181 lines
6.3 KiB
PHP
181 lines
6.3 KiB
PHP
<?php
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declare(strict_types=1);
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namespace OCA\Memories\Db;
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use OCP\DB\QueryBuilder\IQueryBuilder;
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use OCP\IDBConnection;
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trait TimelineQueryPeopleRecognize
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{
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protected IDBConnection $connection;
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public function transformPeopleRecognitionFilter(IQueryBuilder &$query, string $userId, string $faceStr)
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{
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// Get name and uid of face user
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$faceNames = explode('/', $faceStr);
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if (2 !== \count($faceNames)) {
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throw new \Exception('Invalid face query');
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}
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$faceUid = $faceNames[0];
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$faceName = $faceNames[1];
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// Join with cluster
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$nameField = is_numeric($faceName) ? 'rfc.id' : 'rfc.title';
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$query->innerJoin('m', 'recognize_face_clusters', 'rfc', $query->expr()->andX(
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$query->expr()->eq('rfc.user_id', $query->createNamedParameter($faceUid)),
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$query->expr()->eq($nameField, $query->createNamedParameter($faceName)),
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));
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// Join with detections
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$query->innerJoin('m', 'recognize_face_detections', 'rfd', $query->expr()->andX(
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$query->expr()->eq('rfd.file_id', 'm.fileid'),
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$query->expr()->eq('rfd.cluster_id', 'rfc.id'),
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));
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}
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public function transformPeopleRecognizeRect(IQueryBuilder &$query, string $userId)
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{
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// Include detection params in response
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$query->addSelect(
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'rfd.width AS face_w',
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'rfd.height AS face_h',
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'rfd.x AS face_x',
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'rfd.y AS face_y',
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);
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}
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public function getPeopleRecognize(TimelineRoot &$root)
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{
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$query = $this->connection->getQueryBuilder();
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// SELECT all face clusters
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$count = $query->func()->count($query->createFunction('DISTINCT m.fileid'), 'count');
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$query->select('rfc.id', 'rfc.user_id', 'rfc.title', $count)->from('recognize_face_clusters', 'rfc');
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// WHERE there are faces with this cluster
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$query->innerJoin('rfc', 'recognize_face_detections', 'rfd', $query->expr()->eq('rfc.id', 'rfd.cluster_id'));
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// WHERE these items are memories indexed photos
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$query->innerJoin('rfd', 'memories', 'm', $query->expr()->eq('m.fileid', 'rfd.file_id'));
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// WHERE these photos are in the user's requested folder recursively
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$query = $this->joinFilecache($query, $root, true, false);
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// GROUP by ID of face cluster
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$query->groupBy('rfc.id');
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// ORDER by number of faces in cluster
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$query->orderBy($query->createFunction("rfc.title <> ''"), 'DESC');
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$query->addOrderBy('count', 'DESC');
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$query->addOrderBy('rfc.id'); // tie-breaker
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// FETCH all faces
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$cursor = $this->executeQueryWithCTEs($query);
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$faces = $cursor->fetchAll();
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// Post process
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foreach ($faces as &$row) {
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$row['id'] = (int) $row['id'];
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$row['name'] = $row['title'];
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unset($row['title']);
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$row['count'] = (int) $row['count'];
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}
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return $faces;
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}
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public function getPeopleRecognizePreview(TimelineRoot &$root, int $id)
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{
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$query = $this->connection->getQueryBuilder();
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// SELECT face detections for ID
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$query->select(
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'rfd.file_id', // Get actual file
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'rfd.x', // Image cropping
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'rfd.y',
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'rfd.width',
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'rfd.height',
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'm.w as image_width', // Scoring
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'm.h as image_height',
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'm.fileid',
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'm.datetaken', // Just in case, for postgres
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)->from('recognize_face_detections', 'rfd');
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$query->where($query->expr()->eq('rfd.cluster_id', $query->createNamedParameter($id)));
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// WHERE these photos are memories indexed
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$query->innerJoin('rfd', 'memories', 'm', $query->expr()->eq('m.fileid', 'rfd.file_id'));
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// WHERE these photos are in the user's requested folder recursively
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$query = $this->joinFilecache($query, $root, true, false);
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// LIMIT results
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$query->setMaxResults(15);
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// Sort by date taken so we get recent photos
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$query->orderBy('m.datetaken', 'DESC');
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$query->addOrderBy('m.fileid', 'DESC'); // tie-breaker
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// FETCH face detections
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$cursor = $this->executeQueryWithCTEs($query);
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$previews = $cursor->fetchAll();
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if (empty($previews)) {
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return null;
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}
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// Score the face detections
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foreach ($previews as &$p) {
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// Get actual pixel size of face
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$iw = min((int) ($p['image_width'] ?: 512), 2048);
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$ih = min((int) ($p['image_height'] ?: 512), 2048);
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$w = (float) $p['width'];
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$h = (float) $p['height'];
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// Get center of face
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$x = (float) $p['x'] + (float) $p['width'] / 2;
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$y = (float) $p['y'] + (float) $p['height'] / 2;
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// 3D normal distribution - if the face is closer to the center, it's better
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$positionScore = exp(-($x - 0.5) ** 2 * 4) * exp(-($y - 0.5) ** 2 * 4);
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// Root size distribution - if the image is bigger, it's better,
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// but it doesn't matter beyond a certain point
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$imgSizeScore = ($iw * 100) ** (1 / 2) * ($ih * 100) ** (1 / 2);
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// Faces occupying too much of the image don't look particularly good
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$faceSizeScore = (-$w ** 2 + $w) * (-$h ** 2 + $h);
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// Combine scores
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$p['score'] = $positionScore * $imgSizeScore * $faceSizeScore;
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}
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// Sort previews by score descending
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usort($previews, function ($a, $b) {
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return $b['score'] <=> $a['score'];
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});
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return $previews;
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}
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/** Convert face fields to object */
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private function processPeopleRecognizeDetection(&$row, $days = false)
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{
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// Differentiate Recognize queries from Face Recognition
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if (!isset($row) || !isset($row['face_w'])) {
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return;
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}
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if (!$days) {
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$row['facerect'] = [
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'w' => (float) $row['face_w'],
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'h' => (float) $row['face_h'],
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'x' => (float) $row['face_x'],
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'y' => (float) $row['face_y'],
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];
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}
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unset($row['face_w'], $row['face_h'], $row['face_x'], $row['face_y']);
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}
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}
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