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])

Solution

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

Comments

[Dan Mašek]

"result f a multiplication ... returns in general a float` -- Could you, please, provide some demonstrations of this happening?

[Eskapp]

@DanMašek I meant "in general" in the mathematical sense of the term - At least once. I re-wrote what I had in mind in a clearer way. Thank you for pointing out that my answer was confusing.

[skull3r7]

you can also use cv2.normalize(img, dst=None, alpha=0, beta=65535, norm_type=cv2.NORM_MINMAX) to get the right 16bit values