How to convert an image from np.uint16 to np.uint8?
Question:
I am creating an image so:
image = np.empty(shape=(height, width, 1), dtype = np.uint16)
After that I convert the image to BGR model:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
I’d like to convert the image now in a dtype = np.uint8 in order to use that image with cv2.threshold() function. I meant, I would like to convert the image to CV_8UC1.
Answers:
You can use cv2.convertScaleAbs for this problem. See the Documentation.
Check out the command terminal demo below :
>>> img = np.empty((100,100,1),dtype = np.uint16)
>>> image = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
>>> cvuint8 = cv2.convertScaleAbs(image)
>>> cvuint8.dtype
dtype('uint8')
Hope it helps!!!
I suggest you to use this :
outputImg8U = cv2.convertScaleAbs(inputImg16U, alpha=(255.0/65535.0))
this will output a uint8 image & assign value between 0-255 with respect to there previous value between 0-65535
exemple :
pixel with value == 65535 will output with value 255
pixel with value == 1300 will output with value 5 etc...
I assume you want to rescale uint16 range 0..65535 into uint8 range 0..255. In other words, visually image will look the same, just lower color depth.
np_uint16 = np.arange(2 ** 16, dtype=np.uint16).reshape(256, 256)
np_uint8 = (np_int16 // 256).astype(np.uint8)
produces mapping:
[0..255] => 0 (256 values)
[256..511] => 1 (256 values)
...
[65280..65535] => 255 (256 values)
Why two other answers are incorrect:
Accepted one
img = np.empty((100,100,1), dtype = np.uint16)
image = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
cvuint8 = cv2.convertScaleAbs(image)
^ will create an uninitialized uint16 array and then cast it to uint8 taking abs() and clipping values >255 to 255. Resulting mapping:
[0..254] => [0..254]
[255..65535] => 255
Next one:
outputImg8U = cv2.convertScaleAbs(inputImg16U, alpha=(255.0/65535.0))
^ produces slightly wrong mapping:
[0..128] => 0 (129 values)
[129..385] => 1 (257 values)
...
[65407..65535] => 255 (129 values)
so 2,..,254 bins get one extra value at 0 and 255‘s expense.
It’s a bit tricky to get precise mapping with convertScaleAbs, since it uses round half down:
np_int16 = np.arange(2 ** 16, dtype=np.uint16).reshape(256, 256)
np_uint8 = cv2.convertScaleAbs(np_int16, alpha = 1./256., beta=-.49999)
ps: if you care about performance, OpenCV one is ~50% faster than Numpy on my Mac.
I am creating an image so:
image = np.empty(shape=(height, width, 1), dtype = np.uint16)
After that I convert the image to BGR model:
image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
I’d like to convert the image now in a dtype = np.uint8 in order to use that image with cv2.threshold() function. I meant, I would like to convert the image to CV_8UC1.
You can use cv2.convertScaleAbs for this problem. See the Documentation.
Check out the command terminal demo below :
>>> img = np.empty((100,100,1),dtype = np.uint16)
>>> image = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
>>> cvuint8 = cv2.convertScaleAbs(image)
>>> cvuint8.dtype
dtype('uint8')
Hope it helps!!!
I suggest you to use this :
outputImg8U = cv2.convertScaleAbs(inputImg16U, alpha=(255.0/65535.0))
this will output a uint8 image & assign value between 0-255 with respect to there previous value between 0-65535
exemple :
pixel with value == 65535 will output with value 255
pixel with value == 1300 will output with value 5 etc...
I assume you want to rescale uint16 range 0..65535 into uint8 range 0..255. In other words, visually image will look the same, just lower color depth.
np_uint16 = np.arange(2 ** 16, dtype=np.uint16).reshape(256, 256)
np_uint8 = (np_int16 // 256).astype(np.uint8)
produces mapping:
[0..255] => 0 (256 values)
[256..511] => 1 (256 values)
...
[65280..65535] => 255 (256 values)
Why two other answers are incorrect:
Accepted one
img = np.empty((100,100,1), dtype = np.uint16)
image = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
cvuint8 = cv2.convertScaleAbs(image)
^ will create an uninitialized uint16 array and then cast it to uint8 taking abs() and clipping values >255 to 255. Resulting mapping:
[0..254] => [0..254]
[255..65535] => 255
Next one:
outputImg8U = cv2.convertScaleAbs(inputImg16U, alpha=(255.0/65535.0))
^ produces slightly wrong mapping:
[0..128] => 0 (129 values)
[129..385] => 1 (257 values)
...
[65407..65535] => 255 (129 values)
so 2,..,254 bins get one extra value at 0 and 255‘s expense.
It’s a bit tricky to get precise mapping with convertScaleAbs, since it uses round half down:
np_int16 = np.arange(2 ** 16, dtype=np.uint16).reshape(256, 256)
np_uint8 = cv2.convertScaleAbs(np_int16, alpha = 1./256., beta=-.49999)
ps: if you care about performance, OpenCV one is ~50% faster than Numpy on my Mac.