How to improve this pixel remover process?
I have a numpy binary file .npy, containing all the BGR values.
Example
[[ 47 65 82]
[ 48 65 84]
[ 49 64 80]
...
[164 170 169]
[164 173 176]
[165 171 170]]
I can remove a specific bgr pixel value using img[np.where((img == [b,g,r]).all(axis=2))]=[255,255,255] from the entire image.
for all the above bgr values
this is a very simple loop to filter through each pixel
#go through each pixel
for index,x in np.ndindex(__pixels.shape):
if x == 0:
__b = __pixels[index,x]
elif x == 1:
__g = __pixels[index,x]
elif x == 2:
__r = __pixels[index,x]
#Change the color to white
print(__pixels.shape[0]," ",index)
img[np.where((img == [__b,__g,__r]).all(axis=2))] = [255, 255, 255]
It there any other efficient way where in I can remove these range of pixels stored as numpy array from the entire image?
Maybe you can make a mask -- if the value of BGR in every pixel is the BGR_to_be_removed, then use cv2.bitwise_and(img, mask) to get the result.
numpy is written in C so to give you a rough idea, it takes 1100 millisecond for one BGR value to be iterated through 2.7Million Pixels using
np.whereIssue I'm having here is the I don't have a numpy function that lets me iterate through the selected pixels row wise to extract BGR values at once. So in checking
xvalue every time in the loop its delaying the entire loop. I don't know how to optimise it.Try the inRange function to make a mask, then... In c++ I would use .setTo, but in python I'm not sure the next step.