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import numpy as np
import scipy.ndimage.filters as fi
from concern.config import State
from .data_process import DataProcess
class MakeCenterMap(DataProcess):
max_size = State(default=32)
shape = State(default=(64, 256))
sigma_ratio = State(default=16)
function_name = State(default='sample_gaussian')
points_key = 'points'
correlation = 0 # The formulation of guassian is simplified when correlation is 0
def process(self, data):
assert self.points_key in data, '%s in data is required' % self.points_key
points = data['points'] * self.shape[::-1] # N, 2
assert points.shape[0] >= self.max_size
func = getattr(self, self.function_name)
data['charmaps'] = func(points, *self.shape)
return data
def gaussian(self, points, height, width):
index_x, index_y = np.meshgrid(np.linspace(0, width, width),
np.linspace(0, height, height))
index_x = np.repeat(index_x[np.newaxis], points.shape[0], axis=0)
index_y = np.repeat(index_y[np.newaxis], points.shape[0], axis=0)
mu_x = points[:, 0][:, np.newaxis, np.newaxis]
mu_y = points[:, 1][:, np.newaxis, np.newaxis]
mask_is_zero = ((mu_x == 0) + (mu_y == 0)) == 0
result = np.reciprocal(2 * np.pi * width / self.sigma_ratio * height / self.sigma_ratio)\
* np.exp(- 0.5 * (np.square((index_x - mu_x) / width * self.sigma_ratio) +
np.square((index_y - mu_y) / height * self.sigma_ratio)))
result = result / \
np.maximum(result.max(axis=1, keepdims=True).max(
axis=2, keepdims=True), np.finfo(np.float32).eps)
result = result * mask_is_zero
return result.astype(np.float32)
def sample_gaussian(self, points, height, width):
points = (points + 0.5).astype(np.int32)
canvas = np.zeros((self.max_size, height, width), dtype=np.float32)
for index in range(canvas.shape[0]):
point = points[index]
canvas[index, point[1], point[0]] = 1.
if point.sum() > 0:
fi.gaussian_filter(canvas[index], (height // self.sigma_ratio,
width // self.sigma_ratio),
output=canvas[index], mode='mirror')
canvas[index] = canvas[index] / canvas[index].max()
x_range = min(point[0], width - point[0])
canvas[index, :, :point[0] - x_range] = 0
canvas[index, :, point[0] + x_range:] = 0
y_range = min(point[1], width - point[1])
canvas[index, :point[1] - y_range, :] = 0
canvas[index, point[1] + y_range:, :] = 0
return canvas
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