I've got a camera that provides images in Bayer RG8 format.
I'm using skimage for processing images, but I could not find away to convert the Bayer RG8 format to standard RGB (to display on screen).
Is there any way to do this with skimage?
I did find a reference to opencv conversion, but I'm trying to avoid including opencv in my app (unless it is absolutely necessary).
21 Answer
As you have not provided any input data, I took the greyscale image from here and made it into a raw Bayer8 file with GBRG ordering using ImageMagick as follows:
magick mandi.png -trim -depth 8 gray:bayer.bin which gives me an 1013x672 pixel file of 680,736 bytes.
Then I read it like this and made it into an image that skimage can understand like this:
#!/usr/bin/env python3 import numpy as np from skimage.io import imsave # Width and height of Bayer image, not original which is w/2 x h/2 w, h = 1013, 672 ow, oh = w//2, h//2 # Load in Bayer8 image, assumed raw 8-bit GBRG bayer = np.fromfile('bayer.bin', dtype=np.uint8).reshape((h,w)) # Pick up raw uint8 samples R = bayer[1::2, 0::2] # rows 1,3,5,7 columns 0,2,4,6 B = bayer[0::2, 1::2] # rows 0,2,4,6 columns 1,3,5,7 G0 = bayer[0::2, 0::2] # rows 0,2,4,6 columns 0,2,4,6 G1 = bayer[1::2, 1::2] # rows 1,3,5,7 columns 1,3,5,7 # Chop any left-over edges and average the 2 Green values R = R[:oh,:ow] B = B[:oh,:ow] G = G0[:oh,:ow]//2 + G1[:oh,:ow]//2 # Formulate image by stacking R, G and B and save out = np.dstack((R,G,B)) imsave('result.png',out) And get this:
Copyright Mathworks, Inc.
Of course, there are more sophisticated methods of interpolating, but this is the most basic and you are welcome to take it and improve it!
Ok, I had some time and I tried to do a 2d-interpolation of the missing values in the Bayer array. I am not 100% confident of my answer, but I think it should be pretty close.
Basically, I copy the original Bayer array at full resolution, and overwrite all green and blue samples with np.Nan and call that Red. Then I do a 2d-interpolation to replace the Nans.
Same again for green and blue, that gives this:
#!/usr/bin/env python3 import numpy as np from skimage.io import imsave from scipy.interpolate import griddata def interp2d(im): """Interpolate in 2d array, replacing NaNs with interpolated values""" x, y = np.indices(im.shape) im[np.isnan(im)] = griddata( (x[~np.isnan(im)], y[~np.isnan(im)]), im[~np.isnan(im)], (x[np.isnan(im)], y[np.isnan(im)])) im = np.nan_to_num(im) return np.clip((im),0,255) # Width and height of Bayer image w, h = 1013, 672 # Calculate output width and height as multiples of 4 ow = (w//4) * 4 oh = (h//4) * 4 # Load in Bayer8 image, assumed raw 8-bit GBRG, reshape and make sides multiple of 4 bayer = np.fromfile('bayer.bin', dtype=np.uint8).reshape((h,w)).astype(np.float)[:oh, :ow] # In following code you'll see "cell" which is the basic repeating 2x2 cell of a Bayer matrix # # cell = G B # R G # # Set everything not Red in bayer array to Nan, then replace Nans with interpolation cell = np.array([[np.NaN, np.NaN], [1.0 , np.NaN]]) R = bayer*np.tile(cell,(oh//2,ow//2)) R = interp2d(R).astype(np.uint8) # Set everything not Green in bayer array to Nan, then replace Nans with interpolation cell = np.array([[1.0 , np.NaN], [np.NaN, 1.0 ]]) G = bayer*np.tile(cell,(oh//2,ow//2)) G = interp2d(G).astype(np.uint8) # Set everything not Blue in bayer array to Nan, then replace Nans with interpolation cell = np.array([[np.NaN, 1.0 ], [np.NaN, np.NaN]]) B = bayer*np.tile(cell,(oh//2,ow//2)) B = interp2d(B).astype(np.uint8) # Form image by stacking R, G and B and save imsave('result.png',np.dstack((R,G,B))) Keywords: Python, bayer, bayer8, debayer, de-bayer, de-mosaic, de-mosaicking, image, raw, CFA, skimage, scikit-image, image processing.
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