Introduction

PyFastTrack is a Python library that provides an easy-to-use solution to integrate the FastTrack software tracking technology in Python projects. The tracking configuration and results will be entirely compatible with FastTrack.

Installation

pip install pyfasttrack

Examples

 1from pyfasttrack.ft_detector import FtDetector
 2from pyfasttrack.tracker import Tracker
 3from pyfasttrack.data import Result
 4import data as dat
 5import cv2
 6import os
 7
 8example_folder_path = os.path.dirname(os.path.abspath(__file__))
 9
10# Load configuration
11config = dat.Configuration()
12params = config.read_toml(
13    "{}/test/data/images/Groundtruth/Tracking_Result/cfg.toml".format(example_folder_path))
14
15# Data saver
16saver = Result("{}/test/data/images/".format(example_folder_path))
17
18# Set up detector
19detector = FtDetector(params)
20detector.set_background(cv2.imread(
21    "{}/test/data/images/Groundtruth/Tracking_Result/background.pgm".format(example_folder_path), cv2.IMREAD_GRAYSCALE))
22
23# Set up tracker
24tracker = Tracker(params)
25tracker.set_params(params)
26tracker.set_detector(detector)
27
28camera = cv2.VideoCapture(
29    "{}/test/data/images/frame_%06d.pgm".format(example_folder_path))
30dat = tracker.initialize(cv2.cvtColor(camera.read()[1], cv2.COLOR_BGR2GRAY))
31saver.add_data(dat)
32
33ret = True
34while (ret):
35    ret, frame = camera.read()
36    if ret:
37        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
38        dat = tracker.process(frame)
39        saver.add_data(dat)
40
41camera.release()

 1from pyfasttrack.yolo_detector import YoloDetector
 2from pyfasttrack.tracker import Tracker
 3from pyfasttrack.data import Result
 4import cv2
 5import os
 6
 7example_folder_path = os.path.dirname(os.path.abspath(__file__))
 8
 9# Data saver
10saver = Result("{}/test/data/images/".format(example_folder_path))
11
12# Set up detector
13# See https://github.com/ultralytics/ultralytics/blob/44c7c3514d87a5e05cfb14dba5a3eeb6eb860e70/ultralytics/datasets/coco.yaml for equivalence between coco labels and indexes
14yolo_params = {"model": "yolov8l-seg.pt", "classes": [2], "conf": 0.5}
15detector = YoloDetector(yolo_params)
16
17# Set up tracker
18params = {"spot": 2, "normDist": 1, "normAngle": 2,
19          "normArea": 1, "normPerim": 1, "maxDist": 500, "maxTime": 100}
20tracker = Tracker()
21tracker.set_params(params)
22tracker.set_detector(detector)
23
24camera = cv2.VideoCapture(
25    "{}/test/data/images/Nascar.mp4".format(example_folder_path))
26dat = tracker.initialize(camera.read()[1])
27saver.add_data(dat)
28
29ret = True
30while (ret):
31    ret, frame = camera.read()
32    if ret:
33        dat = tracker.process(frame)
34        saver.add_data(dat)
35
36camera.release()