Normalizing images in OpenCV

Question

I wrote the following code to work normalize an image using NORM L1 in OpenCV  But the output image was just black How to solve this    import cv2 import numpy as np import Image  img   cv2 imread  img7 jpg   gray image   cv2 cvtColor img cv2 COLOR BGR2GRAY  a   np asarray gray image    dst   np zeros shape  5 2    b cv2 normalize a dst 0 255 cv2 NORM L1    im   Image fromarray b   im save  img50 jpg    cv2 waitKey 0  cv2 destroyAllWindows

User · Answer

When you normalize a matrix using NORM L1  you are dividing every pixel value by the sum of absolute values of all the pixels in the image  As a result  all pixel values become much less than 1 and you get a black image  Try NORM MINMAX instead of NORM L1

User · Answer

The other answers normalize an image based on the entire image  But if your image has a predominant color  such as black   it will mask out the features that you re trying to enhance since it will not be as pronounced  To get around this limitation  we can normalize the image based on a subsection region of interest  ROI   Essentially we will normalize based on the section of the image that we want to enhance instead of equally treating each pixel with the same weight  Take for instance this earth image    Input image - gt  Normalization based on entire image     If we want to enhance the clouds by normalizing based on the entire image  the result will not be very sharp and will be over saturated due to the black background  The features to enhance are lost  So to obtain a better result we can crop a ROI  normalize based on the ROI  and then apply the normalization back onto the original image  Say we crop the ROI highlighted in green     This gives us this ROI    The idea is to calculate the mean and standard deviation of the ROI and then clip the frame based on the lower and upper range  In addition  we could use an offset to dynamically adjust the clip intensity  From here we normalize the original image to this new range  Here s the result   Before - gt  After     Code  import cv2 import numpy as np    Load image as grayscale and crop ROI image   cv2 imread  1 png   0  x  y  w  h   364  633  791  273 ROI   image y y h  x x w     Calculate mean and STD mean  STD    cv2 meanStdDev ROI     Clip frame to lower and upper STD offset   0 2 clipped   np clip image  mean - offset STD  mean   offset STD  astype np uint8     Normalize to range result   cv2 normalize clipped  clipped  0  255  norm type cv2 NORM MINMAX   cv2 imshow  image   image  cv2 imshow  ROI   ROI  cv2 imshow  result   result  cv2 waitKey       The difference between normalizing based on the entire image vs a specific section of the ROI can be visualized by applying a heatmap to the result  Notice the difference on how the clouds are defined   Input image - gt  heatmap     Normalized on entire image - gt  heatmap     Normalized on ROI - gt  heatmap     Heatmap code  import matplotlib pyplot as plt import numpy as np import cv2  image   cv2 imread  result png   0  colormap   plt get cmap  inferno   heatmap    colormap image    2  16  astype np uint16       3  heatmap   cv2 cvtColor heatmap  cv2 COLOR RGB2BGR   cv2 imshow  image   image  cv2 imshow  heatmap   heatmap  cv2 waitKey     Note  The ROI bounding box coordinates were obtained using how to get ROI Bounding Box Coordinates without Guess  amp  Check and heatmap code was from how to convert a grayscale image to heatmap image with Python OpenCV

User · Answer

If you want to change the range to  0  1   make sure the output data type is float   image   cv2 imread  lenacolor512 tiff   cv2 IMREAD COLOR     uint8 image norm image   cv2 normalize image  None  alpha 0  beta 1  norm type cv2 NORM MINMAX  dtype cv2 CV 32F

[python] Normalizing images in OpenCV

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