def rise(im):
"""Applies Rise filter.
Arguments:
im: An input image.
Returns:
The output image.
"""
cb = util.or_convert(im, 'RGB')
cs1 = util.fill(cb.size, [236, 205, 169, .15])
cm1 = css.blending.multiply(cb, cs1)
cs2 = util.fill(cb.size, [50, 30, 7, .4])
cm2 = css.blending.multiply(cb, cs2)
gradient_mask1 = util.radial_gradient_mask(cb.size, length=.55)
cm = Image.composite(cm1, cm2, gradient_mask1)
cs3 = util.fill(cb.size, [232, 197, 152, .8])
cm3 = css.blending.overlay(cm, cs3)
gradient_mask2 = util.radial_gradient_mask(cb.size, scale=.9)
cm_ = Image.composite(cm3, cm, gradient_mask2)
cr = Image.blend(cm, cm_, .6) # opacity
cr = css.brightness(cr, 1.05)
cr = css.sepia(cr, .2)
cr = css.contrast(cr, .9)
cr = css.saturate(cr, .9)
return cr
def mayfair(im):
"""Applies Mayfair filter.
Arguments:
im: An input image.
Returns:
The output image.
"""
cb = util.or_convert(im, 'RGB')
size = cb.size
pos = (.4, .4)
cs1 = util.fill(size, [255, 255, 255, .8])
cm1 = css.blending.overlay(cb, cs1)
cs2 = util.fill(size, [255, 200, 200, .6])
cm2 = css.blending.overlay(cb, cs2)
cs3 = util.fill(size, [17, 17, 17])
cm3 = css.blending.overlay(cb, cs3)
mask1 = util.radial_gradient_mask(size, scale=.3, center=pos)
cs = Image.composite(cm1, cm2, mask1)
mask2 = util.radial_gradient_mask(size, length=.3, scale=.6, center=pos)
cs = Image.composite(cs, cm3, mask2)
cr = Image.blend(cb, cs, .4) # opacity
cr = css.contrast(cr, 1.1)
cr = css.saturate(cr, 1.1)
return cr
def test_radial_gradient_mask_scale_le_0():
w, h = (4, 4)
mask = util.radial_gradient_mask((w, h), scale=0)
assert list(mask.getdata()) == [0] * (w * h)
assert mask.size == (w, h)
assert mask.mode == 'L'
def test_radial_gradient_mask_length_ge_1():
w, h = (4, 4)
mask = util.radial_gradient_mask((w, h), length=1)
assert list(mask.getdata()) == [255] * (w * h)
assert mask.size == (w, h)
assert mask.mode == 'L'
def test_radial_gradient_mask_length_eq_scale():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h), length=.5, scale=.5)
expected_data = [
0,
0,
0,
0,
0,
0,
255,
255,
255,
0,
0,
255,
255,
255,
0,
0,
255,
255,
255,
0,
0,
0,
0,
0,
0,
]
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
def test_radial_gradient_mask_scale():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h), scale=1.5)
expected_data = [
119,
148,
159,
148,
119,
148,
187,
207,
187,
148,
159,
207,
255,
207,
159,
148,
187,
207,
187,
148,
119,
148,
159,
148,
119,
]
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
def test_radial_gradient_mask_length():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h), length=.5)
expected_data = [
0,
107,
149,
107,
0,
107,
255,
255,
255,
107,
149,
255,
255,
255,
149,
107,
255,
255,
255,
107,
0,
107,
149,
107,
0,
]
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
def brooklyn(im):
"""Applies Brooklyn filter.
Arguments:
im: An input image.
Returns:
The output image.
"""
cb = util.or_convert(im, 'RGB')
cs1 = util.fill(cb.size, [168, 223, 193, .4])
cm1 = css.blending.overlay(cb, cs1)
cs2 = util.fill(cb.size, [196, 183, 200])
cm2 = css.blending.overlay(cb, cs2)
gradient_mask = util.radial_gradient_mask(cb.size, length=.7)
cr = Image.composite(cm1, cm2, gradient_mask)
cr = css.contrast(cr, .9)
cr = css.brightness(cr, 1.1)
return cr
def test_radial_gradient_mask_position():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h), center=(0, 0))
expected_data = [
255,
210,
165,
120,
75,
210,
191,
154,
112,
69,
165,
154,
128,
92,
53,
120,
112,
92,
64,
30,
75,
69,
53,
30,
0,
]
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
def xpro2(im):
"""Applies X-pro II filter.
Arguments:
im: An input image.
Returns:
The output image.
"""
cb = util.or_convert(im, 'RGB')
cs1 = util.fill(cb.size, [230, 231, 224])
cs2 = util.fill(cb.size, [43, 42, 161])
cs2 = Image.blend(cb, cs2, .6)
gradient_mask = util.radial_gradient_mask(cb.size, length=.4, scale=1.1)
cs = Image.composite(cs1, cs2, gradient_mask)
# TODO: improve alpha blending
cm1 = css.blending.color_burn(cb, cs)
cm2 = cm1.copy()
cm2 = Image.blend(cb, cm2, .6)
cr = Image.composite(cm1, cm2, gradient_mask)
cr = css.sepia(cr, .3)
return cr
def test_radial_gradient_255_to_0():
# this case is the same as radial_gradient_mask
w, h = (5, 5)
gradient = util.radial_gradient((w, h), [[255, 255, 255], [0, 0, 0]])
expected = util.radial_gradient_mask((w, h))
assert list(gradient.getdata(0)) == list(expected.getdata())
assert list(gradient.getdata(1)) == list(expected.getdata())
assert list(gradient.getdata(2)) == list(expected.getdata())
assert gradient.size == (w, h)
assert gradient.mode == 'RGB'
def test_radial_gradient_mask_prepared_scale():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h), scale=1.5)
expected_data = [
118, 147, 158, 148, 119,
147, 187, 207, 188, 148,
158, 207, 239, 209, 160,
148, 188, 209, 189, 149,
119, 148, 160, 149, 120,
]
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
def test_radial_gradient_mask_prepared():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h))
expected_data = [
52, 93, 111, 94, 53,
93, 150, 182, 151, 94,
111, 182, 231, 183, 113,
94, 151, 183, 152, 95,
53, 94, 113, 95, 54,
]
# TODO: test rectangle
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
def lofi(im):
"""Applies Lo-Fi filter.
Arguments:
im: An input image.
Returns:
The output image.
"""
cb = util.or_convert(im, 'RGB')
cs = util.fill(cb.size, [34, 34, 34])
cs = css.blending.multiply(cb, cs)
mask = util.radial_gradient_mask(cb.size, length=.7, scale=1.5)
cr = Image.composite(cb, cs, mask)
cr = css.saturate(cr, 1.1)
cr = css.contrast(cr, 1.5)
return cr
def test_radial_gradient_mask():
w, h = (5, 5)
mask = util.radial_gradient_mask((w, h))
expected_data = [
49,
92,
110,
93,
50,
92,
151,
182,
152,
94,
110,
182,
255,
183,
111,
93,
152,
183,
153,
94,
50,
94,
111,
94,
51,
]
# TODO: test position
# TODO: test rectangle
assert list(mask.getdata()) == expected_data
assert mask.size == (w, h)
assert mask.mode == 'L'
