Summing 3D array elements with 1D arrays

I'm using OpenCV开发者_运维技巧 for a computer vision project, however, I need to do a pixel by pixel operation on the image which means accessing every pixel in a 640x480 image and changing it's HSV values. The image is made up of a 3D array X, Y and HSV Values, so a pixel at 130, 230 may have a HSV value of [12, 26, 18] or represented in the image: (130, 230, (12, 26, 18))

I need to perform an operation which allows me to add an amount X into the V value (element index 2) of the HSV values: (130, 230, (12, 26, 18))

I can do this using two loops:

for x in range(image.width):
        for y in range(image.height/2):
            initcolor = cv.Get2D(image, y, x)
            initcolor2 = [0, 0, 10, 0]
            summed = [sum(pair) for pair in zip(initcolor, initcolor2)] 
            cv.Set2D(image, y, x, summed)

But this is awfully slow and for some reason takes around 20 seconds to complete the operation over the entire image.

Is there a simpler, more faster way of achieving this?

The first and easiest thing you should do is to check to see if OpenCV can take numpy arrays as arguments. Numpy is built on fast C algorithms that can handle large loops over data structures in what are called "vectorized" operations. Each loop in Python incurs a very large overhead.

Another alternative might be to put this block of code in Cython, which can handle tight loops like this far better.

If I were doing computer vision in Python, I would definitely use Numpy and get my arrays into numpy format as soon as possible. I suspect you might want numpy.asarray() to convert from PIL to array.