How can I find the brightest point in a CIImage (in Metal maybe)?

I created a custom CIKernel in Metal. This is useful because it is close to real-time. I am avoiding any cgcontext or cicontext that might lag in real time. My kernel essentially does a Hough transform, but I can't seem to figure out how to read the white points from the image buffer.

Here is kernel.metal:

#include <CoreImage/CoreImage.h>

extern "C" {
    namespace coreimage {

        float4 hough(sampler src) {

            // Math

            // More Math

            // eventually:

            if (luminance > 0.8) {
                uint2 position = src.coord()
                // Somehow add this to an array because I need to know the x,y pair
            }

            return float4(luminance, luminance, luminance, 1.0);
        }
    }
}

I am fine if this part can be extracted to a different kernel or function. The caveat to CIKernel, is its return type is a float4 representing the new color of a pixel. Ideally, instead of a image -> image filter, I would like an image -> array sort of deal. E.g. reduce instead of map. I have a bad hunch this will require me to render it and deal with it on the CPU.

Ultimately I want to retrieve the qualifying coordinates (which there can be multiple per image) back in my swift function.

FINAL SOLUTION EDIT:

As per suggestions of the answer, I am doing large per-pixel calculations on the GPU, and some math on the CPU. I designed 2 additional kernels that work like the builtin reduction kernels. One kernel returns a 1 pixel high image of the highest values in each column, and the other kernel returns a 1 pixel high image of the normalized y-coordinate of the highest value:

    /// Returns the maximum value in each column.
    ///
    /// - Parameter src: a sampler for the input texture
    /// - Returns: maximum value in for column
    float4 maxValueForColumn(sampler src) {

        const float2 size = float2(src.extent().z, src.extent().w);

        /// Destination pixel coordinate, normalized
        const float2 pos = src.coord();

        float maxV = 0;

        for (float y = 0; y < size.y; y++) {
            float v = src.sample(float2(pos.x, y / size.y)).x;
            if (v > maxV) {
                maxV = v;
            }
        }

        return float4(maxV, maxV, maxV, 1.0);
    }

    /// Returns the normalized coordinate of the maximum value in each column.
    ///
    /// - Parameter src: a sampler for the input texture
    /// - Returns: normalized y-coordinate of the maximum value in for column
    float4 maxCoordForColumn(sampler src) {

        const float2 size = float2(src.extent().z, src.extent().w);

        /// Destination pixel coordinate, normalized
        const float2 pos = src.coord();

        float maxV = 0;
        float maxY = 0;

        for (float y = 0; y < size.y; y++) {
            float v = src.sample(float2(pos.x, y / size.y)).x;
            if (v > maxV) {
                maxY = y / size.y;
                maxV = v;
            }
        }

        return float4(maxY, maxY, maxY, 1.0);
    }

This won't give every pixel where luminance is greater than 0.8, but for my purposes, it returns enough: the highest value in each column, and its location.

Pro: copying only (2 * image width) bytes over to the CPU instead of every pixel saves TONS of time (a few ms).

Con: If you have two major white points in the same column, you will never know. You might have to alter this and do calculations by row instead of column if that fits your use-case.

FOLLOW UP:

There seems to be a problem in rendering the outputs. The Float values returned in metal are not correlated to the UInt8 values I am getting in swift.

This unanswered question describes the problem.

Edit: This answered question provides a very convenient metal function. When you call it on a metal value (e.g. 0.5) and return it, you will get the correct value (e.g. 128) on the CPU.

Check out the filters in the CICategoryReduction (like CIAreaAverage). They return images that are just a few pixels tall, containing the reduction result. But you still have to render them to be able to read the values in your Swift function.

The problem for using this approach for your problem is that you don't know the number of coordinates you are returning beforehand. Core Image needs to know the extend of the output when it calls your kernel, though. You could just assume a static maximum number of coordinates, but that all sounds tedious.

I think you are better off using Accelerate APIs for iterating the pixels of your image (parallelized, super efficiently) on the CPU to find the corresponding coordinates.

You could do a hybrid approach where you do the per-pixel heavy math on the GPU with Core Image and then do the analysis on the CPU using Accelerate. You can even integrate the CPU part into your Core Image pipeline using a CIImageProcessorKernel.