def grad_for_masked_data(data, mask):
grad_data = gradient(_unmask(data, mask))
grad_mask = gradient(mask) != 0
for i in range(len(grad_data)):
grad_data[i][grad_mask[i]] = 0
return grad_data
def grad_for_masked_data(data,mask):
grad_data = gradient(_unmask(data, mask))
grad_mask = gradient(mask) != 0
for i in range(len(grad_data)):
grad_data[i][grad_mask[i]] = 0
return grad_data
def tv_for_masked_data(data, mask):
"""This function compute the total variation of an image
"""
grad_data = gradient(_unmask(data))
grad_mask = gradient(mask) != 0
for i in range(len(grad_data)):
grad_data[i][grad_mask[i]] = 0
return np.sum(np.sqrt(np.dot(grad_data[:-1], grad_data[:-1])))
def tv_for_masked_data(data, mask):
"""This function compute the total variation of an image
"""
grad_data = gradient(_unmask(data))
grad_mask = gradient(mask) != 0
for i in range(len(grad_data)):
grad_data[i][grad_mask[i]] = 0
return np.sum(np.sqrt(np.dot(grad_data[:-1], grad_data[:-1])))
def tv(img):
"""This function compute the total variation of an image
"""
spatial_grad = gradient(img)
return np.sum(
np.sqrt(np.sum(spatial_grad[:-1] * spatial_grad[:-1], axis=0)))
def tv(img):
"""This function compute the total variation of an image
"""
spatial_grad = gradient(img)
return np.sum(np.sqrt(np.sum(spatial_grad[:-1] * spatial_grad[:-1],
axis=0)))
