opencv计算机视觉学习笔记七
第八章 目标跟踪
1检测目标的移动
基本的运动检测,示例代码如下:
import cv2
import numpy as np
捕获摄像头图像
camera = cv2.VideoCapture(0)
#
es = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (10, 10))
kernel = np.ones((5, 5), np.uint8)
background = None
while (True):
ret, frame = camera.read()
# 将第一帧设为图像的背景
if background is None:
# 转换颜色空间
background = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# 高斯模糊
background = cv2.GaussianBlur(background, (21, 21), 0)
continue
# 转换颜色空间并作模糊处理
gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray_frame = cv2.GaussianBlur(gray_frame, (21, 21), 0)
# 取得差分图
diff = cv2.absdiff(background, gray_frame)
diff = cv2.threshold(diff, 25, 255, cv2.THRESH_BINARY)[1]
# 膨胀
diff = cv2.dilate(diff, es, iterations=2)
# 得到图像中目标的轮廓
image, cnts, hierarchy = cv2.findContours(diff.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
if cv2.contourArea(c) < 1500:
continue
# 计算矩形边框
(x, y, w, h) = cv2.boundingRect(c)
# 绘制矩形
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# 显示图像
cv2.imshow('contours', frame)
cv2.imshow('dif', diff)
if cv2.waitKey(int(1000 / 12)) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
camera.release()
运行结果如下:
2背景分割器 knn mog2和GMG
Opencv3有三种背景分割器
K-nearest(knn)
Mixture of Gaussians(MOG2)
Geometric multigid(GMC)
backgroundSubtractor用于分割前景和背景
示例代码如下:
import cv2
import numpy as np
cv2.ocl.setUseOpenCL(False)
cap = cv2.VideoCapture(0)
mog = cv2.createBackgroundSubtractorMOG2()
while (True):
ret, frame = cap.read()
fgmask = mog.apply(frame)
cv2.imshow(‘frame’, fgmask)
if cv2.waitKey(30) & 0xFF == ord(‘q’):
break
cap.release()
cv2.destroyAllWindows()
运行结果如下:
使用backgroundSubtractorKNN来实现运动检测
示例代码如下:
import cv2
cv2.ocl.setUseOpenCL(False)
bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
读取本地视频
camera = cv2.VideoCapture(‘../traffic.flv’)
while (True):
ret, frame = camera.read()
fgmask = bs.apply(frame.copy())
# 设置阈值
th = cv2.threshold(fgmask, # 源图像
244, # 阈值
255, # 最大值
cv2.THRESH_BINARY)[1] # 阈值类型
# 膨胀
dilated = cv2.dilate(th, # 源图像
cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), # 内核
iterations=2) # 腐蚀次数
# 查找图像中的目标轮廓
image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
if cv2.contourArea(c) > 1600:
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 255, 0), 2)
cv2.imshow('mog', fgmask) # 分割前景与背景
cv2.imshow('thresh', th) #
cv2.imshow('detection', frame) # 运动检测结果
if cv2.waitKey(30) & 0xFF == 27:
break
camera.release()
cv2.destroyAllWindows()
运行结果如下:
均值漂移meanShift
示例代码如下:
import cv2
import numpy as np
取得摄像头图像
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
设置跟踪窗体大小
r, h, c, w = 10, 200, 10, 200
track_window = (c, r, w, h)
提取roi
roi = frame[r:r + h, c:c + w]
转换颜色空间
hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
根据阈值构建掩码
mask = cv2.inRange(hsv_roi, np.array((100., 30., 32.)), np.array((180., 120., 255.)))
计算roi图形的彩色直方图
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
指定停止条件
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
while (True):
ret, frame = cap.read()
if ret == True:
# 更换颜色空间
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# histogram back projection calculation 直方图反向投影
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
# 均值漂移
ret, track_window = cv2.meanShift(dst, track_window, term_crit)
# 绘制矩形显示图像
x, y, w, h = track_window
img2 = cv2.rectangle(frame, (x, y), (x + w, y + h), 255, 2)
cv2.imshow('img2', img2)
# esc退出
if cv2.waitKey(60) & 0xFF == 27:
break
else:
break
cv2.destroyAllWindows()
cap.release()
运行结果如下:
彩色直方图
calHist函数
函数原型:
def calcHist(images, #源图像
channels, #通道列表
mask,#可选的掩码
histSize, #每个维度下直方图数组的大小
ranges,#每一个维度下直方图bin的上下界的数组
hist=None,#输出直方图是一个[]维稠密度的数组
accumulate=None)#累计标志
Camshift
示例代码如下:
!/usr/bin/env python
-- coding: utf-8 --
@Time : 2016/12/15 16:48
@Author : Retacn
@Site : camshift实现物体跟踪
@File : camshift.py
@Software: PyCharm
author = “retacn”
copyright = “property of mankind.”
license = “CN”
version = “0.0.1”
maintainer = “retacn”
email = “[email protected]”
status = “Development”
import cv2
import numpy as np
取得摄像头图像
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
设置跟踪窗体大小
r, h, c, w = 300, 200, 400, 300
track_window = (c, r, w, h)
提取roi
roi = frame[r:r + h, c:c + w]
转换颜色空间
hsv_roi = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
根据阈值构建掩码
mask = cv2.inRange(hsv_roi, np.array((100., 30., 32.)), np.array((180., 120., 255.)))
计算roi图形的彩色直方图
roi_hist = cv2.calcHist([hsv_roi], [0], mask, [180], [0, 180])
cv2.normalize(roi_hist, roi_hist, 0, 255, cv2.NORM_MINMAX)
指定停止条件
term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
while (True):
ret, frame = cap.read()
if ret == True:
# 更换颜色空间
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# histogram back projection calculation 直方图反向投影
dst = cv2.calcBackProject([hsv], [0], roi_hist, [0, 180], 1)
# 均值漂移
ret, track_window = cv2.CamShift(dst, track_window, term_crit)
# 绘制矩形显示图像
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
img2 = cv2.polylines(frame, [pts], True, 255, 2)
cv2.imshow('img2', img2)
# esc退出
if cv2.waitKey(60) & 0xFF == 27:
break
else:
break
cv2.destroyAllWindows()
cap.release()
运行结果如下:
4 卡尔曼滤波器
函数原型为：
def KalmanFilter(dynamParams=None,#状态的维度
measureParams=None, #测量的维度
controlParams=None,#控制的维度
type=None)#矩阵的类型
示例代码如下：
import cv2
import numpy as np
创建空帧
frame = np.zeros((800, 800, 3), np.uint8)
测量坐标
last_measurement = current_measurement = np.array((2, 1), np.float32)
鼠标运动预测
last_prediction = current_predication = np.zeros((2, 1), np.float32)
def mousemove(event, x, y, s, p):
# 设置全局变量
global frame, measurements, current_measurement, last_measurement, current_predication, last_prediction
last_prediction = current_predication
last_measurement = current_measurement
current_measurement = np.array([[np.float32(x)], [np.float32(y)]])
kalman.correct(current_measurement)
current_predication = kalman.predict()
# 实际移动起始点
lmx, lmy = last_measurement[0], last_measurement[1]
cmx, cmy = current_measurement[0], current_measurement[1]
# 预测线起止点
lpx, lpy = last_prediction[0], last_prediction[1]
cpx, cpy = current_predication[0], current_predication[1]
# 绘制连线
cv2.line(frame, (lmx, lmy), (cmx, cmy), (0, 100, 0)) # 绿色
cv2.line(frame, (lpx, lpy), (cpx, cpy), (0, 0, 200)) # 红色
创建窗体
cv2.namedWindow(‘mouse_detection’)
注册鼠标事件的回调函数
cv2.setMouseCallback(‘mouse_detection’, mousemove)
卡尔曼滤波器
kalman = cv2.KalmanFilter(4, 2)
kalman.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32)
kalman.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32)
kalman.processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]], np.float32) * 0.03
while (True):
cv2.imshow(‘mouse_detection’, frame)
if cv2.waitKey(30) & 0xFF == 27:
break
cv2.destroyAllWindows()
运行结果如下：
一个基于行人跟踪的例子
示例代码如下:
import cv2
import numpy as np
import os.path as path
import argparse
font = cv2.FONT_HERSHEY_SIMPLEX
parser = argparse.ArgumentParser()
parser.add_argument(“-a”, “–algorithm”,
help=”m (or nothing) for meanShift and c for camshift”)
args = vars(parser.parse_args())
计算矩阵中心(行人位置)
def center(points):
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
# print(np.array([np.float32(x), np.float32(y)], np.float32))
# [ 588. 257.5]
return np.array([np.float32(x), np.float32(y)], np.float32)
行人
class Pedestrian():
def init(self, id, frame, track_window):
self.id = int(id) # 行人id
x, y, w, h = track_window # 跟踪窗体
self.track_window = track_window
# 更换颜色空间
self.roi = cv2.cvtColor(frame[y:y + h, x:x + w], cv2.COLOR_BGR2HSV)
# 计算roi图形的彩色直方图
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
# 测量坐标
self.measurement = np.array((2, 1), np.float32)
# 鼠标运动预测
self.predication = np.zeros((2, 1), np.float32)
# 指定停止条件
self.term_crit = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 1)
self.center = None
self.update(frame)
def __del__(self):
print('Pedestrian %d destroyed' % self.id)
# 更新图像帧
def update(self, frame):
# 更换颜色空间
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# histogram back projection calculation 直方图反向投影
back_project = cv2.calcBackProject([hsv], [0], self.roi_hist, [0, 180], 1)
# camshift
if args.get('algorithm') == 'c':
ret, self.track_window = cv2.CamShift(back_project, self.track_window, self.term_crit)
# 绘制跟踪框
pts = cv2.boxPoints(ret)
pts = np.int0(pts)
self.center = center(pts)
cv2.polylines(frame, [pts], True, 255, 1)
# 均值漂移
if not args.get('algorithm') or args.get('algorithm') == 'm':
ret, self.track_window = cv2.meanShift(back_project, self.track_window, self.term_crit)
# 绘制跟踪框
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)
self.kalman.correct(self.center)
prediction = self.kalman.predict()
cv2.circle(frame, (int(prediction[0]), int(prediction[1])), 4, (0, 255, 0), -1)
# 计数器
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)
# 跟踪窗口坐标
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)
def main():
# 加载视频
# camera = cv2.VideoCapture(‘../movie.mpg’)
# camera = cv2.VideoCapture(‘../traffic.flv’)
camera = cv2.VideoCapture(‘../768x576.avi’)
# 初始化背景分割器
history = 20
bs = cv2.createBackgroundSubtractorKNN(detectShadows=True)
# 创建显示主窗口
cv2.namedWindow('surveillance')
pedestrians = {} # 行人字典
firstFrame = True
frames = 0
fourcc = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter('../output.avi', fourcc, 20.0, (640, 480))
while (True):
print('----------------------frmae %d----------------' % frames)
grabbed, frane = camera.read()
if (grabbed is False):
print("failed to grab frame")
break
ret, frame = camera.read()
fgmask = bs.apply(frame)
if frames < history:
frames += 1
continue
# 设置阈值
th = cv2.threshold(fgmask.copy(), 127, 255, cv2.THRESH_BINARY)[1]
# 腐蚀
th = cv2.erode(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3)), iterations=2)
# 膨胀
dilated = cv2.dilate(th, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (8, 3)), iterations=2)
# 查找轮廓
image, contours, hier = cv2.findContours(dilated, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
counter = 0
for c in contours:
if cv2.contourArea(c) > 500:
# 边界数组
(x, y, w, h) = cv2.boundingRect(c)
# 绘制矩形
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 1)
if firstFrame is True:
pedestrians[counter] = Pedestrian(counter, frame, (x, y, w, h))
counter += 1
# 更新帧内容
for i, p in pedestrians.items():
p.update(frame)
# false 只跟踪已有的行人
# firstFrame = True
firstFrame = False
frames += 1
# 显示
cv2.imshow('surveillance', frame)
out.write(frame)
if cv2.waitKey(120) & 0xFF == 27: # esc退出
break
out.release()
camera.release()
if name == “main“:
main()
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