How can I change numpy array into grayscale opencv image in python?
After some processing I got an array with following atributes: max value is: 0.99999999988,
min value is 8.269656407e-08 and type is: <type 'numpy.ndarray'>. I can show it as an image using cv2.imshow() function, but I can't pass it into cv2.AdaptiveTreshold() function because it has wrong type:
error: (-215) src.type() == CV_8UC1 in function cv::adaptiveThreshold
How can I convert this np.array to correct format?
As the assertion states, adaptiveThreshold() requires a single-channeled 8-bit image.
Assuming your floating-point image ranges from 0 to 1, which appears to be the case, you can convert the image by multiplying by 255 and casting to np.uint8:
float_img = np.random.random((4,4))
im = np.array(float_img * 255, dtype = np.uint8)
threshed = cv2.adaptiveThreshold(im, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 0)
This one worked for me:
uint_img = np.array(float_arr*255).astype('uint8')
grayImage = cv2.cvtColor(uint_img, cv2.COLOR_GRAY2BGR)
I need to convert closed image(morphological closing) to binary, and after checking @Aurelius solution, This one work for me better than mentioned solution.
mask_gray = cv2.normalize(src=mask_gray, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8UC1)