NumPy convert 8-bit to 16/32-bit image

Question

I am using OpenCV 2 to do some images manipulations in YCbCr color space. For the moment I can detect some noise due to the conversion RGB -> YCbCr and then YCbCr -> RGB, but as said in the documentation :

If you use cvtColor with 8-bit images, the conversion will have some information lost. For many applications, this will not be noticeable but it is recommended to use 32-bit images in applications that need the full range of colors or that convert an image before an operation and then convert back.

So I would like to convert my image in 16 or 32 bits, but I didn't found how to do it with NumPy. Some ideas?

img = cv2.imread(imgNameIn)
# Here I want to convert img in 32 bits
cv2.cvtColor(img, cv2.COLOR_BGR2YCR_CB, img)
# Some image processing ...
cv2.cvtColor(img, cv2.COLOR_YCR_CB2BGR, img)
cv2.imwrite(imgNameOut, img, [cv2.cv.CV_IMWRITE_PNG_COMPRESSION, 0])

Answer 1

Thanks to @moarningsun, problem resolved:

i = cv2.imread(imgNameIn, cv2.CV_LOAD_IMAGE_COLOR) # Need to be sure to have a 8-bit input
img = np.array(i, dtype=np.uint16) # This line only change the type, not values
img *= 256 # Now we get the good values in 16 bit format

Answer 2

The accepted answer is not accurate. A 16-bit image has 65536 intensity levels ( 2^16 ) hence, values ranging from 0 to 65535 .

If one wants to obtain a 16-bit image from an image represented as an array of float ranging from 0 to 1, one has to multiply every coefficient of this array by 65535 .

Also, it is good practice to cast the type of your end result as the very last step of the operations you perform. This is mainly for two reasons: - If you perform divisions or multiplications by float , the result will return a float and you will need to change the type again. - In general (in the mathematical sense of the term), a transformation from float to integer can introduce errors. Casting the type at the very end of the operations prevents error propagation.