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I want to convert a gray-scale image with shape (height,width) to a 3 channels image with shape (height,width,nchannels). The work is done with a for-loop, but there must be a neat way. Here is a piece code in program, can someone give a hint. please advice.
30 if img.shape == (height,width): # if img is grayscale, expand 31 print "convert 1-channel image to ", nchannels, " image." 32 new_img = np.zeros((height,width,nchannels)) 33 for ch in range(nchannels): 34 for xx in range(height): 35 for yy in range(width): 36 new_img[xx,yy,ch] = img[xx,yy] 37 img = new_img 
532
A grayscale image has just one channel.
Conversion of a grayscale to RGB is simple. Simply use R = G = B = gray value. The basic idea is that color (as viewed on a monitor in terms of RGB) is an additive system. Thus adding red to green yields yellow.
Grayscale is a range of gray shades from white to black, as used in a monochrome display or printout. Grayscale images are most commonly used in image processing because smaller data enables developers to do more complex operations in a shorter time.
You can use np.stack to accomplish this much more concisely:
img = np.array([[1, 2], [3, 4]]) stacked_img = np.stack((img,)*3, axis=-1) print(stacked_img) # array([[[1, 1, 1], # [2, 2, 2]], # [[3, 3, 3], # [4, 4, 4]]]) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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