83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
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#!/usr/bin/env python
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import sys
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import math
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import os
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import numpy as np
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dataset_path = sys.argv[1]
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print dataset_path
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timestamps = {}
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exposures = {}
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for sensor in ['cam0', 'cam1', 'imu0']:
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data = np.loadtxt(dataset_path + '/mav0/' + sensor + '/data.csv', usecols=[0], delimiter=',', dtype=np.int64)
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timestamps[sensor] = data
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# check if dataset is OK...
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for key, value in timestamps.iteritems():
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times = value * 1e-9
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min_t = times.min()
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max_t = times.max()
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interval = max_t - min_t
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diff = times[1:] - times[:-1]
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print '=========================================='
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print 'sensor', key
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print 'min timestamp', min_t
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print 'max timestamp', max_t
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print 'interval', interval
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print 'hz', times.shape[0]/interval
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print 'min time between consecutive msgs', diff.min()
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print 'max time between consecutive msgs', diff.max()
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for i, d in enumerate(diff):
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# Note: 0.001 is just a hacky heuristic, since we have nothing faster than 1000Hz. Should maybe be topic-specific.
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if d < 0.001:
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print("ERROR: Difference on consecutive measurements too small: {} - {} = {}".format(times[i+1], times[i], d))
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# check if we have all images for timestamps
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timestamp_to_topic = {}
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for key, value in timestamps.iteritems():
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if not key.startswith('cam'): continue
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for v in value:
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if v not in timestamp_to_topic:
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timestamp_to_topic[v] = list()
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timestamp_to_topic[v].append(key)
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for key in timestamp_to_topic.keys():
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if len(timestamp_to_topic[key]) != 2:
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print 'timestamp', key, 'has topics', timestamp_to_topic[key]
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# check image data.
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img_extensions = ['.png', '.jpg', '.webp']
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for key, value in timestamps.iteritems():
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if not key.startswith('cam'): continue
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for v in value:
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path = dataset_path + '/mav0/' + key + '/data/' + str(v)
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img_exists = False
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for e in img_extensions:
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if os.path.exists(dataset_path + '/mav0/' + key + '/data/' + str(v) + e):
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img_exists = True
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if not img_exists:
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print('No image data for ' + key + ' at timestamp ' + str(v))
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exposure_file = dataset_path + '/mav0/' + key + '/exposure.csv'
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if not os.path.exists(exposure_file):
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print('No exposure data for ' + key)
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continue
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exposure_data = np.loadtxt(exposure_file, delimiter=',', dtype=np.int64)
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for v in value:
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idx = np.searchsorted(exposure_data[:, 0], v)
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if exposure_data[idx, 0] != v:
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print('No exposure data for ' + key + ' at timestamp ' + str(v))
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