sideseeing-tools 0.3.8

A set of tools to load, preprocess and analyze data collected through the MultiSensor Data Collection App

SideSeeing Tools

SideSeeing Tools is a collection of scripts designed to load, preprocess, and analyze data gathered through the MultiSensor Data Collection App.

Installation

pip install sideseeing-tools

Usage

Create a dataset

from sideseeing_tools import sideseeing

ds = sideseeing.SideSeeingDS(root_dir='/home/user/my-project')

# Available iterators
#   .instances  // Tip: dictionary of instances (key=name, value=SideSeeingInstance)
#   .iterator   // Tip: for i in ds.iterator: i.name

# Available attributes and methods
#   .metadata() // Tip: generates and prints the dataset metadata
#   .size       // Tip: shows the number of instances  
#   .sensors    // Tip: lists the names of the available sensors

Get a random sample from the dataset

my_sample = ds.instance

Get accelerometer data from the sample

my_accel_data = ds.instance.sensors3['Accelerometer']
my_accel_data

	Datetime UTC	x	y	z	Time (s)
0	2024-03-21 19:33:01.550000	9.34247	-0.270545	3.10767	0
1	2024-03-21 19:33:01.561000	9.51725	-0.347159	3.00233	0.011
2	2024-03-21 19:33:01.571000	9.46458	-0.407014	2.81079	0.021
3	2024-03-21 19:33:01.581000	9.35205	-0.395043	2.79164	0.031
4	2024-03-21 19:33:01.590000	9.36402	-0.263362	2.77488	0.04

Extract a snippet from a sample (video and sensor data)

my_sample.extract_snippet(
    start_time=2,                        # Start time of the snippet (in seconds)
    end_time=17,                         # End time of the snippet (in seconds)
    output_dir='/home/user/snippet_2_17' # Directory to save the extracted snippet
)

Running the command extract_snippet will generate one file for the video (with audio) and one file for each sensor present in the instance. See an illustrative example in the following file tree.

home/
├─ user/
│  ├─ snippet_2_17/
│  │  ├─ accelerometer_2_12.csv
│  │  ├─ barometer_2_12.csv
│  │  ├─ gravity_2_12.csv
│  │  ├─ gyroscope_2_12.csv
│  │  ├─ gyroscope uncalibrated_2_12.csv
│  │  ├─ light uncalibrated_2_12.csv
│  │  ├─ linear accelerometer_2_12.csv
│  │  ├─ magnetometer_2_12.csv
│  │  ├─ magnetometer uncalibrated_2_12.csv
│  │  ├─ video_2_12.mp4

Extract a snippet for a video

from sideseeing_tools import media

# Extract a 15-second snippet from the video, beginning at the 3-second mark and ending at the 18-second mark
media.extract_video_snippet(
    source_path=my_sample.video,    # Path to the input mp4 file
    start_second=3,                 # Start time of the snippet (in seconds)
    end_second=18,                  # End time of the snippet (in seconds)
    output_path='my_snippet.mp4'    # Path to save the extracted snippet
)

Extract a snippet for sensor data

from sideseeing_tools import utils

# Extract a 15-second snippet from the sensor data, beginning at the 3-second mark and ending at the 18-second mark
utils.extract_sensor_snippet(
    data=my_sample.sensors3['My sensor name'],  # DataFrame containing sensor data
    start_time=3,                               # Start time of the snippet (in seconds)
    end_time=18,                                # End time of the snippet (in seconds)
    output_path='my_sensor_snippet.csv'         # Path to save the extracted sensor snippet
)

Show available sensors in the dataset

ds.sensors

# Output:
# {
#     'Accelerometer',
#     'Barometer',
#     'Gravity',
#     'Gyroscope',
#     'Gyroscope Uncalibrated',
#     'Light Uncalibrated',
#     'Linear Accelerometer',
#     'Magnetometer',
#     'Magnetometer Uncalibrated'
# }

Iterate over the samples

for i in ds.iterator:
    print(i.name, i.video)

Create a plotter

from sideseeing_tools import plot

plotter = plot.SideSeeingPlotter(ds, taxonomy='/home/user/my-project/taxonomy.csv')

# Available methods:
#   .plot_dataset_cities()
#   .plot_dataset_map()
#   .plot_dataset_sensors3()
#   .plot_dataset_tags_matrix()
#   .plot_dataset_tags()
#   .plot_instance_audio()
#   .plot_instance_map()
#   .plot_instance_sensors3_and_audio()
#   .plot_instance_video_frames_at_times()
#   .plot_instance_video_frames()
#   .generate_video_sensor3()

Additional tips

We suggest implementing the following folder structure: create a directory named data to contain all recordings. By doing so, when instantiating the SideSeeingDataset, a metadata.csv file will be generated in the root directory. Here is the command to instantiate a dataset:

ds = sideseeing.SideSeeingDS('/home/user/my-project', subdir='data', name='MyDataset')

And here is the suggested folder structure:

my-project/
├─ data/
│  ├─ place01/
│  │  ├─ route01/
│  │  │  ├─ consumption.csv
│  │  │  ├─ gps.csv
│  │  │  ├─ metadata.json
│  │  │  ├─ sensors.one.csv
│  │  │  ├─ sensors.three.csv
│  │  │  ├─ sensors.three.uncalibrated.csv
│  │  │  ├─ video.gif
│  │  │  ├─ video.mp4
│  │  │  ├─ video.wav
│  │  ├─ route02/
│  ├─ place02/
│  ├─ place03/
├─ metadata.csv
├─ taxonomy.csv

Sensor data specification before SideSeeing conversion

The following data outlines the specifications of sensor content before SideSeeing conversion, i.e., when accessing them directly through the files generated by the MultSensor Data Collection tool.

File consumption.csv

datetime_utc	battery_microamperes
2024-03-21T19:38:04.961Z	-1431
2024-03-21T19:38:05.961Z	-1011
2024-03-21T19:38:06.961Z	-2216

File gps.csv

datetime_utc	gps_interval	accuracy	latitude	longitude
2024-03-21T19:38:10.309Z	15	16.0	-23.5645676	-46.7395994
2024-03-21T19:38:38.033Z	15	57.639	-23.5645617	-46.739602
2024-03-21T19:38:54.120Z	15	26.611	-23.5645528	-46.7396658

File sensors.one.csv

timestamp_nano	datetime_utc	name	axis_x	accuracy
0	2024-03-13T13:40:27.243Z	Palm Proximity Sensor	5.0	3
712657771915658	2024-03-21T19:38:05.015Z	TCS3407 Uncalibrated lux Sensor	1810.0	3
712657931915658	2024-03-21T19:38:05.174Z	TCS3407 Uncalibrated lux Sensor	1812.0	3

File sensors.three.csv

timestamp_nano	datetime_utc	name	axis_x	axis_y	axis_z	accuracy
712657652031560	2024-03-21T19:38:04.895Z	LSM6DSO Acceleration Sensor	9.603442	-0.10295067	3.9959226	3
712657673895658	2024-03-21T19:38:04.916Z	LSM6DSO Acceleration Sensor	9.823709	-0.38067806	3.9097314	3
712657652031560	2024-03-21T19:38:04.895Z	LSM6DSO Gyroscope Sensor	0.113544576	0.42852196	0.083306745	3

File sensors.three.uncalibrated.csv

timestamp_nano	datetime_utc	name	axis_x	axis_y	axis_z	delta_x	delta_y	delta_z	accuracy
712657851915658	2024-03-21T19:38:05.094Z	Uncalibrated Magnetic Sensor	268.56	-9.54	-230.45999	255.48	-2.28	-227.94	3
712657852615658	2024-03-21T19:38:05.096Z	Gyroscope sensor UnCalibrated	0.044593163	-0.13439035	0.07086037	-0.003009122	-0.016193425	-0.0026664268	3
712657862615658	2024-03-21T19:38:05.105Z	Gyroscope sensor UnCalibrated	0.042760566	-0.05009095	0.100792766	-0.003009122	-0.016193425	-0.0026664268	3
712657874678965	2024-03-21T19:38:05.118Z	Uncalibrated Magnetic Sensor	268.62	-8.639999	-231.48	255.48	-2.28	-227.94	3
712657872615658	2024-03-21T19:38:05.116Z	Gyroscope sensor UnCalibrated	0.064751714	-0.007330383	0.118507855	-0.003009122	-0.016193425	-0.0026664268	3

List of SideSeeingInstance attributes/methods

Attribute or method	Description
geolocation_points	List of latitude and longitude coordinates representing geographical points.
geolocation_center	Latitude and longitude coordinates representing the geographic center of a specific area.
audio	Path to the audio file associated with the collected data.
gif	Path to the GIF file associated with the collected data.
video	Path to the video file associated with the collected data.
sensors1	Dictionary containing data from one-axis sensors.
sensors3	Dictionary containing data from three-axis sensors.
sensors6	Dictionary containing data from six-axis sensors, including uncalibrated data.
label	List of categories and tags representing the taxonomy of sidewalks.
video_start_time	Start time of the video associated with the collected data.
video_stop_time	Stop time of the video associated with the collected data.
extract_snippet	Extracting a snippet from the sample (video and sensor data).

Author

Rafael J P Damaceno

About us

The SideSeeing Project aims to develop methods based on Computer Vision and Machine Learning for Urban Informatics applications. Our goal is to devise strategies for obtaining and analyzing data related to urban accessibility. Take a look at our website.