![]() ![]() The remaining sections plot the two (2) images on a graph, adding a title to each image respectively and displaying them. Then, this object is passed to rgb2gray(), converted and saved to gray_img as a Grayscale representation. Next, the erock_rgb.jpg file is read in and saved as an object to rgb_img. The first five (5) lines import the required libraries and functions therein to successfully execute the code below. import matplotlib.pyplot as pltįig, axes = plt.subplots(1, 2, figsize=(8, 4)) This code displays both the RGB and Grayscale images side-by-side on a graph. This method imports the Matplotlib and Scikit-Learn libraries to convert an RGB image to a Grayscale Representation. ![]() Method 4: Use Matplotlib and Sci-Kit-Learn Then, the Grayscale representation is plotted as a graph and output as shown below: This function, when called later converts the argument passed ( img_rgb) into a Grayscale representation and returns the same. Next, a function is created accepting an object as an argument. Plt.imshow(img_gray, cmap=plt.get_cmap('gray'))Ībove, two (2) libraries and called in: NumPy to use the np.dot() function and Matplotlib to handle the other calls. This method uses both the NumPyand Matplotliblibraries to read an RGB image, convert it to a Grayscale representation, plot, and display the image on a graph. Once to display the original RGB image (underneath), and once to display the Grayscale representation (top). □ Note: Notice the RGB color changes when img_rgb is output to the terminal compared to img_gray.įinally, image.show() is called twice. If img_gray was sent to the terminal, the following would display (snippet only): Then, the following line converts the above image to Grayscale ( cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)) and saves to img_gray. If img_rgb was sent to the terminal, the following would display as follows (snippet only): Next, erock_rgb.jpg is read in using cv2.imread('erock_rgb.jpg'). Img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY) This method imports the OpenCVlibrary to call in and use various functions to convert an RGB image to a Grayscale representation. Since the color channels are ordered differently in PIL and cv2 images, we have to convert the color format manually. Here, I will show you how to convert RGB/RGBA/grayscale images from cv2 to PIL or PIL to cv2. Img_rgb = Image.open('erock_rgb.jpg').convert('L') In many circumstances, we need to convert a cv2 image to a PIL image (or PIL to cv2). The results save as erock_gray.jpg.Ī compact way to perform the same task is to append convert('L') to the end of the second line: reducing the code by one (1) full line. Using img_rgb.convert('L'), converts the RGB object to a Grayscale representation of the same. This object allows us access to and manipulation of this particular image. expand will behave as if the data is replicated without actually doing so. repeat will actually replicate the image data (taking 3x the memory of the original image) whereas. This returns an object img_rgb similar to below. You can also convert a 2D grayscale image to a 3D RGB one by doing: img img.view (width, height, 1).expand (-1, -1, 3) Calling. Next, the image saved earlier is opened ( Image.open('erock_rgb.jpg')) and passed the image name as an argument. Click here to install the pillow library, if required. from PIL import ImageĪbove, imports the PIL ( pillow) library. This function converts an RGB image to a Grayscale representation. This method imports the PIL ( pillow) library allowing access to the img.convert() function. Method 4: Use Matplotlib and Scikit-Learn libraries.Method 3: Use NumPyand Matplotliblibraries.We can accomplish this task by one of the following options: □ Question: How would we write Python code to convert an RGB image to a Grayscale representation? You can read the original ITU-R Recommendation 709 6th edition.□ Note: To follow along, right-click on the above image and save it as erock_rgb.jpg, then move to the current working directory. You can read the original ITU-R Recommendation 601 7th edition. L = R * 299/1000 + G * 587/1000 + B * 114/1000īy iterating through each pixel you can convert 24-bit to 8-bit or 3 channel to 1 channel for each pixel by using the formula above. ITU-R 601 7th Edition Construction of Luminance formula: One of the standards that can be used is Recommendation 601 from ITU-R (Radiocommunication Sector of International Telecommunication Union or ITU) organization which is also used by pillow library while converting color images to grayscale. So, how do we achieve one value from those three pixel values? We need some kind of averaging. L mode on the other hand only uses one value between 0-255 for each pixel (8-bit). In summary, color images usually use the RGB format which means every pixel is represented by a tuple of three value (red, green and blue) in Python. There are different image hashes that can be used to transform color images to grayscale.
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