def __init__(self, shape, size, dtype=np.uint8, saturation=None,
hard_radius=None, signal=None, noise=0,
feat_func=feat_gauss, **feat_kwargs):
self.ndim = len(shape)
self.shape = shape
self.dtype = dtype
self.image = np.zeros(shape, dtype=dtype)
if _Frame is not None:
self.image = _Frame(self.image)
self.size = validate_tuple(size, self.ndim)
self.isotropic = np.all([self.size[1:] == self.size[:-1]])
self.feat_func = feat_func
self.feat_kwargs = feat_kwargs
self.noise = noise
if saturation is None and np.issubdtype(dtype, np.integer):
self.saturation = np.iinfo(dtype).max
elif saturation is None and np.issubdtype(dtype, np.floating):
self.saturation = 1
else:
self.saturation = saturation
if signal is None:
self.signal = self.saturation
else:
self.signal = signal
self.center = tuple([s // 2 for s in shape])
self.hard_radius = hard_radius
self._coords = []
self.pos_columns = ['z', 'y', 'x'][-self.ndim:]
if self.isotropic:
self.size_columns = ['size']
else:
self.size_columns = ['size_z', 'size_y', 'size_x'][-self.ndim:]
def noisy_image(self, noise_level):
"""Adds noise to the current image, uniformly distributed
between 0 and `noise_level`, not including noise_level."""
if noise_level <= 0:
return self.image
if np.issubdtype(self.dtype, np.integer):
noise = np.random.poisson(noise_level, self.shape)
else:
noise = np.clip(np.random.normal(noise_level, noise_level/2, self.shape), 0, self.saturation)
noisy_image = np.clip(self.image + noise, 0, self.saturation)
result = np.array(noisy_image, dtype=self.dtype)
if _Frame is not None:
result = _Frame(result)
return result
def noisy_image(self, noise_level):
"""Adds noise to the current image, uniformly distributed
between 0 and `noise_level`, not including noise_level."""
if noise_level <= 0:
return self.image
if np.issubdtype(self.dtype, np.integer):
noise = np.random.poisson(noise_level, self.shape)
else:
noise = np.clip(
np.random.normal(noise_level, noise_level / 2, self.shape), 0,
self.saturation)
noisy_image = np.clip(self.image + noise, 0, self.saturation)
result = np.array(noisy_image, dtype=self.dtype)
if _Frame is not None:
result = _Frame(result)
return result
def __init__(self,
shape,
size,
dtype=np.uint8,
saturation=None,
hard_radius=None,
signal=None,
noise=0,
feat_func=feat_gauss,
**feat_kwargs):
self.ndim = len(shape)
self.shape = shape
self.dtype = dtype
self.image = np.zeros(shape, dtype=dtype)
if _Frame is not None:
self.image = _Frame(self.image)
self.size = validate_tuple(size, self.ndim)
self.isotropic = np.all([self.size[1:] == self.size[:-1]])
self.feat_func = feat_func
self.feat_kwargs = feat_kwargs
self.noise = noise
if saturation is None and np.issubdtype(dtype, np.integer):
self.saturation = np.iinfo(dtype).max
elif saturation is None and np.issubdtype(dtype, np.floating):
self.saturation = 1
else:
self.saturation = saturation
if signal is None:
self.signal = self.saturation
else:
self.signal = signal
self.center = tuple([s // 2 for s in shape])
self.hard_radius = hard_radius
self._coords = []
self.pos_columns = ['z', 'y', 'x'][-self.ndim:]
if self.isotropic:
self.size_columns = ['size']
else:
self.size_columns = ['size_z', 'size_y', 'size_x'][-self.ndim:]