![]() # pass image colors and retrieve corresponding palette color # Step 1: Take precalculated color cube for defined palette and Np.savez_compressed('view', color_cube = precalculated) # for all colors (256*256*256) assign color from palette # Step 2: Create/Load precalculated color cube Take image and use every color in image as index in color cube. In other words, for every existing color assign corresponding palette color that is closest. omarray(result_image.astype(np.uint8)).show()īuild 256x256x256x3 size color cube based on your palette. Result_image = closest(image,colors_container) # NOTE: Dont pass uint8 due to overflow during subtract # Pass pixel position with corresponding position of smallest colorĬolor_view = reshaped_container.reshape(shape,shape,3) Reshaped_container = colors_container.reshape(-1,len(colors),3) Min_index = np.argmin(distances,axis=2).reshape(-1) # reshaped_container shape = (x*y,number of colors,3) # before min_index has shape (x,y), now shape = (x*y) # this means we look for color_container position ?-> (x,y,?,3) # It has same dimensions as image (1000,1000,number of colors,3)Ĭolors_container = np.ones(shape=,image.shape,len(colors),3])ĭistances = np.sqrt(np.sum((color_container-image)**2,axis=3)) Image = im.reshape(im.shape,im.shape,1,3)Ĭolors = ,] 1 is there so its easy to subtract from color container ![]() Im = Image.open(requests.get("", stream=True).raw) Take image, create pallete object with same size as image, calculate distances, retrieve new image with np.argmin indices import numpy as np color cube requires 1.5mb of space on disk in form of compressed np matrix color cube can contain only one palette requires creation of color cube (once, up to 3 minutes) ideal for batch processing if palette doesnt change low memory, independent of image size or pallete size super fast (50ms per image), independent of palette size Option: Batch processing (super fast) Pros memory for large number of colors in paletteĢ. Option: Single image evaluation (slow) Pros Indices = np.uint8().reshape(height, width)ĭist = np.float32().reshape(height, width)ġ. # find nearest neighbor matches for all pixels Index_params = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)įm = cv.FlannBasedMatcher(index_params, search_params)įm.add(np.float32()) # extra dimension is "pictures", unused ![]() # somewhat arbitrary parameters because under-documented The "linear scan", with code specific to this problem, I present in another answer using numba. However, the cost of FLANN's complexity and generality would only be amortized by the better lookup complexity once the palette becomes huge. Due to the index structures in FLANN, it performs sub-linearly (probably O(log(n)) or sth.), i.e. What is unique to FLANN is probably how little (user-side) code it needs.ĭisadvantage: this still takes a few seconds.įLANN uses index structures and can handle arbitrary vectors, and it uses float32 types. One advantage of this approach is that it can handle "large" palettes without requiring lots of memory. The lookups take two seconds on my old computer. This is possible using FLANN (comes with OpenCV). You get an index from that lookup, which you can then turn into the palette color for that pixel. You define a palette, and then you need to find, for every pixel, the nearest neighbor match in the defined palette for that pixel's color. The task is to turn a picture into a palette version of it. ![]()
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