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Merge pull request #691 from matiasdelellis/native-cwc
Implement the Chinese Whispers clustering algorithm in PHP code.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,6 @@ | ||
| <?php | ||
| /** | ||
| * @copyright Copyright (c) 2017-2020 Matias De lellis <[email protected]> | ||
| * @copyright Copyright (c) 2017-2023 Matias De lellis <[email protected]> | ||
| * @copyright Copyright (c) 2018, Branko Kokanovic <[email protected]> | ||
| * | ||
| * @author Branko Kokanovic <[email protected]> | ||
|
|
@@ -33,6 +33,9 @@ | |
| use OCA\FaceRecognition\Db\PersonMapper; | ||
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| use OCA\FaceRecognition\Helper\Euclidean; | ||
| use OCA\FaceRecognition\Helper\Requirements; | ||
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| use OCA\FaceRecognition\Clusterer\ChineseWhispers; | ||
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| use OCA\FaceRecognition\Service\SettingsService; | ||
| /** | ||
|
|
@@ -282,10 +285,9 @@ private function getNewClusters(array $faces): array { | |
| // Clustering parameters | ||
| $sensitivity = $this->settingsService->getSensitivity(); | ||
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| // Create edges for chinese whispers | ||
| $edges = array(); | ||
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| if (version_compare(phpversion('pdlib'), '1.0.2', '>=')) { | ||
| if (Requirements::pdlibLoaded()) { | ||
| // Create edges (neighbors) for Chinese Whispers | ||
| $edges = array(); | ||
| $faces_count = count($faces); | ||
| for ($i = 0; $i < $faces_count; $i++) { | ||
| $face1 = $faces[$i]; | ||
|
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@@ -304,8 +306,14 @@ private function getNewClusters(array $faces): array { | |
| } | ||
| } | ||
| } | ||
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| // Given the edges get the list of labels (found clusters) for each face. | ||
| $newChineseClustersByIndex = dlib_chinese_whispers($edges); | ||
| } else { | ||
| // Create edges (neighbors) for Chinese Whispers | ||
| $edges = array(); | ||
| $faces_count = count($faces); | ||
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| for ($i = 0; $i < $faces_count; $i++) { | ||
| $face1 = $faces[$i]; | ||
| if (!isset($face1->descriptor)) { | ||
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@@ -323,17 +331,27 @@ private function getNewClusters(array $faces): array { | |
| } | ||
| } | ||
| } | ||
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| // The clustering algorithm actually expects ordered lists. | ||
| $oedges = []; | ||
| ChineseWhispers::convert_unordered_to_ordered($edges, $oedges); | ||
| usort($oedges, function($a, $b) { | ||
| if ($a[0] === $b[0]) return $a[1] - $b[1]; | ||
| return $a[0] - $b[0]; | ||
| }); | ||
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| // Given the edges get the list of labels (found clusters) for each face. | ||
| $newChineseClustersByIndex = []; | ||
| ChineseWhispers::predict($oedges, $newChineseClustersByIndex); | ||
| } | ||
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| $newChineseClustersByIndex = dlib_chinese_whispers($edges); | ||
| $newClusters = array(); | ||
| for ($i = 0, $c = count($newChineseClustersByIndex); $i < $c; $i++) { | ||
| if (!isset($newClusters[$newChineseClustersByIndex[$i]])) { | ||
| $newClusters[$newChineseClustersByIndex[$i]] = array(); | ||
| } | ||
| $newClusters[$newChineseClustersByIndex[$i]][] = $faces[$i]->id; | ||
| } | ||
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| return $newClusters; | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,159 @@ | ||
| <?php | ||
| declare(strict_types=1); | ||
| /** | ||
| * @copyright Copyright (c) 2023, Matias De lellis | ||
| * | ||
| * @author Matias De lellis <[email protected]> | ||
| * | ||
| * @license AGPL-3.0-or-later | ||
| * | ||
| * This code is free software: you can redistribute it and/or modify | ||
| * it under the terms of the GNU Affero General Public License, version 3, | ||
| * as published by the Free Software Foundation. | ||
| * | ||
| * This program is distributed in the hope that it will be useful, | ||
| * but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| * GNU Affero General Public License for more details. | ||
| * | ||
| * You should have received a copy of the GNU Affero General Public License, version 3, | ||
| * along with this program. If not, see <http://www.gnu.org/licenses/> | ||
| * | ||
| */ | ||
|
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| namespace OCA\FaceRecognition\Clusterer; | ||
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| /** | ||
| * This class implements the graph clustering algorithm described in the | ||
| * paper: Chinese Whispers - an Efficient Graph Clustering Algorithm and its | ||
| * Application to Natural Language Processing Problems by Chris Biemann. | ||
| * | ||
| * In particular, it tries to be a shameless copy of the original dlib | ||
| * implementation. | ||
| * - https://github.com/davisking/dlib/blob/master/dlib/clustering/chinese_whispers.h | ||
| */ | ||
| class ChineseWhispers { | ||
|
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| /** | ||
| * Cluster the dataset by assigning a label to each sample.from the edges | ||
| */ | ||
| static public function predict(array &$edges, array &$labels, int $num_iterations = 100) | ||
| { | ||
| // To improve the stability of the clusters, we must | ||
| // iterate the neighbors in a pseudo-random way. | ||
| mt_srand(2023); | ||
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| $labels = []; | ||
| if (count($edges) == 0) | ||
| return 0; | ||
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| $neighbors = []; | ||
| self::find_neighbor_ranges($edges, $neighbors); | ||
|
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| // Initialize the labels, each node gets a different label. | ||
| for ($i = 0; $i < count($neighbors); ++$i) | ||
| $labels[$i] = $i; | ||
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| for ($iter = 0; $iter < count($neighbors)*$num_iterations; ++$iter) | ||
| { | ||
| // Pick a random node. | ||
| $idx = mt_rand()%count($neighbors); | ||
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| // Count how many times each label happens amongst our neighbors. | ||
| $labels_to_counts = []; | ||
| $end = $neighbors[$idx][1]; | ||
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| for ($i = $neighbors[$idx][0]; $i != $end; ++$i) | ||
| { | ||
| $iLabelFirst = $edges[$i][1]; | ||
| $iLabel = $labels[$iLabelFirst]; | ||
| if (isset($labels_to_counts[$iLabel])) | ||
| $labels_to_counts[$iLabel]++; | ||
| else | ||
| $labels_to_counts[$iLabel] = 1; | ||
| } | ||
|
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| // find the most common label | ||
| // std::map<unsigned long, double>::iterator i; | ||
| $best_score = PHP_INT_MIN; | ||
| $best_label = $labels[$idx]; | ||
| foreach ($labels_to_counts as $key => $value) | ||
| { | ||
| if ($value > $best_score) | ||
| { | ||
| $best_score = $value; | ||
| $best_label = $key; | ||
| } | ||
| } | ||
|
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| $labels[$idx] = $best_label; | ||
| } | ||
|
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| // Remap the labels into a contiguous range. First we find the | ||
| // mapping. | ||
| $label_remap = []; | ||
| for ($i = 0; $i < count($labels); ++$i) | ||
| { | ||
| $next_id = count($label_remap); | ||
| if (!isset($label_remap[$labels[$i]])) | ||
| $label_remap[$labels[$i]] = $next_id; | ||
| } | ||
| // now apply the mapping to all the labels. | ||
| for ($i = 0; $i < count($labels); ++$i) | ||
| { | ||
| $labels[$i] = $label_remap[$labels[$i]]; | ||
| } | ||
|
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| return count($label_remap); | ||
| } | ||
|
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| static function find_neighbor_ranges (&$edges, &$neighbors) { | ||
| // setup neighbors so that [neighbors[i].first, neighbors[i].second) is the range | ||
| // within edges that contains all node i's edges. | ||
| $num_nodes = self::max_index_plus_one($edges); | ||
| for ($i = 0; $i < $num_nodes; ++$i) $neighbors[$i] = [0, 0]; | ||
| $cur_node = 0; | ||
| $start_idx = 0; | ||
| for ($i = 0; $i < count($edges); ++$i) | ||
| { | ||
| if ($edges[$i][0] != $cur_node) | ||
| { | ||
| $neighbors[$cur_node] = [$start_idx, $i]; | ||
| $start_idx = $i; | ||
| $cur_node = $edges[$i][0]; | ||
| } | ||
| } | ||
| if (count($neighbors) !== 0) | ||
| $neighbors[$cur_node] = [$start_idx, count($edges)]; | ||
| } | ||
|
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| static function max_index_plus_one ($pairs): int { | ||
| if (count($pairs) === 0) | ||
| { | ||
| return 0; | ||
| } | ||
| else { | ||
| $max_idx = 0; | ||
| for ($i = 0; $i < count($pairs); ++$i) | ||
| { | ||
| if ($pairs[$i][0] > $max_idx) | ||
| $max_idx = $pairs[$i][0]; | ||
| if ($pairs[$i][1] > $max_idx) | ||
| $max_idx = $pairs[$i][1]; | ||
| } | ||
| return $max_idx + 1; | ||
| } | ||
| } | ||
|
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| static function convert_unordered_to_ordered (&$edges, &$out_edges) | ||
| { | ||
| $out_edges = []; | ||
| for ($i = 0; $i < count($edges); ++$i) | ||
| { | ||
| $out_edges[] = [$edges[$i][0], $edges[$i][1]]; | ||
| if ($edges[$i][0] != $edges[$i][1]) | ||
| $out_edges[] = [$edges[$i][1], $edges[$i][0]]; | ||
| } | ||
| } | ||
| } |
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