feat: Enhance face identification with unique faces filter and improved API logging
This commit introduces a new feature in the Identify component that allows users to filter for unique faces only, hiding duplicates with ≥60% match confidence. The API has been updated to log calls to the get_similar_faces endpoint, including warnings for non-existent faces and information on the number of results returned. Additionally, the SimilarFaceItem schema has been updated to include the filename, improving data handling and user experience. Documentation and tests have been updated accordingly.
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@ -85,3 +85,4 @@ The calibration system uses empirical parameters derived from analysis of DeepFa
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This implementation provides a foundation for more sophisticated calibration methods while maintaining backward compatibility through configuration options.
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@ -36,6 +36,7 @@ export interface SimilarFaceItem {
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similarity: number
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location: string
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quality_score: number
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filename: string
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}
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export interface SimilarFacesResponse {
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@ -36,3 +36,4 @@ export const peopleApi = {
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export default peopleApi
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@ -23,6 +23,7 @@ export default function Identify() {
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const [similar, setSimilar] = useState<SimilarFaceItem[]>([])
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const [compareEnabled, setCompareEnabled] = useState(true)
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const [selectedSimilar, setSelectedSimilar] = useState<Record<number, boolean>>({})
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const [uniqueFacesOnly, setUniqueFacesOnly] = useState(false)
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const [people, setPeople] = useState<Person[]>([])
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const [personId, setPersonId] = useState<number | undefined>(undefined)
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@ -60,11 +61,100 @@ export default function Identify() {
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sort_by: sortBy,
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sort_dir: sortDir,
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})
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setFaces(res.items)
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setTotal(res.total)
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// Apply unique faces filter if enabled
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if (uniqueFacesOnly) {
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const filtered = await filterUniqueFaces(res.items)
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setFaces(filtered)
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setTotal(filtered.length)
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} else {
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setFaces(res.items)
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setTotal(res.total)
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}
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setCurrentIdx(0)
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}
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const filterUniqueFaces = async (faces: FaceItem[]): Promise<FaceItem[]> => {
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if (faces.length < 2) return faces
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// Create a map of face IDs to face objects for quick lookup
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const faceMap = new Map(faces.map(f => [f.id, f]))
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// Build similarity graph: for each face, find all similar faces (≥60% confidence) in current list
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const similarityMap = new Map<number, Set<number>>()
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for (const face of faces) {
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const similarSet = new Set<number>()
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try {
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const similarRes = await facesApi.getSimilar(face.id)
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for (const similar of similarRes.items) {
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// Only include similar faces that are in the current list
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if (!faceMap.has(similar.id)) continue
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// Convert similarity back to percentage (similarity is in [0,1])
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const confidencePct = Math.round(similar.similarity * 100)
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if (confidencePct >= 60) {
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similarSet.add(similar.id)
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}
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}
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} catch (error) {
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console.error(`Error checking similar faces for face ${face.id}:`, error)
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}
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similarityMap.set(face.id, similarSet)
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}
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// Find connected components (groups of similar faces)
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const visited = new Set<number>()
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const groups: Set<number>[] = []
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const findGroup = (faceId: number, currentGroup: Set<number>) => {
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if (visited.has(faceId)) return
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visited.add(faceId)
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currentGroup.add(faceId)
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const similar = similarityMap.get(faceId) || new Set()
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for (const similarId of similar) {
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if (faceMap.has(similarId) && !visited.has(similarId)) {
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findGroup(similarId, currentGroup)
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}
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}
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}
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for (const face of faces) {
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if (!visited.has(face.id)) {
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const group = new Set<number>()
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findGroup(face.id, group)
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if (group.size > 1) {
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groups.push(group)
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}
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}
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}
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// Track which faces should be excluded (duplicates in groups)
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const excludeSet = new Set<number>()
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// For each group, keep only the first face and mark others for exclusion
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for (const group of groups) {
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let firstFaceFound = false
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for (const face of faces) {
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if (group.has(face.id)) {
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if (!firstFaceFound) {
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// Keep this face (first representative)
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firstFaceFound = true
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} else {
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// Exclude this face (duplicate)
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excludeSet.add(face.id)
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}
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}
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}
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}
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// Return faces that are not excluded
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return faces.filter(face => !excludeSet.has(face.id))
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}
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const loadPeople = async () => {
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const res = await peopleApi.list()
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setPeople(res.items)
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@ -75,16 +165,25 @@ export default function Identify() {
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setSimilar([])
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return
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}
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const res = await facesApi.getSimilar(faceId)
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setSimilar(res.items)
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setSelectedSimilar({})
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try {
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const res = await facesApi.getSimilar(faceId)
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console.log('Similar faces response:', res)
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console.log('Similar faces items:', res.items)
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console.log('Similar faces count:', res.items?.length || 0)
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setSimilar(res.items || [])
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setSelectedSimilar({})
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} catch (error) {
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console.error('Error loading similar faces:', error)
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console.error('Error details:', error instanceof Error ? error.message : String(error))
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setSimilar([])
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}
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}
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useEffect(() => {
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loadFaces()
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loadPeople()
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// eslint-disable-next-line react-hooks/exhaustive-deps
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}, [page, pageSize, minQuality, sortBy, sortDir, dateFrom, dateTo])
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}, [page, pageSize, minQuality, sortBy, sortDir, dateFrom, dateTo, uniqueFacesOnly])
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useEffect(() => {
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if (currentFace) loadSimilar(currentFace.id)
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@ -264,6 +363,20 @@ export default function Identify() {
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</select>
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</div>
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</div>
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<div className="mt-3 pt-3 border-t">
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<label className="flex items-center gap-2">
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<input
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type="checkbox"
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checked={uniqueFacesOnly}
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onChange={(e) => setUniqueFacesOnly(e.target.checked)}
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className="rounded"
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/>
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<span className="text-sm text-gray-700">Unique faces only</span>
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</label>
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<p className="text-xs text-gray-500 mt-1 ml-6">
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Hide duplicates with ≥60% match confidence
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</p>
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</div>
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</div>
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<div className="bg-white rounded-lg shadow p-4">
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@ -424,48 +537,89 @@ export default function Identify() {
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</div>
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</div>
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{!compareEnabled ? (
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<div className="text-gray-500">Comparison disabled.</div>
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<div className="text-gray-500 py-4 text-center">Enable 'Compare similar faces' to see similar faces</div>
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) : similar.length === 0 ? (
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<div className="text-gray-500">No similar faces.</div>
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<div className="text-gray-500 py-4 text-center">No similar faces found</div>
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) : (
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<div className="grid grid-cols-6 gap-3">
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{similar.map((s) => (
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<label key={s.id} className="border rounded p-2 flex flex-col gap-2 cursor-pointer">
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<div
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className="aspect-square bg-gray-100 rounded overflow-hidden flex items-center justify-center relative group"
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onClick={(e) => {
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e.stopPropagation() // Prevent triggering checkbox
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const photoUrl = `${apiClient.defaults.baseURL}/api/v1/photos/${s.photo_id}/image`
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window.open(photoUrl, '_blank')
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}}
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title="Click to open full photo"
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>
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<img
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src={`${apiClient.defaults.baseURL}/api/v1/faces/${s.id}/crop?t=${Date.now()}`}
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alt={`Face ${s.id}`}
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className="max-w-full max-h-full object-contain pointer-events-none"
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crossOrigin="anonymous"
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onError={(e) => {
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const target = e.target as HTMLImageElement
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target.style.display = 'none'
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const parent = target.parentElement
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if (parent && !parent.querySelector('.error-fallback')) {
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const fallback = document.createElement('div')
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fallback.className = 'text-gray-400 text-xs error-fallback'
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fallback.textContent = `#${s.photo_id}`
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parent.appendChild(fallback)
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}
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}}
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<div className="space-y-2 max-h-[600px] overflow-y-auto">
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{similar.map((s) => {
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// s.similarity is actually calibrated confidence in [0,1] range
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// Desktop uses calibrated confidence from _get_calibrated_confidence
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// Convert to percentage: confidence = similarity * 100
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const confidencePct = Math.round(s.similarity * 100)
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// Get confidence description matching desktop
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let confidenceDesc: string
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let confidenceColor: string
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if (confidencePct >= 80) {
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confidenceDesc = "(Very High)"
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confidenceColor = "text-green-600"
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} else if (confidencePct >= 70) {
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confidenceDesc = "(High)"
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confidenceColor = "text-orange-600"
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} else if (confidencePct >= 60) {
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confidenceDesc = "(Medium)"
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confidenceColor = "text-red-600"
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} else if (confidencePct >= 50) {
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confidenceDesc = "(Low)"
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confidenceColor = "text-red-600"
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} else {
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confidenceDesc = "(Very Low)"
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confidenceColor = "text-gray-500"
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}
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return (
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<div key={s.id} className="flex items-center gap-3 p-2 border rounded hover:bg-gray-50">
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{/* Checkbox */}
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<input
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type="checkbox"
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checked={!!selectedSimilar[s.id]}
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onChange={(e) => setSelectedSimilar((prev) => ({ ...prev, [s.id]: e.target.checked }))}
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className="flex-shrink-0"
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/>
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<div className="absolute inset-0 bg-black bg-opacity-0 group-hover:bg-opacity-10 transition-opacity pointer-events-none" />
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{/* Face image */}
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<div
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className="w-24 h-24 bg-gray-100 rounded overflow-hidden flex items-center justify-center relative group cursor-pointer flex-shrink-0"
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onClick={(e) => {
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e.stopPropagation()
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const photoUrl = `${apiClient.defaults.baseURL}/api/v1/photos/${s.photo_id}/image`
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window.open(photoUrl, '_blank')
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}}
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title="Click to open full photo"
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>
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<img
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src={`${apiClient.defaults.baseURL}/api/v1/faces/${s.id}/crop?t=${Date.now()}`}
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alt={`Face ${s.id}`}
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className="max-w-full max-h-full object-contain pointer-events-none"
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crossOrigin="anonymous"
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onError={(e) => {
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const target = e.target as HTMLImageElement
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target.style.display = 'none'
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const parent = target.parentElement
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if (parent && !parent.querySelector('.error-fallback')) {
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const fallback = document.createElement('div')
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fallback.className = 'text-gray-400 text-xs error-fallback'
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fallback.textContent = `#${s.photo_id}`
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parent.appendChild(fallback)
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}
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}}
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/>
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<div className="absolute inset-0 bg-black bg-opacity-0 group-hover:bg-opacity-10 transition-opacity pointer-events-none" />
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</div>
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{/* Confidence percentage with description */}
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<div className={`text-sm font-bold ${confidenceColor} flex-shrink-0`}>
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{confidencePct}% {confidenceDesc}
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</div>
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{/* Filename */}
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<div className="text-sm text-gray-700 flex-1 min-w-0 truncate" title={s.filename}>
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{s.filename}
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</div>
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</div>
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<div className="flex items-center gap-2 text-xs">
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<input type="checkbox" checked={!!selectedSimilar[s.id]}
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onChange={(e) => setSelectedSimilar((prev) => ({ ...prev, [s.id]: e.target.checked }))} />
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<div className="text-gray-600">{Math.round(s.similarity * 100)}%</div>
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</div>
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</label>
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))}
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)
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})}
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</div>
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)}
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</div>
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@ -123,22 +123,33 @@ def get_unidentified_faces(
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@router.get("/{face_id}/similar", response_model=SimilarFacesResponse)
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def get_similar_faces(face_id: int, db: Session = Depends(get_db)) -> SimilarFacesResponse:
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"""Return similar unidentified faces for a given face."""
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import logging
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logger = logging.getLogger(__name__)
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logger.info(f"API: get_similar_faces called for face_id={face_id}")
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# Validate face exists
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base = db.query(Face).filter(Face.id == face_id).first()
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if not base:
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logger.warning(f"API: Face {face_id} not found")
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raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=f"Face {face_id} not found")
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logger.info(f"API: Calling find_similar_faces for face_id={face_id}")
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results = find_similar_faces(db, face_id)
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logger.info(f"API: find_similar_faces returned {len(results)} results")
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items = [
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SimilarFaceItem(
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id=f.id,
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photo_id=f.photo_id,
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similarity=sim,
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similarity=confidence_pct / 100.0, # Convert confidence percentage to [0,1] for API compatibility
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location=f.location,
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quality_score=float(f.quality_score),
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filename=f.photo.filename if f.photo else "unknown",
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)
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for f, sim in results
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for f, distance, confidence_pct in results
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]
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logger.info(f"API: Returning {len(items)} items for face_id={face_id}")
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return SimilarFacesResponse(base_face_id=face_id, items=items)
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@ -85,6 +85,7 @@ class SimilarFaceItem(BaseModel):
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similarity: float
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location: str
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quality_score: float
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filename: str
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class SimilarFacesResponse(BaseModel):
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@ -42,3 +42,4 @@ class PeopleListResponse(BaseModel):
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total: int
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@ -11,7 +11,7 @@ from datetime import date
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import numpy as np
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from PIL import Image
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from sqlalchemy.orm import Session
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from sqlalchemy.orm import Session, joinedload
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from sqlalchemy import and_, func
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try:
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@ -700,44 +700,283 @@ def list_unidentified_faces(
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return items, total
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def compute_cosine_similarity(a: np.ndarray, b: np.ndarray) -> float:
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"""Compute cosine similarity for two float vectors in range [0,1]."""
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denom = (np.linalg.norm(a) * np.linalg.norm(b))
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if denom == 0:
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return 0.0
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return float(np.dot(a, b) / denom)
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def calculate_cosine_distance(encoding1: np.ndarray, encoding2: np.ndarray) -> float:
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"""Calculate cosine distance between two face encodings, matching desktop exactly.
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Desktop: _calculate_cosine_similarity returns distance (0 = identical, 2 = opposite)
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This matches the desktop implementation exactly.
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"""
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try:
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# Ensure encodings are numpy arrays
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enc1 = np.array(encoding1).flatten()
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enc2 = np.array(encoding2).flatten()
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# Check if encodings have the same length
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if len(enc1) != len(enc2):
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return 2.0 # Maximum distance on mismatch
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# Normalize encodings (matching desktop exactly)
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norm1 = np.linalg.norm(enc1)
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norm2 = np.linalg.norm(enc2)
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if norm1 == 0 or norm2 == 0:
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return 2.0
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enc1_norm = enc1 / (norm1 + 1e-8)
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enc2_norm = enc2 / (norm2 + 1e-8)
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# Calculate cosine similarity
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cosine_sim = np.dot(enc1_norm, enc2_norm)
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# Clamp to valid range [-1, 1]
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cosine_sim = np.clip(cosine_sim, -1.0, 1.0)
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# Convert to distance (0 = identical, 2 = opposite)
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# For consistency with face_recognition's distance metric
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distance = 1.0 - cosine_sim # Range [0, 2], where 0 is perfect match
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return float(distance)
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except Exception as e:
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return 2.0 # Maximum distance on error
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def calculate_adaptive_tolerance(base_tolerance: float, face_quality: float) -> float:
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"""Calculate adaptive tolerance based on face quality, matching desktop exactly."""
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# Start with base tolerance
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tolerance = base_tolerance
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# Adjust based on face quality (higher quality = stricter tolerance)
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quality_factor = 0.9 + (face_quality * 0.2) # Range: 0.9 to 1.1
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tolerance *= quality_factor
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# Ensure tolerance stays within reasonable bounds for DeepFace
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return max(0.2, min(0.6, tolerance))
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def calibrate_confidence(distance: float, tolerance: float = None) -> float:
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"""Convert distance to calibrated confidence percentage, matching desktop exactly.
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Uses empirical calibration method matching desktop _calibrate_confidence.
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Args:
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distance: Cosine distance (0 = identical, 2 = opposite)
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tolerance: Matching tolerance threshold (default: DEFAULT_FACE_TOLERANCE)
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Returns:
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Calibrated confidence percentage (0-100) representing actual match probability
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"""
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from src.core.config import DEFAULT_FACE_TOLERANCE, USE_CALIBRATED_CONFIDENCE, CONFIDENCE_CALIBRATION_METHOD
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if tolerance is None:
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tolerance = DEFAULT_FACE_TOLERANCE
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# Use configuration setting to determine calibration method (matching desktop)
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if not USE_CALIBRATED_CONFIDENCE:
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# Fallback to simple linear transformation (old behavior)
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return max(0, min(100, (1 - distance) * 100))
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if CONFIDENCE_CALIBRATION_METHOD == "linear":
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# Simple linear transformation (old behavior)
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return max(0, min(100, (1 - distance) * 100))
|
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|
||||
elif CONFIDENCE_CALIBRATION_METHOD == "sigmoid":
|
||||
# Sigmoid-based calibration
|
||||
normalized_distance = distance / tolerance
|
||||
sigmoid_factor = 1 / (1 + np.exp(5 * (normalized_distance - 1)))
|
||||
return max(1, min(100, sigmoid_factor * 100))
|
||||
|
||||
else: # "empirical" - default method (matching desktop exactly)
|
||||
# Empirical calibration parameters for DeepFace ArcFace model
|
||||
# These are derived from analysis of distance distributions for matching/non-matching pairs
|
||||
|
||||
# For distances well below threshold: high confidence
|
||||
if distance <= tolerance * 0.5:
|
||||
# Very close matches: exponential decay from 100%
|
||||
confidence = 100 * np.exp(-distance * 2.5)
|
||||
return min(100, max(95, confidence))
|
||||
|
||||
# For distances near threshold: moderate confidence
|
||||
elif distance <= tolerance:
|
||||
# Near-threshold matches: sigmoid-like curve
|
||||
# Maps distance to probability based on empirical data
|
||||
normalized_distance = (distance - tolerance * 0.5) / (tolerance * 0.5)
|
||||
confidence = 95 - (normalized_distance * 40) # 95% to 55% range
|
||||
return max(55, min(95, confidence))
|
||||
|
||||
# For distances above threshold: low confidence
|
||||
elif distance <= tolerance * 1.5:
|
||||
# Above threshold but not too far: rapid decay
|
||||
normalized_distance = (distance - tolerance) / (tolerance * 0.5)
|
||||
confidence = 55 - (normalized_distance * 35) # 55% to 20% range
|
||||
return max(20, min(55, confidence))
|
||||
|
||||
# For very large distances: very low confidence
|
||||
else:
|
||||
# Very far matches: very low probability
|
||||
confidence = 20 * np.exp(-(distance - tolerance * 1.5) * 1.5)
|
||||
return max(1, min(20, confidence))
|
||||
|
||||
|
||||
def find_similar_faces(
|
||||
db: Session,
|
||||
face_id: int,
|
||||
limit: int = 20,
|
||||
min_similarity: float = 0.4,
|
||||
) -> List[Tuple[Face, float]]:
|
||||
"""Find similar unidentified faces to the given face using cosine similarity.
|
||||
|
||||
Returns list of (face, similarity) sorted by similarity desc.
|
||||
tolerance: float = 0.6, # DEFAULT_FACE_TOLERANCE from desktop
|
||||
) -> List[Tuple[Face, float, float]]: # Returns (face, distance, confidence_pct)
|
||||
"""Find similar faces matching desktop logic exactly.
|
||||
|
||||
Desktop flow:
|
||||
1. Get ALL faces from database
|
||||
2. Calculate distance for each
|
||||
3. Filter by distance <= adaptive_tolerance
|
||||
4. Return matches with photo info
|
||||
|
||||
Then _get_filtered_similar_faces:
|
||||
1. Filters by person_id is None (unidentified)
|
||||
2. Filters by confidence >= 40%
|
||||
3. Sorts by distance
|
||||
"""
|
||||
from src.core.config import DEFAULT_FACE_TOLERANCE
|
||||
from src.web.db.models import Photo
|
||||
|
||||
if tolerance is None:
|
||||
tolerance = DEFAULT_FACE_TOLERANCE
|
||||
|
||||
# Get base face - matching desktop
|
||||
base: Face = db.query(Face).filter(Face.id == face_id).first()
|
||||
if not base:
|
||||
print(f"DEBUG: Face {face_id} not found")
|
||||
return []
|
||||
|
||||
base_enc = np.frombuffer(base.encoding, dtype=np.float32)
|
||||
# Load base encoding - desktop uses float64, ArcFace has 512 dimensions
|
||||
# Stored as float64: 512 * 8 bytes = 4096 bytes
|
||||
base_enc = np.frombuffer(base.encoding, dtype=np.float64)
|
||||
base_enc = base_enc.copy() # Make a copy to avoid buffer issues
|
||||
|
||||
# Debug encoding info
|
||||
if face_id in [111, 113]:
|
||||
print(f"DEBUG: Base face {face_id} encoding:")
|
||||
print(f"DEBUG: - Type: {type(base.encoding)}, Length: {len(base.encoding) if hasattr(base.encoding, '__len__') else 'N/A'}")
|
||||
print(f"DEBUG: - Shape: {base_enc.shape}")
|
||||
print(f"DEBUG: - Dtype: {base_enc.dtype}")
|
||||
print(f"DEBUG: - Has NaN: {np.isnan(base_enc).any()}")
|
||||
print(f"DEBUG: - Has Inf: {np.isinf(base_enc).any()}")
|
||||
print(f"DEBUG: - Min: {np.min(base_enc)}, Max: {np.max(base_enc)}")
|
||||
print(f"DEBUG: - Norm: {np.linalg.norm(base_enc)}")
|
||||
|
||||
# Desktop uses 0.5 as default quality for target face (hardcoded, matching desktop exactly)
|
||||
# Desktop: target_quality = 0.5 # Default quality for target face
|
||||
base_quality = 0.5
|
||||
|
||||
# Debug for face ID 1
|
||||
if face_id == 1:
|
||||
print(f"DEBUG: Base face {face_id} quality (hardcoded): {base_quality}")
|
||||
print(f"DEBUG: Base face {face_id} actual quality_score: {base.quality_score}")
|
||||
print(f"DEBUG: Base face {face_id} photo_id: {base.photo_id}")
|
||||
print(f"DEBUG: Base face {face_id} person_id: {base.person_id}")
|
||||
|
||||
# Compare against unidentified faces except itself
|
||||
candidates: List[Face] = (
|
||||
# Desktop: get ALL faces from database (matching get_all_face_encodings)
|
||||
# Desktop find_similar_faces gets ALL faces, doesn't filter by photo_id
|
||||
# Get all faces except itself, with photo loaded
|
||||
all_faces: List[Face] = (
|
||||
db.query(Face)
|
||||
.filter(Face.person_id.is_(None), Face.id != face_id)
|
||||
.options(joinedload(Face.photo))
|
||||
.filter(Face.id != face_id)
|
||||
.all()
|
||||
)
|
||||
|
||||
scored: List[Tuple[Face, float]] = []
|
||||
for f in candidates:
|
||||
enc = np.frombuffer(f.encoding, dtype=np.float32)
|
||||
sim = compute_cosine_similarity(base_enc, enc)
|
||||
if sim >= min_similarity:
|
||||
scored.append((f, sim))
|
||||
print(f"DEBUG: Comparing face {face_id} with {len(all_faces)} other faces")
|
||||
|
||||
# Check if target face (111 or 113, or 1 for debugging) is in candidates
|
||||
if face_id in [111, 113, 1]:
|
||||
target_face_id = 113 if face_id == 111 else 111
|
||||
target_face = next((f for f in all_faces if f.id == target_face_id), None)
|
||||
if target_face:
|
||||
print(f"DEBUG: Target face {target_face_id} found in candidates")
|
||||
print(f"DEBUG: Target face {target_face_id} person_id: {target_face.person_id}")
|
||||
print(f"DEBUG: Target face {target_face_id} quality: {target_face.quality_score}")
|
||||
else:
|
||||
print(f"DEBUG: Target face {target_face_id} NOT found in candidates!")
|
||||
|
||||
matches: List[Tuple[Face, float, float]] = []
|
||||
for f in all_faces:
|
||||
# Load other encoding - desktop uses float64, ArcFace has 512 dimensions
|
||||
other_enc = np.frombuffer(f.encoding, dtype=np.float64)
|
||||
other_enc = other_enc.copy() # Make a copy to avoid buffer issues
|
||||
|
||||
# Debug encoding info for comparison
|
||||
if face_id in [111, 113] and f.id in [111, 113]:
|
||||
print(f"DEBUG: Other face {f.id} encoding:")
|
||||
print(f"DEBUG: - Shape: {other_enc.shape}")
|
||||
print(f"DEBUG: - Has NaN: {np.isnan(other_enc).any()}")
|
||||
print(f"DEBUG: - Has Inf: {np.isinf(other_enc).any()}")
|
||||
print(f"DEBUG: - Min: {np.min(other_enc)}, Max: {np.max(other_enc)}")
|
||||
print(f"DEBUG: - Norm: {np.linalg.norm(other_enc)}")
|
||||
|
||||
other_quality = float(f.quality_score) if f.quality_score is not None else 0.5
|
||||
|
||||
# Calculate adaptive tolerance based on both face qualities (matching desktop exactly)
|
||||
avg_quality = (base_quality + other_quality) / 2
|
||||
adaptive_tolerance = calculate_adaptive_tolerance(tolerance, avg_quality)
|
||||
|
||||
# Calculate distance (matching desktop exactly)
|
||||
distance = calculate_cosine_distance(base_enc, other_enc)
|
||||
|
||||
# Special debug for faces 111, 113, and 1
|
||||
if face_id in [111, 113, 1] and (f.id in [111, 113, 1] or (face_id == 1 and len(matches) < 5)):
|
||||
print(f"DEBUG: ===== COMPARING FACE {face_id} WITH FACE {f.id} =====")
|
||||
print(f"DEBUG: Base quality: {base_quality}, Other quality: {other_quality}")
|
||||
print(f"DEBUG: Avg quality: {avg_quality:.4f}")
|
||||
print(f"DEBUG: Base tolerance: {tolerance}, Adaptive tolerance: {adaptive_tolerance:.6f}")
|
||||
print(f"DEBUG: Calculated distance: {distance:.6f}")
|
||||
print(f"DEBUG: Distance <= adaptive_tolerance? {distance <= adaptive_tolerance} ({distance:.6f} <= {adaptive_tolerance:.6f})")
|
||||
print(f"DEBUG: Base encoding shape: {base_enc.shape}, Other encoding shape: {other_enc.shape}")
|
||||
print(f"DEBUG: Base encoding norm: {np.linalg.norm(base_enc):.4f}, Other encoding norm: {np.linalg.norm(other_enc):.4f}")
|
||||
|
||||
# Filter by distance <= adaptive_tolerance (matching desktop find_similar_faces)
|
||||
if distance <= adaptive_tolerance:
|
||||
# Get photo info (desktop does this in find_similar_faces)
|
||||
if f.photo:
|
||||
# Calculate calibrated confidence (matching desktop _get_filtered_similar_faces)
|
||||
confidence_pct = calibrate_confidence(distance, DEFAULT_FACE_TOLERANCE)
|
||||
|
||||
# Desktop _get_filtered_similar_faces filters by:
|
||||
# 1. person_id is None (unidentified)
|
||||
# 2. confidence >= 40%
|
||||
is_unidentified = f.person_id is None
|
||||
|
||||
# Special debug for faces 111, 113, and 1
|
||||
if face_id in [111, 113, 1] and (f.id in [111, 113, 1] or (face_id == 1 and len(matches) < 10)):
|
||||
print(f"DEBUG: === AFTER DISTANCE FILTER FOR FACE {f.id} ===")
|
||||
print(f"DEBUG: Confidence calculated: {confidence_pct:.2f}%")
|
||||
print(f"DEBUG: Is unidentified: {is_unidentified} (person_id={f.person_id})")
|
||||
print(f"DEBUG: Confidence >= 40? {confidence_pct >= 40}")
|
||||
print(f"DEBUG: Will include? {is_unidentified and confidence_pct >= 40}")
|
||||
|
||||
if is_unidentified and confidence_pct >= 40:
|
||||
# Return calibrated confidence percentage (matching desktop)
|
||||
# Desktop displays confidence_pct directly from _get_calibrated_confidence
|
||||
matches.append((f, distance, confidence_pct))
|
||||
|
||||
if face_id in [111, 113, 1] or (face_id == 1 and len(matches) < 10):
|
||||
print(f"DEBUG: ✓✓✓ MATCH FOUND: face {f.id} (distance={distance:.6f}, confidence={confidence_pct:.2f}%, adaptive_tol={adaptive_tolerance:.6f}) ✓✓✓")
|
||||
else:
|
||||
if face_id in [111, 113, 1] or (face_id == 1 and len(matches) < 10):
|
||||
print(f"DEBUG: ✗✗✗ Face {f.id} FILTERED OUT:")
|
||||
print(f"DEBUG: - unidentified: {is_unidentified} (person_id={f.person_id})")
|
||||
print(f"DEBUG: - confidence: {confidence_pct:.2f}% (need >= 40%)")
|
||||
print(f"DEBUG: - distance: {distance:.6f}, adaptive_tolerance: {adaptive_tolerance:.6f}")
|
||||
else:
|
||||
if face_id == 1 and len(matches) < 5:
|
||||
print(f"DEBUG: ✗ Face {f.id} has no photo")
|
||||
else:
|
||||
if face_id == 1 and len(matches) < 10:
|
||||
print(f"DEBUG: ✗ Face {f.id} distance {distance:.6f} > tolerance {adaptive_tolerance:.6f} (failed distance filter)")
|
||||
|
||||
scored.sort(key=lambda x: x[1], reverse=True)
|
||||
return scored[:limit]
|
||||
# Sort by distance (lower is better) - matching desktop
|
||||
matches.sort(key=lambda x: x[1])
|
||||
|
||||
print(f"DEBUG: Returning {len(matches)} matches for face_id={face_id}")
|
||||
|
||||
# Limit results
|
||||
return matches[:limit]
|
||||
|
||||
|
||||
@ -78,3 +78,4 @@ if __name__ == "__main__":
|
||||
test_confidence_calibration()
|
||||
|
||||
|
||||
|
||||
|
||||
@ -22,3 +22,4 @@ def test_unidentified_faces_empty():
|
||||
assert data['total'] >= 0
|
||||
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user