opencv(python)计算LK稀疏光流

光流法详解之一(LK光流）
cv.CalcOpticalFlowPyrLK(官方文档)

参数说明

Python: cv.CalcOpticalFlowPyrLK(prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags, guesses=None) -> (currFeatures, status, track_error)

Parameters:
prevImg – first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid().
nextImg – second input image or pyramid of the same size and the same type as prevImg.
prevPts – vector of 2D points for which the flow needs to be found; point coordinates must be single-precision floating-point numbers.
nextPts – output vector of 2D points (with single-precision floating-point coordinates) containing the calculated new positions of input features in the second image; when OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
status – output status vector (of unsigned chars); each element of the vector is set to 1 if the flow for the corresponding features has been found, otherwise, it is set to 0.
err – output vector of errors; each element of the vector is set to an error for the corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn’t found then the error is not defined (use the status parameter to find such cases).
winSize – size of the search window at each pyramid level.
maxLevel – 0-based maximal pyramid level number; if set to 0, pyramids are not used (single level), if set to 1, two levels are used, and so on; if pyramids are passed to input then algorithm will use as many levels as pyramids have but no more than maxLevel.
criteria – parameter, specifying the termination criteria of the iterative search algorithm (after the specified maximum number of iterations criteria.maxCount or when the search window moves by less than criteria.epsilon.

flags –
operation flags:
OPTFLOW_USE_INITIAL_FLOW uses initial estimations, stored in nextPts; if the flag is not set, then prevPts is copied to nextPts and is considered the initial estimate.
OPTFLOW_LK_GET_MIN_EIGENVALS use minimum eigen values as an error measure (see minEigThreshold description); if the flag is not set, then L1 distance between patches around the original and a moved point, divided by number of pixels in a window, is used as a error measure.
minEigThreshold – the algorithm calculates the minimum eigen value of a 2x2 normal matrix of optical flow equations (this matrix is called a spatial gradient matrix in [Bouguet00]), divided by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding feature is filtered out and its flow is not processed, so it allows to remove bad points and get a performance boost.

代码

参考：Opencv Python版学习笔记（五）光流跟踪 Lucas-Kanade（LK）算法

修改：

1. 支持视频(视频路径) / 图片列表(list[ndarray]) 两种输入方式
2. 修改了__init__()
3. 限制了跟踪的角点数self.max_points

import numpy as np
import cv2

lk_params = dict(winSize=(15, 15),
                 maxLevel=2,
                 criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))

feature_params = dict(maxCorners=500,
                      qualityLevel=0.3,
                      minDistance=7,
                      blockSize=