def replace_windows_with_nearest_neighbors(im, window_size, nbrs, fout_name):
pixels = im.load()
windows = ImageSeg.window_segmentation(im, window_size)
im_width, im_height = im.size
window_width, window_height = window_size
for offset in windows:
window_to_replace = windows[offset]
vector_to_match = window_to_vector(window_to_replace)
distances, indices = nbrs.kneighbors(np.array(vector_to_match))
window_replacement_vector = nbrs._fit_X[indices[0]][0]
# print window_replacement_vector
window_pixels = vector_to_window(window_replacement_vector, window_size)
ImageSeg.replace_window(pixels, offset, window_pixels)
im.save(fout_name)
def NN_nbrs(images, window_size):
windows_of_images = map(lambda im: ImageSeg.window_segmentation(im, window_size).values(), images)
# LOG("images segmented")
all_windows = reduce(lambda a, b: a + b, windows_of_images)
vectors = map(window_to_vector, all_windows)
# LOG("vectors created")
# print len(vectors)
X = np.array(vectors)
# LOG("np array created")
# print len(X)
nbrs = NearestNeighbors(n_neighbors=1).fit(X)
# LOG("nn learned")
return nbrs
