244 lines
7.4 KiB
Python
244 lines
7.4 KiB
Python
import sys
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import json
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import os
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import piexif
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import sqlite3
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from sqlite3 import Error
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from PIL import Image
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import numpy as np
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import functools
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from ketrface.util import *
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from ketrface.dbscan import *
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from ketrface.db import *
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from ketrface.config import *
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config = read_config()
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html_path = merge_config_path(config['path'], 'frontend')
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pictures_path = merge_config_path(config['path'], config['picturesPath'])
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faces_path = merge_config_path(config['path'], config['facesPath'])
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db_path = merge_config_path(config['path'], config["db"]["photos"]["host"])
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html_base = config['basePath']
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# TODO
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# Switch to using DBSCAN
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#
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# Thoughts for determining number of clusters to try and target...
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#
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# Augment DBSCAN to rule out identity matching for the same face
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# appearing more than once in a photo
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#
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# NOTE: This means twins or reflections won't both identify in the
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# same photo -- those faces would then identify as a second face pairing
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# which could merge with a cluster, but can not be used to match
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def gen_html(identities):
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for identity in identities:
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print('<div>')
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print(f'<div><b>Identity {identity["id"]} has {len(identity["faces"])}</b><br></div>')
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print('<div>')
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for face in identity['faces']:
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faceId = face['id']
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photoId = face['photoId']
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distance = "{:0.4f}".format(face['distance'])
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confidence = "{:0.3f}".format(face['confidence'])
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focus = int(face['focus'])
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label = face['cluster']
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if type(label) != str:
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label = f'Cluster ({face["cluster"]["id"]})'
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print('<div style="position:relative;display:inline-flex;flex-direction:column">')
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path = f'{html_base}/faces/{"{:02d}".format(faceId % 100)}'
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print(f'<img src="{path}/{faceId}.jpg"/>')
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print(f'<div style="background-color:rgba(255, 255, 255, 0.4);position:absolute;top:0px;left:0px;right:0px;padding:0.25rem">{label}: {distance}</div>')
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print(f'<div style="background-color:rgba(255, 255, 255, 0.4);position:absolute;bottom:0px;left:0px;right:0px;padding:0.25rem">{faceId} {photoId} {confidence} {focus}</div>')
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print('</div>')
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print('</div>')
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print('</div>')
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def update_cluster_averages(identities):
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for identity in identities:
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average = []
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for face in identity['faces']:
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if len(average) == 0:
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average = face['descriptors']
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else:
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average = np.add(average, face['descriptors'])
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average = np.divide(average, len(identity['faces']))
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identity['descriptors'] = average
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return identities
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def load_faces(db_path = db_path):
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print(f'Connecting to database: {db_path}')
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conn = create_connection(db_path)
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faces = []
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with conn:
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print('Querying faces')
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cur = conn.cursor()
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res = cur.execute('''
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SELECT faces.id,facedescriptors.descriptors,faces.faceConfidence,faces.photoId,faces.focus
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FROM faces
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JOIN facedescriptors ON (faces.descriptorId=facedescriptors.id)
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WHERE faces.identityId IS null AND faces.faceConfidence>0.99
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''')
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for row in res.fetchall():
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id, descriptors, confidence, photoId, focus = row
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if focus is None:
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focus = 100 # Assume full focus if focus not set
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face = {
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'id': id,
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'type': 'face',
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'confidence': confidence,
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'distance': 0,
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'photoId': photoId,
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'descriptors': np.frombuffer(descriptors),
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'cluster': Undefined,
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'focus': focus
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}
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face['faces'] = [ face ]
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faces.append(face)
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return faces
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def update_distances(identities, prune = False):
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removed = 0
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for identity in identities:
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for face in identity['faces']:
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average = identity['descriptors']
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distance = findCosineDistance(average, face['descriptors'])
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if prune and distance > MAX_EPOCH_DISTANCE:
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average = np.dot(average, len(identity['faces']))
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average = np.subtract(average, face['descriptors'])
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face['cluster'] = Undefined
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face['distance'] = 0
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identity['faces'].remove(face)
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identity['descriptors'] = np.divide(average, len(identity['faces']))
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removed += 1
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else:
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face['distance'] = distance
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return removed
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def sort_identities(identities):
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identities.sort(reverse = True, key = lambda x: len(x['faces']))
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for identity in identities:
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identity['faces'].sort(reverse = False, key = lambda x: x['distance'])
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def cluster_sort(A, B):
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diff = A['cluster'] - B['cluster']
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if diff > 0:
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return 1
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elif diff < 0:
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return -1
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diff = A['confidence'] - B['confidence']
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if diff > 0:
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return 1
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elif diff < 0:
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return -1
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return 0
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def build_straglers(faces):
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noise = []
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undefined = []
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for face in faces:
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if face['cluster'] == Noise:
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noise.append(face)
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elif face['cluster'] == Undefined:
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undefined.append(face)
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return noise + undefined
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print('Loading faces from database')
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faces = load_faces()
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print(f'{len(faces)} faces loaded')
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print('Scanning for clusters')
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identities = DBSCAN(faces) # process_faces(faces)
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print(f'{len(identities)} clusters grouped')
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MAX_CLUSTER_DISTANCE = 0.15 # Used to merge clusters
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MAX_EPOCH_DISTANCE = 0.14 # Used to prune outliers
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# Compute average center for all clusters
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identities = update_cluster_averages(identities)
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removed = -1
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epoch = 1
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# Filter each cluster removing any face that is > cluster_max_distance
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# from the average center point of the cluster
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while removed != 0:
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print(f'Epoch {epoch}...')
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epoch += 1
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removed = update_distances(identities, prune = True)
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if removed > 0:
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print(f'Excluded {removed} faces this epoch')
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print(f'{len(identities)} identities seeded.')
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# Cluster the clusters...
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print('Reducing clusters via DBSCAN')
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reduced = DBSCAN(identities, eps = MAX_CLUSTER_DISTANCE, minPts = 2)
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if len(reduced) == 0:
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reduced = identities
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# For each cluster, merge the lists of faces referenced in the cluster's
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# "faces" field, which is pointing to clusters (and not actual faces)
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for cluster in reduced:
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merged = []
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for identity in cluster['faces']:
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merged = merged + identity['faces']
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cluster['faces'] = merged
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# Creating a set containing those faces which have not been bound
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# to an identity to recluster them in isolation from the rest of
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# the faces
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straglers = build_straglers(faces)
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reduced = reduced + DBSCAN(straglers)
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# Build a final cluster with all remaining uncategorized faces
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remaining_cluster = {
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'id': len(reduced) + 1,
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'distance': 0,
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'descriptors': [],
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'cluster': Undefined,
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'faces': []
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}
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straglers = build_straglers(faces)
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for face in straglers:
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face['cluster'] = remaining_cluster
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remaining_cluster['faces'].append(face)
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reduced.append(remaining_cluster)
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# Give all merged identity lists a unique ID
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for id, identity in enumerate(reduced):
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identity['id'] = id
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for face in identity['faces']:
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face['cluster'] = identity
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reduced = update_cluster_averages(reduced)
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update_distances(reduced)
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sort_identities(reduced)
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# This generates a set of differences between clusters and makes
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# a recommendation to merge clusters (outside of DBSCAN)
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#
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# Worth testing on larger data set
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for i, A in enumerate(reduced):
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for k, B in enumerate(reduced):
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if k < i:
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continue
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if A == B:
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continue
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distance = findCosineDistance(A['descriptors'], B['descriptors'])
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if distance < MAX_CLUSTER_DISTANCE:
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distance = "{:0.4f}".format(distance)
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print(f'{A["id"]} to {B["id"]} = {distance}: MERGE')
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print('Writing to "auto-clusters.html"')
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redirect_on(os.path.join(html_path, 'auto-clusters.html'))
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gen_html(reduced)
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redirect_off()
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