运动估计

import numpy as np
import cv2 as cv

videoFileName = '运动估计2.mp4'
cap = cv.VideoCapture(videoFileName)
fgbg = cv.createBackgroundSubtractorMOG2()  # 创建一个背景提取类的对象
thresh = 200

while True:
    ret, frame = cap.read()  # 读取视频中每一帧
    if not ret:  # 没读到当前帧，结束
        break

    # Foreground Mask（前景掩模）
    fgmask = fgbg.apply(frame)  # 实时更新当前背景图像 计算出前景对应的掩模图像
    _, fgmask = cv.threshold(fgmask, 30, 0xff, cv.THRESH_BINARY)  # 使用threshold初步对前景掩模图像过滤
    bgImage = fgbg.getBackgroundImage()  # 得到的实时更新的背景图像
    _, cnts, _ = cv.findContours(fgmask.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)  # 寻找轮廓

    count = 0
    for c in cnts:
        area = cv.contourArea(c)
        if area < thresh:  # 区域面积小于指定阈值则认为是噪声
            continue
        count += 1

        x, y, w, h = cv.boundingRect(c)
        cv.rectangle(frame, (x, y), (x + w, y + h), (0, 0xff, 0), 2)
    print('共监测到', count, '个目标', '
')
    cv.imshow('frame', frame)
    cv.imshow('Background', bgImage)

    key = cv.waitKey(30)  # 每一帧间隔30ms
    if key == 27:  # 按下ESC键退出
        break
cap.release()
cv.destroyAllWindows()

feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7)
lk_params = dict(winSize=(15, 15),
                 maxLevel=2,
                 criteria=(cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 0.03))
cap = cv.VideoCapture(videoFileName)

ret, prev = cap.read()  # 得到第一帧
prevGray = cv.cvtColor(prev, cv.COLOR_BGR2GRAY)  # OpvnCV 只支持灰度图像
p0 = cv.goodFeaturesToTrack(prevGray, mask=None, **feature_params)  # 得到好的特征点

while True:
    ret, frame = cap.read()
    if not ret:
        break

    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)  # 转化为灰度
    # prevGray 上一帧灰度图像 gray 当前帧灰度图像
    # p1 检测到所有光流点的列表 st 每一个特征点计算的执行度
    p1, st, err = cv.calcOpticalFlowPyrLK(prevGray, gray, p0, None, **lk_params)

    # 选取好的跟踪点
    goodPoints = p1[st == 1]
    goodPrevPoints = p0[st == 1]
    # 在结果图像中迭加画出特征点和计算出来的光流向量
    res = frame.copy()
    drawColor = (0, 0, 255)
    for i, (cur, prev) in enumerate(zip(goodPoints, goodPrevPoints)):
        x0, y0 = cur.ravel()
        x1, y1 = prev.ravel()
        cv.line(res, (x0, y0), (x1, y1), drawColor)
        cv.circle(res, (x0, y0), 3, drawColor)
    # 更新上一帧
    prevGray = gray.copy()
    p0 = goodPoints.reshape(-1, 1, 2)

    cv.imshow('res', res)
    key = cv.waitKey(30)
    if key == 27:
        break