def test_to_husl_2d():
img = np.ascontiguousarray(_img()[:, 4])
float_img = transform.ensure_rgb_float(img)
husl_new = nphusl.to_husl(img)
for row in range(img.shape[0]):
husl_old = husl.rgb_to_husl(*float_img[row])
_diff_husl(husl_new[row], husl_old)
def test_rgb_to_xyz_3d():
img = _img()
float_img = transform.ensure_rgb_float(img)
xyz_arr = _nphusl._rgb_to_xyz(img)
for row in range(img.shape[0]):
for col in range(img.shape[1]):
_diff(xyz_arr[row, col], husl.rgb_to_xyz(float_img[row, col]))
def test_lch_to_rgb():
img = _img()
float_img = transform.ensure_rgb_float(img)
lch = _nphusl._rgb_to_lch(float_img)
rgb = _nphusl._lch_to_rgb(lch)
int_rgb = transform.ensure_rgb_int(rgb)
_diff(int_rgb, img, diff=1)
def test_rgb_to_xyz():
rgb_arr = _img()[:, 0]
float_arr = transform.ensure_rgb_float(rgb_arr)
xyz_arr = _nphusl._rgb_to_xyz(rgb_arr)
for xyz, rgb, rgbf in zip(xyz_arr, rgb_arr, float_arr):
diff = xyz - husl.rgb_to_xyz(rgbf)
_diff(xyz, husl.rgb_to_xyz(rgbf))
def test_to_husl_3d():
img = _img()
float_img = transform.ensure_rgb_float(img)
husl_new = nphusl.to_husl(img)
for row in range(img.shape[0]):
for col in range(img.shape[1]):
husl_old = husl.rgb_to_husl(*float_img[row, col])
_diff_husl(husl_new[row, col], husl_old)
def test_rgb_to_husl_3d():
rgb_arr = np.ascontiguousarray(_img()[:5, :5])
float_arr = transform.ensure_rgb_float(rgb_arr)
husl_new = _nphusl._rgb_to_husl(rgb_arr)
for row in range(husl_new.shape[0]):
for col in range(husl_new.shape[1]):
husl_old = husl.rgb_to_husl(*float_arr[row, col])
_diff_husl(husl_new[row, col], husl_old)
def test_to_husl_rgba():
rgb = transform.ensure_rgb_float(_img())
rgba = np.zeros(shape=rgb.shape[:-1] + (4, ), dtype=rgb.dtype)
rgba[..., :3] = rgb
rgba[..., 3] = 0.5 # 50% for a float RGBA array
new_rgb = rgb * 0.5
hsl_from_rgba = nphusl.to_husl(rgba)
hsl_from_rgb = nphusl.to_husl(new_rgb)
_diff_husl(hsl_from_rgba, hsl_from_rgb)
def test_husl_to_lch():
img = _img()
float_img = transform.ensure_rgb_float(img)
lch = _nphusl._rgb_to_lch(float_img)
husl = nphusl.to_husl(img)
lch_2 = _nphusl._husl_to_lch(husl)
img_2 = _nphusl._lch_to_rgb(lch_2)
img_2 = transform.ensure_rgb_int(img_2)
_diff(img_2, img, diff=1)
def test_to_husl_gray():
img = transform.ensure_rgb_float(_img())
img[..., 1] = img[..., 0]
img[..., 2] = img[..., 0]
gray_arr = img[..., 0] # single channel
husl_new = nphusl.to_husl(gray_arr)
for row in range(gray_arr.shape[0]):
for col in range(gray_arr.shape[1]):
husl_old = husl.rgb_to_husl(*img[row, col])
_diff_husl(husl_new[row, col], husl_old)
def test_to_hue_gray():
img = _img()
img = transform.ensure_rgb_float(img)
img[..., 1] = img[..., 0]
img[..., 2] = img[..., 0]
gray_arr = img[..., 0] # single channel
hue_new = nphusl.to_hue(gray_arr)
for row in range(gray_arr.shape[0]):
for col in range(gray_arr.shape[1]):
husl_old = husl.rgb_to_husl(*img[row, col])
assert husl_old[1] < 1 # low saturation
# inaccurate hue values at low saturation
_diff(hue_new[row, col], husl_old[0], diff=5)
def test_luv_to_lch():
rgb_arr = _img()[:, 14]
float_arr = transform.ensure_rgb_float(rgb_arr)
rgb_arr = rgb_arr.reshape((rgb_arr.size // 3, 3))
xyz_arr = _nphusl._rgb_to_xyz(rgb_arr)
luv_arr = _nphusl._xyz_to_luv(xyz_arr)
lch_arr = _nphusl._luv_to_lch(luv_arr)
for i in range(rgb_arr.shape[0]):
xyz = husl.rgb_to_xyz(float_arr[i])
_diff(xyz, xyz_arr[i])
luv = husl.xyz_to_luv(xyz)
_diff(luv, luv_arr[i])
lch = husl.luv_to_lch(luv)
_diff(lch, lch_arr[i])
def test_to_husl_gray_3d():
img = _img()
img[..., 1] = img[..., 0] # makes things gray
img[..., 2] = img[..., 0] # makes things gray
img_float = transform.ensure_rgb_float(img)
husl_new = nphusl.to_husl(img)
was_wrong = False
for row in range(img.shape[0]):
for col in range(img.shape[1]):
husl_old = husl.rgb_to_husl(*img_float[row, col])
a = husl.husl_to_rgb(*husl_old)
b = husl.husl_to_rgb(*husl_new[row, col])
a = np.asarray(a)
b = np.asarray(b)
i = row * img.shape[1] * 3 + col * 3
_diff_husl(husl_new[row, col], husl_old)