Convert np array of type float64 to type uint8 scaling values

Question

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 I need to convert this image  obviously having a quality-loss   I ve already tried   import numpy as np import cv2       data   data   data max    normalizes data in range 0 - 255 data   255   data img   data astype np uint8  cv2 imshow  Window   img    But cv2 imshow is not giving the image as expected  but with strange distortion     In the end  I only need to convert a np float64 to np uint8 scaling all the values and truncating the rest  eg  65535 becomes 255  65534 becomes 254 and so on     Any help   Thanks

User · Answer

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

User · Answer

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

User · Answer

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

[python] Convert np.array of type float64 to type uint8 scaling values

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