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