53 lines
1.5 KiB
Python
53 lines
1.5 KiB
Python
import numpy as np
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def compute_split_parts(num_samples, num_parts):
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part_samples = num_samples // num_parts
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remain_samples = num_samples % num_parts
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if part_samples == 0:
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return [num_samples]
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if remain_samples == 0:
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return [part_samples] * num_parts
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else:
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return [part_samples] * num_parts + [remain_samples]
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def overall_filter(boxes):
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ignore = np.zeros(boxes.shape[0], dtype=bool) # all false
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return ignore
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def distance_filter(boxes, level):
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ignore = np.ones(boxes.shape[0], dtype=bool) # all true
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dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
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if level == 0: # 0-30m
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flag = dist < 30
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elif level == 1: # 30-50m
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flag = (dist >= 30) & (dist < 50)
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elif level == 2: # 50m-inf
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flag = dist >= 50
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else:
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assert False, 'level < 3 for distance metric, found level %s' % (str(level))
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ignore[flag] = False
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return ignore
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def overall_distance_filter(boxes, level):
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ignore = np.ones(boxes.shape[0], dtype=bool) # all true
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dist = np.sqrt(np.sum(boxes[:, 0:3] * boxes[:, 0:3], axis=1))
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if level == 0:
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flag = np.ones(boxes.shape[0], dtype=bool)
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elif level == 1: # 0-30m
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flag = dist < 30
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elif level == 2: # 30-50m
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flag = (dist >= 30) & (dist < 50)
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elif level == 3: # 50m-inf
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flag = dist >= 50
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else:
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assert False, 'level < 4 for overall & distance metric, found level %s' % (str(level))
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ignore[flag] = False
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return ignore |