def test_to_husl_triplet():
assert type(nphusl.to_husl([10, 30, 40])) == np.ndarray
_diff(nphusl.to_husl([0x80, 0x80, 0x80]),
nphusl.to_husl([0.5, 0.5, 0.5]),
diff=0.3)
assert nphusl.to_husl([30, 30, 30])[1] < 0.01 # low saturation gray value
assert nphusl.to_husl(np.asarray([255, 0, 0]))[0] < 13 # red hue
def test_to_husl_1d_grayscale():
hsl_from_gray = nphusl.to_husl([0.1, 0.1]) # interpreted as 2 gray pixels
hsl_from_rgb = nphusl.to_husl([[0.1, 0.1, 0.1], [0.1, 0.1, 0.1]])
expected = [[2.88467242e+02, 1.02865661e-05, 9.30666400],
[2.88467242e+02, 1.02865661e-05, 9.30666400]]
assert np.all(hsl_from_gray == hsl_from_rgb)
_diff_husl(hsl_from_gray, expected)
_diff_husl(hsl_from_rgb, expected)
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_to_husl_rgba():
rgb = _img()
rgba = np.zeros(shape=rgb.shape[:-1] + (4,), dtype=rgb.dtype)
rgba[..., :3] = rgb
alpha = 0x80 # 50%
rgba[..., 3] = alpha
ratio = alpha / 255.0
new_rgb = np.round(rgb * ratio).astype(dtype=np.uint8)
hsl_from_rgba = nphusl.to_husl(rgba)
hsl_from_rgb = nphusl.to_husl(new_rgb)
assert _diff(hsl_from_rgba, hsl_from_rgb)
def test_to_husl_2d():
img = _img()[0]
rgb_arr = img * 255
husl_new = nphusl.to_husl(rgb_arr)
for row in range(rgb_arr.shape[0]):
husl_old = husl.rgb_to_husl(*img[row])
assert _diff(husl_new[row], husl_old)
def melonize(img, n_frames):
hsl = nphusl.to_husl(img)
hue, sat, lit = (hsl[..., n] for n in range(3))
#sat[:] = 99
pink = 360 # very end of the H spectrum
green = 130
def gen_chunksizes():
yield from range(1, 100)
yield from range(100, 1, -1)
for chunksize in gen_chunksizes():
hsl_out = hsl.copy()
hue_out, sat_out, lit_out = (hsl_out[..., i] for i in range(3))
for low, high in nphusl.chunk(100,
chunksize): # chunks of the hue range
select = np.logical_and(lit > low, lit < high)
is_odd = low % (chunksize * 2)
color = pink if is_odd else green
hue_out[select] = color
select = np.logical_and(lit > (low - 1), lit < low)
select = np.logical_and(select, lit > 60)
ave = (low + high) / 2
select = np.logical_and(lit > (ave - 2), lit < (ave + 2))
sat_out[select] = 100
yield nphusl.to_rgb(hsl_out)
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 clump_hues(img):
hsl = nphusl.to_husl(img)
H, L = hsl[..., 0], hsl[..., 2]
light = mask(img, L > 1)
travel = (1,)
effects = (clump_vert(s, travel) for s in _select_ranges(H, 50, light))
return Group(img, *effects()).zip()
def melonize(img, n_frames):
hsl = nphusl.to_husl(img)
hue, sat, lit = (hsl[..., n] for n in range(3))
#sat[:] = 99
pink = 360 # very end of the H spectrum
green = 130
def gen_chunksizes():
yield from range(1, 100)
yield from range(100, 1, -1)
for chunksize in gen_chunksizes():
hsl_out = hsl.copy()
hue_out, sat_out, lit_out = (hsl_out[..., i] for i in range(3))
for low, high in nphusl.chunk(100, chunksize): # chunks of the hue range
select = np.logical_and(lit > low, lit < high)
is_odd = low % (chunksize * 2)
color = pink if is_odd else green
hue_out[select] = color
select = np.logical_and(lit > (low - 1), lit < low)
select = np.logical_and(select, lit > 60)
ave = (low + high) / 2
select = np.logical_and(lit > (ave - 2), lit < (ave + 2))
sat_out[select] = 100
yield nphusl.to_rgb(hsl_out)
def img(request):
if CachedImg.rgb is None:
path = request.config.getoption("--img")
CachedImg.path = path
CachedImg.rgb = imageio.imread(path)
CachedImg.hsl = nphusl.to_husl(CachedImg.rgb)
return CachedImg
def test_to_husl_3d():
img = _img()
rgb_arr = img * 255
husl_new = nphusl.to_husl(rgb_arr)
for row in range(rgb_arr.shape[0]):
for col in range(rgb_arr.shape[1]):
husl_old = husl.rgb_to_husl(*img[row, col])
assert _diff_hue(husl_new[row, col], husl_old)
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 clump_dark(img):
hsl = nphusl.to_husl(img)
H, L = hsl[..., 0], hsl[..., 2]
dark = mask(img, L < 5)
travel = (5,)
vert = clump_vert(dark, travel)
horz = clump_horz(dark, travel)
return Group(img, vert, horz).zip(effects_per_frame=5)
def disperse_light(img):
hsl = nphusl.to_husl(img)
H, L = hsl[..., 0], hsl[..., 2]
light = mask(img, L > 80)
travel = (1,)
vert = disperse_vert(img, light, travel)
horz = disperse_horz(img, light, travel)
return Group(img, horz, vert).zip()
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 reveal_blue(img):
"""Easy mode! Selecting bluish pixels with the HUSL color space."""
# convert an integer RGB image to HUSL array of floats
hsl = nphusl.to_husl(img)
hue = hsl[..., 0] # separate out the hue channel
# create a mask for pixels with hues between 250 and 290 (blue)
bluish = np.logical_and(hue > 250, hue < 290)
hsl[..., 2][~bluish] *= 0.5 # halve lightness of non-bluish areas
return nphusl.to_rgb(hsl), "blue"
def reveal_blue(img):
"""Easy mode! Selecting bluish pixels with the HUSL color space."""
# convert an integer RGB image to HUSL array of floats
hsl = nphusl.to_husl(img)
hue = hsl[..., 0] # separate out the hue channel
# create a mask for pixels with hues between 250 and 290 (blue)
bluish = np.logical_and(hue > 250, hue < 290)
hsl[..., 2][~bluish] *= 0.5 # halve lightness of non-bluish areas
return nphusl.to_rgb(hsl), "blue"
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_husl_gray():
img = _img()
img[..., 1] = img[..., 0]
img[..., 2] = img[..., 0]
rgb_arr = img[..., 0] * 255 # single channel
husl_new = nphusl.to_husl(rgb_arr)
for row in range(rgb_arr.shape[0]):
for col in range(rgb_arr.shape[1]):
husl_old = husl.rgb_to_husl(*img[row, col])
assert _diff_hue(husl_new[row, col], husl_old)
def slide_colors(img):
hsl = nphusl.to_husl(img)
H, L = hsl[..., 0], hsl[..., 2]
light = mask(img, L > 4)
blue = mask(img, light, H > 240, H < 290)
red = mask(img, light, _or(H < 40, H > 320))
travel = (4,)
blue_up = slide_vert(blue, travel)
red_right = slide_horz(red, travel)
light_right = slide_horz(light, travel)
return Group(img, light_right).zip()
def hue_watermelon(img):
hsl = nphusl.to_husl(img)
hue, saturation, lightness = (hsl[..., n] for n in range(3))
hue_out = hue.copy()
pink = 360 # very end of the H spectrum
green = 130
chunksize = 45
for low, high in nphusl.chunk(360, chunksize): # chunks of the hue range
select = np.logical_and(hue > low, hue < high)
is_odd = low % (chunksize * 2)
color = pink if is_odd else green
hue_out[select] = color
hue[:] = hue_out
return nphusl.to_rgb(hsl), "watermelon"
def hue_watermelon(img):
hsl = nphusl.to_husl(img)
hue, saturation, lightness = (hsl[..., n] for n in range(3))
hue_out = hue.copy()
pink = 360 # very end of the H spectrum
green = 130
chunksize = 45
for low, high in nphusl.chunk(360, chunksize): # chunks of the hue range
select = np.logical_and(hue > low, hue < high)
is_odd = low % (chunksize * 2)
color = pink if is_odd else green
hue_out[select] = color
hue[:] = hue_out
return nphusl.to_rgb(hsl), "watermelon"
def microwave(img):
hsl = nphusl.to_husl(img)
hue = hsl[..., 0]
rows, cols = hue.shape
yield nphusl.to_rgb(hsl)
while True:
for chunk, ((rs, re), (cs, ce)) in nphusl.chunk_img(hue, chunksize=8):
hue_left = hue[rs, cs-1]
hue_up = hue[rs-1, cs]
this_hue = chunk[0, 0]
new_hue = (-random.randrange(30, 50) * (hue_up / 360)
-10*random.randrange(1, 10) * (hue_left / 360))
new_hue = (15*this_hue + 2*new_hue) / 17
chunk[:] = new_hue
np.mod(hue, 360, out=hue)
yield nphusl.to_rgb(hsl)
def microwave(img):
hsl = nphusl.to_husl(img)
hue = hsl[..., 0]
rows, cols = hue.shape
yield nphusl.to_rgb(hsl)
while True:
for chunk, ((rs, re), (cs, ce)) in nphusl.chunk_img(hue, chunksize=8):
hue_left = hue[rs, cs - 1]
hue_up = hue[rs - 1, cs]
this_hue = chunk[0, 0]
new_hue = (-random.randrange(30, 50) * (hue_up / 360) -
10 * random.randrange(1, 10) * (hue_left / 360))
new_hue = (15 * this_hue + 2 * new_hue) / 17
chunk[:] = new_hue
np.mod(hue, 360, out=hue)
yield nphusl.to_rgb(hsl)
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)
def clump_gradient(img):
hsl = nphusl.to_husl(img)
H, L = hsl[..., 0], hsl[..., 2]
dark = mask(img, L < 5)
travel = (5,)
ranges = list(range(1, 5))
def effects(ranges):
min_L = ranges[0]
for max_L in ranges[1:]:
m = mask(img, L < max_L, L > min_L)
yield clump_vert(m, travel)
yield clump_horz(m, travel)
min_L = max_L
effs = effects(ranges)
return Group(img, *effs).zip(effects_per_frame=5)
def test_to_hue():
img = _img()[0] # 2D
as_husl = nphusl.to_husl(img)
just_hue = nphusl.to_hue(img)
assert _diff(as_husl[..., 0], just_hue)
def test_to_rgb_2d():
img = np.ascontiguousarray(_img()[:, 17])
husl = nphusl.to_husl(img)
rgb = nphusl.to_rgb(husl)
_diff(rgb, img, diff=1)
def highlight_saturation(img):
hsl = nphusl.to_husl(img)
hsl[..., 2][hsl[..., 1] < 80] = 0
return nphusl.to_rgb(hsl), "saturation"
def reveal_light(img):
hsl = nphusl.to_husl(img)
lightness = hsl[..., 2] # just the lightness channel
dark = lightness < 62
hsl[..., 2][dark] = 0 # darkish areas to completely dark
return nphusl.to_rgb(hsl), "light"
def test_transform_rgb():
img = _img()
as_husl = nphusl.to_husl(img)
chunk_husl = transform.in_chunks(img, nphusl.to_husl, 10)
_diff(as_husl, chunk_husl)
def test_to_hue_2d():
img = _img()[:, 14] # 2D RGB
as_husl = nphusl.to_husl(img)
just_hue = nphusl.to_hue(img)
_diff(as_husl[..., 0], just_hue)
def test_to_husl_rgba_quadruplet():
hsl = nphusl.to_husl([0.5, 0.2, 0, 0.5])
_diff_husl(hsl, [28.42153418, 100.0, 14.39285828])
def highlight_saturation(img):
hsl = nphusl.to_husl(img)
hsl[..., 2][hsl[..., 1] < 80] = 0
return nphusl.to_rgb(hsl), "saturation"
def test_accuracy(img):
with nphusl.simd_enabled():
hsl = nphusl.to_husl(img.rgb)
with nphusl.numpy_enabled():
rgb = nphusl.to_rgb(hsl)
hsl_ref = nphusl.to_husl(img.rgb)
size = hsl.shape[0] * hsl.shape[1]
hsl_flat = hsl.reshape((size, 3))
rgb_flat = rgb.reshape((size, 3))
hsl_ref_flat = hsl_ref.reshape((size, 3))
rgb_ref_flat = img.rgb.reshape((size, 3))
rgb_diff = np.abs(rgb_flat.astype(int) - rgb_ref_flat)
hsl_diff = np.abs(hsl_flat - hsl_ref_flat)
h_err, s_err, l_err = (hsl_diff[..., n] for n in range(3))
h, s, l = (hsl_flat[..., n] for n in range(3))
percentiles = [0, 25, 50, 90, 95, 96, 97, 98,
99.5, 99.6, 99.7, 99.8, 99.9, 100]
def print_err_tables():
fields = "Percentile", "Red error", "Green error", "Blue error", " "
print(BOLD + "\nIMG->HUSL->RGB roundtrip error" + END)
print(_error_table(rgb_diff, percentiles, fields))
fields = "Percentile", "Hue error", "Sat error", "Light error", " "
print(BOLD + "\nIMG->HUSL error vs. reference impl." + END)
print(_error_table(hsl_diff, percentiles, fields))
for i, name in enumerate("hue saturation lightness".split()):
c = hsl_flat[..., i]
c_err = hsl_diff[..., i]
err_99 = np.percentile(c_err, 99)
print(BOLD + "\nTypical RGB for {} error above 99th "
"percentile: ".format(name) + END)
print(img.rgb[c_err.reshape(rgb.shape[:-1]) > err_99])
print(BOLD + "\nTypical HUSL for {} error above 99th "
"percentile: ".format(name) + END)
print(hsl[c_err.reshape(rgb.shape[:-1]) > err_99])
print(BOLD + "\nAll RGB & HUSL errors")
print( "=====================" + END)
print_err_tables()
h_err[s < 0.1] = 0 # hue errors for low saturation have no meaning
rgb_diff[s < 0.1] = 0
s_err[l > 99.5] = 0 # saturation errors when very bright not meaningful
rgb_diff[l > 99.5] = 0
s_err[l < 1] = 0 # saturation errors when very dim not meaningful
rgb_diff[l < 1] = 0
print(BOLD + "\nPerceptible RGB & HUSL errors")
print( "=============================" + END)
print_err_tables()
max_err = max(np.percentile(rgb_diff, 100, axis=0))
mask = np.any(rgb_diff == max_err, axis=1)
print(BOLD + "\nMost challenging pixel")
print( "======================" + END)
print("RGB input vs. output: {} -> {}".format(
rgb_ref_flat[mask].squeeze(), rgb_flat[mask].squeeze()))
print("HUSL ouput vs. reference impl.: {} vs. {}".format(
hsl_flat[mask].squeeze(), hsl_ref_flat[mask].squeeze()))
src = "_accuracy_test_source.png"
rec = "_accuracy_test_recreated.png"
print("\nWriting PNGs: {}, {}".format(src, rec))
imageio.imwrite(src, img.rgb)
imageio.imwrite(rec, rgb)
def reveal_light(img):
hsl = nphusl.to_husl(img)
lightness = hsl[..., 2] # just the lightness channel
dark = lightness < 62
hsl[..., 2][dark] = 0 # darkish areas to completely dark
return nphusl.to_rgb(hsl), "light"
