1241 lines
70 KiB
C#
1241 lines
70 KiB
C#
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using OpenCVForUnity.CoreModule;
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using OpenCVForUnity.UtilsModule;
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using System;
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using System.Collections.Generic;
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using System.Runtime.InteropServices;
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namespace OpenCVForUnity.VideoModule
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{
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// C++: class Video
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public class Video
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{
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private const int CV_LKFLOW_INITIAL_GUESSES = 4;
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private const int CV_LKFLOW_GET_MIN_EIGENVALS = 8;
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// C++: enum <unnamed>
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public const int OPTFLOW_USE_INITIAL_FLOW = 4;
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public const int OPTFLOW_LK_GET_MIN_EIGENVALS = 8;
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public const int OPTFLOW_FARNEBACK_GAUSSIAN = 256;
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public const int MOTION_TRANSLATION = 0;
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public const int MOTION_EUCLIDEAN = 1;
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public const int MOTION_AFFINE = 2;
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public const int MOTION_HOMOGRAPHY = 3;
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// C++: enum cv.detail.TrackerSamplerCSC.MODE
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public const int TrackerSamplerCSC_MODE_INIT_POS = 1;
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public const int TrackerSamplerCSC_MODE_INIT_NEG = 2;
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public const int TrackerSamplerCSC_MODE_TRACK_POS = 3;
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public const int TrackerSamplerCSC_MODE_TRACK_NEG = 4;
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public const int TrackerSamplerCSC_MODE_DETECT = 5;
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//
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// C++: Ptr_BackgroundSubtractorMOG2 cv::createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true)
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//
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/**
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* Creates MOG2 Background Subtractor
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*
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* param history Length of the history.
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* param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
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* to decide whether a pixel is well described by the background model. This parameter does not
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* affect the background update.
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* param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history, double varThreshold, bool detectShadows)
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{
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return BackgroundSubtractorMOG2.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorMOG2_10(history, varThreshold, detectShadows)));
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}
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/**
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* Creates MOG2 Background Subtractor
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*
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* param history Length of the history.
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* param varThreshold Threshold on the squared Mahalanobis distance between the pixel and the model
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* to decide whether a pixel is well described by the background model. This parameter does not
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* affect the background update.
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history, double varThreshold)
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{
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return BackgroundSubtractorMOG2.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorMOG2_11(history, varThreshold)));
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}
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/**
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* Creates MOG2 Background Subtractor
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*
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* param history Length of the history.
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* to decide whether a pixel is well described by the background model. This parameter does not
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* affect the background update.
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2(int history)
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{
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return BackgroundSubtractorMOG2.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorMOG2_12(history)));
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}
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/**
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* Creates MOG2 Background Subtractor
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*
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* to decide whether a pixel is well described by the background model. This parameter does not
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* affect the background update.
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorMOG2 createBackgroundSubtractorMOG2()
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{
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return BackgroundSubtractorMOG2.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorMOG2_13()));
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}
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//
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// C++: Ptr_BackgroundSubtractorKNN cv::createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true)
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//
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/**
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* Creates KNN Background Subtractor
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*
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* param history Length of the history.
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* param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
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* whether a pixel is close to that sample. This parameter does not affect the background update.
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* param detectShadows If true, the algorithm will detect shadows and mark them. It decreases the
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history, double dist2Threshold, bool detectShadows)
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{
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return BackgroundSubtractorKNN.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorKNN_10(history, dist2Threshold, detectShadows)));
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}
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/**
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* Creates KNN Background Subtractor
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*
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* param history Length of the history.
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* param dist2Threshold Threshold on the squared distance between the pixel and the sample to decide
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* whether a pixel is close to that sample. This parameter does not affect the background update.
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history, double dist2Threshold)
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{
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return BackgroundSubtractorKNN.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorKNN_11(history, dist2Threshold)));
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}
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/**
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* Creates KNN Background Subtractor
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*
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* param history Length of the history.
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* whether a pixel is close to that sample. This parameter does not affect the background update.
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorKNN createBackgroundSubtractorKNN(int history)
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{
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return BackgroundSubtractorKNN.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorKNN_12(history)));
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}
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/**
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* Creates KNN Background Subtractor
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*
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* whether a pixel is close to that sample. This parameter does not affect the background update.
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* speed a bit, so if you do not need this feature, set the parameter to false.
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* return automatically generated
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*/
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public static BackgroundSubtractorKNN createBackgroundSubtractorKNN()
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{
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return BackgroundSubtractorKNN.__fromPtr__(DisposableObject.ThrowIfNullIntPtr(video_Video_createBackgroundSubtractorKNN_13()));
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}
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//
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// C++: RotatedRect cv::CamShift(Mat probImage, Rect& window, TermCriteria criteria)
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//
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/**
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* Finds an object center, size, and orientation.
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*
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* param probImage Back projection of the object histogram. See calcBackProject.
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* param window Initial search window.
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* param criteria Stop criteria for the underlying meanShift.
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* returns
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* (in old interfaces) Number of iterations CAMSHIFT took to converge
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* The function implements the CAMSHIFT object tracking algorithm CITE: Bradski98 . First, it finds an
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* object center using meanShift and then adjusts the window size and finds the optimal rotation. The
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* function returns the rotated rectangle structure that includes the object position, size, and
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* orientation. The next position of the search window can be obtained with RotatedRect::boundingRect()
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*
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* See the OpenCV sample camshiftdemo.c that tracks colored objects.
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*
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* <b>Note:</b>
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* <ul>
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* <li>
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* (Python) A sample explaining the camshift tracking algorithm can be found at
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* opencv_source_code/samples/python/camshift.py
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* </li>
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* </ul>
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* return automatically generated
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*/
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public static RotatedRect CamShift(Mat probImage, Rect window, TermCriteria criteria)
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{
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if (probImage != null) probImage.ThrowIfDisposed();
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double[] window_out = new double[4];
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double[] tmpArray = new double[5];
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video_Video_CamShift_10(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon, tmpArray);
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RotatedRect retVal = new RotatedRect(tmpArray);
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if (window != null) { window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3]; }
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return retVal;
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}
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//
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// C++: int cv::meanShift(Mat probImage, Rect& window, TermCriteria criteria)
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//
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/**
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* Finds an object on a back projection image.
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*
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* param probImage Back projection of the object histogram. See calcBackProject for details.
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* param window Initial search window.
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* param criteria Stop criteria for the iterative search algorithm.
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* returns
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* : Number of iterations CAMSHIFT took to converge.
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* The function implements the iterative object search algorithm. It takes the input back projection of
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* an object and the initial position. The mass center in window of the back projection image is
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* computed and the search window center shifts to the mass center. The procedure is repeated until the
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* specified number of iterations criteria.maxCount is done or until the window center shifts by less
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* than criteria.epsilon. The algorithm is used inside CamShift and, unlike CamShift , the search
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* window size or orientation do not change during the search. You can simply pass the output of
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* calcBackProject to this function. But better results can be obtained if you pre-filter the back
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* projection and remove the noise. For example, you can do this by retrieving connected components
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* with findContours , throwing away contours with small area ( contourArea ), and rendering the
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* remaining contours with drawContours.
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* return automatically generated
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*/
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public static int meanShift(Mat probImage, Rect window, TermCriteria criteria)
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{
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if (probImage != null) probImage.ThrowIfDisposed();
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double[] window_out = new double[4];
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int retVal = video_Video_meanShift_10(probImage.nativeObj, window.x, window.y, window.width, window.height, window_out, criteria.type, criteria.maxCount, criteria.epsilon);
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if (window != null) { window.x = (int)window_out[0]; window.y = (int)window_out[1]; window.width = (int)window_out[2]; window.height = (int)window_out[3]; }
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return retVal;
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}
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//
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// C++: int cv::buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
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//
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/**
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* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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*
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* param img 8-bit input image.
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* param pyramid output pyramid.
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* param winSize window size of optical flow algorithm. Must be not less than winSize argument of
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* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
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* param maxLevel 0-based maximal pyramid level number.
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* param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
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* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
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* param pyrBorder the border mode for pyramid layers.
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* param derivBorder the border mode for gradients.
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* param tryReuseInputImage put ROI of input image into the pyramid if possible. You can pass false
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* to force data copying.
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* return number of levels in constructed pyramid. Can be less than maxLevel.
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*/
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public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder, bool tryReuseInputImage)
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{
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if (img != null) img.ThrowIfDisposed();
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Mat pyramid_mat = new Mat();
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int retVal = video_Video_buildOpticalFlowPyramid_10(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder, derivBorder, tryReuseInputImage);
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Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
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pyramid_mat.release();
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return retVal;
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}
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/**
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* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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*
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* param img 8-bit input image.
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* param pyramid output pyramid.
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* param winSize window size of optical flow algorithm. Must be not less than winSize argument of
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* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
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* param maxLevel 0-based maximal pyramid level number.
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* param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
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* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
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* param pyrBorder the border mode for pyramid layers.
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* param derivBorder the border mode for gradients.
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* to force data copying.
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* return number of levels in constructed pyramid. Can be less than maxLevel.
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*/
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public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder, int derivBorder)
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{
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if (img != null) img.ThrowIfDisposed();
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Mat pyramid_mat = new Mat();
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int retVal = video_Video_buildOpticalFlowPyramid_11(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder, derivBorder);
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Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
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pyramid_mat.release();
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return retVal;
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}
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/**
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* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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*
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* param img 8-bit input image.
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* param pyramid output pyramid.
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* param winSize window size of optical flow algorithm. Must be not less than winSize argument of
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* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
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* param maxLevel 0-based maximal pyramid level number.
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* param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
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* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
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* param pyrBorder the border mode for pyramid layers.
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* to force data copying.
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* return number of levels in constructed pyramid. Can be less than maxLevel.
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*/
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public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, bool withDerivatives, int pyrBorder)
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{
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if (img != null) img.ThrowIfDisposed();
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Mat pyramid_mat = new Mat();
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int retVal = video_Video_buildOpticalFlowPyramid_12(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives, pyrBorder);
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Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
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pyramid_mat.release();
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return retVal;
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}
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/**
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* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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*
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* param img 8-bit input image.
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* param pyramid output pyramid.
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* param winSize window size of optical flow algorithm. Must be not less than winSize argument of
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* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
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* param maxLevel 0-based maximal pyramid level number.
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* param withDerivatives set to precompute gradients for the every pyramid level. If pyramid is
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* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
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* to force data copying.
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* return number of levels in constructed pyramid. Can be less than maxLevel.
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*/
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public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel, bool withDerivatives)
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{
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if (img != null) img.ThrowIfDisposed();
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Mat pyramid_mat = new Mat();
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int retVal = video_Video_buildOpticalFlowPyramid_13(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel, withDerivatives);
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Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
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pyramid_mat.release();
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return retVal;
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}
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/**
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* Constructs the image pyramid which can be passed to calcOpticalFlowPyrLK.
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*
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* param img 8-bit input image.
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* param pyramid output pyramid.
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* param winSize window size of optical flow algorithm. Must be not less than winSize argument of
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* calcOpticalFlowPyrLK. It is needed to calculate required padding for pyramid levels.
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* param maxLevel 0-based maximal pyramid level number.
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* constructed without the gradients then calcOpticalFlowPyrLK will calculate them internally.
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* to force data copying.
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* return number of levels in constructed pyramid. Can be less than maxLevel.
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*/
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public static int buildOpticalFlowPyramid(Mat img, List<Mat> pyramid, Size winSize, int maxLevel)
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{
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if (img != null) img.ThrowIfDisposed();
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Mat pyramid_mat = new Mat();
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int retVal = video_Video_buildOpticalFlowPyramid_14(img.nativeObj, pyramid_mat.nativeObj, winSize.width, winSize.height, maxLevel);
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Converters.Mat_to_vector_Mat(pyramid_mat, pyramid);
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pyramid_mat.release();
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return retVal;
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}
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//
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// C++: void cv::calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
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//
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/**
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* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
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* pyramids.
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*
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* param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
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* param nextImg second input image or pyramid of the same size and the same type as prevImg.
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* param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
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* single-precision floating-point numbers.
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* param nextPts output vector of 2D points (with single-precision floating-point coordinates)
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* containing the calculated new positions of input features in the second image; when
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* OPTFLOW_USE_INITIAL_FLOW flag is passed, the vector must have the same size as in the input.
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* param status output status vector (of unsigned chars); each element of the vector is set to 1 if
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* the flow for the corresponding features has been found, otherwise, it is set to 0.
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* param err output vector of errors; each element of the vector is set to an error for the
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* corresponding feature, type of the error measure can be set in flags parameter; if the flow wasn't
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* found then the error is not defined (use the status parameter to find such cases).
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* param winSize size of the search window at each pyramid level.
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* param maxLevel 0-based maximal pyramid level number; if set to 0, pyramids are not used (single
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* level), if set to 1, two levels are used, and so on; if pyramids are passed to input then
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* algorithm will use as many levels as pyramids have but no more than maxLevel.
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* param criteria parameter, specifying the termination criteria of the iterative search algorithm
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* (after the specified maximum number of iterations criteria.maxCount or when the search window
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* moves by less than criteria.epsilon.
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* param flags operation flags:
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* <ul>
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* <li>
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* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
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* not set, then prevPts is copied to nextPts and is considered the initial estimate.
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* </li>
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* <li>
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* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> use minimum eigen values as an error measure (see
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* minEigThreshold description); if the flag is not set, then L1 distance between patches
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* around the original and a moved point, divided by number of pixels in a window, is used as a
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* error measure.
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* </li>
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* </ul>
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* param minEigThreshold the algorithm calculates the minimum eigen value of a 2x2 normal matrix of
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* optical flow equations (this matrix is called a spatial gradient matrix in CITE: Bouguet00), divided
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* by number of pixels in a window; if this value is less than minEigThreshold, then a corresponding
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* feature is filtered out and its flow is not processed, so it allows to remove bad points and get a
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* performance boost.
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*
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* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
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* CITE: Bouguet00 . The function is parallelized with the TBB library.
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/cpp/lkdemo.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/python/lk_track.py
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
|
|
* opencv_source_code/samples/python/lk_homography.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags, double minEigThreshold)
|
|
{
|
|
if (prevImg != null) prevImg.ThrowIfDisposed();
|
|
if (nextImg != null) nextImg.ThrowIfDisposed();
|
|
if (prevPts != null) prevPts.ThrowIfDisposed();
|
|
if (nextPts != null) nextPts.ThrowIfDisposed();
|
|
if (status != null) status.ThrowIfDisposed();
|
|
if (err != null) err.ThrowIfDisposed();
|
|
Mat prevPts_mat = prevPts;
|
|
Mat nextPts_mat = nextPts;
|
|
Mat status_mat = status;
|
|
Mat err_mat = err;
|
|
video_Video_calcOpticalFlowPyrLK_10(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon, flags, minEigThreshold);
|
|
|
|
|
|
}
|
|
|
|
/**
|
|
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
|
|
* pyramids.
|
|
*
|
|
* param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
|
|
* param nextImg second input image or pyramid of the same size and the same type as prevImg.
|
|
* param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
|
|
* single-precision floating-point numbers.
|
|
* param 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.
|
|
* param 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.
|
|
* param 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).
|
|
* param winSize size of the search window at each pyramid level.
|
|
* param 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.
|
|
* param 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.
|
|
* param flags operation flags:
|
|
* <ul>
|
|
* <li>
|
|
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
|
|
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
|
|
* </li>
|
|
* <li>
|
|
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> 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.
|
|
* </li>
|
|
* </ul>
|
|
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: 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.
|
|
*
|
|
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
|
|
* CITE: Bouguet00 . The function is parallelized with the TBB library.
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/cpp/lkdemo.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/python/lk_track.py
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
|
|
* opencv_source_code/samples/python/lk_homography.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria, int flags)
|
|
{
|
|
if (prevImg != null) prevImg.ThrowIfDisposed();
|
|
if (nextImg != null) nextImg.ThrowIfDisposed();
|
|
if (prevPts != null) prevPts.ThrowIfDisposed();
|
|
if (nextPts != null) nextPts.ThrowIfDisposed();
|
|
if (status != null) status.ThrowIfDisposed();
|
|
if (err != null) err.ThrowIfDisposed();
|
|
Mat prevPts_mat = prevPts;
|
|
Mat nextPts_mat = nextPts;
|
|
Mat status_mat = status;
|
|
Mat err_mat = err;
|
|
video_Video_calcOpticalFlowPyrLK_11(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon, flags);
|
|
|
|
|
|
}
|
|
|
|
/**
|
|
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
|
|
* pyramids.
|
|
*
|
|
* param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
|
|
* param nextImg second input image or pyramid of the same size and the same type as prevImg.
|
|
* param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
|
|
* single-precision floating-point numbers.
|
|
* param 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.
|
|
* param 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.
|
|
* param 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).
|
|
* param winSize size of the search window at each pyramid level.
|
|
* param 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.
|
|
* param 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.
|
|
* <ul>
|
|
* <li>
|
|
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
|
|
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
|
|
* </li>
|
|
* <li>
|
|
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> 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.
|
|
* </li>
|
|
* </ul>
|
|
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: 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.
|
|
*
|
|
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
|
|
* CITE: Bouguet00 . The function is parallelized with the TBB library.
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/cpp/lkdemo.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/python/lk_track.py
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
|
|
* opencv_source_code/samples/python/lk_homography.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel, TermCriteria criteria)
|
|
{
|
|
if (prevImg != null) prevImg.ThrowIfDisposed();
|
|
if (nextImg != null) nextImg.ThrowIfDisposed();
|
|
if (prevPts != null) prevPts.ThrowIfDisposed();
|
|
if (nextPts != null) nextPts.ThrowIfDisposed();
|
|
if (status != null) status.ThrowIfDisposed();
|
|
if (err != null) err.ThrowIfDisposed();
|
|
Mat prevPts_mat = prevPts;
|
|
Mat nextPts_mat = nextPts;
|
|
Mat status_mat = status;
|
|
Mat err_mat = err;
|
|
video_Video_calcOpticalFlowPyrLK_12(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel, criteria.type, criteria.maxCount, criteria.epsilon);
|
|
|
|
|
|
}
|
|
|
|
/**
|
|
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
|
|
* pyramids.
|
|
*
|
|
* param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
|
|
* param nextImg second input image or pyramid of the same size and the same type as prevImg.
|
|
* param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
|
|
* single-precision floating-point numbers.
|
|
* param 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.
|
|
* param 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.
|
|
* param 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).
|
|
* param winSize size of the search window at each pyramid level.
|
|
* param 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.
|
|
* (after the specified maximum number of iterations criteria.maxCount or when the search window
|
|
* moves by less than criteria.epsilon.
|
|
* <ul>
|
|
* <li>
|
|
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
|
|
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
|
|
* </li>
|
|
* <li>
|
|
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> 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.
|
|
* </li>
|
|
* </ul>
|
|
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: 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.
|
|
*
|
|
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
|
|
* CITE: Bouguet00 . The function is parallelized with the TBB library.
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/cpp/lkdemo.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/python/lk_track.py
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
|
|
* opencv_source_code/samples/python/lk_homography.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize, int maxLevel)
|
|
{
|
|
if (prevImg != null) prevImg.ThrowIfDisposed();
|
|
if (nextImg != null) nextImg.ThrowIfDisposed();
|
|
if (prevPts != null) prevPts.ThrowIfDisposed();
|
|
if (nextPts != null) nextPts.ThrowIfDisposed();
|
|
if (status != null) status.ThrowIfDisposed();
|
|
if (err != null) err.ThrowIfDisposed();
|
|
Mat prevPts_mat = prevPts;
|
|
Mat nextPts_mat = nextPts;
|
|
Mat status_mat = status;
|
|
Mat err_mat = err;
|
|
video_Video_calcOpticalFlowPyrLK_13(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height, maxLevel);
|
|
|
|
|
|
}
|
|
|
|
/**
|
|
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
|
|
* pyramids.
|
|
*
|
|
* param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
|
|
* param nextImg second input image or pyramid of the same size and the same type as prevImg.
|
|
* param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
|
|
* single-precision floating-point numbers.
|
|
* param 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.
|
|
* param 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.
|
|
* param 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).
|
|
* param winSize size of the search window at each pyramid level.
|
|
* 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.
|
|
* (after the specified maximum number of iterations criteria.maxCount or when the search window
|
|
* moves by less than criteria.epsilon.
|
|
* <ul>
|
|
* <li>
|
|
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
|
|
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
|
|
* </li>
|
|
* <li>
|
|
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> 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.
|
|
* </li>
|
|
* </ul>
|
|
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: 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.
|
|
*
|
|
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
|
|
* CITE: Bouguet00 . The function is parallelized with the TBB library.
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/cpp/lkdemo.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/python/lk_track.py
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
|
|
* opencv_source_code/samples/python/lk_homography.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err, Size winSize)
|
|
{
|
|
if (prevImg != null) prevImg.ThrowIfDisposed();
|
|
if (nextImg != null) nextImg.ThrowIfDisposed();
|
|
if (prevPts != null) prevPts.ThrowIfDisposed();
|
|
if (nextPts != null) nextPts.ThrowIfDisposed();
|
|
if (status != null) status.ThrowIfDisposed();
|
|
if (err != null) err.ThrowIfDisposed();
|
|
Mat prevPts_mat = prevPts;
|
|
Mat nextPts_mat = nextPts;
|
|
Mat status_mat = status;
|
|
Mat err_mat = err;
|
|
video_Video_calcOpticalFlowPyrLK_14(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj, winSize.width, winSize.height);
|
|
|
|
|
|
}
|
|
|
|
/**
|
|
* Calculates an optical flow for a sparse feature set using the iterative Lucas-Kanade method with
|
|
* pyramids.
|
|
*
|
|
* param prevImg first 8-bit input image or pyramid constructed by buildOpticalFlowPyramid.
|
|
* param nextImg second input image or pyramid of the same size and the same type as prevImg.
|
|
* param prevPts vector of 2D points for which the flow needs to be found; point coordinates must be
|
|
* single-precision floating-point numbers.
|
|
* param 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.
|
|
* param 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.
|
|
* param 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).
|
|
* 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.
|
|
* (after the specified maximum number of iterations criteria.maxCount or when the search window
|
|
* moves by less than criteria.epsilon.
|
|
* <ul>
|
|
* <li>
|
|
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses initial estimations, stored in nextPts; if the flag is
|
|
* not set, then prevPts is copied to nextPts and is considered the initial estimate.
|
|
* </li>
|
|
* <li>
|
|
* <b>OPTFLOW_LK_GET_MIN_EIGENVALS</b> 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.
|
|
* </li>
|
|
* </ul>
|
|
* optical flow equations (this matrix is called a spatial gradient matrix in CITE: 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.
|
|
*
|
|
* The function implements a sparse iterative version of the Lucas-Kanade optical flow in pyramids. See
|
|
* CITE: Bouguet00 . The function is parallelized with the TBB library.
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/cpp/lkdemo.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade optical flow algorithm can be found at
|
|
* opencv_source_code/samples/python/lk_track.py
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the Lucas-Kanade tracker for homography matching can be found at
|
|
* opencv_source_code/samples/python/lk_homography.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, MatOfPoint2f prevPts, MatOfPoint2f nextPts, MatOfByte status, MatOfFloat err)
|
|
{
|
|
if (prevImg != null) prevImg.ThrowIfDisposed();
|
|
if (nextImg != null) nextImg.ThrowIfDisposed();
|
|
if (prevPts != null) prevPts.ThrowIfDisposed();
|
|
if (nextPts != null) nextPts.ThrowIfDisposed();
|
|
if (status != null) status.ThrowIfDisposed();
|
|
if (err != null) err.ThrowIfDisposed();
|
|
Mat prevPts_mat = prevPts;
|
|
Mat nextPts_mat = nextPts;
|
|
Mat status_mat = status;
|
|
Mat err_mat = err;
|
|
video_Video_calcOpticalFlowPyrLK_15(prevImg.nativeObj, nextImg.nativeObj, prevPts_mat.nativeObj, nextPts_mat.nativeObj, status_mat.nativeObj, err_mat.nativeObj);
|
|
|
|
|
|
}
|
|
|
|
|
|
//
|
|
// C++: void cv::calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
|
|
//
|
|
|
|
/**
|
|
* Computes a dense optical flow using the Gunnar Farneback's algorithm.
|
|
*
|
|
* param prev first 8-bit single-channel input image.
|
|
* param next second input image of the same size and the same type as prev.
|
|
* param flow computed flow image that has the same size as prev and type CV_32FC2.
|
|
* param pyr_scale parameter, specifying the image scale (<1) to build pyramids for each image;
|
|
* pyr_scale=0.5 means a classical pyramid, where each next layer is twice smaller than the previous
|
|
* one.
|
|
* param levels number of pyramid layers including the initial image; levels=1 means that no extra
|
|
* layers are created and only the original images are used.
|
|
* param winsize averaging window size; larger values increase the algorithm robustness to image
|
|
* noise and give more chances for fast motion detection, but yield more blurred motion field.
|
|
* param iterations number of iterations the algorithm does at each pyramid level.
|
|
* param poly_n size of the pixel neighborhood used to find polynomial expansion in each pixel;
|
|
* larger values mean that the image will be approximated with smoother surfaces, yielding more
|
|
* robust algorithm and more blurred motion field, typically poly_n =5 or 7.
|
|
* param poly_sigma standard deviation of the Gaussian that is used to smooth derivatives used as a
|
|
* basis for the polynomial expansion; for poly_n=5, you can set poly_sigma=1.1, for poly_n=7, a
|
|
* good value would be poly_sigma=1.5.
|
|
* param flags operation flags that can be a combination of the following:
|
|
* <ul>
|
|
* <li>
|
|
* <b>OPTFLOW_USE_INITIAL_FLOW</b> uses the input flow as an initial flow approximation.
|
|
* </li>
|
|
* <li>
|
|
* <b>OPTFLOW_FARNEBACK_GAUSSIAN</b> uses the Gaussian \(\texttt{winsize}\times\texttt{winsize}\)
|
|
* filter instead of a box filter of the same size for optical flow estimation; usually, this
|
|
* option gives z more accurate flow than with a box filter, at the cost of lower speed;
|
|
* normally, winsize for a Gaussian window should be set to a larger value to achieve the same
|
|
* level of robustness.
|
|
* </li>
|
|
* </ul>
|
|
*
|
|
* The function finds an optical flow for each prev pixel using the CITE: Farneback2003 algorithm so that
|
|
*
|
|
* \(\texttt{prev} (y,x) \sim \texttt{next} ( y + \texttt{flow} (y,x)[1], x + \texttt{flow} (y,x)[0])\)
|
|
*
|
|
* <b>Note:</b>
|
|
*
|
|
* <ul>
|
|
* <li>
|
|
* An example using the optical flow algorithm described by Gunnar Farneback can be found at
|
|
* opencv_source_code/samples/cpp/fback.cpp
|
|
* </li>
|
|
* <li>
|
|
* (Python) An example using the optical flow algorithm described by Gunnar Farneback can be
|
|
* found at opencv_source_code/samples/python/opt_flow.py
|
|
* </li>
|
|
* </ul>
|
|
*/
|
|
public static void calcOpticalFlowFarneback(Mat prev, Mat next, Mat flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
|
|
{
|
|
if (prev != null) prev.ThrowIfDisposed();
|
|
if (next != null) next.ThrowIfDisposed();
|
|
if (flow != null) flow.ThrowIfDisposed();
|
|
|
|
video_Video_calcOpticalFlowFarneback_10(prev.nativeObj, next.nativeObj, flow.nativeObj, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags);
|
|
|
|
|
|
}
|
|
|
|
|
|
//
|
|
// C++: double cv::computeECC(Mat templateImage, Mat inputImage, Mat inputMask = Mat())
|
|
//
|
|
|
|
/**
|
|
* Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 .
|
|
*
|
|
* param templateImage single-channel template image; CV_8U or CV_32F array.
|
|
* param inputImage single-channel input image to be warped to provide an image similar to
|
|
* templateImage, same type as templateImage.
|
|
* param inputMask An optional mask to indicate valid values of inputImage.
|
|
*
|
|
* SEE:
|
|
* findTransformECC
|
|
* return automatically generated
|
|
*/
|
|
public static double computeECC(Mat templateImage, Mat inputImage, Mat inputMask)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
if (inputMask != null) inputMask.ThrowIfDisposed();
|
|
|
|
return video_Video_computeECC_10(templateImage.nativeObj, inputImage.nativeObj, inputMask.nativeObj);
|
|
|
|
|
|
}
|
|
|
|
/**
|
|
* Computes the Enhanced Correlation Coefficient value between two images CITE: EP08 .
|
|
*
|
|
* param templateImage single-channel template image; CV_8U or CV_32F array.
|
|
* param inputImage single-channel input image to be warped to provide an image similar to
|
|
* templateImage, same type as templateImage.
|
|
*
|
|
* SEE:
|
|
* findTransformECC
|
|
* return automatically generated
|
|
*/
|
|
public static double computeECC(Mat templateImage, Mat inputImage)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
|
|
return video_Video_computeECC_11(templateImage.nativeObj, inputImage.nativeObj);
|
|
|
|
|
|
}
|
|
|
|
|
|
//
|
|
// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize)
|
|
//
|
|
|
|
/**
|
|
* Finds the geometric transform (warp) between two images in terms of the ECC criterion CITE: EP08 .
|
|
*
|
|
* param templateImage single-channel template image; CV_8U or CV_32F array.
|
|
* param inputImage single-channel input image which should be warped with the final warpMatrix in
|
|
* order to provide an image similar to templateImage, same type as templateImage.
|
|
* param warpMatrix floating-point \(2\times 3\) or \(3\times 3\) mapping matrix (warp).
|
|
* param motionType parameter, specifying the type of motion:
|
|
* <ul>
|
|
* <li>
|
|
* <b>MOTION_TRANSLATION</b> sets a translational motion model; warpMatrix is \(2\times 3\) with
|
|
* the first \(2\times 2\) part being the unity matrix and the rest two parameters being
|
|
* estimated.
|
|
* </li>
|
|
* <li>
|
|
* <b>MOTION_EUCLIDEAN</b> sets a Euclidean (rigid) transformation as motion model; three
|
|
* parameters are estimated; warpMatrix is \(2\times 3\).
|
|
* </li>
|
|
* <li>
|
|
* <b>MOTION_AFFINE</b> sets an affine motion model (DEFAULT); six parameters are estimated;
|
|
* warpMatrix is \(2\times 3\).
|
|
* </li>
|
|
* <li>
|
|
* <b>MOTION_HOMOGRAPHY</b> sets a homography as a motion model; eight parameters are
|
|
* estimated;\{code warpMatrix\} is \(3\times 3\).
|
|
* </li>
|
|
* </ul>
|
|
* param criteria parameter, specifying the termination criteria of the ECC algorithm;
|
|
* criteria.epsilon defines the threshold of the increment in the correlation coefficient between two
|
|
* iterations (a negative criteria.epsilon makes criteria.maxcount the only termination criterion).
|
|
* Default values are shown in the declaration above.
|
|
* param inputMask An optional mask to indicate valid values of inputImage.
|
|
* param gaussFiltSize An optional value indicating size of gaussian blur filter; (DEFAULT: 5)
|
|
*
|
|
* The function estimates the optimum transformation (warpMatrix) with respect to ECC criterion
|
|
* (CITE: EP08), that is
|
|
*
|
|
* \(\texttt{warpMatrix} = \arg\max_{W} \texttt{ECC}(\texttt{templateImage}(x,y),\texttt{inputImage}(x',y'))\)
|
|
*
|
|
* where
|
|
*
|
|
* \(\begin{bmatrix} x' \\ y' \end{bmatrix} = W \cdot \begin{bmatrix} x \\ y \\ 1 \end{bmatrix}\)
|
|
*
|
|
* (the equation holds with homogeneous coordinates for homography). It returns the final enhanced
|
|
* correlation coefficient, that is the correlation coefficient between the template image and the
|
|
* final warped input image. When a \(3\times 3\) matrix is given with motionType =0, 1 or 2, the third
|
|
* row is ignored.
|
|
*
|
|
* Unlike findHomography and estimateRigidTransform, the function findTransformECC implements an
|
|
* area-based alignment that builds on intensity similarities. In essence, the function updates the
|
|
* initial transformation that roughly aligns the images. If this information is missing, the identity
|
|
* warp (unity matrix) is used as an initialization. Note that if images undergo strong
|
|
* displacements/rotations, an initial transformation that roughly aligns the images is necessary
|
|
* (e.g., a simple euclidean/similarity transform that allows for the images showing the same image
|
|
* content approximately). Use inverse warping in the second image to take an image close to the first
|
|
* one, i.e. use the flag WARP_INVERSE_MAP with warpAffine or warpPerspective. See also the OpenCV
|
|
* sample image_alignment.cpp that demonstrates the use of the function. Note that the function throws
|
|
* an exception if algorithm does not converges.
|
|
*
|
|
* SEE:
|
|
* computeECC, estimateAffine2D, estimateAffinePartial2D, findHomography
|
|
* return automatically generated
|
|
*/
|
|
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
if (warpMatrix != null) warpMatrix.ThrowIfDisposed();
|
|
if (inputMask != null) inputMask.ThrowIfDisposed();
|
|
|
|
return video_Video_findTransformECC_10(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon, inputMask.nativeObj, gaussFiltSize);
|
|
|
|
|
|
}
|
|
|
|
|
|
//
|
|
// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
|
|
//
|
|
|
|
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria, Mat inputMask)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
if (warpMatrix != null) warpMatrix.ThrowIfDisposed();
|
|
if (inputMask != null) inputMask.ThrowIfDisposed();
|
|
|
|
return video_Video_findTransformECC_11(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon, inputMask.nativeObj);
|
|
|
|
|
|
}
|
|
|
|
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType, TermCriteria criteria)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
if (warpMatrix != null) warpMatrix.ThrowIfDisposed();
|
|
|
|
return video_Video_findTransformECC_12(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType, criteria.type, criteria.maxCount, criteria.epsilon);
|
|
|
|
|
|
}
|
|
|
|
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix, int motionType)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
if (warpMatrix != null) warpMatrix.ThrowIfDisposed();
|
|
|
|
return video_Video_findTransformECC_13(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj, motionType);
|
|
|
|
|
|
}
|
|
|
|
public static double findTransformECC(Mat templateImage, Mat inputImage, Mat warpMatrix)
|
|
{
|
|
if (templateImage != null) templateImage.ThrowIfDisposed();
|
|
if (inputImage != null) inputImage.ThrowIfDisposed();
|
|
if (warpMatrix != null) warpMatrix.ThrowIfDisposed();
|
|
|
|
return video_Video_findTransformECC_14(templateImage.nativeObj, inputImage.nativeObj, warpMatrix.nativeObj);
|
|
|
|
|
|
}
|
|
|
|
|
|
//
|
|
// C++: Mat cv::readOpticalFlow(String path)
|
|
//
|
|
|
|
/**
|
|
* Read a .flo file
|
|
*
|
|
* param path Path to the file to be loaded
|
|
*
|
|
* The function readOpticalFlow loads a flow field from a file and returns it as a single matrix.
|
|
* Resulting Mat has a type CV_32FC2 - floating-point, 2-channel. First channel corresponds to the
|
|
* flow in the horizontal direction (u), second - vertical (v).
|
|
* return automatically generated
|
|
*/
|
|
public static Mat readOpticalFlow(string path)
|
|
{
|
|
|
|
|
|
return new Mat(DisposableObject.ThrowIfNullIntPtr(video_Video_readOpticalFlow_10(path)));
|
|
|
|
|
|
}
|
|
|
|
|
|
//
|
|
// C++: bool cv::writeOpticalFlow(String path, Mat flow)
|
|
//
|
|
|
|
/**
|
|
* Write a .flo to disk
|
|
*
|
|
* param path Path to the file to be written
|
|
* param flow Flow field to be stored
|
|
*
|
|
* The function stores a flow field in a file, returns true on success, false otherwise.
|
|
* The flow field must be a 2-channel, floating-point matrix (CV_32FC2). First channel corresponds
|
|
* to the flow in the horizontal direction (u), second - vertical (v).
|
|
* return automatically generated
|
|
*/
|
|
public static bool writeOpticalFlow(string path, Mat flow)
|
|
{
|
|
if (flow != null) flow.ThrowIfDisposed();
|
|
|
|
return video_Video_writeOpticalFlow_10(path, flow.nativeObj);
|
|
|
|
|
|
}
|
|
|
|
|
|
#if (UNITY_IOS || UNITY_WEBGL) && !UNITY_EDITOR
|
|
const string LIBNAME = "__Internal";
|
|
#else
|
|
const string LIBNAME = "opencvforunity";
|
|
#endif
|
|
|
|
|
|
|
|
// C++: Ptr_BackgroundSubtractorMOG2 cv::createBackgroundSubtractorMOG2(int history = 500, double varThreshold = 16, bool detectShadows = true)
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorMOG2_10(int history, double varThreshold, [MarshalAs(UnmanagedType.U1)] bool detectShadows);
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorMOG2_11(int history, double varThreshold);
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorMOG2_12(int history);
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorMOG2_13();
|
|
|
|
// C++: Ptr_BackgroundSubtractorKNN cv::createBackgroundSubtractorKNN(int history = 500, double dist2Threshold = 400.0, bool detectShadows = true)
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorKNN_10(int history, double dist2Threshold, [MarshalAs(UnmanagedType.U1)] bool detectShadows);
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorKNN_11(int history, double dist2Threshold);
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorKNN_12(int history);
|
|
[DllImport(LIBNAME)]
|
|
private static extern IntPtr video_Video_createBackgroundSubtractorKNN_13();
|
|
|
|
// C++: RotatedRect cv::CamShift(Mat probImage, Rect& window, TermCriteria criteria)
|
|
[DllImport(LIBNAME)]
|
|
private static extern void video_Video_CamShift_10(IntPtr probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon, double[] retVal);
|
|
|
|
// C++: int cv::meanShift(Mat probImage, Rect& window, TermCriteria criteria)
|
|
[DllImport(LIBNAME)]
|
|
private static extern int video_Video_meanShift_10(IntPtr probImage_nativeObj, int window_x, int window_y, int window_width, int window_height, double[] window_out, int criteria_type, int criteria_maxCount, double criteria_epsilon);
|
|
|
|
// C++: int cv::buildOpticalFlowPyramid(Mat img, vector_Mat& pyramid, Size winSize, int maxLevel, bool withDerivatives = true, int pyrBorder = BORDER_REFLECT_101, int derivBorder = BORDER_CONSTANT, bool tryReuseInputImage = true)
|
|
[DllImport(LIBNAME)]
|
|
private static extern int video_Video_buildOpticalFlowPyramid_10(IntPtr img_nativeObj, IntPtr pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, [MarshalAs(UnmanagedType.U1)] bool withDerivatives, int pyrBorder, int derivBorder, [MarshalAs(UnmanagedType.U1)] bool tryReuseInputImage);
|
|
[DllImport(LIBNAME)]
|
|
private static extern int video_Video_buildOpticalFlowPyramid_11(IntPtr img_nativeObj, IntPtr pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, [MarshalAs(UnmanagedType.U1)] bool withDerivatives, int pyrBorder, int derivBorder);
|
|
[DllImport(LIBNAME)]
|
|
private static extern int video_Video_buildOpticalFlowPyramid_12(IntPtr img_nativeObj, IntPtr pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, [MarshalAs(UnmanagedType.U1)] bool withDerivatives, int pyrBorder);
|
|
[DllImport(LIBNAME)]
|
|
private static extern int video_Video_buildOpticalFlowPyramid_13(IntPtr img_nativeObj, IntPtr pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, [MarshalAs(UnmanagedType.U1)] bool withDerivatives);
|
|
[DllImport(LIBNAME)]
|
|
private static extern int video_Video_buildOpticalFlowPyramid_14(IntPtr img_nativeObj, IntPtr pyramid_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
|
|
|
|
// C++: void cv::calcOpticalFlowPyrLK(Mat prevImg, Mat nextImg, vector_Point2f prevPts, vector_Point2f& nextPts, vector_uchar& status, vector_float& err, Size winSize = Size(21,21), int maxLevel = 3, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 30, 0.01), int flags = 0, double minEigThreshold = 1e-4)
|
|
[DllImport(LIBNAME)]
|
|
private static extern void video_Video_calcOpticalFlowPyrLK_10(IntPtr prevImg_nativeObj, IntPtr nextImg_nativeObj, IntPtr prevPts_mat_nativeObj, IntPtr nextPts_mat_nativeObj, IntPtr status_mat_nativeObj, IntPtr err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon, int flags, double minEigThreshold);
|
|
[DllImport(LIBNAME)]
|
|
private static extern void video_Video_calcOpticalFlowPyrLK_11(IntPtr prevImg_nativeObj, IntPtr nextImg_nativeObj, IntPtr prevPts_mat_nativeObj, IntPtr nextPts_mat_nativeObj, IntPtr status_mat_nativeObj, IntPtr err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon, int flags);
|
|
[DllImport(LIBNAME)]
|
|
private static extern void video_Video_calcOpticalFlowPyrLK_12(IntPtr prevImg_nativeObj, IntPtr nextImg_nativeObj, IntPtr prevPts_mat_nativeObj, IntPtr nextPts_mat_nativeObj, IntPtr status_mat_nativeObj, IntPtr err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel, int criteria_type, int criteria_maxCount, double criteria_epsilon);
|
|
[DllImport(LIBNAME)]
|
|
private static extern void video_Video_calcOpticalFlowPyrLK_13(IntPtr prevImg_nativeObj, IntPtr nextImg_nativeObj, IntPtr prevPts_mat_nativeObj, IntPtr nextPts_mat_nativeObj, IntPtr status_mat_nativeObj, IntPtr err_mat_nativeObj, double winSize_width, double winSize_height, int maxLevel);
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[DllImport(LIBNAME)]
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private static extern void video_Video_calcOpticalFlowPyrLK_14(IntPtr prevImg_nativeObj, IntPtr nextImg_nativeObj, IntPtr prevPts_mat_nativeObj, IntPtr nextPts_mat_nativeObj, IntPtr status_mat_nativeObj, IntPtr err_mat_nativeObj, double winSize_width, double winSize_height);
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[DllImport(LIBNAME)]
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private static extern void video_Video_calcOpticalFlowPyrLK_15(IntPtr prevImg_nativeObj, IntPtr nextImg_nativeObj, IntPtr prevPts_mat_nativeObj, IntPtr nextPts_mat_nativeObj, IntPtr status_mat_nativeObj, IntPtr err_mat_nativeObj);
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// C++: void cv::calcOpticalFlowFarneback(Mat prev, Mat next, Mat& flow, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags)
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[DllImport(LIBNAME)]
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private static extern void video_Video_calcOpticalFlowFarneback_10(IntPtr prev_nativeObj, IntPtr next_nativeObj, IntPtr flow_nativeObj, double pyr_scale, int levels, int winsize, int iterations, int poly_n, double poly_sigma, int flags);
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// C++: double cv::computeECC(Mat templateImage, Mat inputImage, Mat inputMask = Mat())
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[DllImport(LIBNAME)]
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private static extern double video_Video_computeECC_10(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj, IntPtr inputMask_nativeObj);
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[DllImport(LIBNAME)]
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private static extern double video_Video_computeECC_11(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj);
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// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType, TermCriteria criteria, Mat inputMask, int gaussFiltSize)
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[DllImport(LIBNAME)]
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private static extern double video_Video_findTransformECC_10(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj, IntPtr warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon, IntPtr inputMask_nativeObj, int gaussFiltSize);
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// C++: double cv::findTransformECC(Mat templateImage, Mat inputImage, Mat& warpMatrix, int motionType = MOTION_AFFINE, TermCriteria criteria = TermCriteria(TermCriteria::COUNT+TermCriteria::EPS, 50, 0.001), Mat inputMask = Mat())
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[DllImport(LIBNAME)]
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private static extern double video_Video_findTransformECC_11(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj, IntPtr warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon, IntPtr inputMask_nativeObj);
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[DllImport(LIBNAME)]
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private static extern double video_Video_findTransformECC_12(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj, IntPtr warpMatrix_nativeObj, int motionType, int criteria_type, int criteria_maxCount, double criteria_epsilon);
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[DllImport(LIBNAME)]
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private static extern double video_Video_findTransformECC_13(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj, IntPtr warpMatrix_nativeObj, int motionType);
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[DllImport(LIBNAME)]
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private static extern double video_Video_findTransformECC_14(IntPtr templateImage_nativeObj, IntPtr inputImage_nativeObj, IntPtr warpMatrix_nativeObj);
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// C++: Mat cv::readOpticalFlow(String path)
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[DllImport(LIBNAME)]
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private static extern IntPtr video_Video_readOpticalFlow_10(string path);
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// C++: bool cv::writeOpticalFlow(String path, Mat flow)
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[DllImport(LIBNAME)]
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[return: MarshalAs(UnmanagedType.U1)]
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private static extern bool video_Video_writeOpticalFlow_10(string path, IntPtr flow_nativeObj);
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}
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}
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