CODEWITHSUNDEEP
pythonimagenumpyimage-processingpython-3.6
RobiNoob
RobiNoob

Reputation: 275

How to efficiently update np array depending on index and value?

I have an image of the sun, I found center and radius and now I want to process pixels differently if they are inside or outside the disk. The ideal solution would be to imterpolate the parameters of the processing function, in order to smoothly transition from disk to background.

Here is what I'm doing now:

for index,value in np.ndenumerate(sun_img):
    if distance.euclidean(index,center) > radius:
        sun_img[index] = processing_function(index,value)

Like this it works but it takes forever to compute the image. I'm sure there is a more efficient way to do that. How would you solve this?

Image shape is around (1000, 1000) Processing_function is basically not doing anything right now: value += 1

The function should be something like a non-linear "step function" with 0.0 value till radius and 1.0 5px after. something like: _______/''''''''''''''''''''' multiplied by the value of the pixel. The slope should be on the value of the radius. I wanna do this in order to enhance the protuberances

Upvotes: 1

Views: 1476

Answers (1)

Divakar
Divakar

Reputation: 221614

Here's a vectorized way leveraging NumPy broadcasting -

m,n = sun_img.shape  
I,J = np.ogrid[:m,:n]      
sq_dist = (I - center[0])**2 + (J - center[1])**2
valid_mask = sq_dist > radius**2

Now, for a processing_function that just adds 1 to the valid places, defined by the IF-conditional, do -

sun_img[valid_mask] += 1

If you need to implement a custom operation with processing_function that needs those row, column indices, use np.where to get those indices and then iterate through the valid elements, like so -

r,c = np.where(valid_mask)
for index in zip(r,c):
    sun_img[index] = processing_function(index,sun_img[r,c])

If you have a lot of such valid places, then computing r,c might make things slow. In that case, directly use the mask, like so -

for index,value in np.ndenumerate(sun_img):
    if valid_mask[index]:
        sun_img[index] = processing_function(index,value)

Compared to the original code, the benefit is that we have the conditional values pre-computed before going into the loop. The best way again would be to vectorize processing_function itself so that it works on a bigger chunk of data, but that would depend on its implementation.

Upvotes: 2

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