e75c4d6f07
- Remove session-ses_2f27.md (161KB raw session log) - Remove 49 ROOT_* duplicate files across REFERENCE/ - Remove 14 duplicate files between REFERENCE/ root and history/ - Remove asr_legacy.rs (dead code, replaced by asr.rs) - Remove src/core/worker/ (duplicate JobWorker) - Remove src/core/layers/ (empty directory) - Remove 4 .bak files in src/ - Remove 7 dead private methods in worker/processor.rs - Remove backup directory from git tracking
372 lines
12 KiB
Python
372 lines
12 KiB
Python
#!/opt/homebrew/bin/python3.11
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"""
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Face Registration Script
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Register new faces to the face database
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"""
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import sys
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import json
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import argparse
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import os
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import time
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from typing import Dict, Any, Optional
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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class FaceRegistration:
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def __init__(self):
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self.face_model = None
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self.face_database = {}
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self.database_path = None
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def load_models(self, use_mps: bool = False):
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"""Load InsightFace models with MPS support"""
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try:
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import insightface
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from insightface.app import FaceAnalysis
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# Determine execution providers based on configuration
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providers = ["CPUExecutionProvider"]
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if use_mps:
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try:
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# Try to import MPS provider
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import onnxruntime as ort
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available_providers = ort.get_available_providers()
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if "CoreMLExecutionProvider" in available_providers:
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print(
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"[INFO] Using CoreMLExecutionProvider for MPS acceleration"
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)
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providers = ["CoreMLExecutionProvider", "CPUExecutionProvider"]
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elif "CUDAExecutionProvider" in available_providers:
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print("[INFO] Using CUDAExecutionProvider")
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providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
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else:
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print("[INFO] MPS/CUDA not available, using CPU")
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providers = ["CPUExecutionProvider"]
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except ImportError:
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print("[WARNING] ONNX Runtime not available, using CPU")
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providers = ["CPUExecutionProvider"]
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print(f"[INFO] Using execution providers: {providers}")
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# Initialize face analysis app
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self.face_model = FaceAnalysis(
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name="buffalo_l", # or 'buffalo_s' for smaller model
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providers=providers,
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)
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# For MPS/CoreML, we need to adjust context
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ctx_id = -1 # Default for CPU
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if use_mps and "CoreMLExecutionProvider" in providers:
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ctx_id = 0 # CoreML uses device 0
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self.face_model.prepare(ctx_id=ctx_id, det_size=(640, 640))
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print("[INFO] InsightFace models loaded successfully")
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return True
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except ImportError as e:
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print(f"[ERROR] Failed to import InsightFace: {e}")
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print("[INFO] Install with: pip install insightface")
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return False
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except Exception as e:
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print(f"[ERROR] Failed to load models: {e}")
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return False
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def load_database(self, database_path: str):
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"""Load existing face database"""
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self.database_path = database_path
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if os.path.exists(database_path):
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try:
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with open(database_path, "r") as f:
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self.face_database = json.load(f)
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print(f"[INFO] Loaded {len(self.face_database)} faces from database")
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except Exception as e:
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print(f"[WARNING] Failed to load database: {e}")
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self.face_database = {}
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else:
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print("[INFO] Creating new face database")
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self.face_database = {}
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def save_database(self):
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"""Save face database to file"""
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if not self.database_path:
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print("[ERROR] No database path specified")
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return False
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try:
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# Create directory if it doesn't exist
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os.makedirs(os.path.dirname(self.database_path), exist_ok=True)
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with open(self.database_path, "w") as f:
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json.dump(self.face_database, f, indent=2)
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print(f"[INFO] Saved {len(self.face_database)} faces to database")
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return True
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except Exception as e:
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print(f"[ERROR] Failed to save database: {e}")
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return False
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def register_face(
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self, image_path: str, name: str, metadata: Optional[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""Register a new face from image"""
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# Check if image exists
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if not os.path.exists(image_path):
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return {
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"success": False,
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"message": f"Image not found: {image_path}",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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# Load models if not already loaded
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if self.face_model is None:
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if not self.load_models():
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return {
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"success": False,
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"message": "Failed to load face models",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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# Load image
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try:
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import cv2
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image = cv2.imread(image_path)
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if image is None:
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return {
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"success": False,
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"message": f"Failed to load image: {image_path}",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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except ImportError:
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return {
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"success": False,
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"message": "OpenCV not installed",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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# Detect faces
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try:
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faces = self.face_model.get(image)
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if len(faces) == 0:
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return {
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"success": False,
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"message": "No faces detected in image",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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if len(faces) > 1:
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print("[WARNING] Multiple faces detected, using the first one")
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# Use the first face
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face = faces[0]
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# Get embedding
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embedding = face.embedding.tolist() if hasattr(face, "embedding") else None
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if embedding is None:
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return {
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"success": False,
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"message": "Failed to extract face embedding",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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# Get attributes
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attributes = {}
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if hasattr(face, "age") and face.age is not None:
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attributes["age"] = int(face.age)
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if hasattr(face, "gender") and face.gender is not None:
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attributes["gender"] = "female" if face.gender == 0 else "male"
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# Get pose if available
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pose = None
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if hasattr(face, "pose") and face.pose is not None:
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pose = {
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"yaw": float(face.pose[0]),
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"pitch": float(face.pose[1]),
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"roll": float(face.pose[2]),
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}
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# Generate face ID
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import uuid
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face_id = str(uuid.uuid4())
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# Create face record
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face_record = {
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"face_id": face_id,
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"name": name,
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"embedding": embedding,
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"attributes": {
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"age": attributes.get("age"),
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"gender": attributes.get("gender"),
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"emotion": None,
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"glasses": None,
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"mask": None,
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"pose": pose,
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}
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if any([attributes.get("age"), attributes.get("gender"), pose])
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else None,
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"metadata": metadata or {},
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"registration_time": time.time(),
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"image_path": image_path,
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}
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# Add to database
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self.face_database[face_id] = face_record
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# Save database
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if not self.save_database():
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return {
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"success": False,
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"message": "Failed to save face to database",
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"face_id": face_id,
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"embedding": embedding,
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"attributes": face_record["attributes"],
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}
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return {
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"success": True,
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"message": f"Face registered successfully as '{name}'",
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"face_id": face_id,
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"embedding": embedding,
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"attributes": face_record["attributes"],
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}
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except Exception as e:
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return {
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"success": False,
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"message": f"Face registration failed: {str(e)}",
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"face_id": None,
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"embedding": None,
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"attributes": None,
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}
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def list_faces(self) -> Dict[str, Any]:
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"""List all registered faces"""
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faces = []
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for face_id, face_data in self.face_database.items():
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faces.append(
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{
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"face_id": face_id,
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"name": face_data.get("name", "Unknown"),
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"registration_time": face_data.get("registration_time"),
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"metadata": face_data.get("metadata", {}),
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}
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)
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return {
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"success": True,
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"message": f"Found {len(faces)} registered faces",
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"faces": faces,
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}
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def delete_face(self, face_id: str) -> Dict[str, Any]:
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"""Delete a face from database"""
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if face_id not in self.face_database:
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return {"success": False, "message": f"Face ID not found: {face_id}"}
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# Remove from database
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deleted_face = self.face_database.pop(face_id)
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# Save database
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if not self.save_database():
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# Try to restore
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self.face_database[face_id] = deleted_face
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return {
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"success": False,
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"message": "Failed to save database after deletion",
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}
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return {
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"success": True,
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"message": f"Face '{deleted_face.get('name', 'Unknown')}' deleted successfully",
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}
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def main():
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parser = argparse.ArgumentParser(description="Face Registration Tool")
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parser.add_argument("image_path", help="Path to face image")
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parser.add_argument("output_path", help="Output JSON path")
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parser.add_argument("name", help="Name for the registered face")
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parser.add_argument(
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"--metadata", "-m", help="Path to metadata JSON file", default=""
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)
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parser.add_argument(
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"--database", "-d", help="Path to face database", default="face_database.json"
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)
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parser.add_argument(
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"--list", "-l", help="List registered faces", action="store_true"
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)
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parser.add_argument("--delete", help="Delete face by ID", default="")
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args = parser.parse_args()
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# Initialize registration
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registration = FaceRegistration()
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# Load database
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registration.load_database(args.database)
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# Handle list command
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if args.list:
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result = registration.list_faces()
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with open(args.output_path, "w") as f:
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json.dump(result, f, indent=2)
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print(result["message"])
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return
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# Handle delete command
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if args.delete:
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result = registration.delete_face(args.delete)
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with open(args.output_path, "w") as f:
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json.dump(result, f, indent=2)
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print(result["message"])
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return
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# Load metadata if provided
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metadata = {}
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if args.metadata and os.path.exists(args.metadata):
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try:
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with open(args.metadata, "r") as f:
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metadata = json.load(f)
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except Exception as e:
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print(f"[WARNING] Failed to load metadata: {e}")
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# Register face
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result = registration.register_face(
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image_path=args.image_path, name=args.name, metadata=metadata
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)
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# Save result
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with open(args.output_path, "w") as f:
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json.dump(result, f, indent=2)
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print(result["message"])
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if __name__ == "__main__":
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main()
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