def get_image_clusters(self, cluster_size, hard_radius=1., noise=0,
signal_dev=0, size_dev=0, angle=None):
N = self.repeats
separation = [int(sep + 2 * hard_radius * s)
for (sep, s) in zip(self.separation, self.size)]
margin = self.separation
Nsqrt = int(N**(1/self.ndim) + 0.9999)
pos = np.meshgrid(*[np.arange(0, s * Nsqrt, s) for s in separation],
indexing='ij')
pos = np.array([p.ravel() for p in pos], dtype=np.float).T[:N] + margin
pos += (np.random.random(pos.shape) - 0.5) #randomize subpixel location
if self.ndim == 2 and angle is None:
angles = np.random.uniform(0, 2*np.pi, N)
elif self.ndim == 3 and angle is None:
angles = np.random.uniform(0, 2*np.pi, (N, 3))
elif self.ndim == 2 and angle is not None:
angles = np.repeat(angle, N)
elif self.ndim == 3 and angle is not None:
angles = np.repeat([angle], N, axis=0)
shape = tuple(np.max(pos, axis=0).astype(np.int) + margin)
if signal_dev > 0:
signal = self.signal * np.random.uniform(1-signal_dev, 1+signal_dev,
N)
else:
signal = np.repeat(self.signal, N)
if size_dev > 0:
size = np.array([self.size]) * np.random.uniform(1-size_dev,
1+size_dev,
(N, 1))
else:
size = np.repeat([self.size], N, axis=0)
image = np.zeros(shape, dtype=self.dtype)
coords = []
for _pos, _signal, _size, _angle in zip(pos, signal, size, angles):
coords.extend(draw_cluster(image, _pos, _size, cluster_size,
hard_radius, _angle, max_value=_signal,
feat_func=self.feat_func,
**self.feat_kwargs))
coords = np.array(coords)
signal = np.repeat(signal, cluster_size)
size = np.repeat(size, cluster_size, axis=0)
if self.isotropic:
size = size[:, 0]
if noise > 0:
image = image + np.random.poisson(noise, shape)
if image.max() <= 255:
image = image.astype(np.uint8)
return image, (coords, signal, size), (pos, angles)
def get_image_clusters(self, cluster_size, hard_radius=1., noise=0,
signal_dev=0, size_dev=0, angle=None):
N = self.repeats
separation = [int(sep + 2 * hard_radius * s)
for (sep, s) in zip(self.separation, self.size)]
margin = self.separation
Nsqrt = int(N**(1/self.ndim) + 0.9999)
pos = np.meshgrid(*[np.arange(0, s * Nsqrt, s) for s in separation],
indexing='ij')
pos = np.array([p.ravel() for p in pos], dtype=np.float).T[:N] + margin
pos += (np.random.random(pos.shape) - 0.5) #randomize subpixel location
if self.ndim == 2 and angle is None:
angles = np.random.uniform(0, 2*np.pi, N)
elif self.ndim == 3 and angle is None:
angles = np.random.uniform(0, 2*np.pi, (N, 3))
elif self.ndim == 2 and angle is not None:
angles = np.repeat(angle, N)
elif self.ndim == 3 and angle is not None:
angles = np.repeat([angle], N, axis=0)
shape = tuple(np.max(pos, axis=0).astype(np.int) + margin)
if signal_dev > 0:
signal = self.signal * np.random.uniform(1-signal_dev, 1+signal_dev,
N)
else:
signal = np.repeat(self.signal, N)
if size_dev > 0:
size = np.array([self.size]) * np.random.uniform(1-size_dev,
1+size_dev,
(N, 1))
else:
size = np.repeat([self.size], N, axis=0)
image = np.zeros(shape, dtype=self.dtype)
coords = []
for _pos, _signal, _size, _angle in zip(pos, signal, size, angles):
coords.extend(draw_cluster(image, _pos, _size, cluster_size,
hard_radius, _angle, max_value=_signal,
feat_func=self.feat_func,
**self.feat_kwargs))
coords = np.array(coords)
signal = np.repeat(signal, cluster_size)
size = np.repeat(size, cluster_size, axis=0)
if self.isotropic:
size = size[:, 0]
if noise > 0:
image = image + np.random.poisson(noise, shape)
if image.max() <= 255:
image = image.astype(np.uint8)
return image, (coords, signal, size), (pos, angles)