numpy: unique list of colors in the image

One straightforward way to do this is to leverage the de-duplication that occurs when casting a list of all pixels as a set:

unique_pixels = np.vstack({tuple(r) for r in img.reshape(-1,3)})

Another way that might be of practical use, depending on your reasons for extracting unique pixels, would be to use Numpy’s histogramdd function to bin image pixels to some pre-specified fidelity as follows (where it is assumed pixel values range from 0 to 1 for a given image channel):

n_bins = 10
bin_edges = np.linspace(0, 1, n_bins + 1)
bin_centres = (bin_edges[0:-1] + bin_edges[1::]) / 2.
hist, _ = np.histogramdd(img.reshape(-1, 3), bins=np.vstack(3 * [bin_edges]))
unique_pixels = np.column_stack(bin_centres[dim] for dim in np.where(hist))

You could do this:

set( tuple(v) for m2d in img for v in m2d )

Your initial idea to use numpy.unique() actually can do the job perfectly with the best performance:

numpy.unique(img.reshape(-1, img.shape[2]), axis=0)

At first, we flatten rows and columns of matrix. Now the matrix has as much rows as there're pixels in the image. Columns are color components of each pixels.

Then we count unique rows of flattened matrix.