349 lines
10 KiB
Python
349 lines
10 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright 2018 Slightech Co., Ltd. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# pylint: disable=missing-docstring
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from __future__ import print_function
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import os
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import sys
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TOOLBOX_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.append(os.path.join(TOOLBOX_DIR, 'internal'))
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# pylint: disable=import-error,wrong-import-position
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from data import ROSBag, MYNTEYE, What
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ANGLE_DEGREES = 'd'
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ANGLE_RADIANS = 'r'
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ANGLE_UNITS = (ANGLE_DEGREES, ANGLE_RADIANS)
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BIN_IMG_NAME = 'stamp_analytics_img.bin'
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BIN_IMU_NAME = 'stamp_analytics_imu.bin'
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RESULT_FIGURE = 'stamp_analytics.png'
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class BinDataset(object):
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def __init__(self, path, dataset_creator):
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self.path = path
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self.dataset_creator = dataset_creator
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self._digest()
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def _digest(self):
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bindir = os.path.splitext(self.path)[0]
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binimg = os.path.join(bindir, BIN_IMG_NAME)
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binimu = os.path.join(bindir, BIN_IMU_NAME)
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if os.path.isfile(binimg) and os.path.isfile(binimu):
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print('find binary files ...')
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print(' binimg: {}'.format(binimg))
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print(' binimu: {}'.format(binimu))
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while True:
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sys.stdout.write('Do you want to use it directly? [Y/n] ')
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choice = raw_input().lower()
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if choice == '' or choice == 'y':
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self._binimg = binimg
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self._binimu = binimu
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self._has_img = True
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self._has_imu = True
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return
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elif choice == 'n':
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break
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else:
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print('Please respond with \'y\' or \'n\'.')
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self._convert()
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def _convert(self):
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import numpy as np
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dataset = self.dataset_creator(self.path)
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bindir = os.path.splitext(self.path)[0]
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if not os.path.exists(bindir):
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os.makedirs(bindir)
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binimg = os.path.join(bindir, BIN_IMG_NAME)
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binimu = os.path.join(bindir, BIN_IMU_NAME)
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print('save to binary files ...')
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print(' binimg: {}'.format(binimg))
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print(' binimu: {}'.format(binimu))
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has_img = False
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has_imu = False
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with open(binimg, 'wb') as f_img, open(binimu, 'wb') as f_imu:
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img_count = 0
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imu_count = 0
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for result in dataset.generate(What.img_left, What.imu):
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if What.img_left in result:
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img = result[What.img_left]
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np.array([(
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img.timestamp
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)], dtype="f8").tofile(f_img)
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img_count = img_count + 1
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has_img = True
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if What.imu in result:
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imu = result[What.imu]
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np.array([(
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imu.timestamp,
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imu.accel_x, imu.accel_y, imu.accel_z,
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imu.gyro_x, imu.gyro_y, imu.gyro_z
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)], dtype="f8, f8, f8, f8, f8, f8, f8").tofile(f_imu)
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imu_count = imu_count + 1
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has_imu = True
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sys.stdout.write('\r img: {}, imu: {}'.format(img_count, imu_count))
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sys.stdout.write('\n')
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# pylint: disable=attribute-defined-outside-init
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self._binimg = binimg
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self._binimu = binimu
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self._has_img = has_img
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self._has_imu = has_imu
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def stamp_analytics(self, args):
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outdir = args.outdir
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import numpy as np
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if self.has_img:
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# pd.cut fails on readonly arrays
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# https://github.com/pandas-dev/pandas/issues/18773
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# imgs = np.memmap(self._binimg, dtype=[
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# ('t', 'f8')
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# ], mode='r')
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imgs = np.fromfile(self._binimg, dtype=[
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('t', 'f8')
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])
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else:
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sys.exit("Error: there are no imgs.")
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if self.has_imu:
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imus = np.memmap(self._binimu, dtype=[
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('t', 'f8'),
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('accel_x', 'f8'), ('accel_y', 'f8'), ('accel_z', 'f8'),
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('gyro_x', 'f8'), ('gyro_y', 'f8'), ('gyro_z', 'f8'),
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], mode='r')
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else:
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sys.exit("Error: there are no imus.")
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period_img = 1. / args.rate_img
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period_imu = 1. / args.rate_imu
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print('\nrate (Hz)')
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print(' img: {}, imu: {}'.format(args.rate_img, args.rate_imu))
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print('sample period (s)')
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print(' img: {}, imu: {}'.format(period_img, period_imu))
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imgs_t_diff = np.diff(imgs['t'])
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imus_t_diff = np.diff(imus['t'])
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print('\ndiff count')
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print(' imgs: {}, imus: {}'.format(imgs['t'].size, imus['t'].size))
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print(' imgs_t_diff: {}, imus_t_diff: {}'
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.format(imgs_t_diff.size, imus_t_diff.size))
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print('\ndiff where (factor={})'.format(args.factor))
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where = np.argwhere(imgs_t_diff > period_img * (1 + args.factor))
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print(' imgs where diff > {}*{} ({})'.format(period_img,
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1 + args.factor, where.size))
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for x in where:
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print(' {:8d}: {:.16f}'.format(x[0], imgs_t_diff[x][0]))
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where = np.argwhere(imgs_t_diff < period_img * (1 - args.factor))
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print(' imgs where diff < {}*{} ({})'.format(period_img,
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1 - args.factor, where.size))
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for x in where:
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print(' {:8d}: {:.16f}'.format(x[0], imgs_t_diff[x][0]))
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where = np.argwhere(imus_t_diff > period_imu * (1 + args.factor))
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print(' imus where diff > {}*{} ({})'.format(period_imu,
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1 + args.factor, where.size))
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for x in where:
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print(' {:8d}: {:.16f}'.format(x[0], imus_t_diff[x][0]))
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where = np.argwhere(imus_t_diff < period_imu * (1 - args.factor))
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print(' imus where diff < {}*{} ({})'.format(period_imu,
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1 - args.factor, where.size))
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for x in where:
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print(' {:8d}: {:.16f}'.format(x[0], imus_t_diff[x][0]))
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import pandas as pd
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bins = imgs['t']
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bins_n = imgs['t'].size
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bins = pd.Series(data=bins).drop_duplicates(keep='first')
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cats = pd.cut(imus['t'], bins)
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print('\nimage timestamp duplicates: {}'.format(bins_n - bins.size))
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self._plot(outdir, imgs_t_diff, imus_t_diff, cats.value_counts())
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def _plot(self, outdir, imgs_t_diff, imus_t_diff, imgs_t_imus):
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import matplotlib.pyplot as plt
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import numpy as np
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fig_1 = plt.figure(1, [16, 6])
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fig_1.suptitle('Stamp Analytics')
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fig_1.subplots_adjust(
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left=0.1,
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right=0.95,
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top=0.85,
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bottom=0.15,
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wspace=0.4)
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ax_imgs_t_diff = fig_1.add_subplot(131)
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ax_imgs_t_diff.set_title('Image Timestamp Diff')
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ax_imgs_t_diff.set_xlabel('diff index')
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ax_imgs_t_diff.set_ylabel('diff (s)')
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ax_imgs_t_diff.axis('auto')
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ax_imus_t_diff = fig_1.add_subplot(132)
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ax_imus_t_diff.set_title('Imu Timestamp Diff')
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ax_imus_t_diff.set_xlabel('diff index')
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ax_imus_t_diff.set_ylabel('diff (s)')
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ax_imus_t_diff.axis('auto')
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ax_imgs_t_imus = fig_1.add_subplot(133)
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ax_imgs_t_imus.set_title('Imu Count Per Image Intervel')
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ax_imgs_t_imus.set_xlabel('intervel index')
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ax_imgs_t_imus.set_ylabel('imu count')
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ax_imgs_t_imus.axis('auto')
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ax_imgs_t_diff.set_xlim([0, imgs_t_diff.size])
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ax_imgs_t_diff.plot(imgs_t_diff)
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ax_imus_t_diff.set_xlim([0, imus_t_diff.size])
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ax_imus_t_diff.plot(imus_t_diff)
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# print(imgs_t_imus.values)
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# imgs_t_imus.plot(kind='line', ax=ax_imgs_t_imus)
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data = imgs_t_imus.values
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ax_imgs_t_imus.set_xlim([0, data.size])
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ax_imgs_t_imus.set_ylim([np.min(data) - 1, np.max(data) + 1])
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ax_imgs_t_imus.plot(data)
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if outdir:
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figpath = os.path.join(outdir, RESULT_FIGURE)
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print('\nsave figure to:\n {}'.format(figpath))
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if not os.path.exists(outdir):
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os.makedirs(outdir)
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fig_1.savefig(figpath, dpi=100)
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plt.show()
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@property
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def has_img(self):
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return self._has_img
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@property
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def has_imu(self):
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return self._has_imu
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def _parse_args():
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import argparse
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parser = argparse.ArgumentParser(
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prog=os.path.basename(__file__),
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formatter_class=argparse.RawTextHelpFormatter,
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description='usage examples:'
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'\n python %(prog)s -i DATASET')
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parser.add_argument(
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'-i',
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'--input',
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dest='input',
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metavar='DATASET',
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required=True,
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help='the input dataset path')
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parser.add_argument(
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'-o',
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'--outdir',
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dest='outdir',
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metavar='OUTDIR',
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help='the output directory')
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parser.add_argument(
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'-c',
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'--config',
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dest='config',
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metavar='CONFIG',
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help='yaml config file about input dataset')
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parser.add_argument(
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'-f',
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'--factor',
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dest='factor',
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metavar='FACTOR',
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default=0.1,
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type=float,
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help='the wave factor (default: %(default)s)')
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parser.add_argument(
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'--rate-img',
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dest='rate_img',
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metavar='RATE',
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default=60,
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type=int,
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help='the img rate (default: %(default)s)')
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parser.add_argument(
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'--rate-imu',
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dest='rate_imu',
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metavar='RATE',
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default=200,
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type=int,
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help='the imu rate (default: %(default)s)')
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return parser.parse_args()
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def _main():
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args = _parse_args()
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dataset_path = args.input
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if not dataset_path or not os.path.exists(dataset_path):
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sys.exit('Error: the dataset path not exists, %s' % dataset_path)
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dataset_path = os.path.normpath(dataset_path)
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outdir = args.outdir
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if not args.outdir:
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outdir = os.path.splitext(dataset_path)[0]
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else:
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outdir = os.path.abspath(outdir)
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args.outdir = outdir
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print('stamp analytics ...')
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print(' input: %s' % dataset_path)
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print(' outdir: %s' % outdir)
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def dataset_creator(path):
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print('open dataset ...')
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if args.config:
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import yaml
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config = yaml.load(file(args.config, 'r'))
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model = config['dataset']
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if model == 'rosbag':
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dataset = ROSBag(path, **config['rosbag'])
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elif model == 'mynteye':
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dataset = MYNTEYE(path)
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else:
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sys.exit('Error: dataset model not supported {}'.format(model))
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else:
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dataset = ROSBag(path,
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topic_img_left='/mynteye/left/image_raw',
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topic_imu='/mynteye/imu/data_raw')
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return dataset
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dataset = BinDataset(dataset_path, dataset_creator)
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dataset.stamp_analytics(args)
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print('stamp analytics done')
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if __name__ == '__main__':
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_main()
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