| 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 |
|
|
| def process(self, data): |
| assert self.points_key in data, '%s in data is required' % self.points_key |
| points = data['points'] * self.shape[::-1] |
| 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 |
|
|
