def test_rgb_lch_roundtrip(self):
     rgb = img_as_float(self.img_rgb)
     lab = rgb2lab(rgb)
     lch = lab2lch(lab)
     lab2 = lch2lab(lch)
     rgb2 = lab2rgb(lab2)
     assert_array_almost_equal(rgb, rgb2)

def rgb2lch(rgb):
    """Convert RBG to LCH colorspace (via LAB)
    Input and output are in (bands, cols, rows) order
    """
    # reshape for skimage (bands, cols, rows) -> (cols, rows, bands)
    srgb = np.swapaxes(rgb, 0, 2)
    # convert colorspace
    lch = lab2lch(rgb2lab(srgb))
    # return in (bands, cols, rows) order
    return np.swapaxes(lch, 2, 0)

 def test_lab_lch_3d(self):
     lab0 = self._get_lab0()
     lch0 = lab2lch(lab0)
     lch3 = lab2lch(lab0[None, None, None, :])
     assert_array_almost_equal(lch0, lch3[0, 0, 0, :])

 def test_lab_lch_roundtrip(self):
     rgb = img_as_float(self.img_rgb)
     lab = rgb2lab(rgb)
     lab2 = lch2lab(lab2lch(lab))
     assert_array_almost_equal(lab2, lab)

if img.dtype != 'float32':
    img = cv2.normalize(img.astype('float32'), None, 0.0, 1.0, cv2.NORM_MINMAX)

# get dimensions of input image
x, y, z = img.shape

# store a copy of original image for comparison
img1 = np.zeros((x, y, z))
img1 = img.copy()

alpha = None

# Convert to colorspaces CIELAB, CIELUV, and CIELCH
lab = color.rgb2lab(img)  # - RGB to LAB
luv = color.rgb2luv(img)  # - RGB to LUV
LCH = color.lab2lch(img)  # - LAB to LCH

# Compute G(x, y) for image
Gx = np.zeros((x, y))   # Create empty array for Gx component
Gy = np.zeros((x, y))   # Create empty array for Gy component
for i in range(1, x-1):
    for j in range(1, y-1):
        #Calculate the Gx and Gy per pixel using color difference from Eq 3 (on pg 2 of paper)
        Gx[i, j] = color_difference(lab[i + 1, j, :], lab[i - 1, j, :], luv[i + 1, j, :], luv[i - 1, j, :], alpha)
        Gy[i, j] = color_difference(lab[i, j + 1, :], lab[i, j - 1, :], luv[i, j + 1, :], luv[i, j - 1, :], alpha)

# Assign Lightness, chroma, and hue arrays from LCH to their own respective arrays
L = LCH[:, :, 0]
C = LCH[:, :, 1]
H = LCH[:, :, 2]
T = np.zeros((x, y, 9))