so, my app needs to find the dominant palette and the position in the image of the k-most dominant colors. I followed the very useful sample project from the vImage documentation https://developer.apple.com/documentation/accelerate/bnns/calculating_the_dominant_colors_in_an_image and the algorithm works fine although I can't wrap my head around how should I go on about and linking said colors with a point in the image. Since the algorithm works by filling storages first, I tried also filling an array of CGPoints called LocationStorage and working with that //filling the array for i in 0...width { for j in 0...height { locationStorage.append( CGPoint(x: i, y: j)) } . . . //working with the array let randomIndex = Int.random(in: 0 ..< width * height) centroids.append(Centroid(red: redStorage[randomIndex], green: greenStorage[randomIndex], blue: blueStorage[randomIndex], position: locationStorage[randomIndex])) } struct Centroid { /// The red channel value. var red: Float /// The green channel value. var green: Float /// The blue channel value. var blue: Float /// The number of pixels assigned to this cluster center. var pixelCount: Int = 0 var position: CGPoint = CGPointZero init(red: Float, green: Float, blue: Float, position: CGPoint) { self.red = red self.green = green self.blue = blue self.position = position } } although it's not accurate. I also tried force trying every pixel in the image to get as close to each color but I think it's too slow. What do you think my approach should be? Let me know if you need additional info Please be kind I'm learning Swift.

I'm using M1pro and have successfully installed Numpy with Accelerate following, and it really speedup my programs. I also ran np.test() to check the correctness and every test passed. However, I can't install Scipy with Accelerate, since the official document said Accelerate has a LAPACK of too old version. I can't even find a scipy that can pass scipy.test(). I tried the codes below: conda install numpy 'libblas=*=*accelerate' conda install scipy np.test() as fails, sp.test() can't even finish conda install numpy 'libblas=*=*openblas' conda install scipy Both np.test() and sp.test() can finish, but with many failures. I believe the bugs are due to Conda. pip install --no-binary :all: --no-use-pep517 numpy pip install scipy np.test() has no failure and went fast, sp.test() uses OpenBLAS and has 3 failures. This is the best version I have found. So my question is: can we find a reliable version of scipy on M1? Considering the popularity of scipy, I think it's not a high-living expectation. And a question for Apple: is there really a plan to upgrade the LAPACK in Accelerate?