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stripes_solver.cpp
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stripes_solver.cpp
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#include <stripes_solver.h>
StripesSolver::StripesSolver(const string & _puzzle_folder, int _stripes_n, int _samples_n, bool _real_flag, double _word_conf_thres, double _lambda0, double _lambda1, double _U_a, double _filter_rate, int _candidate_factor) :
puzzle_folder(_puzzle_folder),
stripes_n(_stripes_n),
candidate_seqs_n(_samples_n),
path_manager(_stripes_n, _samples_n),
real_flag(_real_flag),
word_conf_thres(_word_conf_thres),
lambda0(_lambda0),
lambda1(_lambda1),
U_a(_U_a),
filter_rate(_filter_rate),
candidate_factor(_candidate_factor) {
// Timestamp array
ts_arr.clear();
ts_arr.push_back(time(0));
white_chars = "";
for (int i = 0; i < 10; i++) white_chars += to_string(i);
for (int i = 0; i < 26; i++) white_chars += char(int('A') + i);
for (int i = 0; i < 26; i++) white_chars += char(int('a') + i);
black_chars = ",<.>/?;:\'\"[{]}\\|";
// Read ground truth order.
ifstream fin(puzzle_folder + "order.txt", ios::in);
if (!fin.is_open()) {
cerr << "[ERRO] " << puzzle_folder + "order.txt" << " does not exist." << endl;
exit(-1);
}
cout << "[INFO] Import groundtruth order." << endl;
gt_order = vector<int>(stripes_n);
for (int i = 0; i < stripes_n; i++) fin >> gt_order[i];
fin.close();
// Import stripes.
cout << "[INFO] Import stripes." << endl;
for (int i = 0; i < stripes_n; i++) {
const string stripe_img_path = puzzle_folder + to_string(i) + ".png";
cv::Mat stripe_img = cv::imread(stripe_img_path);
if (stripe_img.empty()) {
cerr << "[ERR] Stripe img does not exist." << endl;
exit(-1);
}
stripes.push_back(move(stripe_img));
}
omp_init_lock(&omp_lock);
}
StripesSolver::~StripesSolver() {
// ocr->End();
omp_destroy_lock(&omp_lock);
}
bool StripesSolver::reassemble( Metric _metric_mode,
Composition _composition_mode,
const string & case_name,
bool benchmark_flag) {
metric_mode = _metric_mode;
composition_mode = _composition_mode;
if (composition_mode == Composition::GREEDY ||
composition_mode == Composition::GCOM ||
composition_mode == Composition::GREEDY_GCOM) {
m_metric();
ts_arr.push_back(time(0));
}
vector< vector<int> > fragments;
vector<int> seq_x;
switch (composition_mode) {
// 0
case Composition::GREEDY:
cout << "[INFO] Composition: Greedy." << endl;
fragments = reassemble_greedy();
for (const auto & frag: fragments) {
composition_order.insert(
composition_order.end(),
frag.begin(),
frag.end()
);
}
composition_img = compose_img(composition_order, real_flag, &seq_x);
composition_img_seams = add_seams(composition_img, composition_order, true, &seq_x);
save_result(case_name, benchmark_flag);
ts_arr.push_back(time(0));
break;
// 1
case Composition::GCOM:
assert(metric_mode == Metric::WORD);
cout << "[INFO] Composition: GCOM." << endl;
reassemble_GCOM();
composition_img = compose_img(composition_order, real_flag, &seq_x);
composition_img_seams = add_seams(composition_img, composition_order, true, &seq_x);
save_result(case_name, benchmark_flag);
ts_arr.push_back(time(0));
break;
// 2
case Composition::GREEDY_GCOM:
assert(metric_mode == Metric::WORD);
// Greedy part
cout << "[INFO] Composition: Greedy + GCOM: Greedy" << endl;
composition_mode = Composition::GREEDY;
fragments = reassemble_greedy();
for (const auto & frag: fragments) {
composition_order.insert(
composition_order.end(),
frag.begin(),
frag.end()
);
}
composition_img = compose_img(composition_order, real_flag, &seq_x);
if (real_flag) {
composition_img_bar = add_colorbar(composition_img, composition_order, true, &seq_x);
} else {
composition_img_seams = add_seams(composition_img, composition_order, true, &seq_x);
}
save_result(case_name, benchmark_flag);
ts_arr.push_back(time(0));
// GCOM Part
cout << "\n\n[INFO] Composition: Greedy + GCOM: GCOM." << endl;
composition_mode = Composition::GCOM;
reassemble_GCOM();
composition_img = compose_img(composition_order, real_flag, &seq_x);
if (real_flag) {
composition_img_bar = add_colorbar(composition_img, composition_order, true, &seq_x);
} else {
composition_img_seams = add_seams(composition_img, composition_order, true, &seq_x);
}
save_result(case_name, benchmark_flag);
ts_arr.push_back(time(0));
break;
// 3
case Composition::GT:
composition_order = gt_order;
composition_img = compose_img(gt_order, real_flag, &seq_x);
composition_img_seams = add_seams(composition_img, gt_order, false, &seq_x);
save_result(case_name, false);
break;
// 4
case Composition::USER:
composition_order = vector<int>({1, 22, 8, 21, 7, 5, 18, 24, 17, 9, 2, 23, 19, 15, 3, 13, 26, 25, 0, 11, 10, 6, 14, 12, 4, 16, 20});
// composition_order = vector<int>({18, 6, 12, 37, 56, 0, 22, 7, 5, 27, 31, 21, 10, 48, 29, 50, 15, 52, 8, 44, 46, 35, 45, 51, 41, 39, 58, 4, 34, 17, 14, 38, 47, 36, 20, 49, 43, 30, 57, 3, 16, 1, 19, 32, 33, 13, 40, 54, 53, 25, 9, 2, 59, 55, 26, 28, 23, 24, 11, 42});
composition_img = compose_img(composition_order, real_flag, &seq_x);
composition_img_seams = add_seams(composition_img, composition_order, real_flag, &seq_x);
// composition_img_bar = add_colorbar(composition_img, composition_order, real_flag, &seq_x);
save_result(case_name, false);
break;
default:
return false;
}
// Computation time
switch (ts_arr.size()) {
case 3:
printf("Computation time: %ds\n", int(ts_arr[2] - ts_arr[0]));
break;
case 4:
printf("Computation time for Greedy: %ds\n", int(ts_arr[2] - ts_arr[0]));
printf("Computation time for GCOM: %ds\n", int(ts_arr[3] - ts_arr[2] + ts_arr[1] - ts_arr[0]));
break;
default:
printf("Computation time is not recorded.\n");
break;
}
return true;
}
/* *****************
Metric character
****************** */
double StripesSolver::m_metric_char(const cv::Mat & piece0, const cv::Mat & piece1, tesseract::TessBaseAPI * ocr, int idx) {
int seam_x = piece0.cols;
int margin_piece1;
cv::Mat merged_img = merge_imgs(piece0, piece1, seam_x, margin_piece1, real_flag);
const int max_m_width = min(piece0.cols, piece1.cols);
const int bias = real_flag ? 2 : 1;
const double char_conf_thres {80};
ocr->SetImage(merged_img.data, merged_img.cols, merged_img.rows, 3, merged_img.step);
ocr->Recognize(0);
tesseract::ResultIterator * ocr_iter = ocr->GetIterator();
double m_metric_score = 0;
if (ocr_iter != 0) {
do {
const float conf = ocr_iter->Confidence(tesseract::RIL_SYMBOL);
if (conf < char_conf_thres) continue;
// Boundary cross constraint
int x0, y0, x1, y1;
ocr_iter->BoundingBox(tesseract::RIL_SYMBOL, &x0, &y0, &x1, &y1);
const cv::Rect o_bbox(x0 + bias, y0, x1 - x0 - bias * 2, y1 - y0);
if (!cross_seam(o_bbox, seam_x)) continue;
m_metric_score += conf;
#ifdef DEBUG
// const string ocr_char = ocr_iter->GetUTF8Text(tesseract::RIL_SYMBOL);
// cv::rectangle(merged_img, o_bbox, cv::Scalar(0, 0, 255));
// printf("word: '%s'; \tconf: %.2f; \t\tBoundingBox: %d,%d,%d,%d; Seam: %d\n",
// ocr_char.c_str(), conf, x0, y0, x1, y1, seam_x);
#endif
} while (ocr_iter->Next(tesseract::RIL_SYMBOL));
}
#ifdef DEBUG
// cv::line(merged_img, cv::Point(seam_x, 0), cv::Point(seam_x, merged_img.rows-1), cv::Scalar(255, 0, 0));
// string merged_path = "tmp/merged/" + to_string(idx) + ".png";
// cv::imwrite(merged_path, merged_img);
#endif
m_metric_score = 1 - exp(-m_metric_score / 200);
return m_metric_score;
}
/* *****************
Metric word-path
****************** */
void StripesSolver::compute_mutual_graph(vector< vector<double> > & mutual_graph) {
// Check from left to right
vector< vector<double> > s_l(stripes_n, vector<double>(stripes_n, 0));
// Check from right to left
vector< vector<double> > s_r(stripes_n, vector<double>(stripes_n, 0));
// Compute s_l
for (int i = 0; i < stripes_n; ++i) {
double score_max = numeric_limits<double>::min();
double score_min = numeric_limits<double>::max();
bool valid_flag = false;
for (int j = 0; j < stripes_n; j++) {
if (i == j) continue;
if (!real_flag && low_level_graph[i][j] > 1) continue;
score_max = max(score_max, low_level_graph[i][j]);
score_min = min(score_min, low_level_graph[i][j]);
valid_flag = true;
}
if (!valid_flag) continue;
double score_delta = score_max - score_min;
if (abs(score_delta) < eps) continue;
for (int j = 0; j < stripes_n; j++) {
if (i == j) continue;
if (!real_flag && low_level_graph[i][j] > 1) continue;
s_l[i][j] = (low_level_graph[i][j] - score_min) / score_delta;
}
}
// Compute s_r
for (int j = 0; j < stripes_n; j++) {
double score_max = numeric_limits<double>::min();
double score_min = numeric_limits<double>::max();
bool valid_flag = false;
for (int i = 0; i < stripes_n; i++) {
if (i == j) continue;
if (!real_flag && low_level_graph[i][j] > 1) continue;
score_max = max(score_max, low_level_graph[i][j]);
score_min = min(score_min, low_level_graph[i][j]);
valid_flag = true;
}
if (!valid_flag) continue;
double score_delta = score_max - score_min;
if (abs(score_delta) < eps) continue;
for (int i = 0; i < stripes_n; i++) {
if (i == j) continue;
if (!real_flag && low_level_graph[i][j] > 1) continue;
s_r[i][j] = (low_level_graph[i][j] - score_min) / score_delta;
}
}
for (int i = 0; i < stripes_n; i++) {
for (int j = 0; j < stripes_n; j++) {
mutual_graph[i][j] = s_l[i][j] + s_r[i][j];
}
}
}
void StripesSolver::stochastic_search(vector<int> & seq, const vector< vector<StripePair> > & compose_next) {
random_device rand_device;
default_random_engine rand_engine(rand_device());
uniform_int_distribution<int> uniform_st_dist(0, stripes_n-1);
uniform_real_distribution<double> uniform_unit_dist(0, 1);
vector<bool> stripe_visited(stripes_n, false);
int cur_p = uniform_st_dist(rand_engine);
seq.push_back(cur_p);
stripe_visited[cur_p] = true;
const int searchs_n = 20;
for (int i = 1; i < candidate_seq_len; i++) {
if (compose_next[cur_p].size() == 0) break;
for (const StripePair & sp: compose_next[cur_p]) {
if (stripe_visited[sp.stripe_idx1]) continue;
double rand_p = uniform_unit_dist(rand_engine);
if (rand_p > sp.ac_prob) continue;
cur_p = sp.stripe_idx1;
seq.push_back(cur_p);
stripe_visited[cur_p] = true;
break;
}
if (seq.size() == i) break;
}
}
cv::Mat StripesSolver::word_detection( const cv::Mat & img,
const vector<int> & seq,
vector<int> & seq_x,
tesseract::TessBaseAPI * ocr) {
const tesseract::PageIteratorLevel tesseract_level {tesseract::RIL_WORD};
const cv::Scalar color_blue(200, 0, 0);
ocr->SetImage(img.data, img.cols, img.rows, 3, img.step);
ocr->Recognize(0);
tesseract::ResultIterator * ocr_iter = ocr->GetIterator();
double m_metric_score = 0;
cv::Mat img_bbox = img.clone();
if (seq_x.size() == 0) {
int cum_x = 0;
for (int i = 0; i < seq.size(); i++) {
seq_x.push_back(cum_x);
cum_x += stripes[seq[i]].cols;
}
}
map< vector<int>, int > seq_words;
int bias = real_flag ? 3 : 1;
if (ocr_iter != 0) {
do {
const float conf = ocr_iter->Confidence(tesseract_level);
if (conf < word_conf_thres) continue;
const string word = ocr_iter->GetUTF8Text(tesseract_level);
if (word.length() < 3 || !ocr_iter->WordIsFromDictionary()) continue;
// Boundary cross constraint
int x0, y0, x1, y1;
ocr_iter->BoundingBox(tesseract_level, &x0, &y0, &x1, &y1);
const cv::Rect bbox(x0 + bias, y0, x1 - x0 - bias * 2, y1 - y0);
int seq_path_st = upper_bound(seq_x.begin(), seq_x.end(), x0) - seq_x.begin() - 1;
int seq_path_ed = lower_bound(seq_x.begin(), seq_x.end(), x1) - seq_x.begin();
if (seq_path_ed - seq_path_st > 1) {
seq_words[vector<int>(seq.begin()+seq_path_st, seq.begin()+seq_path_ed)]++;
}
#ifdef DEBUG
// cv::rectangle(img_bbox, bbox, color_blue);
printf("word: '%s'; \tconf: %.2f; \tDict: %d; \tBoundingBox: %d,%d,%d,%d;\n",
word.c_str(), conf, ocr_iter->WordIsFromDictionary(), x0, y0, x1, y1);
cout << endl;
#endif
} while (ocr_iter->Next(tesseract_level));
}
omp_set_lock(&omp_lock);
path_manager.add_seq_words(seq_words);
omp_unset_lock(&omp_lock);
return img_bbox;
}
void StripesSolver::compute_word_scores(const vector< vector<int> > & candidate_seqs) {
int seq_idx = 0;
int group = 24;
#pragma omp parallel for
for (int i = 0; i < group; i++) {
tesseract::TessBaseAPI * ocr = new tesseract::TessBaseAPI();
if (ocr->Init(tesseract_model_path.c_str(), "eng", tesseract::OEM_TESSERACT_ONLY)) {
cerr << "Could not initialize tesseract." << endl;
exit(-1);
}
ocr->SetVariable("tessedit_char_whitelist", white_chars.c_str());
ocr->SetVariable("tessedit_char_blacklist", black_chars.c_str());
ocr->SetVariable("language_model_penalty_non_freq_dict_word", "5");
ocr->SetVariable("language_model_penalty_non_dict_word", "1");
for (int j = i; j < candidate_seqs.size(); j += group) {
const auto & seq = candidate_seqs[j];
vector<int> seq_x;
cv::Mat composition_img = compose_img(seq, real_flag, &seq_x);
// cv::imwrite("tmp/comp_" + to_string(seq_idx + 1) + ".png", composition_img);
cv::Mat tmp_img = word_detection(composition_img, seq, seq_x, ocr);
#ifdef DEBUG
// cout << seq_idx << endl;
// tmp_img = add_seams(tmp_img, seq, false, &seq_x);
// ++seq_idx;
// cv::imwrite("tmp/seq_" + to_string(seq_idx) + ".png", tmp_img);
#else
++seq_idx;
if (seq_idx % 100 == 0) {
cout << "Detect words on solution: " << seq_idx << " / " << candidate_seqs.size() << "\r" << flush;
}
#endif
}
ocr->End();
}
}
void StripesSolver::m_metric_word() {
// Compute mutual low level graph
vector< vector<double> > mutual_graph = vector< vector<double> >(stripes_n, vector<double>(stripes_n, 0));
compute_mutual_graph(mutual_graph);
// Compute stripe_pairs
int preserve_n = min(int(stripes_n * (1 - filter_rate)), stripes_n - 1); // 1- filter_rate
cout << stripes_n - 1 << " " << stripes_n * (1 - filter_rate) << " " << "Preserve stripes: \t" << preserve_n << endl;
vector< vector<StripePair> > compose_next;
int occur_cnt = 0;
for (int i = 0; i < stripes_n; i++) {
vector<StripePair> next_pairs;
for (int j = 0; j < stripes_n; j++) {
if (i == j) continue;
next_pairs.push_back(StripePair(i, j, mutual_graph[i][j]));
}
sort(next_pairs.begin(), next_pairs.end());
double worst_score = next_pairs[preserve_n].m_score;
for (int j = preserve_n; j >= 0; j--) {
if (next_pairs[j].m_score > eps) {
worst_score = next_pairs[j].m_score;
break;
}
}
if (abs(worst_score) < eps) {
compose_next.push_back(vector<StripePair>());
continue;
}
next_pairs.erase(next_pairs.begin() + preserve_n, next_pairs.end());
// #ifdef DEBUG
int gt_next_idx = -1;
for (int j = 0; j < stripes_n - 1; j++) {
if (gt_order[j] == i) {
gt_next_idx = gt_order[j + 1];
break;
}
}
// #endif
for (auto & stripe_pair: next_pairs) {
double alpha = U_a * (stripe_pair.m_score / worst_score - 1);
double exp_alpha = exp(alpha);
stripe_pair.ac_prob = (exp_alpha - 1) / (exp_alpha + 1);
// #ifdef DEBUG
if (gt_next_idx == stripe_pair.stripe_idx1) {
occur_cnt++;
#ifdef DEBUG
cout << stripe_pair << endl;
#endif
}
// #endif
}
compose_next.push_back(move(next_pairs));
}
// #ifdef DEBUG
printf("Stripe pairs in searching space: %d / %d\n", occur_cnt, stripes_n-1);
// #endif
map< vector<int>, bool > seq_visited;
vector< vector<int> > candidate_seqs;
candidate_seq_len = stripes_n / candidate_factor;
int min_seq_len = candidate_seq_len / 2;
cout << "Candidate length: \t" << candidate_seq_len << endl;
cout << "[INFO] Search candidate seqs." << endl;
vector< vector<int> > composition_cnt(stripes_n, vector<int>(stripes_n, 0));
int research_cnt = 0;
while (candidate_seqs.size() < candidate_seqs_n) {
vector<int> seq;
stochastic_search(seq, compose_next);
if (seq.size() > min_seq_len && seq_visited[seq] == false) {
seq_visited[seq] = true;
candidate_seqs.push_back(seq);
for (int i = 1; i < seq.size(); i++) {
composition_cnt[seq[i-1]][seq[i]]++;
}
research_cnt = 0;
} else {
research_cnt++;
if (research_cnt == 100) break;
}
}
cout << "Candidate seqs: " << candidate_seqs.size() << endl;
occur_cnt = 0;
for (int i = 0; i < stripes_n; i++) {
for (int j = 0; j < stripes_n - 1; j++) {
if (gt_order[j] == i) {
// #ifdef DEBUG
cout << "Occurence cnt " << i << " " << gt_order[j+1] << " " << composition_cnt[i][gt_order[j+1]] << endl;
// #endif
if (composition_cnt[i][gt_order[j+1]] > 0) occur_cnt++;
break;
}
}
}
printf("Candidate sequence in word detection: %d / %d\n", occur_cnt, stripes_n-1);
compute_word_scores(candidate_seqs);
cout << endl;
path_manager.build_path_graph();
#ifdef DEBUG
// path_manager.print_seq_paths();
// path_manager.print_path_graph();
#endif
stripe_pairs = path_manager.build_stripe_pairs();
}
/* *****************
Metric manager
****************** */
void StripesSolver::m_metric() {
// Compute matching score for each pair
cout << "[INFO] Compute matching score for each pair." << endl;
low_level_graph = vector< vector<double> >(stripes_n, vector<double>(stripes_n, 0));
#pragma omp parallel for
for (int i = 0; i < stripes_n; i++) {
tesseract::TessBaseAPI * ocr = new tesseract::TessBaseAPI();
if (ocr->Init(tesseract_model_path.c_str(), "eng", tesseract::OEM_TESSERACT_ONLY)) {
cerr << "Could not initialize tesseract." << endl;
exit(-1);
}
ocr->SetVariable("tessedit_char_whitelist", white_chars.c_str());
ocr->SetVariable("tessedit_char_blacklist", black_chars.c_str());
ocr->SetVariable("language_model_penalty_non_freq_dict_word", "5");
ocr->SetVariable("language_model_penalty_non_dict_word", "1");
for (int j = 0; j < stripes_n; j++) {
if (i == j) continue;
double m_score_p, m_score_c, m_score = 0;
switch (metric_mode) {
case Metric::PIXEL:
m_score = m_metric_pixel(stripes[i], stripes[j], real_flag);
break;
case Metric::CHAR:
m_score = m_metric_char(stripes[i], stripes[j], ocr);
case Metric::WORD:
m_score_p = m_metric_pixel(stripes[i], stripes[j], real_flag, i * stripes_n + j);
if (fsign(lambda0) > 0) {
m_score_c = m_metric_char(stripes[i], stripes[j], ocr, i * stripes_n + j);
}
if (m_score_p > 1) {
m_score = m_score_p;
} else {
m_score = lambda0 * m_score_c + (1 - lambda0) * m_score_p;
}
#ifdef DEBUG
// printf("Idx: %d, metric (%d, %d)\tc: %.3lf, p: %.3lf, low: %.3lf\n", i*stripes_n + j, i, j, m_score_c, m_score_p, m_score);
#endif
break;
default:
break;
}
low_level_graph[i][j] = m_score;
if (metric_mode == Metric::PIXEL || metric_mode == Metric::CHAR) {
omp_set_lock(&omp_lock);
stripe_pairs.push_back(StripePair(i, j, m_score, 1.0, false));
omp_unset_lock(&omp_lock);
}
}
ocr->End();
}
sort(stripe_pairs.begin(), stripe_pairs.end());
#ifdef DEBUG
// for (const StripePair & sp : stripe_pairs) {
// cout << sp << endl;
// }
#endif
if (metric_mode == Metric::WORD) {
cout << "[INFO] Calculate word metric." << endl;
m_metric_word();
};
}
/* *****************
Reassemble greedy
****************** */
vector< vector<int> > StripesSolver::reassemble_greedy() {
// random_device rand_device;
// default_random_engine rand_engine(rand_device());
// uniform_real_distribution<double> uniform_unit_dist(0, 1);
vector<int> stripe_left(stripes_n, -1);
vector<int> stripe_right(stripes_n, -1);
int merged_cnt = 0;
for (const StripePair & sp: stripe_pairs) {
if (metric_mode != Metric::WORD && sp.m_score > 2) continue;
if (stripe_right[sp.stripe_idx0] != -1) continue;
if (stripe_left[sp.stripe_idx1] != -1) continue;
int left_most = sp.stripe_idx0;
while (stripe_left[left_most] != -1) {
left_most = stripe_left[left_most];
}
if (left_most == sp.stripe_idx1) continue;
stripe_right[sp.stripe_idx0] = sp.stripe_idx1;
stripe_left[sp.stripe_idx1] = sp.stripe_idx0;
merged_cnt++;
if (merged_cnt == stripes_n - 1) break;
}
vector< vector<int> > fragments;
vector<bool> visited(stripes_n, false);
for (int order_idx = 0; order_idx < stripes_n; order_idx++) {
if (visited[order_idx]) continue;
int cur_idx = order_idx;
while (stripe_left[cur_idx] != -1) {
cur_idx = stripe_left[cur_idx];
}
vector<int> seq;
seq.push_back(cur_idx);
visited[cur_idx] = true;
int splice_x0, splice_x1;
while (stripe_right[cur_idx] != -1) {
cur_idx = stripe_right[cur_idx];
seq.push_back(cur_idx);
visited[cur_idx] = true;
}
fragments.push_back(move(seq));
}
return fragments;
}
/* *****************
Reassemble GCOM
****************** */
void StripesSolver::compute_bigraph_w(vector< vector<int> > & fragments, vector< vector<double> > & bigraph_w) {
vector<cv::Mat> frag_imgs;
vector<int> seq_x;
for (const auto & fragment: fragments) {
cv::Mat frag_img = compose_img(fragment, real_flag, &seq_x);
frag_imgs.push_back(frag_img);
#ifdef DEBUG
// cv::imshow("frag", frag_img);
// cv::waitKey();
#endif
}
int nodes_n = fragments.size();
bigraph_w = vector< vector<double> >(nodes_n, vector<double>(nodes_n, 0));
#pragma omp parallel for
for (int i = 0; i < fragments.size(); i++) {
tesseract::TessBaseAPI * ocr = new tesseract::TessBaseAPI();
if (ocr->Init(tesseract_model_path.c_str(), "eng", tesseract::OEM_TESSERACT_ONLY)) {
cerr << "Could not initialize tesseract." << endl;
exit(-1);
}
ocr->SetVariable("tessedit_char_whitelist", white_chars.c_str());
ocr->SetVariable("tessedit_char_blacklist", black_chars.c_str());
ocr->SetVariable("language_model_penalty_non_freq_dict_word", "5");
ocr->SetVariable("language_model_penalty_non_dict_word", "1");
int right_end = fragments[i].back();
for (int j = 0; j < fragments.size(); j++) {
if (i == j) continue;
const int bias = real_flag ? 3 : 1;
int seam_x = frag_imgs[i].cols;
int margin_piece1;
cv::Mat merged_img = merge_imgs(frag_imgs[i], frag_imgs[j], seam_x, margin_piece1, real_flag);
ocr->SetImage(merged_img.data, merged_img.cols, merged_img.rows, 3, merged_img.step);
ocr->Recognize(0);
tesseract::ResultIterator * ocr_iter = ocr->GetIterator();
double word_path_score = 0;
if (ocr_iter != 0) {
do {
const float conf = ocr_iter->Confidence(tesseract::RIL_WORD);
if (conf < word_conf_thres) continue;
const string word = ocr_iter->GetUTF8Text(tesseract::RIL_WORD);
if (word.length() < 3 || !ocr_iter->WordIsFromDictionary()) continue;
// Boundary cross constraint
int x0, y0, x1, y1;
ocr_iter->BoundingBox(tesseract::RIL_WORD, &x0, &y0, &x1, &y1);
const cv::Rect bbox(x0 + bias, y0, x1 - x0 - bias * 2, y1 - y0);
if (!cross_seam(bbox, seam_x)) continue;
word_path_score += conf;
#ifdef DEBUG
// const string ocr_char = ocr_iter->GetUTF8Text(tesseract::RIL_WORD);
// cv::rectangle(merged_img, bbox, cv::Scalar(0, 0, 255));
// printf("word: '%s'; \tconf: %.2f; \t\tBoundingBox: %d,%d,%d,%d; Seam: %d\n",
// ocr_char.c_str(), conf, x0, y0, x1, y1, seam_x);
#endif
} while (ocr_iter->Next(tesseract::RIL_WORD));
}
int left_end = fragments[j].front();
word_path_score = 1 - exp(-word_path_score / 100);
double low_level_score = low_level_graph[right_end][left_end];
if (fsign(low_level_score - 3) == 0) low_level_score = 1;
bigraph_w[i][j] = lambda1 * word_path_score + (1 - lambda1) * low_level_score;
#ifdef DEBUG
// printf("word-path score: (%d, %d), %.3lf\n", i, j, word_path_score);
// cv::line(merged_img, cv::Point(seam_x, 0), cv::Point(seam_x, merged_img.rows-1), cv::Scalar(255, 0, 0));
// string merged_path = "tmp/bigraph/" + to_string(i) + "_" + to_string(j) + ".png";
// cv::imwrite(merged_path, merged_img);
// printf("right %d, left: %d, low: %.3lf\n", fragments[i].back(), fragments[j].front(), low_level_graph[right_end][left_end]);
#endif
}
ocr->End();
}
}
void StripesSolver::optimal_match(vector< vector<int> > & fragments) {
cout << "Fragments before greedy composition:" << endl;
for (int i = 0; i < fragments.size(); i++) {
cout << "[" << i << "]\t";
for (const int & x: fragments[i]) cout << x << " ";
cout << endl;
}
cout << endl;
if (fragments.size() == 1) {
composition_order = fragments[0];
return;
}
vector< vector<double> > bigraph_w;
compute_bigraph_w(fragments, bigraph_w);
KM KM_solver(bigraph_w);
KM_solver.solve();
vector< vector<int> > groups = KM_solver.cut_loops();
#ifdef DEBUG
KM_solver.print_edges();
KM_solver.print_matches();
#endif
// KM return groups. We find the maximum dissimilarity groups.
int groups_n = groups.size();
int st_group_idx = 0;
int score_min = INF_INT;
for (int i = 0; i < groups_n; i++) {
int right_end = groups[i].back();
for (int j = 0; j < groups_n; j++) {
if (i == j) continue;
int left_end = groups[j].front();
if (score_min > bigraph_w[right_end][left_end]) {
score_min = bigraph_w[right_end][left_end];
st_group_idx = j;
}
}
}
vector<int> frag_order;
frag_order.insert(
frag_order.end(),
groups[st_group_idx].begin(),
groups[st_group_idx].end()
);
int cur_group_idx = st_group_idx;
vector<bool> frag_visited(groups_n, false);
for (int i = 1; i < groups_n; i++) {
frag_visited[cur_group_idx] = true;
int next_group_idx = 0;
double score_max = 0;
int right_end = groups[st_group_idx].back();
for (int j = 0; j < groups_n; j++) {
if (frag_visited[j]) continue;
int left_end = groups[j].front();
if (score_max < bigraph_w[right_end][left_end]) {
score_max = bigraph_w[right_end][left_end];
next_group_idx = j;
}
}
frag_order.insert(
frag_order.end(),
groups[next_group_idx].begin(),
groups[next_group_idx].end()
);
cur_group_idx = next_group_idx;
}
composition_order.clear();
for (int frag_idx: frag_order) {
composition_order.insert(
composition_order.end(),
fragments[frag_idx].begin(),
fragments[frag_idx].end()
);
}
}
void StripesSolver::reassemble_GCOM() {
vector< vector<int> > && fragments = reassemble_greedy();
optimal_match(fragments);
}
/* *****************
Utils
****************** */
cv::Mat StripesSolver::compose_img( const vector<int> & composition_order,
bool shift_flag,
vector<int> * seq_x) {
cv::Mat composition_img;