"SimpleFlow" optical flow estimation algorithm (GSoC project) declaration in includes, implementation, usage example, test
Yury Zemlyanskiy
13 years ago
committed by
Vadim Pisarevsky
parent
2367a195c5
commit
c77d099018
6 changed files with 1235 additions and 1 deletions
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp" |
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#include "simpleflow.hpp" |
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//
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// 2D dense optical flow algorithm from the following paper:
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// Michael Tao, Jiamin Bai, Pushmeet Kohli, and Sylvain Paris.
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// "SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm"
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// Computer Graphics Forum (Eurographics 2012)
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// http://graphics.berkeley.edu/papers/Tao-SAN-2012-05/
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//
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namespace cv |
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{ |
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WeightedCrossBilateralFilter::WeightedCrossBilateralFilter( |
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const Mat& _image, |
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int _windowSize, |
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double _sigmaDist, |
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double _sigmaColor) |
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: image(_image),
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windowSize(_windowSize),
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sigmaDist(_sigmaDist),
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sigmaColor(_sigmaColor) { |
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expDist.resize(2*windowSize*windowSize+1); |
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const double sigmaDistSqr = 2 * sigmaDist * sigmaDist; |
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for (int i = 0; i <= 2*windowSize*windowSize; ++i) { |
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expDist[i] = exp(-i/sigmaDistSqr); |
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} |
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const double sigmaColorSqr = 2 * sigmaColor * sigmaColor; |
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wc.resize(image.rows); |
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for (int row = 0; row < image.rows; ++row) { |
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wc[row].resize(image.cols); |
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for (int col = 0; col < image.cols; ++col) { |
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int beginRow = max(0, row - windowSize); |
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int beginCol = max(0, col - windowSize); |
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int endRow = min(image.rows - 1, row + windowSize); |
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int endCol = min(image.cols - 1, col + windowSize); |
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wc[row][col] = build<double>(endRow - beginRow + 1, endCol - beginCol + 1); |
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for (int r = beginRow; r <= endRow; ++r) { |
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for (int c = beginCol; c <= endCol; ++c) { |
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wc[row][col][r - beginRow][c - beginCol] =
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exp(-dist(image.at<Vec3b>(row, col),
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image.at<Vec3b>(r, c))
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/ sigmaColorSqr); |
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} |
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} |
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} |
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} |
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} |
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Mat WeightedCrossBilateralFilter::apply(Mat& matrix, Mat& weights) { |
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int rows = matrix.rows; |
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int cols = matrix.cols; |
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Mat result = Mat::zeros(rows, cols, CV_64F); |
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for (int row = 0; row < rows; ++row) { |
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for(int col = 0; col < cols; ++col) { |
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result.at<double>(row, col) =
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convolution(matrix, row, col, weights); |
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} |
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} |
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return result; |
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} |
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double WeightedCrossBilateralFilter::convolution(Mat& matrix,
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int row, int col,
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Mat& weights) { |
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double result = 0, weightsSum = 0; |
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int beginRow = max(0, row - windowSize); |
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int beginCol = max(0, col - windowSize); |
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int endRow = min(matrix.rows - 1, row + windowSize); |
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int endCol = min(matrix.cols - 1, col + windowSize); |
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for (int r = beginRow; r <= endRow; ++r) { |
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double* ptr = matrix.ptr<double>(r); |
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for (int c = beginCol; c <= endCol; ++c) { |
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const double w = expDist[dist(row, col, r, c)] * |
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wc[row][col][r - beginRow][c - beginCol] * |
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weights.at<double>(r, c); |
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result += ptr[c] * w; |
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weightsSum += w; |
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} |
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} |
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return result / weightsSum; |
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} |
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static void removeOcclusions(const Flow& flow,
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const Flow& flow_inv, |
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double occ_thr, |
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Mat& confidence) { |
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const int rows = flow.u.rows; |
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const int cols = flow.v.cols; |
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int occlusions = 0; |
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for (int r = 0; r < rows; ++r) { |
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for (int c = 0; c < cols; ++c) { |
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if (dist(flow.u.at<double>(r, c), flow.v.at<double>(r, c), |
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-flow_inv.u.at<double>(r, c), -flow_inv.v.at<double>(r, c)) > occ_thr) { |
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confidence.at<double>(r, c) = 0; |
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occlusions++; |
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} |
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} |
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} |
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} |
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static Mat wd(int top_shift, int bottom_shift, int left_shift, int right_shift, double sigma) { |
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const double factor = 1.0 / (2.0 * sigma * sigma); |
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Mat d = Mat(top_shift + bottom_shift + 1, right_shift + left_shift + 1, CV_64F); |
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for (int dr = -top_shift, r = 0; dr <= bottom_shift; ++dr, ++r) { |
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for (int dc = -left_shift, c = 0; dc <= right_shift; ++dc, ++c) { |
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d.at<double>(r, c) = -(dr*dr + dc*dc) * factor; |
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} |
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} |
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Mat ed; |
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exp(d, ed); |
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return ed; |
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} |
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static Mat wc(const Mat& image, int r0, int c0, int top_shift, int bottom_shift, int left_shift, int right_shift, double sigma) { |
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const double factor = 1.0 / (2.0 * sigma * sigma); |
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Mat d = Mat(top_shift + bottom_shift + 1, right_shift + left_shift + 1, CV_64F); |
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for (int dr = r0-top_shift, r = 0; dr <= r0+bottom_shift; ++dr, ++r) { |
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for (int dc = c0-left_shift, c = 0; dc <= c0+right_shift; ++dc, ++c) { |
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d.at<double>(r, c) = -dist(image.at<Vec3b>(r0, c0), image.at<Vec3b>(dr, dc)) * factor; |
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} |
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} |
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Mat ed; |
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exp(d, ed); |
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return ed; |
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} |
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inline static void dist(const Mat& m1, const Mat& m2, Mat& result) { |
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const int rows = m1.rows; |
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const int cols = m1.cols; |
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for (int r = 0; r < rows; ++r) { |
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const Vec3b *m1_row = m1.ptr<Vec3b>(r); |
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const Vec3b *m2_row = m2.ptr<Vec3b>(r); |
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double* row = result.ptr<double>(r); |
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for (int c = 0; c < cols; ++c) { |
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row[c] = dist(m1_row[c], m2_row[c]); |
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} |
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} |
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} |
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static void calcOpticalFlowSingleScaleSF(const Mat& prev,
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const Mat& next, |
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const Mat& mask, |
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Flow& flow, |
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Mat& confidence, |
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int averaging_radius,
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int max_flow, |
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double sigma_dist, |
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double sigma_color) { |
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const int rows = prev.rows; |
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const int cols = prev.cols; |
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confidence = Mat::zeros(rows, cols, CV_64F); |
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for (int r0 = 0; r0 < rows; ++r0) { |
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for (int c0 = 0; c0 < cols; ++c0) { |
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int u0 = floor(flow.u.at<double>(r0, c0) + 0.5); |
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int v0 = floor(flow.v.at<double>(r0, c0) + 0.5); |
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const int min_row_shift = -min(r0 + u0, max_flow); |
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const int max_row_shift = min(rows - 1 - (r0 + u0), max_flow); |
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const int min_col_shift = -min(c0 + v0, max_flow); |
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const int max_col_shift = min(cols - 1 - (c0 + v0), max_flow); |
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double min_cost = DBL_MAX, best_u = u0, best_v = v0; |
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Mat w_full_window; |
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double w_full_window_sum; |
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Mat diff_storage; |
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if (r0 - averaging_radius >= 0 &&
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r0 + averaging_radius < rows && |
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c0 - averaging_radius >= 0 && |
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c0 + averaging_radius < cols && |
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mask.at<uchar>(r0, c0)) { |
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w_full_window = wd(averaging_radius,
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averaging_radius,
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averaging_radius,
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averaging_radius,
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sigma_dist).mul( |
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wc(prev, r0, c0,
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averaging_radius,
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averaging_radius,
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averaging_radius,
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averaging_radius,
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sigma_color)); |
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w_full_window_sum = sum(w_full_window)[0]; |
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diff_storage = Mat::zeros(averaging_radius*2 + 1, averaging_radius*2 + 1, CV_64F); |
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} |
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bool first_flow_iteration = true; |
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double sum_e, min_e; |
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for (int u = min_row_shift; u <= max_row_shift; ++u) { |
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for (int v = min_col_shift; v <= max_col_shift; ++v) { |
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double e = dist(prev.at<Vec3b>(r0, c0), next.at<Vec3b>(r0 + u0 + u, c0 + v0 + v)); |
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if (first_flow_iteration) { |
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sum_e = e; |
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min_e = e; |
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first_flow_iteration = false; |
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} else { |
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sum_e += e; |
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min_e = std::min(min_e, e); |
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} |
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if (!mask.at<uchar>(r0, c0)) { |
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continue; |
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} |
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const int window_top_shift = min(r0, r0 + u + u0, averaging_radius); |
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const int window_bottom_shift = min(rows - 1 - r0,
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rows - 1 - (r0 + u + u0),
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averaging_radius); |
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const int window_left_shift = min(c0, c0 + v + v0, averaging_radius); |
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const int window_right_shift = min(cols - 1 - c0,
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cols - 1 - (c0 + v + v0),
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averaging_radius); |
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const Range prev_row_range(r0 - window_top_shift, r0 + window_bottom_shift + 1); |
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const Range prev_col_range(c0 - window_left_shift, c0 + window_right_shift + 1); |
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const Range next_row_range(r0 + u0 + u - window_top_shift,
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r0 + u0 + u + window_bottom_shift + 1); |
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const Range next_col_range(c0 + v0 + v - window_left_shift,
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c0 + v0 + v + window_right_shift + 1);
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Mat diff2; |
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Mat w; |
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double w_sum; |
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if (window_top_shift == averaging_radius && |
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window_bottom_shift == averaging_radius && |
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window_left_shift == averaging_radius && |
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window_right_shift == averaging_radius) { |
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w = w_full_window; |
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w_sum = w_full_window_sum; |
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diff2 = diff_storage; |
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dist(prev(prev_row_range, prev_col_range), next(next_row_range, next_col_range), diff2); |
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} else { |
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diff2 = Mat::zeros(window_bottom_shift + window_top_shift + 1,
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window_right_shift + window_left_shift + 1, CV_64F); |
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dist(prev(prev_row_range, prev_col_range), next(next_row_range, next_col_range), diff2); |
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w = wd(window_top_shift, window_bottom_shift, window_left_shift, window_right_shift, sigma_dist).mul(
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wc(prev, r0, c0, window_top_shift, window_bottom_shift, window_left_shift, window_right_shift, sigma_color)); |
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w_sum = sum(w)[0]; |
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} |
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multiply(diff2, w, diff2); |
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const double cost = sum(diff2)[0] / w_sum; |
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if (cost < min_cost) { |
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min_cost = cost; |
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best_u = u + u0; |
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best_v = v + v0; |
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} |
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} |
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} |
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int square = (max_row_shift - min_row_shift + 1) * |
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(max_col_shift - min_col_shift + 1); |
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confidence.at<double>(r0, c0) = (square == 0) ? 0 |
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: sum_e / square - min_e; |
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if (mask.at<uchar>(r0, c0)) { |
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flow.u.at<double>(r0, c0) = best_u; |
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flow.v.at<double>(r0, c0) = best_v; |
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} |
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} |
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} |
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} |
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static Flow upscaleOpticalFlow(int new_rows,
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int new_cols, |
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const Mat& image, |
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const Mat& confidence, |
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const Flow& flow, |
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int averaging_radius, |
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double sigma_dist, |
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double sigma_color) { |
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const int rows = image.rows; |
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const int cols = image.cols; |
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Flow new_flow(new_rows, new_cols); |
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for (int r = 0; r < rows; ++r) { |
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for (int c = 0; c < cols; ++c) { |
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const int window_top_shift = min(r, averaging_radius); |
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const int window_bottom_shift = min(rows - 1 - r, averaging_radius); |
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const int window_left_shift = min(c, averaging_radius); |
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const int window_right_shift = min(cols - 1 - c, averaging_radius); |
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const Range row_range(r - window_top_shift, r + window_bottom_shift + 1); |
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const Range col_range(c - window_left_shift, c + window_right_shift + 1); |
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const Mat w = confidence(row_range, col_range).mul( |
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wd(window_top_shift, window_bottom_shift, window_left_shift, window_right_shift, sigma_dist)).mul(
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wc(image, r, c, window_top_shift, window_bottom_shift, window_left_shift, window_right_shift, sigma_color)); |
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const double w_sum = sum(w)[0]; |
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double new_u, new_v; |
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if (fabs(w_sum) < 1e-9) { |
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new_u = flow.u.at<double>(r, c); |
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new_v = flow.v.at<double>(r, c); |
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} else { |
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new_u = sum(flow.u(row_range, col_range).mul(w))[0] / w_sum; |
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new_v = sum(flow.v(row_range, col_range).mul(w))[0] / w_sum; |
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} |
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for (int dr = 0; dr <= 1; ++dr) { |
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int nr = 2*r + dr; |
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for (int dc = 0; dc <= 1; ++dc) { |
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int nc = 2*c + dc; |
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if (nr < new_rows && nc < new_cols) { |
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new_flow.u.at<double>(nr, nc) = 2 * new_u; |
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new_flow.v.at<double>(nr, nc) = 2 * new_v; |
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} |
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} |
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} |
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} |
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} |
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return new_flow; |
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} |
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static Mat calcIrregularityMat(const Flow& flow, int radius) { |
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const int rows = flow.u.rows; |
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const int cols = flow.v.cols; |
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Mat irregularity = Mat::zeros(rows, cols, CV_64F); |
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for (int r = 0; r < rows; ++r) { |
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const int start_row = max(0, r - radius); |
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const int end_row = min(rows - 1, r + radius); |
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for (int c = 0; c < cols; ++c) { |
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const int start_col = max(0, c - radius); |
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const int end_col = min(cols - 1, c + radius); |
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for (int dr = start_row; dr <= end_row; ++dr) { |
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for (int dc = start_col; dc <= end_col; ++dc) { |
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const double diff = dist(flow.u.at<double>(r, c), flow.v.at<double>(r, c),
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flow.u.at<double>(dr, dc), flow.v.at<double>(dr, dc)); |
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if (diff > irregularity.at<double>(r, c)) { |
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irregularity.at<double>(r, c) = diff; |
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} |
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} |
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} |
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} |
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} |
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return irregularity; |
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} |
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static void selectPointsToRecalcFlow(const Flow& flow,
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int irregularity_metric_radius, |
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int speed_up_thr, |
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int curr_rows, |
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int curr_cols, |
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const Mat& prev_speed_up, |
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Mat& speed_up, |
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Mat& mask) { |
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const int prev_rows = flow.u.rows; |
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const int prev_cols = flow.v.cols; |
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Mat is_flow_regular = calcIrregularityMat(flow,
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irregularity_metric_radius) |
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< speed_up_thr; |
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Mat done = Mat::zeros(prev_rows, prev_cols, CV_8U); |
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speed_up = Mat::zeros(curr_rows, curr_cols, CV_8U); |
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mask = Mat::zeros(curr_rows, curr_cols, CV_8U); |
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for (int r = 0; r < is_flow_regular.rows; ++r) { |
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for (int c = 0; c < is_flow_regular.cols; ++c) { |
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if (!done.at<uchar>(r, c)) { |
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if (is_flow_regular.at<uchar>(r, c) &&
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2*r + 1 < curr_rows && 2*c + 1< curr_cols) { |
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bool all_flow_in_region_regular = true; |
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int speed_up_at_this_point = prev_speed_up.at<uchar>(r, c); |
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int step = (1 << speed_up_at_this_point) - 1; |
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int prev_top = r; |
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int prev_bottom = std::min(r + step, prev_rows - 1); |
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int prev_left = c; |
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int prev_right = std::min(c + step, prev_cols - 1); |
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for (int rr = prev_top; rr <= prev_bottom; ++rr) { |
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for (int cc = prev_left; cc <= prev_right; ++cc) { |
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done.at<uchar>(rr, cc) = 1; |
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if (!is_flow_regular.at<uchar>(rr, cc)) { |
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all_flow_in_region_regular = false; |
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} |
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} |
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} |
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int curr_top = std::min(2 * r, curr_rows - 1); |
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int curr_bottom = std::min(2*(r + step) + 1, curr_rows - 1); |
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int curr_left = std::min(2 * c, curr_cols - 1); |
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int curr_right = std::min(2*(c + step) + 1, curr_cols - 1); |
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if (all_flow_in_region_regular &&
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curr_top != curr_bottom && |
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curr_left != curr_right) { |
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mask.at<uchar>(curr_top, curr_left) = MASK_TRUE_VALUE; |
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mask.at<uchar>(curr_bottom, curr_left) = MASK_TRUE_VALUE; |
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mask.at<uchar>(curr_top, curr_right) = MASK_TRUE_VALUE; |
||||
mask.at<uchar>(curr_bottom, curr_right) = MASK_TRUE_VALUE; |
||||
for (int rr = curr_top; rr <= curr_bottom; ++rr) { |
||||
for (int cc = curr_left; cc <= curr_right; ++cc) { |
||||
speed_up.at<uchar>(rr, cc) = speed_up_at_this_point + 1;
|
||||
} |
||||
} |
||||
} else { |
||||
for (int rr = curr_top; rr <= curr_bottom; ++rr) { |
||||
for (int cc = curr_left; cc <= curr_right; ++cc) { |
||||
mask.at<uchar>(rr, cc) = MASK_TRUE_VALUE; |
||||
} |
||||
} |
||||
} |
||||
} else { |
||||
done.at<uchar>(r, c) = 1; |
||||
for (int dr = 0; dr <= 1; ++dr) { |
||||
int nr = 2*r + dr; |
||||
for (int dc = 0; dc <= 1; ++dc) { |
||||
int nc = 2*c + dc; |
||||
if (nr < curr_rows && nc < curr_cols) { |
||||
mask.at<uchar>(nr, nc) = MASK_TRUE_VALUE; |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
static inline double extrapolateValueInRect(int height, int width, |
||||
double v11, double v12, |
||||
double v21, double v22, |
||||
int r, int c) { |
||||
if (r == 0 && c == 0) { return v11;} |
||||
if (r == 0 && c == width) { return v12;} |
||||
if (r == height && c == 0) { return v21;} |
||||
if (r == height && c == width) { return v22;} |
||||
|
||||
double qr = double(r) / height; |
||||
double pr = 1.0 - qr; |
||||
double qc = double(c) / width; |
||||
double pc = 1.0 - qc; |
||||
|
||||
return v11*pr*pc + v12*pr*qc + v21*qr*pc + v22*qc*qr;
|
||||
} |
||||
|
||||
static void extrapolateFlow(Flow& flow, |
||||
const Mat& speed_up) { |
||||
const int rows = flow.u.rows; |
||||
const int cols = flow.u.cols; |
||||
Mat done = Mat::zeros(rows, cols, CV_8U); |
||||
for (int r = 0; r < rows; ++r) { |
||||
for (int c = 0; c < cols; ++c) { |
||||
if (!done.at<uchar>(r, c) && speed_up.at<uchar>(r, c) > 1) { |
||||
int step = (1 << speed_up.at<uchar>(r, c)) - 1; |
||||
int top = r; |
||||
int bottom = std::min(r + step, rows - 1); |
||||
int left = c; |
||||
int right = std::min(c + step, cols - 1); |
||||
|
||||
int height = bottom - top; |
||||
int width = right - left; |
||||
for (int rr = top; rr <= bottom; ++rr) { |
||||
for (int cc = left; cc <= right; ++cc) { |
||||
done.at<uchar>(rr, cc) = 1; |
||||
flow.u.at<double>(rr, cc) = extrapolateValueInRect( |
||||
height, width,
|
||||
flow.u.at<double>(top, left), |
||||
flow.u.at<double>(top, right), |
||||
flow.u.at<double>(bottom, left), |
||||
flow.u.at<double>(bottom, right), |
||||
rr-top, cc-left);
|
||||
|
||||
flow.v.at<double>(rr, cc) = extrapolateValueInRect( |
||||
height, width,
|
||||
flow.v.at<double>(top, left), |
||||
flow.v.at<double>(top, right), |
||||
flow.v.at<double>(bottom, left), |
||||
flow.v.at<double>(bottom, right), |
||||
rr-top, cc-left);
|
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
} |
||||
|
||||
static void buildPyramidWithResizeMethod(Mat& src, |
||||
vector<Mat>& pyramid, |
||||
int layers, |
||||
int interpolation_type) { |
||||
pyramid.push_back(src); |
||||
for (int i = 1; i <= layers; ++i) { |
||||
Mat prev = pyramid[i - 1]; |
||||
if (prev.rows <= 1 || prev.cols <= 1) { |
||||
break; |
||||
} |
||||
|
||||
Mat next; |
||||
resize(prev, next, Size((prev.cols + 1) / 2, (prev.rows + 1) / 2), 0, 0, interpolation_type); |
||||
pyramid.push_back(next); |
||||
} |
||||
} |
||||
|
||||
static Flow calcOpticalFlowSF(Mat& from,
|
||||
Mat& to,
|
||||
int layers, |
||||
int averaging_block_size,
|
||||
int max_flow, |
||||
double sigma_dist, |
||||
double sigma_color, |
||||
int postprocess_window,
|
||||
double sigma_dist_fix,
|
||||
double sigma_color_fix, |
||||
double occ_thr, |
||||
int upscale_averaging_radius, |
||||
double upscale_sigma_dist, |
||||
double upscale_sigma_color, |
||||
double speed_up_thr) { |
||||
vector<Mat> pyr_from_images; |
||||
vector<Mat> pyr_to_images; |
||||
|
||||
buildPyramidWithResizeMethod(from, pyr_from_images, layers - 1, INTER_CUBIC); |
||||
buildPyramidWithResizeMethod(to, pyr_to_images, layers - 1, INTER_CUBIC); |
||||
// buildPyramid(from, pyr_from_images, layers - 1, BORDER_WRAP);
|
||||
// buildPyramid(to, pyr_to_images, layers - 1, BORDER_WRAP);
|
||||
|
||||
if ((int)pyr_from_images.size() != layers) { |
||||
exit(1); |
||||
} |
||||
|
||||
if ((int)pyr_to_images.size() != layers) { |
||||
exit(1); |
||||
} |
||||
|
||||
Mat first_from_image = pyr_from_images[layers - 1]; |
||||
Mat first_to_image = pyr_to_images[layers - 1]; |
||||
|
||||
Mat mask = Mat::ones(first_from_image.rows, first_from_image.cols, CV_8U); |
||||
Mat mask_inv = Mat::ones(first_from_image.rows, first_from_image.cols, CV_8U); |
||||
|
||||
Flow flow(first_from_image.rows, first_from_image.cols); |
||||
Flow flow_inv(first_to_image.rows, first_to_image.cols); |
||||
|
||||
Mat confidence; |
||||
Mat confidence_inv; |
||||
|
||||
calcOpticalFlowSingleScaleSF(first_from_image,
|
||||
first_to_image,
|
||||
mask, |
||||
flow, |
||||
confidence, |
||||
averaging_block_size,
|
||||
max_flow,
|
||||
sigma_dist,
|
||||
sigma_color); |
||||
|
||||
calcOpticalFlowSingleScaleSF(first_to_image,
|
||||
first_from_image,
|
||||
mask_inv, |
||||
flow_inv, |
||||
confidence_inv, |
||||
averaging_block_size,
|
||||
max_flow,
|
||||
sigma_dist,
|
||||
sigma_color); |
||||
|
||||
removeOcclusions(flow,
|
||||
flow_inv, |
||||
occ_thr, |
||||
confidence); |
||||
|
||||
removeOcclusions(flow_inv,
|
||||
flow, |
||||
occ_thr, |
||||
confidence_inv); |
||||
|
||||
Mat speed_up = Mat::zeros(first_from_image.rows, first_from_image.cols, CV_8U); |
||||
Mat speed_up_inv = Mat::zeros(first_from_image.rows, first_from_image.cols, CV_8U); |
||||
|
||||
for (int curr_layer = layers - 2; curr_layer >= 0; --curr_layer) { |
||||
const Mat curr_from = pyr_from_images[curr_layer]; |
||||
const Mat curr_to = pyr_to_images[curr_layer]; |
||||
const Mat prev_from = pyr_from_images[curr_layer + 1]; |
||||
const Mat prev_to = pyr_to_images[curr_layer + 1]; |
||||
|
||||
const int curr_rows = curr_from.rows; |
||||
const int curr_cols = curr_from.cols; |
||||
|
||||
Mat new_speed_up, new_speed_up_inv; |
||||
|
||||
selectPointsToRecalcFlow(flow, |
||||
averaging_block_size, |
||||
speed_up_thr, |
||||
curr_rows, |
||||
curr_cols, |
||||
speed_up, |
||||
new_speed_up, |
||||
mask); |
||||
|
||||
int points_to_recalculate = sum(mask)[0] / MASK_TRUE_VALUE; |
||||
|
||||
selectPointsToRecalcFlow(flow_inv, |
||||
averaging_block_size, |
||||
speed_up_thr, |
||||
curr_rows, |
||||
curr_cols, |
||||
speed_up_inv, |
||||
new_speed_up_inv, |
||||
mask_inv); |
||||
|
||||
points_to_recalculate = sum(mask_inv)[0] / MASK_TRUE_VALUE; |
||||
|
||||
speed_up = new_speed_up; |
||||
speed_up_inv = new_speed_up_inv; |
||||
|
||||
flow = upscaleOpticalFlow(curr_rows, |
||||
curr_cols, |
||||
prev_from, |
||||
confidence, |
||||
flow,
|
||||
upscale_averaging_radius, |
||||
upscale_sigma_dist, |
||||
upscale_sigma_color); |
||||
|
||||
flow_inv = upscaleOpticalFlow(curr_rows, |
||||
curr_cols, |
||||
prev_to, |
||||
confidence_inv, |
||||
flow_inv, |
||||
upscale_averaging_radius, |
||||
upscale_sigma_dist, |
||||
upscale_sigma_color); |
||||
|
||||
calcOpticalFlowSingleScaleSF(curr_from,
|
||||
curr_to,
|
||||
mask, |
||||
flow, |
||||
confidence, |
||||
averaging_block_size,
|
||||
max_flow,
|
||||
sigma_dist,
|
||||
sigma_color); |
||||
|
||||
calcOpticalFlowSingleScaleSF(curr_to, |
||||
curr_from,
|
||||
mask_inv, |
||||
flow_inv, |
||||
confidence_inv, |
||||
averaging_block_size,
|
||||
max_flow,
|
||||
sigma_dist,
|
||||
sigma_color); |
||||
|
||||
extrapolateFlow(flow, speed_up); |
||||
extrapolateFlow(flow_inv, speed_up_inv); |
||||
|
||||
removeOcclusions(flow, flow_inv, occ_thr, confidence); |
||||
removeOcclusions(flow_inv, flow, occ_thr, confidence_inv); |
||||
} |
||||
|
||||
WeightedCrossBilateralFilter filter_postprocess(pyr_from_images[0],
|
||||
postprocess_window, |
||||
sigma_dist_fix, |
||||
sigma_color_fix); |
||||
|
||||
flow.u = filter_postprocess.apply(flow.u, confidence); |
||||
flow.v = filter_postprocess.apply(flow.v, confidence); |
||||
|
||||
Mat blured_u, blured_v; |
||||
GaussianBlur(flow.u, blured_u, Size(3, 3), 5); |
||||
GaussianBlur(flow.v, blured_v, Size(3, 3), 5); |
||||
|
||||
return Flow(blured_v, blured_u); |
||||
} |
||||
|
||||
void calcOpticalFlowSF(Mat& from,
|
||||
Mat& to,
|
||||
Mat& flowX,
|
||||
Mat& flowY, |
||||
int layers, |
||||
int averaging_block_size,
|
||||
int max_flow, |
||||
double sigma_dist, |
||||
double sigma_color, |
||||
int postprocess_window,
|
||||
double sigma_dist_fix,
|
||||
double sigma_color_fix, |
||||
double occ_thr, |
||||
int upscale_averaging_radius, |
||||
double upscale_sigma_dist, |
||||
double upscale_sigma_color, |
||||
double speed_up_thr) { |
||||
|
||||
Flow flow = calcOpticalFlowSF(from, to,
|
||||
layers, |
||||
averaging_block_size, |
||||
max_flow, |
||||
sigma_dist, |
||||
sigma_color, |
||||
postprocess_window, |
||||
sigma_dist_fix, |
||||
sigma_color_fix, |
||||
occ_thr, |
||||
upscale_averaging_radius, |
||||
upscale_sigma_dist, |
||||
upscale_sigma_color, |
||||
speed_up_thr); |
||||
flowX = flow.u; |
||||
flowY = flow.v; |
||||
} |
||||
|
||||
} |
||||
|
||||
@ -0,0 +1,125 @@ |
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
||||
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of the copyright holders may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#ifndef __OPENCV_SIMPLEFLOW_H__ |
||||
#define __OPENCV_SIMPLEFLOW_H__ |
||||
|
||||
#include <vector> |
||||
|
||||
using namespace std; |
||||
|
||||
#define MASK_TRUE_VALUE 255 |
||||
#define UNKNOWN_FLOW_THRESH 1e9 |
||||
|
||||
namespace cv { |
||||
|
||||
struct Flow { |
||||
Mat u, v; |
||||
|
||||
Flow() {;} |
||||
|
||||
Flow(Mat& _u, Mat& _v) |
||||
: u(_u), v(_v) {;} |
||||
|
||||
Flow(int rows, int cols) { |
||||
u = Mat::zeros(rows, cols, CV_64F); |
||||
v = Mat::zeros(rows, cols, CV_64F); |
||||
} |
||||
}; |
||||
|
||||
inline static double dist(const Vec3b& p1, const Vec3b& p2) { |
||||
return (p1[0] - p2[0]) * (p1[0] - p2[0]) + |
||||
(p1[1] - p2[1]) * (p1[1] - p2[1]) + |
||||
(p1[2] - p2[2]) * (p1[2] - p2[2]); |
||||
} |
||||
|
||||
inline static double dist(const Point2f& p1, const Point2f& p2) { |
||||
return (p1.x - p2.x) * (p1.x - p2.x) + |
||||
(p1.y - p2.y) * (p1.y - p2.y); |
||||
} |
||||
|
||||
inline static double dist(double x1, double y1, double x2, double y2) { |
||||
return (x1 - x2) * (x1 - x2) + |
||||
(y1 - y2) * (y1 - y2); |
||||
} |
||||
|
||||
inline static int dist(int x1, int y1, int x2, int y2) { |
||||
return (x1 - x2) * (x1 - x2) + |
||||
(y1 - y2) * (y1 - y2); |
||||
} |
||||
|
||||
template<class T> |
||||
inline static T min(T t1, T t2, T t3) { |
||||
return (t1 <= t2 && t1 <= t3) ? t1 : min(t2, t3); |
||||
} |
||||
|
||||
template<class T> |
||||
vector<vector<T> > build(int n, int m) { |
||||
vector<vector<T> > res(n); |
||||
for (int i = 0; i < n; ++i) { |
||||
res[i].resize(m, 0); |
||||
} |
||||
return res; |
||||
} |
||||
|
||||
class WeightedCrossBilateralFilter { |
||||
public: |
||||
WeightedCrossBilateralFilter(const Mat& _image, |
||||
int _windowSize, |
||||
double _sigmaDist, |
||||
double _sigmaColor); |
||||
|
||||
Mat apply(Mat& matrix, Mat& weights); |
||||
|
||||
private: |
||||
double convolution(Mat& matrix, int row, int col, Mat& weights); |
||||
|
||||
Mat image; |
||||
int windowSize; |
||||
double sigmaDist, sigmaColor; |
||||
|
||||
vector<double> expDist; |
||||
vector<vector<vector<vector<double> > > > wc; |
||||
}; |
||||
} |
||||
|
||||
#endif |
||||
@ -0,0 +1,193 @@ |
||||
/*M///////////////////////////////////////////////////////////////////////////////////////
|
||||
//
|
||||
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
||||
//
|
||||
// By downloading, copying, installing or using the software you agree to this license.
|
||||
// If you do not agree to this license, do not download, install,
|
||||
// copy or use the software.
|
||||
//
|
||||
//
|
||||
// Intel License Agreement
|
||||
// For Open Source Computer Vision Library
|
||||
//
|
||||
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
||||
// Third party copyrights are property of their respective owners.
|
||||
//
|
||||
// Redistribution and use in source and binary forms, with or without modification,
|
||||
// are permitted provided that the following conditions are met:
|
||||
//
|
||||
// * Redistribution's of source code must retain the above copyright notice,
|
||||
// this list of conditions and the following disclaimer.
|
||||
//
|
||||
// * Redistribution's in binary form must reproduce the above copyright notice,
|
||||
// this list of conditions and the following disclaimer in the documentation
|
||||
// and/or other materials provided with the distribution.
|
||||
//
|
||||
// * The name of Intel Corporation may not be used to endorse or promote products
|
||||
// derived from this software without specific prior written permission.
|
||||
//
|
||||
// This software is provided by the copyright holders and contributors "as is" and
|
||||
// any express or implied warranties, including, but not limited to, the implied
|
||||
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
||||
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
||||
// indirect, incidental, special, exemplary, or consequential damages
|
||||
// (including, but not limited to, procurement of substitute goods or services;
|
||||
// loss of use, data, or profits; or business interruption) however caused
|
||||
// and on any theory of liability, whether in contract, strict liability,
|
||||
// or tort (including negligence or otherwise) arising in any way out of
|
||||
// the use of this software, even if advised of the possibility of such damage.
|
||||
//
|
||||
//M*/
|
||||
|
||||
#include "test_precomp.hpp" |
||||
|
||||
#include <string> |
||||
|
||||
using namespace std; |
||||
|
||||
/* ///////////////////// simpleflow_test ///////////////////////// */ |
||||
|
||||
class CV_SimpleFlowTest : public cvtest::BaseTest |
||||
{ |
||||
public: |
||||
CV_SimpleFlowTest(); |
||||
protected: |
||||
void run(int); |
||||
}; |
||||
|
||||
|
||||
CV_SimpleFlowTest::CV_SimpleFlowTest() {} |
||||
|
||||
static void readOpticalFlowFromFile(FILE* file, cv::Mat& flowX, cv::Mat& flowY) { |
||||
char header[5]; |
||||
if (fread(header, 1, 4, file) < 4 && (string)header != "PIEH") { |
||||
return; |
||||
} |
||||
|
||||
int cols, rows; |
||||
if (fread(&cols, sizeof(int), 1, file) != 1|| |
||||
fread(&rows, sizeof(int), 1, file) != 1) { |
||||
return; |
||||
} |
||||
|
||||
flowX = cv::Mat::zeros(rows, cols, CV_64F); |
||||
flowY = cv::Mat::zeros(rows, cols, CV_64F); |
||||
|
||||
for (int i = 0; i < rows; ++i) { |
||||
for (int j = 0; j < cols; ++j) { |
||||
float uPoint, vPoint; |
||||
if (fread(&uPoint, sizeof(float), 1, file) != 1 || |
||||
fread(&vPoint, sizeof(float), 1, file) != 1) { |
||||
flowX.release(); |
||||
flowY.release(); |
||||
return; |
||||
} |
||||
|
||||
flowX.at<double>(i, j) = uPoint; |
||||
flowY.at<double>(i, j) = vPoint; |
||||
} |
||||
} |
||||
} |
||||
|
||||
static bool isFlowCorrect(double u) { |
||||
return !isnan(u) && (fabs(u) < 1e9); |
||||
} |
||||
|
||||
static double calc_rmse(cv::Mat flow1X, cv::Mat flow1Y, cv::Mat flow2X, cv::Mat flow2Y) { |
||||
long double sum; |
||||
int counter = 0; |
||||
const int rows = flow1X.rows; |
||||
const int cols = flow1X.cols; |
||||
|
||||
for (int y = 0; y < rows; ++y) { |
||||
for (int x = 0; x < cols; ++x) { |
||||
double u1 = flow1X.at<double>(y, x); |
||||
double v1 = flow1Y.at<double>(y, x); |
||||
double u2 = flow2X.at<double>(y, x); |
||||
double v2 = flow2Y.at<double>(y, x); |
||||
if (isFlowCorrect(u1) && isFlowCorrect(u2) && isFlowCorrect(v1) && isFlowCorrect(v2)) { |
||||
sum += (u1-u2)*(u1-u2) + (v1-v2)*(v1-v2); |
||||
counter++; |
||||
} |
||||
} |
||||
} |
||||
return sqrt((double)sum / (1e-9 + counter)); |
||||
} |
||||
|
||||
void CV_SimpleFlowTest::run(int) { |
||||
int code = cvtest::TS::OK; |
||||
|
||||
const double MAX_RMSE = 0.6; |
||||
const string frame1_path = ts->get_data_path() + "optflow/RubberWhale1.png"; |
||||
const string frame2_path = ts->get_data_path() + "optflow/RubberWhale2.png"; |
||||
const string gt_flow_path = ts->get_data_path() + "optflow/RubberWhale.flo"; |
||||
|
||||
cv::Mat frame1 = cv::imread(frame1_path); |
||||
cv::Mat frame2 = cv::imread(frame2_path); |
||||
|
||||
if (frame1.empty()) { |
||||
ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str()); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
||||
return; |
||||
} |
||||
|
||||
if (frame2.empty()) { |
||||
ts->printf(cvtest::TS::LOG, "could not read image %s\n", frame2_path.c_str()); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
||||
return; |
||||
} |
||||
|
||||
if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) { |
||||
ts->printf(cvtest::TS::LOG, "images should be of equal sizes (%s and %s)", |
||||
frame1_path.c_str(), frame2_path.c_str()); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
||||
return; |
||||
} |
||||
|
||||
if (frame1.type() != 16 || frame2.type() != 16) { |
||||
ts->printf(cvtest::TS::LOG, "images should be of equal type CV_8UC3 (%s and %s)", |
||||
frame1_path.c_str(), frame2_path.c_str()); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
||||
return; |
||||
} |
||||
|
||||
cv::Mat flowX_gt, flowY_gt; |
||||
|
||||
FILE* gt_flow_file = fopen(gt_flow_path.c_str(), "rb"); |
||||
if (gt_flow_file == NULL) { |
||||
ts->printf(cvtest::TS::LOG, "could not read ground-thuth flow from file %s", |
||||
gt_flow_path.c_str()); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
||||
return; |
||||
} |
||||
readOpticalFlowFromFile(gt_flow_file, flowX_gt, flowY_gt); |
||||
if (flowX_gt.empty() || flowY_gt.empty()) { |
||||
ts->printf(cvtest::TS::LOG, "error while reading flow data from file %s", |
||||
gt_flow_path.c_str()); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
||||
return; |
||||
} |
||||
fclose(gt_flow_file); |
||||
|
||||
cv::Mat flowX, flowY; |
||||
cv::calcOpticalFlowSF(frame1, frame2,
|
||||
flowX, flowY, |
||||
3, 4, 2, 4.1, 25.5, 18, 55.0, 25.5, 0.35, 18, 55.0, 25.5, 10); |
||||
|
||||
double rmse = calc_rmse(flowX_gt, flowY_gt, flowX, flowY); |
||||
|
||||
ts->printf(cvtest::TS::LOG, "Optical flow estimation RMSE for SimpleFlow algorithm : %lf\n", |
||||
rmse); |
||||
|
||||
if (rmse > MAX_RMSE) { |
||||
ts->printf( cvtest::TS::LOG, |
||||
"Too big rmse error : %lf ( >= %lf )\n", rmse, MAX_RMSE); |
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY); |
||||
return; |
||||
} |
||||
} |
||||
|
||||
|
||||
TEST(Video_OpticalFlowSimpleFlow, accuracy) { CV_SimpleFlowTest test; test.safe_run(); } |
||||
|
||||
/* End of file. */ |
||||
@ -0,0 +1,96 @@ |
||||
#include "opencv2/video/tracking.hpp" |
||||
#include "opencv2/imgproc/imgproc.hpp" |
||||
#include "opencv2/highgui/highgui.hpp" |
||||
|
||||
#include <cstdio> |
||||
#include <iostream> |
||||
|
||||
using namespace cv; |
||||
using namespace std; |
||||
|
||||
static void help() |
||||
{ |
||||
// print a welcome message, and the OpenCV version
|
||||
printf("This is a demo of SimpleFlow optical flow algorithm,\n" |
||||
"Using OpenCV version %s\n\n", CV_VERSION); |
||||
|
||||
printf("Usage: simpleflow_demo frame1 frame2 output_flow" |
||||
"\nApplication will write estimated flow " |
||||
"\nbetween 'frame1' and 'frame2' in binary format" |
||||
"\ninto file 'output_flow'" |
||||
"\nThen one can use code from http://vision.middlebury.edu/flow/data/" |
||||
"\nto convert flow in binary file to image\n"); |
||||
} |
||||
|
||||
// binary file format for flow data specified here:
|
||||
// http://vision.middlebury.edu/flow/data/
|
||||
static void writeOpticalFlowToFile(const Mat& u, const Mat& v, FILE* file) { |
||||
int cols = u.cols; |
||||
int rows = u.rows; |
||||
|
||||
fprintf(file, "PIEH"); |
||||
|
||||
if (fwrite(&cols, sizeof(int), 1, file) != 1 || |
||||
fwrite(&rows, sizeof(int), 1, file) != 1) { |
||||
fprintf(stderr, "writeOpticalFlowToFile : problem writing header\n"); |
||||
exit(1); |
||||
} |
||||
|
||||
for (int i= 0; i < u.rows; ++i) { |
||||
for (int j = 0; j < u.cols; ++j) { |
||||
float uPoint = u.at<double>(i, j); |
||||
float vPoint = v.at<double>(i, j); |
||||
|
||||
if (fwrite(&uPoint, sizeof(float), 1, file) != 1 || |
||||
fwrite(&vPoint, sizeof(float), 1, file) != 1) { |
||||
fprintf(stderr, "writeOpticalFlowToFile : problem writing data\n"); |
||||
exit(1); |
||||
} |
||||
} |
||||
} |
||||
} |
||||
int main(int argc, char** argv) { |
||||
help(); |
||||
|
||||
if (argc < 4) { |
||||
fprintf(stderr, "Wrong number of command line arguments : %d (expected %d)\n", argc, 4); |
||||
exit(1); |
||||
} |
||||
|
||||
Mat frame1 = imread(argv[1]); |
||||
Mat frame2 = imread(argv[2]); |
||||
|
||||
if (frame1.empty() || frame2.empty()) { |
||||
fprintf(stderr, "simpleflow_demo : Images cannot be read\n"); |
||||
exit(1); |
||||
} |
||||
|
||||
if (frame1.rows != frame2.rows && frame1.cols != frame2.cols) { |
||||
fprintf(stderr, "simpleflow_demo : Images should be of equal sizes\n"); |
||||
exit(1); |
||||
} |
||||
|
||||
if (frame1.type() != 16 || frame2.type() != 16) { |
||||
fprintf(stderr, "simpleflow_demo : Images should be of equal type CV_8UC3\n"); |
||||
exit(1); |
||||
} |
||||
|
||||
printf("simpleflow_demo : Read two images of size [rows = %d, cols = %d]\n",
|
||||
frame1.rows, frame1.cols); |
||||
|
||||
Mat flowX, flowY; |
||||
|
||||
calcOpticalFlowSF(frame1, frame2,
|
||||
flowX, flowY, |
||||
3, 2, 4, 4.1, 25.5, 18, 55.0, 25.5, 0.35, 18, 55.0, 25.5, 10); |
||||
|
||||
FILE* file = fopen(argv[3], "wb"); |
||||
if (file == NULL) { |
||||
fprintf(stderr, "simpleflow_demo : Unable to open file '%s' for writing\n", argv[3]);
|
||||
exit(1); |
||||
} |
||||
printf("simpleflow_demo : Writing to file\n"); |
||||
writeOpticalFlowToFile(flowX, flowY, file); |
||||
fclose(file); |
||||
return 0; |
||||
} |
||||
Loading…
Reference in new issue