Python OpenCV学习笔记之：图像Lucas-Kanad流光算法

# -*- coding: utf-8 -*-
"""
图像Lucas-Kanad流光算法
Lucas-Kanad算法请参考:http://www.cnblogs.com/hrlnw/p/3600291.html
"""

import numpy as np
import cv2
cap = cv2.VideoCapture(0)

# ShiTomasi角点检测参数
feature_params = dict( maxCorners = 100,
qualityLevel = 0.3,
minDistance = 7,
blockSize = 7 )
# lucas kanade算法参数
lk_params = dict( winSize  = (15,15),
maxLevel = 2,
criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
# 随机颜色
color = np.random.randint(0,255,(100,3))

# 读取第一张帧并进行角点检测
ret, old_frame = cap.read()
while True:
ret, old_frame = cap.read()
if ret == True:
break

old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)

mask = np.zeros_like(old_frame)

while True:
ret, frame = cap.read()
frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

# 计算流光
p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
if p1 is None or p0 is None:
cv2.imshow("frame",frame)
continue
# 选择条件好的点
good_new = p1[st == 1]
good_old = p0[st == 1]

# 绘制跟踪
for i, (new, old) in enumerate(zip(good_new, good_old)):
a, b = new.ravel()
c, d = old.ravel()
mask = cv2.line(mask, (a, b), (c, d), color[i].tolist(), 2)
frame = cv2.circle(frame, (a, b), 5, color[i].tolist(), -1)

img = cv2.add(frame, mask)

cv2.imshow('frame',mask)

k = cv2.waitKey(10) & 0xFF
if k == 27:
break
# 更新点和帧
old_gray = frame_gray.copy()
p0 = good_new.reshape(-1, 1, 2)
cap.release()
cv2.destroyAllWindows()