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689 lines
26 KiB
C++
689 lines
26 KiB
C++
/*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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//
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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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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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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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//
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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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static const uchar MASK_TRUE_VALUE = (uchar)255;
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inline static float dist(const Vec3b& p1, const Vec3b& p2) {
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return (float)((p1[0] - p2[0]) * (p1[0] - p2[0]) +
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(p1[1] - p2[1]) * (p1[1] - p2[1]) +
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(p1[2] - p2[2]) * (p1[2] - p2[2]));
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}
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inline static float dist(const Vec2f& p1, const Vec2f& p2) {
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return (p1[0] - p2[0]) * (p1[0] - p2[0]) +
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(p1[1] - p2[1]) * (p1[1] - p2[1]);
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}
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inline static float dist(const Point2f& p1, const Point2f& p2) {
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return (p1.x - p2.x) * (p1.x - p2.x) +
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(p1.y - p2.y) * (p1.y - p2.y);
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}
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inline static float dist(float x1, float y1, float x2, float y2) {
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return (x1 - x2) * (x1 - x2) +
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(y1 - y2) * (y1 - y2);
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}
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inline static int dist(int x1, int y1, int x2, int y2) {
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return (x1 - x2) * (x1 - x2) +
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(y1 - y2) * (y1 - y2);
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}
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template<class T>
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inline static T min(T t1, T t2, T t3) {
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return (t1 <= t2 && t1 <= t3) ? t1 : min(t2, t3);
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}
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static void removeOcclusions(const Mat& flow,
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const Mat& flow_inv,
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float occ_thr,
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Mat& confidence) {
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const int rows = flow.rows;
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const int cols = flow.cols;
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if (!confidence.data) {
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confidence = Mat::zeros(rows, cols, CV_32F);
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}
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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.at<Vec2f>(r, c), -flow_inv.at<Vec2f>(r, c)) > occ_thr) {
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confidence.at<float>(r, c) = 0;
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} else {
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confidence.at<float>(r, c) = 1;
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}
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}
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}
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}
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static void wd(Mat& d, int top_shift, int bottom_shift, int left_shift, int right_shift, float sigma) {
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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<float>(r, c) = (float)-(dr*dr + dc*dc);
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}
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}
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d *= 1.0 / (2.0 * sigma * sigma);
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exp(d, d);
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}
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static void wc(const Mat& image, Mat& d, int r0, int c0,
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int top_shift, int bottom_shift, int left_shift, int right_shift, float sigma) {
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const Vec3b centeral_point = image.at<Vec3b>(r0, c0);
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int left_border = c0-left_shift, right_border = c0+right_shift;
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for (int dr = r0-top_shift, r = 0; dr <= r0+bottom_shift; ++dr, ++r) {
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const Vec3b *row = image.ptr<Vec3b>(dr);
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float *d_row = d.ptr<float>(r);
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for (int dc = left_border, c = 0; dc <= right_border; ++dc, ++c) {
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d_row[c] = -dist(centeral_point, row[dc]);
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}
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}
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d *= 1.0 / (2.0 * sigma * sigma);
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exp(d, d);
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}
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static void crossBilateralFilter(const Mat& image,
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const Mat& edge_image,
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const Mat confidence,
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Mat& dst, int d,
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float sigma_color, float sigma_space,
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bool flag=false) {
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const int rows = image.rows;
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const int cols = image.cols;
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Mat image_extended, edge_image_extended, confidence_extended;
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copyMakeBorder(image, image_extended, d, d, d, d, BORDER_DEFAULT);
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copyMakeBorder(edge_image, edge_image_extended, d, d, d, d, BORDER_DEFAULT);
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copyMakeBorder(confidence, confidence_extended, d, d, d, d, BORDER_CONSTANT, Scalar(0));
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Mat weights_space(2*d+1, 2*d+1, CV_32F);
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wd(weights_space, d, d, d, d, sigma_space);
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Mat weights(2*d+1, 2*d+1, CV_32F);
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Mat weighted_sum(2*d+1, 2*d+1, CV_32F);
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std::vector<Mat> image_extended_channels;
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split(image_extended, image_extended_channels);
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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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wc(edge_image_extended, weights, row+d, col+d, d, d, d, d, sigma_color);
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Range window_rows(row,row+2*d+1);
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Range window_cols(col,col+2*d+1);
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multiply(weights, confidence_extended(window_rows, window_cols), weights);
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multiply(weights, weights_space, weights);
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float weights_sum = (float)sum(weights)[0];
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for (int ch = 0; ch < 2; ++ch) {
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multiply(weights, image_extended_channels[ch](window_rows, window_cols), weighted_sum);
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float total_sum = (float)sum(weighted_sum)[0];
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dst.at<Vec2f>(row, col)[ch] = (flag && fabs(weights_sum) < 1e-9)
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? image.at<float>(row, col)
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: total_sum / weights_sum;
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}
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}
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}
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}
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static void calcConfidence(const Mat& prev,
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const Mat& next,
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const Mat& flow,
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Mat& confidence,
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int max_flow) {
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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_32F);
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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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Vec2f flow_at_point = flow.at<Vec2f>(r0, c0);
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int u0 = cvRound(flow_at_point[0]);
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if (r0 + u0 < 0) { u0 = -r0; }
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if (r0 + u0 >= rows) { u0 = rows - 1 - r0; }
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int v0 = cvRound(flow_at_point[1]);
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if (c0 + v0 < 0) { v0 = -c0; }
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if (c0 + v0 >= cols) { v0 = cols - 1 - c0; }
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const int top_row_shift = -std::min(r0 + u0, max_flow);
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const int bottom_row_shift = std::min(rows - 1 - (r0 + u0), max_flow);
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const int left_col_shift = -std::min(c0 + v0, max_flow);
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const int right_col_shift = std::min(cols - 1 - (c0 + v0), max_flow);
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bool first_flow_iteration = true;
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float sum_e = 0, min_e = 0;
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for (int u = top_row_shift; u <= bottom_row_shift; ++u) {
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for (int v = left_col_shift; v <= right_col_shift; ++v) {
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float 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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}
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}
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int windows_square = (bottom_row_shift - top_row_shift + 1) *
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(right_col_shift - left_col_shift + 1);
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confidence.at<float>(r0, c0) = (windows_square == 0) ? 0
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: sum_e / windows_square - min_e;
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CV_Assert(confidence.at<float>(r0, c0) >= 0);
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}
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}
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}
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static void calcOpticalFlowSingleScaleSF(const Mat& prev_extended,
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const Mat& next_extended,
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const Mat& mask,
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Mat& flow,
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int averaging_radius,
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int max_flow,
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float sigma_dist,
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float sigma_color) {
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const int averaging_radius_2 = averaging_radius << 1;
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const int rows = prev_extended.rows - averaging_radius_2;
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const int cols = prev_extended.cols - averaging_radius_2;
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Mat weight_window(averaging_radius_2 + 1, averaging_radius_2 + 1, CV_32F);
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Mat space_weight_window(averaging_radius_2 + 1, averaging_radius_2 + 1, CV_32F);
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wd(space_weight_window, averaging_radius, averaging_radius, averaging_radius, averaging_radius, sigma_dist);
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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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if (!mask.at<uchar>(r0, c0)) {
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continue;
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}
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// TODO: do smth with this creepy staff
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Vec2f flow_at_point = flow.at<Vec2f>(r0, c0);
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int u0 = cvRound(flow_at_point[0]);
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if (r0 + u0 < 0) { u0 = -r0; }
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if (r0 + u0 >= rows) { u0 = rows - 1 - r0; }
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int v0 = cvRound(flow_at_point[1]);
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if (c0 + v0 < 0) { v0 = -c0; }
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if (c0 + v0 >= cols) { v0 = cols - 1 - c0; }
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const int top_row_shift = -std::min(r0 + u0, max_flow);
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const int bottom_row_shift = std::min(rows - 1 - (r0 + u0), max_flow);
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const int left_col_shift = -std::min(c0 + v0, max_flow);
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const int right_col_shift = std::min(cols - 1 - (c0 + v0), max_flow);
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float min_cost = FLT_MAX, best_u = (float)u0, best_v = (float)v0;
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wc(prev_extended, weight_window, r0 + averaging_radius, c0 + averaging_radius,
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averaging_radius, averaging_radius, averaging_radius, averaging_radius, sigma_color);
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multiply(weight_window, space_weight_window, weight_window);
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const int prev_extended_top_window_row = r0;
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const int prev_extended_left_window_col = c0;
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for (int u = top_row_shift; u <= bottom_row_shift; ++u) {
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const int next_extended_top_window_row = r0 + u0 + u;
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for (int v = left_col_shift; v <= right_col_shift; ++v) {
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const int next_extended_left_window_col = c0 + v0 + v;
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float cost = 0;
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for (int r = 0; r <= averaging_radius_2; ++r) {
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const Vec3b *prev_extended_window_row = prev_extended.ptr<Vec3b>(prev_extended_top_window_row + r);
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const Vec3b *next_extended_window_row = next_extended.ptr<Vec3b>(next_extended_top_window_row + r);
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const float* weight_window_row = weight_window.ptr<float>(r);
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for (int c = 0; c <= averaging_radius_2; ++c) {
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cost += weight_window_row[c] *
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dist(prev_extended_window_row[prev_extended_left_window_col + c],
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next_extended_window_row[next_extended_left_window_col + c]);
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}
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}
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// cost should be divided by sum(weight_window), but because
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// we interested only in min(cost) and sum(weight_window) is constant
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// for every point - we remove it
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if (cost < min_cost) {
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min_cost = cost;
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best_u = (float)(u + u0);
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best_v = (float)(v + v0);
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}
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}
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}
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flow.at<Vec2f>(r0, c0) = Vec2f(best_u, best_v);
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}
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}
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}
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static Mat 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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Mat& flow,
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int averaging_radius,
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float sigma_dist,
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float sigma_color) {
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crossBilateralFilter(flow, image, confidence, flow, averaging_radius, sigma_color, sigma_dist, true);
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Mat new_flow;
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resize(flow, new_flow, Size(new_cols, new_rows), 0, 0, INTER_NEAREST);
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new_flow *= 2;
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return new_flow;
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}
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static Mat calcIrregularityMat(const Mat& flow, int radius) {
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const int rows = flow.rows;
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const int cols = flow.cols;
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Mat irregularity(rows, cols, CV_32F);
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for (int r = 0; r < rows; ++r) {
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const int start_row = std::max(0, r - radius);
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const int end_row = std::min(rows - 1, r + radius);
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for (int c = 0; c < cols; ++c) {
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const int start_col = std::max(0, c - radius);
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const int end_col = std::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 float diff = dist(flow.at<Vec2f>(r, c), flow.at<Vec2f>(dr, dc));
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if (diff > irregularity.at<float>(r, c)) {
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irregularity.at<float>(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 Mat& flow,
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int irregularity_metric_radius,
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float 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.rows;
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const int prev_cols = flow.cols;
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Mat is_flow_regular = calcIrregularityMat(flow, 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;
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mask.at<uchar>(curr_bottom, curr_right) = MASK_TRUE_VALUE;
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for (int rr = curr_top; rr <= curr_bottom; ++rr) {
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for (int cc = curr_left; cc <= curr_right; ++cc) {
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speed_up.at<uchar>(rr, cc) = (uchar)(speed_up_at_this_point + 1);
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}
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}
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} else {
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for (int rr = curr_top; rr <= curr_bottom; ++rr) {
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for (int cc = curr_left; cc <= curr_right; ++cc) {
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mask.at<uchar>(rr, cc) = MASK_TRUE_VALUE;
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}
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}
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}
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} else {
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done.at<uchar>(r, c) = 1;
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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 < curr_rows && nc < curr_cols) {
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mask.at<uchar>(nr, nc) = MASK_TRUE_VALUE;
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}
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}
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}
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}
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}
|
|
}
|
|
}
|
|
}
|
|
|
|
static inline float extrapolateValueInRect(int height, int width,
|
|
float v11, float v12,
|
|
float v21, float 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;}
|
|
|
|
float qr = float(r) / height;
|
|
float pr = 1.0f - qr;
|
|
float qc = float(c) / width;
|
|
float pc = 1.0f - qc;
|
|
|
|
return v11*pr*pc + v12*pr*qc + v21*qr*pc + v22*qc*qr;
|
|
}
|
|
|
|
static void extrapolateFlow(Mat& flow,
|
|
const Mat& speed_up) {
|
|
const int rows = flow.rows;
|
|
const int cols = flow.cols;
|
|
Mat done(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;
|
|
Vec2f flow_at_point;
|
|
Vec2f top_left = flow.at<Vec2f>(top, left);
|
|
Vec2f top_right = flow.at<Vec2f>(top, right);
|
|
Vec2f bottom_left = flow.at<Vec2f>(bottom, left);
|
|
Vec2f bottom_right = flow.at<Vec2f>(bottom, right);
|
|
|
|
flow_at_point[0] = extrapolateValueInRect(height, width,
|
|
top_left[0], top_right[0],
|
|
bottom_left[0], bottom_right[0],
|
|
rr-top, cc-left);
|
|
|
|
flow_at_point[1] = extrapolateValueInRect(height, width,
|
|
top_left[1], top_right[1],
|
|
bottom_left[1], bottom_right[1],
|
|
rr-top, cc-left);
|
|
flow.at<Vec2f>(rr, cc) = flow_at_point;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
static void buildPyramidWithResizeMethod(const Mat& src,
|
|
std::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);
|
|
}
|
|
}
|
|
|
|
CV_EXPORTS_W void calcOpticalFlowSF(InputArray _from,
|
|
InputArray _to,
|
|
OutputArray _resulted_flow,
|
|
int layers,
|
|
int averaging_radius,
|
|
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)
|
|
{
|
|
Mat from = _from.getMat();
|
|
Mat to = _to.getMat();
|
|
|
|
std::vector<Mat> pyr_from_images;
|
|
std::vector<Mat> pyr_to_images;
|
|
|
|
buildPyramidWithResizeMethod(from, pyr_from_images, layers - 1, INTER_CUBIC);
|
|
buildPyramidWithResizeMethod(to, pyr_to_images, layers - 1, INTER_CUBIC);
|
|
|
|
CV_Assert((int)pyr_from_images.size() == layers && (int)pyr_to_images.size() == layers);
|
|
|
|
Mat curr_from, curr_to, prev_from, prev_to;
|
|
Mat curr_from_extended, curr_to_extended;
|
|
|
|
curr_from = pyr_from_images[layers - 1];
|
|
curr_to = pyr_to_images[layers - 1];
|
|
|
|
copyMakeBorder(curr_from, curr_from_extended,
|
|
averaging_radius, averaging_radius, averaging_radius, averaging_radius,
|
|
BORDER_DEFAULT);
|
|
copyMakeBorder(curr_to, curr_to_extended,
|
|
averaging_radius, averaging_radius, averaging_radius, averaging_radius,
|
|
BORDER_DEFAULT);
|
|
|
|
Mat mask = Mat::ones(curr_from.size(), CV_8U);
|
|
Mat mask_inv = Mat::ones(curr_from.size(), CV_8U);
|
|
|
|
Mat flow(curr_from.size(), CV_32FC2);
|
|
Mat flow_inv(curr_to.size(), CV_32FC2);
|
|
|
|
Mat confidence;
|
|
Mat confidence_inv;
|
|
|
|
|
|
calcOpticalFlowSingleScaleSF(curr_from_extended,
|
|
curr_to_extended,
|
|
mask,
|
|
flow,
|
|
averaging_radius,
|
|
max_flow,
|
|
(float)sigma_dist,
|
|
(float)sigma_color);
|
|
|
|
calcOpticalFlowSingleScaleSF(curr_to_extended,
|
|
curr_from_extended,
|
|
mask_inv,
|
|
flow_inv,
|
|
averaging_radius,
|
|
max_flow,
|
|
(float)sigma_dist,
|
|
(float)sigma_color);
|
|
|
|
removeOcclusions(flow,
|
|
flow_inv,
|
|
(float)occ_thr,
|
|
confidence);
|
|
|
|
removeOcclusions(flow_inv,
|
|
flow,
|
|
(float)occ_thr,
|
|
confidence_inv);
|
|
|
|
Mat speed_up = Mat::zeros(curr_from.size(), CV_8U);
|
|
Mat speed_up_inv = Mat::zeros(curr_from.size(), CV_8U);
|
|
|
|
for (int curr_layer = layers - 2; curr_layer >= 0; --curr_layer) {
|
|
curr_from = pyr_from_images[curr_layer];
|
|
curr_to = pyr_to_images[curr_layer];
|
|
prev_from = pyr_from_images[curr_layer + 1];
|
|
prev_to = pyr_to_images[curr_layer + 1];
|
|
|
|
copyMakeBorder(curr_from, curr_from_extended,
|
|
averaging_radius, averaging_radius, averaging_radius, averaging_radius,
|
|
BORDER_DEFAULT);
|
|
copyMakeBorder(curr_to, curr_to_extended,
|
|
averaging_radius, averaging_radius, averaging_radius, averaging_radius,
|
|
BORDER_DEFAULT);
|
|
|
|
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_radius,
|
|
(float)speed_up_thr,
|
|
curr_rows,
|
|
curr_cols,
|
|
speed_up,
|
|
new_speed_up,
|
|
mask);
|
|
|
|
selectPointsToRecalcFlow(flow_inv,
|
|
averaging_radius,
|
|
(float)speed_up_thr,
|
|
curr_rows,
|
|
curr_cols,
|
|
speed_up_inv,
|
|
new_speed_up_inv,
|
|
mask_inv);
|
|
|
|
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,
|
|
(float)upscale_sigma_dist,
|
|
(float)upscale_sigma_color);
|
|
|
|
flow_inv = upscaleOpticalFlow(curr_rows,
|
|
curr_cols,
|
|
prev_to,
|
|
confidence_inv,
|
|
flow_inv,
|
|
upscale_averaging_radius,
|
|
(float)upscale_sigma_dist,
|
|
(float)upscale_sigma_color);
|
|
|
|
calcConfidence(curr_from, curr_to, flow, confidence, max_flow);
|
|
calcOpticalFlowSingleScaleSF(curr_from_extended,
|
|
curr_to_extended,
|
|
mask,
|
|
flow,
|
|
averaging_radius,
|
|
max_flow,
|
|
(float)sigma_dist,
|
|
(float)sigma_color);
|
|
|
|
calcConfidence(curr_to, curr_from, flow_inv, confidence_inv, max_flow);
|
|
calcOpticalFlowSingleScaleSF(curr_to_extended,
|
|
curr_from_extended,
|
|
mask_inv,
|
|
flow_inv,
|
|
averaging_radius,
|
|
max_flow,
|
|
(float)sigma_dist,
|
|
(float)sigma_color);
|
|
|
|
extrapolateFlow(flow, speed_up);
|
|
extrapolateFlow(flow_inv, speed_up_inv);
|
|
|
|
//TODO: should we remove occlusions for the last stage?
|
|
removeOcclusions(flow, flow_inv, (float)occ_thr, confidence);
|
|
removeOcclusions(flow_inv, flow, (float)occ_thr, confidence_inv);
|
|
}
|
|
|
|
crossBilateralFilter(flow, curr_from, confidence, flow,
|
|
postprocess_window, (float)sigma_color_fix, (float)sigma_dist_fix);
|
|
|
|
GaussianBlur(flow, flow, Size(3, 3), 5);
|
|
|
|
_resulted_flow.create(flow.size(), CV_32FC2);
|
|
Mat resulted_flow = _resulted_flow.getMat();
|
|
int from_to[] = {0,1 , 1,0};
|
|
mixChannels(&flow, 1, &resulted_flow, 1, from_to, 2);
|
|
}
|
|
|
|
CV_EXPORTS_W void calcOpticalFlowSF(InputArray from,
|
|
InputArray to,
|
|
OutputArray flow,
|
|
int layers,
|
|
int averaging_block_size,
|
|
int max_flow) {
|
|
calcOpticalFlowSF(from, to, flow, layers, averaging_block_size, max_flow,
|
|
4.1, 25.5, 18, 55.0, 25.5, 0.35, 18, 55.0, 25.5, 10);
|
|
}
|
|
|
|
}
|
|
|