How to change numpy array into grayscale opencv image
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)
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)