![]() I came to this question via Google, searching for a way to convert an already loaded image to grayscale. Use img.Convert(), supports “L”, “RGB” and “CMYK.” mode import numpy as np If you're using NumPy/SciPy already you may as well use: However, the GIMP converting color to grayscale image software has three algorithms to do the task. Plt.imshow(gray, cmap = plt.get_cmap(name = 'gray')) You could also go all in and roll your own luma-only converter, though that's probably overkill. It's not quite the same as a luma value, but it means you can do it all in matplotlib.Īlternatively, you could use PIL or the builtin colorsys.rgb_to_yiq() to convert to a colorspace with a true luma value. Try using _to_hsv(img) then slicing the last value (V) from the array for your grayscale. ![]() matplotlib does not appear to provide a mechanism to convert to YUV/YIQ, but it does let you convert to HSV. The basic steps you need to do are to transform from the RGB colorspace to a colorspace that encodes with something approximating the luma/chroma model, such as YUV/YIQ or HSL/HSV, then slice off the luma-like channel and use that as your greyscale image. The tutorial is cheating because it is starting with a greyscale image encoded in RGB, so they are just slicing a single color channel and treating it as greyscale. Img = Image.open('input_file.jpg').convert('L') The fastest and current way is to use Pillow, installed via pip install Pillow. You can always read the image file as grayscale right from the beginning using imread from OpenCV: img = cv2.imread('messi5.jpg', 0)įurthermore, in case you want to read the image as RGB, do some processing and then convert to Gray Scale you could use cvtcolor from OpenCV: gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) Img_diff = np.ndarray(shape=img1.shape, dtype='float32') Img2 = np.array(Image.open(z).convert('L')) Print(' seconds'.format(k, sum(v) / len(v))) Img = np.array(Image.open(z).convert('L')) Run_times.append(time.time() - start_time) Run_times = dict(sk=list(), pil=list(), scipy=list()) ![]() In addition the colors are converted slightly different, see the example from the CUB-200 dataset. PIL and SciPy gave identical numpy arrays (ranging from 0 to 255). Three of the suggested methods were tested for speed with 1000 RGBA PNG images (224 x 256 pixels) running with Python 3.5 on Ubuntu 16.04 LTS (Xeon E5 2670 with SSD). Img = io.imread('image.png', as_gray=True) Notes: The weights used in this conversion are calibrated for contemporary CRT phosphors: Y = 0.2125 R + 0.7154 G + 0.0721 BĪlternatively, you can read image in grayscale by: from skimage import io Img = color.rgb2gray(io.imread('image.png')) You can also use scikit-image, which provides some functions to convert an image in ndarray, like rgb2gray. Plt.imshow(gray, cmap=plt.get_cmap('gray'), vmin=0, vmax=1) If an alpha (transparency) channel is present in the input image and should be preserved, use mode LA: img = Image.open('image.png').convert('LA') ![]() Img = Image.open('image.png').convert('L') How about doing it with Pillow: from PIL import Image
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