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// Copyright (C) 2018-2019 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
#include <algorithm>
#include <limits>
#include <vector>
#include <utils/kuhn_munkres.hpp>
KuhnMunkres::KuhnMunkres(bool greedy) : n_(), greedy_(greedy) {}
std::vector<size_t> KuhnMunkres::Solve(const cv::Mat& dissimilarity_matrix) {
CV_Assert(dissimilarity_matrix.type() == CV_32F);
double min_val;
cv::minMaxLoc(dissimilarity_matrix, &min_val);
n_ = std::max(dissimilarity_matrix.rows, dissimilarity_matrix.cols);
dm_ = cv::Mat(n_, n_, CV_32F, cv::Scalar(0));
marked_ = cv::Mat(n_, n_, CV_8S, cv::Scalar(0));
points_ = std::vector<cv::Point>(n_ * 2);
dissimilarity_matrix.copyTo(dm_(
cv::Rect(0, 0, dissimilarity_matrix.cols, dissimilarity_matrix.rows)));
is_row_visited_ = std::vector<int>(n_, 0);
is_col_visited_ = std::vector<int>(n_, 0);
Run();
std::vector<size_t> results(dissimilarity_matrix.rows, -1);
for (int i = 0; i < dissimilarity_matrix.rows; i++) {
const auto ptr = marked_.ptr<char>(i);
for (int j = 0; j < dissimilarity_matrix.cols; j++) {
if (ptr[j] == kStar) {
results[i] = (size_t)j;
}
}
}
return results;
}
void KuhnMunkres::TrySimpleCase() {
auto is_row_visited = std::vector<int>(n_, 0);
auto is_col_visited = std::vector<int>(n_, 0);
for (int row = 0; row < n_; row++) {
auto ptr = dm_.ptr<float>(row);
auto marked_ptr = marked_.ptr<char>(row);
auto min_val = *std::min_element(ptr, ptr + n_);
for (int col = 0; col < n_; col++) {
ptr[col] -= min_val;
if (ptr[col] == 0 && !is_col_visited[col] && !is_row_visited[row]) {
marked_ptr[col] = kStar;
is_col_visited[col] = 1;
is_row_visited[row] = 1;
}
}
}
}
bool KuhnMunkres::CheckIfOptimumIsFound() {
int count = 0;
for (int i = 0; i < n_; i++) {
const auto marked_ptr = marked_.ptr<char>(i);
for (int j = 0; j < n_; j++) {
if (marked_ptr[j] == kStar) {
is_col_visited_[j] = 1;
count++;
}
}
}
return count >= n_;
}
cv::Point KuhnMunkres::FindUncoveredMinValPos() {
auto min_val = std::numeric_limits<float>::max();
cv::Point min_val_pos(-1, -1);
for (int i = 0; i < n_; i++) {
if (!is_row_visited_[i]) {
auto dm_ptr = dm_.ptr<float>(i);
for (int j = 0; j < n_; j++) {
if (!is_col_visited_[j] && dm_ptr[j] < min_val) {
min_val = dm_ptr[j];
min_val_pos = cv::Point(j, i);
}
}
}
}
return min_val_pos;
}
void KuhnMunkres::UpdateDissimilarityMatrix(float val) {
for (int i = 0; i < n_; i++) {
auto dm_ptr = dm_.ptr<float>(i);
for (int j = 0; j < n_; j++) {
if (is_row_visited_[i]) dm_ptr[j] += val;
if (!is_col_visited_[j]) dm_ptr[j] -= val;
}
}
}
int KuhnMunkres::FindInRow(int row, int what) {
for (int j = 0; j < n_; j++) {
if (marked_.at<char>(row, j) == what) {
return j;
}
}
return -1;
}
int KuhnMunkres::FindInCol(int col, int what) {
for (int i = 0; i < n_; i++) {
if (marked_.at<char>(i, col) == what) {
return i;
}
}
return -1;
}
void KuhnMunkres::Run() {
TrySimpleCase();
if (greedy_)
return;
while (!CheckIfOptimumIsFound()) {
while (true) {
auto point = FindUncoveredMinValPos();
auto min_val = dm_.at<float>(point.y, point.x);
if (min_val > 0) {
UpdateDissimilarityMatrix(min_val);
} else {
marked_.at<char>(point.y, point.x) = kPrime;
int col = FindInRow(point.y, kStar);
if (col >= 0) {
is_row_visited_[point.y] = 1;
is_col_visited_[col] = 0;
} else {
int count = 0;
points_[count] = point;
while (true) {
int row = FindInCol(points_[count].x, kStar);
if (row >= 0) {
count++;
points_[count] = cv::Point(points_[count - 1].x, row);
int col = FindInRow(points_[count].y, kPrime);
count++;
points_[count] = cv::Point(col, points_[count - 1].y);
} else {
break;
}
}
for (int i = 0; i < count + 1; i++) {
auto& mark = marked_.at<char>(points_[i].y, points_[i].x);
mark = mark == kStar ? 0 : kStar;
}
is_row_visited_ = std::vector<int>(n_, 0);
is_col_visited_ = std::vector<int>(n_, 0);
marked_.setTo(0, marked_ == kPrime);
break;
}
}
}
}
}
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