opencv-084-视频分析(移动对象的KLT光流跟踪算法)

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知识点

光流跟踪方法分为稠密光流跟踪与稀疏光流跟踪算法，KLT是稀疏光流跟踪算法，这个算法最早是由Bruce D. Lucas and Takeo Kanade两位作者提出来的，所以又被称为KLT。稀疏光流算法工作有三个假设前提条件：

  • 亮度恒定
  • 短距离移动
  • 空间一致性

API

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void cv::calcOpticalFlowPyrLK(
	InputArray 	prevImg, // 前一帧图像
	InputArray 	nextImg, // 后一帧图像
	InputArray 	prevPts, // 前一帧的稀疏光流点
	InputOutputArray 	nextPts, // 后一帧光流点
	OutputArray 	status, // 输出状态，1 表示正常该点保留，否则丢弃
	OutputArray 	err, // 表示错误
	Size winSize = Size(21, 21), // 光流法对象窗口大小
	int maxLevel = 3, // 金字塔层数，0表示只检测当前图像，不构建金字塔图像
	TermCriteria 	criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), // 窗口搜索时候停止条件
	int flags = 0, // 操作标志
	double 	minEigThreshold = 1e-4 // 最小特征值响应，低于最小值不做处理
)

代码（c++,python）

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#include <iostream>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace cv;

vector<Point2f> featurePoints;
RNG rng(12345);

/*
 * 视频分析(移动对象的KLT光流跟踪算法)
 */
int main() {
    VideoCapture capture("../images/vtest.avi");
    if (!capture.isOpened()) {
        cout << "could not open video..." << endl;
        return -1;
    }

    // 角点检测参数
    double qualityLevel = 0.01;
    int minDistance = 10;
    int maxCorners = 100;

    // KLT光流跟踪参数
    vector<Point2f> pts[2];
    vector<uchar> status;
    vector<float> err;

    // 读取第一帧及其角点
    Mat old_frame, old_gray;
    capture.read(old_frame);
    cvtColor(old_frame, old_gray, COLOR_BGR2GRAY);
    goodFeaturesToTrack(old_gray, featurePoints, maxCorners,
                        qualityLevel, minDistance, Mat());

    pts[0].insert(pts[0].end(), featurePoints.begin(), featurePoints.end());
    int width = capture.get(CAP_PROP_FRAME_WIDTH);
    int height = capture.get(CAP_PROP_FRAME_HEIGHT);
    Rect roi(0, 0, width, height);

    Mat gray, frame;

    while (true) {
        bool ret = capture.read(frame);
        if (!ret) break;
        imshow("frame", frame);
        roi.x = 0;
        cvtColor(frame, gray, COLOR_BGR2GRAY);

        // 计算光流
        calcOpticalFlowPyrLK(old_gray, gray, pts[0], pts[1],
                             status, err, Size(31, 31));
        size_t i, k;
        for (int i = k = 0; i < pts[1].size(); ++i) {
            // 根据状态选择
            if (status[i]){
                pts[0][k] = pts[0][i];
                pts[1][k++] = pts[1][i];
                int b = rng.uniform(0, 256);
                int g = rng.uniform(0, 256);
                int r = rng.uniform(0, 256);
                Scalar color(b, g, r);
                // 绘制跟踪线
                circle(frame, pts[1][i], 4, color, -1);
                line(frame, pts[0][i], pts[1][i], color, 2);
            }
        }

        // resize 有用特征点
        pts[0].resize(k);
        pts[1].resize(k);
        imshow("result", frame);
        roi.x = width;
        char c = waitKey(50);
        if (c == 27) break;

        // 更新old
        std::swap(pts[1], pts[0]);
        cv::swap(old_gray, gray);
    }

    return 0;
}
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import numpy as np
import cv2 as cv
cap = cv.VideoCapture('D:/images/video/vtest.avi')

# 角点检测参数
feature_params = dict(maxCorners=100, qualityLevel=0.01, minDistance=10, blockSize=3)

# KLT光流参数
lk_params = dict(winSize=(31, 31), maxLevel=3, criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 30, 0.01))

# 随机颜色
color = np.random.randint(0,255,(100,3))

# 读取第一帧
ret, old_frame = cap.read()
old_gray = cv.cvtColor(old_frame, cv.COLOR_BGR2GRAY)
p0 = cv.goodFeaturesToTrack(old_gray, mask=None, **feature_params)

# 光流跟踪
while True:
    ret, frame = cap.read()
    if ret is False:
        break
    frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
    # 计算光流
    p1, st, err = cv.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
    # 根据状态选择
    good_new = p1[st == 1]
    good_old = p0[st == 1]

    # 绘制跟踪线
    for i, (new, old) in enumerate(zip(good_new,good_old)):
        a,b = new.ravel()
        c,d = old.ravel()
        frame = cv.line(frame, (a,b),(c,d), color[i].tolist(), 2)
        frame = cv.circle(frame,(a,b),5,color[i].tolist(),-1)
    cv.imshow('frame',frame)
    k = cv.waitKey(30) & 0xff
    if k == 27:
        break

    # 更新
    old_gray = frame_gray.copy()
    p0 = good_new.reshape(-1, 1, 2)

cv.destroyAllWindows()
cap.release()

结果

代码地址

github