Tracking Result
After a tracking analysis (or an analysis preview), FastTrack saves several files inside the Tracking_Result (or inside the Tracking_Result_VideoFileName for a video file) folder:
- tracking.db: the tracking result as a SQLite database
- tracking.txt: the tracking result
- annotation.txt: the annotation
- background.pgm: the background image
- cfg.toml: the parameters used for the tracking
The tracking result file is simply a text file with 23 columns separated by a '\t' character. This file can easily be loaded to subsequent analysis see this Python and this Julia.
- xHead, yHead, tHead: the position (x, y) and the absolute angle of the object's head.
- xTail, yTail, tTail: the position (x, y) and the absolute angle of the object's tail.
- xBody, yBody, tBody: the position (x, y) and the absolute angle of the object.
- curvature, areaBody, perimeterBody: curvature of the object, area and perimeter of the object (in pixels).
- headMajorAxisLength, headMinorAxisLength, headExcentricity: parameters of the head's ellipse (headMinorAxisLength and headExcentricity are semi-axis length).
- bodyMajorAxisLength, bodyMinorAxisLength, bodyExcentricity: parameters of the body's ellipse (bodyMinorAxisLength and bodyExcentricity are semi-axis length).
- tailMajorAxisLength, tailMinorAxisLength, tailExcentricity: parameters of the tail's ellipse (bodyMinorAxisLength and bodyExcentricity are semi-axis length).
- imageNumber: index of the frame.
- id: object unique identification number.
| xHead | yHead | tHead | xTail | yTail | tTail | xBody | yBody | tBody | |
|---|---|---|---|---|---|---|---|---|---|
| Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | Float64 | |
| 1 | 514.327 | 333.12 | 5.81619 | 499.96 | 327.727 | 6.10226 | 508.345 | 330.876 | 5.94395 |
| 2 | 463.603 | 327.051 | 0.301279 | 449.585 | 330.323 | 0.245547 | 458.058 | 328.346 | 0.238877 |
| 3 | 23.9978 | 287.715 | 3.70646 | 34.9722 | 278.836 | 3.99819 | 29.2056 | 283.505 | 3.84844 |
| 4 | 372.536 | 230.143 | 0.194641 | 354.226 | 231.604 | 6.08737 | 364.822 | 230.759 | 0.0515087 |
| 5 | 480.58 | 213.482 | 1.28236 | 478.125 | 228.52 | 1.53303 | 479.428 | 220.543 | 1.42567 |
| 6 | 171.682 | 143.55 | 6.09077 | 155.507 | 140.116 | 6.1146 | 164.913 | 142.113 | 6.08216 |
| 7 | 498.151 | 121.32 | 6.00177 | 483.712 | 119.285 | 0.0223247 | 492.683 | 120.55 | 6.15298 |
| 8 | 329.56 | 123.418 | 6.08726 | 312.526 | 119.042 | 5.9098 | 322.531 | 121.614 | 6.01722 |
| 9 | 465.256 | 115.045 | 4.44359 | 470.057 | 99.911 | 4.40559 | 467.106 | 109.205 | 4.40862 |
| 10 | 423.663 | 66.3789 | 0.0888056 | 409.105 | 67.2971 | 6.12053 | 417.615 | 66.7623 | 0.0292602 |
| 11 | 424.487 | 40.4232 | 5.48198 | 411.594 | 30.3912 | 5.88869 | 418.96 | 36.1192 | 5.64923 |
| 12 | 370.591 | 35.2147 | 5.99688 | 354.672 | 29.5633 | 5.89121 | 364.007 | 32.8767 | 5.94008 |
| 13 | 498.502 | 20.2527 | 5.66339 | 487.254 | 9.19499 | 5.39497 | 493.758 | 15.5781 | 5.5026 |
| 14 | 367.791 | 5.03034 | 6.05933 | 352.076 | 6.75603 | 0.653641 | 361.12 | 5.75904 | 0.152688 |
| 15 | 512.965 | 332.575 | 5.86617 | 499.435 | 327.759 | 6.052 | 507.626 | 330.673 | 5.95102 |
| 16 | 463.385 | 324.659 | 0.707 | 451.431 | 332.193 | 0.246265 | 458.959 | 327.443 | 0.542368 |
| 17 | 19.4579 | 293.022 | 4.28861 | 25.5579 | 281.206 | 4.18379 | 21.8962 | 288.302 | 4.23379 |
| 18 | 379.037 | 230.527 | 6.10571 | 361.728 | 229.616 | 0.199343 | 371.74 | 230.144 | 6.25939 |
| 19 | 478.884 | 206.712 | 1.27832 | 475.454 | 221.757 | 1.40929 | 477.197 | 214.108 | 1.35472 |
| 20 | 173.923 | 143.042 | 0.00732468 | 157.261 | 142.182 | 6.00453 | 167.066 | 142.689 | 6.20403 |
| 21 | 498.561 | 122.687 | 5.83253 | 486.357 | 118.196 | 6.13893 | 493.718 | 120.906 | 5.95151 |
| 22 | 328.812 | 124.134 | 6.05932 | 312.848 | 119.605 | 5.98617 | 322.331 | 122.294 | 6.00901 |
| 23 | 461.738 | 116.731 | 4.47649 | 466.371 | 101.736 | 4.40285 | 463.615 | 110.656 | 4.41641 |
| 24 | 428.631 | 69.2715 | 5.87139 | 415.665 | 64.6444 | 6.13862 | 423.218 | 67.3364 | 5.96558 |
| 25 | 425.821 | 44.9942 | 5.59983 | 414.84 | 33.2028 | 5.37159 | 421.248 | 40.0897 | 5.461 |
| 26 | 368.362 | 35.6219 | 5.97427 | 353.22 | 30.4625 | 5.88261 | 362.109 | 33.4891 | 5.94605 |
| 27 | 503.484 | 22.7293 | 5.76026 | 489.632 | 16.6315 | 5.92136 | 497.924 | 20.2857 | 5.86668 |
| 28 | 369.184 | 5.84074 | 6.15994 | 352.622 | 4.25328 | 6.24787 | 362.144 | 5.16766 | 6.19236 |
| 29 | 510.519 | 331.417 | 5.88883 | 495.784 | 327.366 | 6.12889 | 504.484 | 329.758 | 6.02088 |
| 30 | 464.242 | 323.533 | 0.290639 | 451.756 | 328.194 | 0.532686 | 459.432 | 325.326 | 0.37736 |
Positions are in pixels, in the frame of reference of the original image, zero is in the top left corner. Lengths and areas are in pixels. Angles are in radians in the intervale 0 to 2*pi.
0 -- > x
¦
v
y
Note: If several tracking analyses are performed on the same image sequence, the previous folder is not erased. It will be renamed as Tracking_result_DateOfTheNewAnalysis.
Data analysis
The tracking.txt file can be opened for subsequent analysis:
# Python
import pandas
data = pandas.read_csv("tracking.txt", sep='\t')
# Julia
using CSV
using DataFrames
data = CSV.read("tracking.txt", delim='\t', DataFrame)
# R
read.csv("tracking.txt", header=T sep="\t")
The tracking.db file can be opened for subsequent analysis:
# Python
import pandas
import sqlite3
con = sqlite3.connect("tracking.db")
data = pandas.read_sql_query("SELECT * from tracking", con)
# Julia
using SQLite, DataFrames
db = SQLite.DB("tracking.db")
df = DataFrame(SQLite.Source(db, "SELECT * FROM tracking;"))