OpenCV3-Python基于Kalman和CAMShift算法应用
admin 于 2018年09月17日 发表在 计算机视觉
上一篇《OpenCV3-Python卡尔曼滤波器介绍》介绍了OpenCV中Kalman滤波器，本篇来介绍CAMShift算法结合Kalman滤波器来实现行人跟踪的方法。
应用程序遵循以下流程：
<1> 检测第一帧；
<2> 检测后面输入的帧，从场景的开始通过背景分割器来识别场景中的行人；
<3> 为每个行人建立ROI，并利用Kalman/CAMShift来跟踪行人ID；
<4> 检查下一帧是否有进入场景的行人；
1. 类Pedestrian介绍
class Pedestrian():
"""
Pedestrian class
each pedestrian is composed of a ROI, an ID and a Kalman filter
so we create a Pedestrian class to hold the object state
"""
def __init__(self, id, frame, track_window):
"""init the pedestrian object with track window coordinates"""
# set up the roi
self.id = int(id)
x,y,w,h = track_window
self.track_window = track_window
self.roi = cv2.cvtColor(frame[y:y+h, x:x+w], cv2.COLOR_BGR2HSV)
#计算ROI的直方图
roi_hist = cv2.calcHist([self.roi], [0], None, [16], [0, 180])
self.roi_hist = cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
#设置卡尔曼滤波器
self.kalman = cv2.KalmanFilter(4,2)
self.kalman.measurementMatrix = np.array([[1,0,0,0],[0,1,0,0]],np.float32)
self.kalman.transitionMatrix = np.array([[1,0,1,0],[0,1,0,1],[0,0,1,0],[0,0,0,1]],np.float32)
self.kalman.processNoiseCov = np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0],[0,0,0,1]],np.float32) * 0.03
self.measurement = np.array((2,1), np.float32)
self.prediction = np.zeros((2,1), np.float32)
self.term_crit = ( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1 )
self.center = None
#传递当前帧
self.update(frame)
def __del__(self):
print ("Pedestrian %d destroyed" % self.id)
def update(self, frame):
# print "updating %d " % self.id
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
back_project = cv2.calcBackProject([hsv],[0], self.roi_hist,[0,180],1)
#根据传递的参数，选择采用CAMShift/均值漂移
if args.get("algorithm") == "c":
ret, self.track_window = cv2.CamShift(back_project, self.track_window, self.term_crit)
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
self.center = center(pts)
cv2.polylines(frame,[pts],True, 255,1)
if not args.get("algorithm") or args.get("algorithm") == "m":
ret, self.track_window = cv2.meanShift(back_project, self.track_window, self.term_crit)
x,y,w,h = self.track_window
self.center = center([[x,y],[x+w, y],[x,y+h],[x+w, y+h]])
cv2.rectangle(frame, (x,y), (x+w, y+h), (255, 255, 0), 2)
#Updates the predicted state from the measurement.
self.kalman.correct(self.center)
#Computes a predicted state.
prediction = self.kalman.predict()
cv2.circle(frame, (int(prediction[0]), int(prediction[1])), 4, (255, 0, 0), -1)
# fake shadow
cv2.putText(frame, "ID: %d -> %s" % (self.id, self.center), (11, (self.id + 1) * 25 + 1),
font, 0.6,
(0, 0, 0),
1,
cv2.LINE_AA)
# actual info
cv2.putText(frame, "ID: %d -> %s" % (self.id, self.center), (10, (self.id + 1) * 25),
font, 0.6,
(0, 255, 0),
1,
cv2.LINE_AA)2. 主函数内容
#! /usr/bin/python
"""Surveillance Demo: Tracking Pedestrians in Camera Feed
The application opens a video (could be a camera or a video file)
and tracks pedestrians in the video.
"""
__author__ = "joe minichino"
__copyright__ = "property of mankind."
__license__ = "MIT"
__version__ = "0.0.1"
__maintainer__ = "Joe Minichino"
__email__ = "joe.minichino@gmail.com"
__status__ = "Development"
import cv2
import numpy as np
import os.path as path
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("-a", "--algorithm",
help = "m (or nothing) for meanShift and c for camshift")
args = vars(parser.parse_args())
def center(points):
"""calculates centroid of a given matrix"""
x = (points[0][0] + points[1][0] + points[2][0] + points[3][0]) / 4
y = (points[0][1] + points[1][1] + points[2][1] + points[3][1]) / 4
return np.array([np.float32(x), np.float32(y)], np.float32)
font = cv2.FONT_HERSHEY_SIMPLEX
##################################################################
###注意：此处需要添加类Pedestrian的内容才可运行此例程，可参考1中内容###
##################################################################
def main():
camera = cv2.VideoCapture("./768x576.avi")
#设置20帧作为影响背景模型的帧
history = 20
# KNN background subtractor
bs = cv2.createBackgroundSubtractorKNN()
# MOG subtractor
# bs = cv2.bgsegm.createBackgroundSubtractorMOG(history = history)
# bs.setHistory(history)
# GMG
# bs = cv2.bgsegm.createBackgroundSubtractorGMG(initializationFrames = history)
#创建主显示窗口
cv2.namedWindow("surveillance")
pedestrians = {}
firstFrame = True
frames = 0
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('output.avi',fourcc, 20.0, (640,480))
while True:
print (" -------------------- FRAME %d --------------------" % frames)
grabbed, frame = camera.read()
if (grabbed is False):
print ("failed to grab frame.")
break
fgmask = bs.apply(frame)
# this is just to let the background subtractor build a bit of history
if frames < history:
frames += 1
continue
#通过对前景掩模采用膨胀和腐蚀的方法来识别斑点及其周围边框
th = cv2.threshold(fgmask.copy(), 127, 255, cv2.THRESH_BINARY)[1]
th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3)), iterations = 2)
dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8,3)), iterations = 2)
image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#对第一帧中的行人轮廓进行实例化
counter = 0
for c in contours:
if cv2.contourArea(c) > 500:
(x,y,w,h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x,y), (x+w, y+h), (0, 255, 0), 1)
# only create pedestrians in the first frame, then just follow the ones you have
if firstFrame is True:
pedestrians[counter] = Pedestrian(counter, frame, (x,y,w,h))
counter += 1
#对于检测到的行人，都执行update()函数来传递当前帧
for i, p in pedestrians.items():
p.update(frame)
#只跟踪第一帧中已有的人
firstFrame = False
frames += 1
cv2.imshow("surveillance", frame)
out.write(frame)
if cv2.waitKey(110) & 0xFF == ord('q'):
break
out.release()
camera.release()
cv2.destroyAllWindows()
if __name__ == "__main__":
main()3. 实验结果