Setup Opencv and run tracking algorithm on local computer

In this section we install opencv on Ubuntu and implement multi-object tracking using built-in algorithm.

Setup

First we need to setup opencv environment in ubuntu.

If you are running ubuntu 18.04, then do the following

First we install pip:

sudo apt-get install python-pip

then install libopencv-dev:

sudo apt-get install libopencv-dev

then install opencv-python as follows:

sudo pip install opencv-python

If you have reached this point, you can verify your installation in terminal and check the version as follows:

$ python
Python 2.7.15+ (default, Nov 27 2018, 23:36:35)
[GCC 7.3.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
>>> cv2.__version__
>>> '4.1.0'

Multi-object tracking algorithm in opencv

Let’s make a new directory under techx19 for cv-related code(you may need to change the path to go into the directory you want):

cd ~/catkin_ws/src/techx2019/script
mkdir cv_local
cd cv_local
touch cv_multi_test.py
gedit cv_multi_test.py

Then copy the following code and save it.

from __future__ import print_function
import sys
import cv2
from random import randint

trackerTypes = ['BOOSTING', 'MIL', 'KCF','TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT']

def createTrackerByName(trackerType):
  # Create a tracker based on tracker name
  if trackerType == trackerTypes[0]:
    tracker = cv2.TrackerBoosting_create()
  elif trackerType == trackerTypes[1]:
    tracker = cv2.TrackerMIL_create()
  elif trackerType == trackerTypes[2]:
    tracker = cv2.TrackerKCF_create()
  elif trackerType == trackerTypes[3]:
    tracker = cv2.TrackerTLD_create()
  elif trackerType == trackerTypes[4]:
    tracker = cv2.TrackerMedianFlow_create()
  elif trackerType == trackerTypes[5]:
    tracker = cv2.TrackerGOTURN_create()
  elif trackerType == trackerTypes[6]:
    tracker = cv2.TrackerMOSSE_create()
  elif trackerType == trackerTypes[7]:
    tracker = cv2.TrackerCSRT_create()
  else:
    tracker = None
    print('Incorrect tracker name')
    print('Available trackers are:')
    for t in trackerTypes:
      print(t)

  return tracker

# Create a video capture object to read videos
cap = cv2.VideoCapture(0)

# Read first frame
success, frame = cap.read()
# quit if unable to read the video file
if not success:
  print('Failed to read video')
  sys.exit(1)

## Select boxes
bboxes = []
colors = []

# OpenCV's selectROI function doesn't work for selecting multiple objects in Python
# So we will call this function in a loop till we are done selecting all objects
while True:
  # draw bounding boxes over objects
  # selectROI's default behaviour is to draw box starting from the center
  # when fromCenter is set to false, you can draw box starting from top left corner
  bbox = cv2.selectROI('MultiTracker', frame)
  bboxes.append(bbox)
  colors.append((randint(0, 255), randint(0, 255), randint(0, 255)))
  print("Press q to quit selecting boxes and start tracking")
  print("Press any other key to select next object")
  k = cv2.waitKey(0) & 0xFF
  if (k == 113):  # q is pressed
    break

print('Selected bounding boxes {}'.format(bboxes))

# Specify the tracker type
trackerType = "CSRT"

# Create MultiTracker object
multiTracker = cv2.MultiTracker_create()

# Initialize MultiTracker
for bbox in bboxes:
  multiTracker.add(createTrackerByName(trackerType), frame, bbox)

# Process video and track objects
while cap.isOpened():
  success, frame = cap.read()
  if not success:
    break

  # get updated location of objects in subsequent frames
  success, boxes = multiTracker.update(frame)

  # draw tracked objects
  for i, newbox in enumerate(boxes):
    p1 = (int(newbox[0]), int(newbox[1]))
    p2 = (int(newbox[0] + newbox[2]), int(newbox[1] + newbox[3]))
    cv2.rectangle(frame, p1, p2, colors[i], 2, 1)

  # show frame
  cv2.imshow('MultiTracker', frame)


  # quit on ESC button
  if cv2.waitKey(1) & 0xFF == 27:  # Esc pressed
    break

Save the file, go back to terminal and excute the program:

python cv_multi_test.py

follow the instruction in terminal, and you should be able to track multiple objects.