I have a particular np.array data which represents a particular grayscale image. I need to use SimpleBlobDetector() that unfortunately only accepts 8bit images, so
Considering that you are using OpenCV, the best way to convert between data types is to use normalize
function.
img_n = cv2.normalize(src=img, dst=None, alpha=0, beta=255, norm_type=cv2.NORM_MINMAX, dtype=cv2.CV_8U)
However, if you don't want to use OpenCV, you can do this in numpy
def convert(img, target_type_min, target_type_max, target_type):
imin = img.min()
imax = img.max()
a = (target_type_max - target_type_min) / (imax - imin)
b = target_type_max - a * imax
new_img = (a * img + b).astype(target_type)
return new_img
And then use it like this
imgu8 = convert(img16u, 0, 255, np.uint8)
This is based on the answer that I found on crossvalidated board in comments under this solution https://stats.stackexchange.com/a/70808/277040
A better way to normalize your image is to take each value and divide by the largest value experienced by the data type. This ensures that images that have a small dynamic range in your image remain small and they're not inadvertently normalized so that they become gray. For example, if your image had a dynamic range of [0-2]
, the code right now would scale that to have intensities of [0, 128, 255]
. You want these to remain small after converting to np.uint8
.
Therefore, divide every value by the largest value possible by the image type, not the actual image itself. You would then scale this by 255 to produced the normalized result. Use numpy.iinfo and provide it the type (dtype
) of the image and you will obtain a structure of information for that type. You would then access the max
field from this structure to determine the maximum value.
So with the above, do the following modifications to your code:
import numpy as np
import cv2
[...]
info = np.iinfo(data.dtype) # Get the information of the incoming image type
data = data.astype(np.float64) / info.max # normalize the data to 0 - 1
data = 255 * data # Now scale by 255
img = data.astype(np.uint8)
cv2.imshow("Window", img)
Note that I've additionally converted the image into np.float64
in case the incoming data type is not so and to maintain floating-point precision when doing the division.
you can use skimage.img_as_ubyte(yourdata)
it will make you numpy array ranges from 0->255
from skimage import img_as_ubyte
img = img_as_ubyte(data)
cv2.imshow("Window", img)