def _draw_circle(self, radius, thickness,
center_x=0, center_y=0, a=1, b=1,
turb_size=0, turb_power=0):
"""
:param radius:
:param thickness:
:param center_x:
:param center_y:
:param a: float, optional, ellipse: radius on x axis
:param b: float, optional, ellipse: radius on y axis
:return:
"""
noise = Noise(self.width, self.height)
a = 1 / (a * a)
b = 1 / (b * b)
for x in xrange(self.width):
for y in xrange(self.height):
xv = (x - self.width / 2.0) / self.width
yv = (y - self.height / 2.0) / self.height
dist = math.sqrt((xv - center_x) * (xv - center_x) * a +
(yv - center_y) * (yv - center_y) * b)
dist += turb_power * noise.turbulence(x, y, turb_size)
if ((radius - thickness) <= dist) and \
(dist <= (radius + thickness)):
theta = np.interp(dist,
[radius - thickness, radius + thickness],
[0, math.pi])
multiplier = random.uniform(0.2, 0.25)
value = math.sin(theta) * multiplier
self.data[x, y] -= value
def _draw_circle(self,
radius,
thickness,
center_x=0,
center_y=0,
a=1,
b=1,
turb_size=0,
turb_power=0):
"""
:param radius:
:param thickness:
:param center_x:
:param center_y:
:param a: float, optional, ellipse: radius on x axis
:param b: float, optional, ellipse: radius on y axis
:return:
"""
noise = Noise(self.width, self.height)
a = 1 / (a * a)
b = 1 / (b * b)
for x in xrange(self.width):
for y in xrange(self.height):
xv = (x - self.width / 2.0) / self.width
yv = (y - self.height / 2.0) / self.height
dist = math.sqrt((xv - center_x) * (xv - center_x) * a +
(yv - center_y) * (yv - center_y) * b)
dist += turb_power * noise.turbulence(x, y, turb_size)
if ((radius - thickness) <= dist) and \
(dist <= (radius + thickness)):
theta = np.interp(dist,
[radius - thickness, radius + thickness],
[0, math.pi])
multiplier = random.uniform(0.2, 0.25)
value = math.sin(theta) * multiplier
self.data[x, y] -= value
class LiposomeBasic(object):
def __init__(self, width, height, prob_deviation=0):
"""
:param width: int
:param height: int
:param prob_deviation: float, optional, probability of some deviation
"""
self.width = width
self.height = height
self._noise = Noise(width, height, low=0.2, high=0.7)
self.data = self._get_background()
self._prob_deviation = prob_deviation
self._prob_deviation_internal = 0.3
def _get_background(self):
"""
Makes background of microscope image
:return: numpy 2d array of shape(self.width, self.height)
"""
data = np.zeros((self.width, self.height))
turbulence_size = 4
for x in xrange(self.width):
for y in xrange(self.height):
data[x, y] = self._noise.turbulence(x, y, turbulence_size)
return data
@abstractmethod
def _draw(self):
pass
#@abstractmethod
def _deviate(self):
if random.random() > 0.4:
self._deviation_spot()
if random.random() > 0.97:
self._deviation_patch()
if random.random() < self._prob_deviation_internal:
self._deviation_overlay_internal(self._center_x, self._center_y,
self._radius)
if random.random() > 0.5:
self._deviation_arc_unilameral()
if random.random() > 0.9:
self._deviation_arc_multilamellar()
def _draw_circle(self,
radius,
thickness,
center_x=0,
center_y=0,
a=1,
b=1,
turb_size=0,
turb_power=0):
"""
:param radius:
:param thickness:
:param center_x:
:param center_y:
:param a: float, optional, ellipse: radius on x axis
:param b: float, optional, ellipse: radius on y axis
:return:
"""
noise = Noise(self.width, self.height)
a = 1 / (a * a)
b = 1 / (b * b)
for x in xrange(self.width):
for y in xrange(self.height):
xv = (x - self.width / 2.0) / self.width
yv = (y - self.height / 2.0) / self.height
dist = math.sqrt((xv - center_x) * (xv - center_x) * a +
(yv - center_y) * (yv - center_y) * b)
dist += turb_power * noise.turbulence(x, y, turb_size)
if ((radius - thickness) <= dist) and \
(dist <= (radius + thickness)):
theta = np.interp(dist,
[radius - thickness, radius + thickness],
[0, math.pi])
multiplier = random.uniform(0.2, 0.25)
value = math.sin(theta) * multiplier
self.data[x, y] -= value
def make(self):
self._draw()
if random.random() < self._prob_deviation:
self._deviate()
def _deviation_spot(self):
# add a spot like on image 544825
p = random.random()
n = 1
if p > 0.5:
n = 2
if p > 0.75:
n = 3
for _ in xrange(n):
x = random.uniform(-0.3, 0.3)
y = random.uniform(-0.3, 0.3)
radius = random.uniform(0.01, 0.03)
self._draw_circle(radius, 0.05, center_x=x, center_y=y)
def _deviation_patch(self):
# a patch like on image 545302
x = random.uniform(-0.3, 0.3)
y = random.uniform(-0.3, 0.3)
radius = 0.05
thickness = 0.6
turb_size = 4
turb_power = 0.1
self._draw_circle(radius,
thickness,
center_x=x,
center_y=y,
turb_size=turb_size,
turb_power=turb_power)
def _deviation_overlay_internal(self,
center_x,
center_y,
center_radius,
n=None,
radius=None):
"""
Overlay of liposomes like on image 539202
:param n: int, optional, number of inscribed liposomes, randomized if None
:return: nothing
"""
if n is None:
n = random.randint(1, 2)
for _ in xrange(n):
if radius is None:
radius = random.uniform(0.2, 0.3)
thickness = random.uniform(0.04, 0.05)
theta = random.randint(1, 360)
x = center_x + (center_radius - radius) * math.cos(theta)
y = center_y + (center_radius - radius) * math.sin(theta)
a = random.uniform(0.7, 1.3)
b = random.uniform(0.7, 1.3)
self._draw_circle(radius,
thickness,
center_y=y,
center_x=x,
turb_size=32,
turb_power=0.1,
a=a,
b=b)
def _deviation_arc_unilameral(self, n=None):
"""
Draw an arc(s) as on image 545216, 545413, 547997
:param n: int, optional, number of arcs, randomized between 1 and 5 if None
:return:
"""
if n is None:
p = random.random()
n = 1
if p > 0.4:
n = 2
if p > 0.6:
n = 3
if p > 0.9:
n = 4
if p > 0.97:
n = 5
thetas = np.linspace(0, 360, 6)
thetas = random.sample(thetas, n)
for theta in thetas:
radius = random.uniform(0.4, 0.7)
x = radius * math.cos(theta)
y = radius * math.sin(theta)
thickness = random.uniform(0.04, 0.05)
a = random.uniform(0.8, 1.2)
b = random.uniform(0.8, 1.2)
radius = random.uniform(0.2, 0.4)
self._draw_circle(radius,
thickness,
center_y=y,
center_x=x,
a=a,
b=b)
def _deviation_arc_multilamellar(self, n=None):
"""
Deviation like on image 546292
:param n: int, optional, number of arcs, randomized between 2 and 3 if None
:return:
"""
if n is None:
n = 1
if random.random() > 0.5:
n = 2
thetas = np.linspace(0, 360, num=6)
thetas = random.sample(thetas, n)
for theta in thetas:
circles = 2
if random.random() > 0.5:
circles = 3
radius = random.uniform(0.5, 0.7)
x = radius * math.cos(theta)
y = radius * math.sin(theta)
thickness = random.uniform(0.03, 0.05)
a = random.uniform(0.8, 1.2)
b = random.uniform(0.8, 1.2)
radius = random.uniform(0.4, 0.6)
for _ in xrange(circles):
self._draw_circle(radius,
thickness,
center_y=y,
center_x=x,
a=a,
b=b)
radius -= random.uniform(0.08, 0.11)
class LiposomeBasic(object):
def __init__(self, width, height, prob_deviation=0):
"""
:param width: int
:param height: int
:param prob_deviation: float, optional, probability of some deviation
"""
self.width = width
self.height = height
self._noise = Noise(width, height, low=0.2, high=0.7)
self.data = self._get_background()
self._prob_deviation = prob_deviation
self._prob_deviation_internal = 0.3
def _get_background(self):
"""
Makes background of microscope image
:return: numpy 2d array of shape(self.width, self.height)
"""
data = np.zeros((self.width, self.height))
turbulence_size = 4
for x in xrange(self.width):
for y in xrange(self.height):
data[x, y] = self._noise.turbulence(x, y, turbulence_size)
return data
@abstractmethod
def _draw(self):
pass
#@abstractmethod
def _deviate(self):
if random.random() > 0.4:
self._deviation_spot()
if random.random() > 0.97:
self._deviation_patch()
if random.random() < self._prob_deviation_internal:
self._deviation_overlay_internal(self._center_x, self._center_y, self._radius)
if random.random() > 0.5:
self._deviation_arc_unilameral()
if random.random() > 0.9:
self._deviation_arc_multilamellar()
def _draw_circle(self, radius, thickness,
center_x=0, center_y=0, a=1, b=1,
turb_size=0, turb_power=0):
"""
:param radius:
:param thickness:
:param center_x:
:param center_y:
:param a: float, optional, ellipse: radius on x axis
:param b: float, optional, ellipse: radius on y axis
:return:
"""
noise = Noise(self.width, self.height)
a = 1 / (a * a)
b = 1 / (b * b)
for x in xrange(self.width):
for y in xrange(self.height):
xv = (x - self.width / 2.0) / self.width
yv = (y - self.height / 2.0) / self.height
dist = math.sqrt((xv - center_x) * (xv - center_x) * a +
(yv - center_y) * (yv - center_y) * b)
dist += turb_power * noise.turbulence(x, y, turb_size)
if ((radius - thickness) <= dist) and \
(dist <= (radius + thickness)):
theta = np.interp(dist,
[radius - thickness, radius + thickness],
[0, math.pi])
multiplier = random.uniform(0.2, 0.25)
value = math.sin(theta) * multiplier
self.data[x, y] -= value
def make(self):
self._draw()
if random.random() < self._prob_deviation:
self._deviate()
def _deviation_spot(self):
# add a spot like on image 544825
p = random.random()
n = 1
if p > 0.5:
n = 2
if p > 0.75:
n = 3
for _ in xrange(n):
x = random.uniform(-0.3, 0.3)
y = random.uniform(-0.3, 0.3)
radius = random.uniform(0.01, 0.03)
self._draw_circle(radius, 0.05, center_x=x, center_y=y)
def _deviation_patch(self):
# a patch like on image 545302
x = random.uniform(-0.3, 0.3)
y = random.uniform(-0.3, 0.3)
radius = 0.05
thickness = 0.6
turb_size = 4
turb_power = 0.1
self._draw_circle(radius, thickness, center_x=x, center_y=y, turb_size=turb_size, turb_power=turb_power)
def _deviation_overlay_internal(self, center_x, center_y, center_radius, n=None, radius=None):
"""
Overlay of liposomes like on image 539202
:param n: int, optional, number of inscribed liposomes, randomized if None
:return: nothing
"""
if n is None:
n = random.randint(1, 2)
for _ in xrange(n):
if radius is None:
radius = random.uniform(0.2, 0.3)
thickness = random.uniform(0.04, 0.05)
theta = random.randint(1, 360)
x = center_x + (center_radius - radius) * math.cos(theta)
y = center_y + (center_radius - radius) * math.sin(theta)
a = random.uniform(0.7, 1.3)
b = random.uniform(0.7, 1.3)
self._draw_circle(radius, thickness, center_y=y, center_x=x,
turb_size=32, turb_power=0.1, a=a, b=b)
def _deviation_arc_unilameral(self, n=None):
"""
Draw an arc(s) as on image 545216, 545413, 547997
:param n: int, optional, number of arcs, randomized between 1 and 5 if None
:return:
"""
if n is None:
p = random.random()
n = 1
if p > 0.4:
n = 2
if p > 0.6:
n = 3
if p > 0.9:
n = 4
if p > 0.97:
n = 5
thetas = np.linspace(0, 360, 6)
thetas = random.sample(thetas, n)
for theta in thetas:
radius = random.uniform(0.4, 0.7)
x = radius * math.cos(theta)
y = radius * math.sin(theta)
thickness = random.uniform(0.04, 0.05)
a = random.uniform(0.8, 1.2)
b = random.uniform(0.8, 1.2)
radius = random.uniform(0.2, 0.4)
self._draw_circle(radius, thickness, center_y=y, center_x=x, a=a, b=b)
def _deviation_arc_multilamellar(self, n=None):
"""
Deviation like on image 546292
:param n: int, optional, number of arcs, randomized between 2 and 3 if None
:return:
"""
if n is None:
n = 1
if random.random() > 0.5:
n = 2
thetas = np.linspace(0, 360, num=6)
thetas = random.sample(thetas, n)
for theta in thetas:
circles = 2
if random.random() > 0.5:
circles = 3
radius = random.uniform(0.5, 0.7)
x = radius * math.cos(theta)
y = radius * math.sin(theta)
thickness = random.uniform(0.03, 0.05)
a = random.uniform(0.8, 1.2)
b = random.uniform(0.8, 1.2)
radius = random.uniform(0.4, 0.6)
for _ in xrange(circles):
self._draw_circle(radius, thickness, center_y=y, center_x=x, a=a, b=b)
radius -= random.uniform(0.08, 0.11)
