def test_process_pixels_gray(): in_array = np.random.rand(128, 64, 3) byte_arr = generate_compressed_data(in_array) out_array = process_pixels(byte_arr, True) assert out_array.shape == (128, 64, 1) assert np.mean(in_array.mean(axis=2, keepdims=True) - out_array) < 0.01 assert (in_array.mean(axis=2, keepdims=True) - out_array < 0.01).all()
def test_process_pixels(): in_array = np.random.rand(128, 64, 3) byte_arr = generate_compressed_data(in_array) out_array = process_pixels(byte_arr, 3) assert out_array.shape == (128, 64, 3) assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01 assert np.allclose(in_array, out_array, atol=0.01)
def test_process_pixels(): in_array = np.random.rand(128, 128, 3) byte_arr = generate_compressed_data(in_array) out_array = process_pixels(byte_arr, False) assert out_array.shape == (128, 128, 3) assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01 assert (in_array - out_array < 0.01).all()
def test_process_pixels_multi_png(): height = 128 width = 64 num_channels = 7 in_array = np.random.rand(height, width, num_channels) byte_arr = generate_compressed_data(in_array) out_array = process_pixels(byte_arr, num_channels) assert out_array.shape == (height, width, num_channels) assert np.sum(in_array - out_array) / np.prod(in_array.shape) < 0.01 assert np.allclose(in_array, out_array, atol=0.01)