Add punimtag.py script and update requirements.txt

punimtag.py

@@ -0,0 +1,94 @@
+import os
+import sqlite3
+import face_recognition
+from PIL import Image
+import numpy as np
+import hdbscan
+
+# Constants
+PHOTOS_DIR = 'photos'
+DB_FILE = 'faces.db'
+
+# Create or connect to database
+def init_db():
+    conn = sqlite3.connect(DB_FILE)
+    c = conn.cursor()
+    c.execute('''CREATE TABLE IF NOT EXISTS images
+                 (id INTEGER PRIMARY KEY, path TEXT UNIQUE)''')
+    c.execute('''CREATE TABLE IF NOT EXISTS faces
+                 (id INTEGER PRIMARY KEY, image_id INTEGER, location TEXT, encoding BLOB, cluster_id INTEGER,
+                 FOREIGN KEY(image_id) REFERENCES images(id))''')
+    c.execute('''CREATE TABLE IF NOT EXISTS clusters
+                 (id INTEGER PRIMARY KEY, label TEXT)''')
+    conn.commit()
+    return conn
+
+# Walk folder and process images
+def process_images(conn):
+    c = conn.cursor()
+    encodings = []
+    face_data = []
+
+    for root, _, files in os.walk(PHOTOS_DIR):
+        for file in files:
+            if file.lower().endswith(('.jpg', '.png')):
+                path = os.path.join(root, file)
+                try:
+                    image = face_recognition.load_image_file(path)
+                    locations = face_recognition.face_locations(image)
+                    face_encodings = face_recognition.face_encodings(image, locations)
+
+                    c.execute("INSERT OR IGNORE INTO images (path) VALUES (?)", (path,))
+                    c.execute("SELECT id FROM images WHERE path = ?", (path,))
+                    image_id = c.fetchone()[0]
+
+                    for loc, enc in zip(locations, face_encodings):
+                        loc_str = str(loc)
+                        enc_blob = enc.tobytes()
+                        encodings.append(enc)
+                        face_data.append((image_id, loc_str, enc_blob))
+                except Exception as e:
+                    print(f"Error processing {path}: {e}")
+
+    conn.commit()
+    return np.array(encodings), face_data
+
+# Cluster encodings
+def cluster_encodings(encodings):
+    if len(encodings) == 0:
+        return []
+    clusterer = hdbscan.HDBSCAN(min_cluster_size=2)
+    labels = clusterer.fit_predict(encodings)
+    return labels
+
+# Store clusters and update faces
+def store_clusters(conn, labels, face_data):
+    c = conn.cursor()
+    unique_labels = set(labels)
+    for label in unique_labels:
+        if label != -1:  # Ignore noise
+            c.execute("INSERT INTO clusters (label) VALUES (?)", (f"Cluster {label}",))
+            c.execute("SELECT last_insert_rowid()")
+            cluster_id = c.fetchone()[0]
+        else:
+            cluster_id = None
+
+        # Update faces with this label
+        for i, (image_id, loc_str, enc_blob) in enumerate(face_data):
+            if labels[i] == label:
+                c.execute("INSERT INTO faces (image_id, location, encoding, cluster_id) VALUES (?, ?, ?, ?)",
+                          (image_id, loc_str, enc_blob, cluster_id))
+
+    conn.commit()
+
+# Main function
+def main():
+    conn = init_db()
+    encodings, face_data = process_images(conn)
+    labels = cluster_encodings(encodings)
+    store_clusters(conn, labels, face_data)
+    conn.close()
+    print("Processing complete. Data stored in faces.db")
+
+if __name__ == "__main__":
+    main() 

requirements.txt

@@ -2,5 +2,10 @@ click==8.2.1
 dlib==20.0.0
 face-recognition==1.3.0
 face-recognition-models==0.3.0
+hdbscan==0.8.40
+joblib==1.5.1
 numpy==2.2.6
 pillow==11.3.0
+scikit-learn==1.7.0
+scipy==1.15.3
+threadpoolctl==3.6.0