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(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)))