opencv_090.py

import cv2 as cv
import numpy as np


cap = cv.VideoCapture('test.mp4')

# 读取第一帧
ret,frame = cap.read()
cv.namedWindow("Demo", cv.WINDOW_AUTOSIZE)

# 选择ROI区域
x, y, w, h = cv.selectROI("Demo", frame, True, False)
track_window = (x, y, w, h)

# 获取ROI直方图
roi = frame[y:y+h, x:x+w]
hsv_roi = cv.cvtColor(roi, cv.COLOR_BGR2HSV)
mask = cv.inRange(hsv_roi, (26, 43, 46), (34, 255, 255))
roi_hist = cv.calcHist([hsv_roi],[0],mask,[180],[0,180])
cv.normalize(roi_hist,roi_hist,0,255,cv.NORM_MINMAX)

# 设置搜索跟踪分析
term_crit = ( cv.TERM_CRITERIA_EPS | cv.TERM_CRITERIA_COUNT, 10, 1)
tracking_path = []
while True:
    ret, frame = cap.read()
    if ret is False:
        break;
    hsv = cv.cvtColor(frame, cv.COLOR_BGR2HSV)
    dst = cv.calcBackProject([hsv],[0],roi_hist,[0,180],1)

    # ，搜索更新roi区域， 保持运行轨迹
    ret, track_box = cv.CamShift(dst, track_window, term_crit)
    track_window = track_box

    # 椭圆中心
    pt = np.int32(ret[0])
    if pt[0] > 0 and pt[1] > 0:
        tracking_path.append(pt)

    # 绘制跟踪对象位置窗口与对象运行轨迹
    #cv.ellipse(frame, ret, (0, 0, 255), 3, 8)
    for i in range(1, len(tracking_path)):
        cv.line(frame, (tracking_path[i - 1][0], tracking_path[i - 1][1]),
                (tracking_path[i][0], tracking_path[i][1]), (0, 255, 0), 2, 6, 0)

    cv.imshow('Demo',frame)
    k = cv.waitKey(50) & 0xff
    if k == 27:
        break
    else:
        cv.imwrite(chr(k)+".jpg",frame)

cv.destroyAllWindows()
cap.release()