opencv计算机视觉学习笔记七

其他 2018-08-08 05:16:40 阅读次数: 0

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)

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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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