a new file, name it url_to_image.py , and let’s get started:
# 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
The first thing we’ll do is import our necessary packages. We’ll use NumPy for converting the byte-sequence from the download to a NumPy array, urllib to perform the actual request, and cv2 for our OpenCV bindings.
We then define our url_to_image function on Line 7. This function requires a single argument, url , which is the URL of the image we want to download.
Next, we utilize the urllib library to open a connection to the supplied URL on Line 10. The raw byte-sequence from the request is then converted to a NumPy array on Line 11.
At this point the NumPy array is a 1-dimensional array (i.e. a long list of pixels). To reshape the array into a 2D format, assuming 3 components per pixel (i.e. the Red, Green, and Blue components, respectively), we make a call to cv2.imdecode on Line 12. Finally, we return the decoded image to the calling function on Line 15.
# 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)
Lines 18-21 define a list of image URLs that we are going to download and convert to OpenCV format.
We start looping over each of these URLs on Line 25, make a call to oururl_to_image function on Line 28, and then finally display our downloaded image to our screen on Lines 29 and 30. At this point our image can be manipulated with any other OpenCV functions as we normally would.
To see our work in action, open up a terminal and execute the following command:
The second method assumes that you have the scikit-image library installed on your system. Let’s take a look at how we can leverage scikit-image to download an image from a URL and convert it to OpenCV format:
# 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)