![]() 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-imageĬv2.imshow("Correct", cv2.cvtColor(image, cv2.COLOR_BGR2RGB)) The second method assumes that you have the scikit-image library installed on your system. Now, let’s move on to the alternative method to downloading an image and converting it to OpenCV format. If all goes well, you should first see the OpenCV logo: Figure 1: Downloading the OpenCV logo from a URL and converting it to OpenCV format.Īnd next the Google logo: Figure 2: Downloading the Google logo from a URL and converting it to OpenCV format.Īnd here’s an example of me demonstrating face detection in my book, Practical Python and OpenCV: Figure 3: Converting an image URL to OpenCV format with Python. To see our work in action, open up a terminal and execute the following command: $ python url_to_image.py At this point our image can be manipulated with any other OpenCV functions as we normally would. We start looping over each of these URLs on Line 25, make a call to our url_to_image function on Line 28, and then finally display our downloaded image to our screen on Lines 29 and 30. Lines 18-21 define a list of image URLs that we are going to download and convert to OpenCV format. Finally, we return the decoded image to the calling function on Line 15.Īlright, time to put this function to work: # initialize the list of image URLs to download the Red, Green, and Blue components, respectively), we make a call to cv2.imdecode on Line 12. To reshape the array into a 2D format, assuming 3 components per pixel (i.e. The raw byte-sequence from the request is then converted to a NumPy array on Line 11.Īt this point the NumPy array is a 1-dimensional array (i.e. Next, we utilize the urllib library to open a connection to the supplied URL on Line 10. This function requires a single argument, url, which is the URL of the image we want to download. We then define our url_to_image function on Line 7. 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. The first thing we’ll do is import our necessary packages. Image = cv2.imdecode(image, cv2.IMREAD_COLOR) Image = np.asarray(bytearray(resp.read()), dtype="uint8") # download the image, convert it to a NumPy array, and then read Open up a new file, name it url_to_image.py, and let’s get started: # import the necessary packages The first method we’ll explore is converting a URL to an image using the OpenCV, NumPy, and the urllib libraries. In order to run this example, you’ll need Python 2.7 and OpenCV 2.4.X. But we won't set any hard limits for this tool it's your call.Looking for the source code to this post? Jump Right To The Downloads Section If you have many small icons, consider using CSS sprites instead. Please keep in mind it's usually not useful to embed anything larger than few kilobytes, as it will likely impact performance negatively because these images won't be cached by the browser if used in HTML and will increase CSS parsing time if used in CSS. This tool lets you select one of both ways or just a raw data URI string without the additional code.Īll major browsers currently support data URI in a CSS background property.ĭata URI in a HTML image src property is not supported by Internet Explorer. They can be used both in HTML, where the image data is placed inside the src attribute or in a CSS file as a background image. It's useful when you want to improve performance by reducing the number of HTTP requests needed to load a webpage or when you want to distribute a script/HTML page with some icons or other small images but prefer to keep it in a single file, or include images in email signatures. Data URI is a method for embedding small images directly in your HTML or CSS code using base64 encoding without the need for additional image files.
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