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Answer : Here's a version that's much simpler - not sure how performant it is. Heavily based on some django snippet I found while building RGBA -> JPG + BG support for sorl thumbnails. from PIL import Image png = Image.open(object.logo.path) png.load() # required for png.split() background = Image.new("RGB", png.size, (255, 255, 255)) background.paste(png, mask=png.split()[3]) # 3 is the alpha channel background.save('foo.jpg', 'JPEG', quality=80) Result @80% Result @ 50% By using Image.alpha_composite , the solution by Yuji 'Tomita' Tomita become simpler. This code can avoid a tuple index out of range error if png has no alpha channel. from PIL import Image png = Image.open(img_path).convert('RGBA') background = Image.new('RGBA', png.size, (255,255,255)) alpha_composite = Image.alpha_composite(background, png) alpha_composite.save('foo.jpg', 'JPEG', quality=80) The transparent parts mostly have RGBA valu...

Answer : This is simple with numpy and pylab . You can set the colormap to be whatever you like, here I use spectral. from pylab import imshow, show, get_cmap from numpy import random Z = random.random((50,50)) # Test data imshow(Z, cmap=get_cmap("Spectral"), interpolation='nearest') show() Your target image looks to have a grayscale colormap with a higher pixel density than 100x100: import pylab as plt import numpy as np Z = np.random.random((500,500)) # Test data plt.imshow(Z, cmap='gray', interpolation='nearest') plt.show() If you want to create an image file (and display it elsewhere, with or without Matplotlib), you could use NumPy and Pillow as follows: import numpy, from PIL import Image imarray = numpy.random.rand(100,100,3) * 255 im = Image.fromarray(imarray.astype('uint8')).convert('RGBA') im.save('result_image.png') The idea here is to create a numeric array, convert it to a RGB image, and sa...

Answer : You probably want to use an image's convert method: import PIL.Image rgba_image = PIL.Image.open(path_to_image) rgb_image = rgba_image.convert('RGB') In case of numpy array, I use this solution: def rgba2rgb( rgba, background=(255,255,255) ): row, col, ch = rgba.shape if ch == 3: return rgba assert ch == 4, 'RGBA image has 4 channels.' rgb = np.zeros( (row, col, 3), dtype='float32' ) r, g, b, a = rgba[:,:,0], rgba[:,:,1], rgba[:,:,2], rgba[:,:,3] a = np.asarray( a, dtype='float32' ) / 255.0 R, G, B = background rgb[:,:,0] = r * a + (1.0 - a) * R rgb[:,:,1] = g * a + (1.0 - a) * G rgb[:,:,2] = b * a + (1.0 - a) * B return np.asarray( rgb, dtype='uint8' ) in which the argument rgba is a numpy array of type uint8 with 4 channels. The output is a numpy array with 3 channels of type uint8 . This array is easy to do I/O with library imageio using imread and imsave .