opencv跟踪算法
前言
OpenCV有8种不同的跟踪器类型：BOOSTING，MIL，KCF，TLD，MEDIANFLOW，GOTURN，MOSSE，CSRT。
一、OPENCV TRACK
c++代码如下：
C++代码：
#include "pch.h"
#include python版本代码如下：
#python代码
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
if __name__ == '__main__':
print("Default tracking algoritm is CSRT \n"
"Available tracking algorithms are:\n")
for t in trackerTypes:
print(t)
trackerType = "CSRT"
# Set video to load
videoPath = "video/run.mp4"
# Create a video capture object to read videos
cap = cv2.VideoCapture(videoPath)
# 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(64, 255), randint(64, 255), randint(64, 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))
## Initialize MultiTracker
# There are two ways you can initialize multitracker
# 1. tracker = cv2.MultiTracker("CSRT")
# All the trackers added to this multitracker
# will use CSRT algorithm as default
# 2. tracker = cv2.MultiTracker()
# No default algorithm specified
# Initialize MultiTracker with tracking algo
# Specify tracker type
# 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