Spaces:
Runtime error
Runtime error
File size: 11,039 Bytes
701628b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | """
One Step Greener β Face recognition attendance (waste management).
Flask application entry point.
"""
import os
import logging
from flask import Flask, render_template, request, jsonify, redirect
from database.db import init_db, add_employee, get_employee, get_all_employees, mark_attendance, get_today_attendance
from models.embeddings_store import EmbeddingStore
from models.face_engine import (
decode_image,
get_face_embedding,
check_face_in_frame,
get_embeddings_from_frames,
get_embeddings_and_crops_from_frames,
get_face_crops_from_frames,
)
from models.anti_spoof import check_liveness, check_liveness_sequence
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
app = Flask(__name__)
app.secret_key = os.environ.get("SECRET_KEY", "constable-secret-2025")
REGISTER_PIN = os.environ.get("REGISTER_PIN", "3620")
# ββ Initialise database and embedding store βββββββββββββββββββββββββββββββββ
init_db()
store = EmbeddingStore()
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# Page routes
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
@app.route("/")
@app.route("/dashboard")
def dashboard():
return render_template("dashboard.html")
@app.route("/register")
def register_page():
return render_template("register.html")
@app.route("/manage")
def manage_redirect():
return redirect("/dashboard", code=302)
@app.route("/attendance")
def attendance_page():
return render_template("attendance.html")
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
# API routes
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
@app.route("/api/face-check", methods=["POST"])
def api_face_check():
"""
Lightweight face-in-frame check for registration flow.
Body: { frame: base64DataUrl }
Returns: { face_detected, centered, big_enough, ready } (ready = all true).
"""
data = request.get_json(force=True)
frame = data.get("frame", "")
if not frame:
return jsonify({"face_detected": False, "centered": False, "big_enough": False, "ready": False})
try:
img = decode_image(frame)
except Exception:
return jsonify({"face_detected": False, "centered": False, "big_enough": False, "ready": False})
r = check_face_in_frame(img)
r["ready"] = r["face_detected"] and r["centered"] and r["big_enough"]
return jsonify(r)
@app.route("/api/register", methods=["POST"])
def api_register():
"""
Body JSON:
Manforce: { user_type: 'manforce', aadhaar, name, mobile, frames }
Employee: { user_type: 'employee', employee_id, frames }
"""
data = request.get_json(force=True)
user_type = (data.get("user_type") or "employee").strip().lower()
frames = data.get("frames", [])
if not frames:
return jsonify({"status": "error", "message": "No frames provided."}), 400
if user_type == "manforce":
aadhaar = data.get("aadhaar", "").strip()
name = data.get("name", "").strip()
mobile = data.get("mobile", "").strip()
if not aadhaar or not name or not mobile:
return jsonify({"status": "error", "message": "Aadhaar number, full name and mobile number are required for Manforce."}), 400
employee_id = aadhaar
else:
employee_id = data.get("employee_id", "").strip()
if not employee_id:
return jsonify({"status": "error", "message": "Employee code is required."}), 400
name = data.get("name", "").strip() or employee_id
aadhaar = ""
mobile = ""
logger.info(f"Registering {employee_id} ({name}, type={user_type}) with {len(frames)} frames β¦")
embeddings, face_crops = get_embeddings_and_crops_from_frames(frames)
if not embeddings:
return jsonify({
"status": "error",
"message": "No face detected in the provided frames. "
"Please ensure good lighting and that your face is clearly visible."
}), 400
# Anti-spoofing: reject photo/screen/video (motion + blink + texture)
if len(face_crops) >= 2:
liveness = check_liveness_sequence([c for c in face_crops if c is not None and c.size > 0])
else:
liveness = check_liveness(face_crops[0]) if face_crops and face_crops[0] is not None else {"is_live": False}
if not liveness.get("is_live", True):
logger.warning(f"Registration rejected (spoof): {liveness.get('reason', 'liveness failed')}")
return jsonify({
"status": "spoof",
"message": liveness.get("reason", "Liveness check failed. Use a live face, not a photo or screen."),
"reason": liveness.get("reason", "Liveness check failed"),
"composite": liveness.get("score", 0.0),
}), 400
# Check for duplicate face registration
for emb in embeddings:
match_id, score = store.search(emb)
if match_id:
match_emp = get_employee(match_id)
match_name = match_emp["name"] if match_emp else match_id
logger.warning(f"Registration rejected: face already registered to {match_name} ({match_id})")
return jsonify({
"status": "error",
"message": f"This face is already registered to {match_name} ({match_id})."
}), 400
# Persist employee in DB and embeddings in FAISS
add_employee(employee_id, name, user_type=user_type, aadhaar=aadhaar, mobile=mobile)
store.add(employee_id, embeddings)
logger.info(f"Registered {employee_id} with {len(embeddings)} embedding(s).")
return jsonify({
"status": "registered",
"employee_id": employee_id,
"name": name,
"user_type": user_type,
"embeddings_stored": len(embeddings),
})
@app.route("/api/recognize", methods=["POST"])
def api_recognize():
"""
Body JSON:
{ frame: base64DataUrl } or { frames: [base64DataUrl, ...] }
When frames is provided, uses sequence liveness (motion + blink).
Response JSON (one of):
{ status: 'success', name, timestamp }
{ status: 'already_marked', name }
{ status: 'spoof', reason, composite }
{ status: 'unknown' }
{ status: 'no_face' }
"""
data = request.get_json(force=True)
frame = data.get("frame", "")
frames = data.get("frames", [])
# Prefer frames for sequence liveness (motion + blink) when available
if frames and len(frames) >= 2:
try:
face_crops = get_face_crops_from_frames(frames)
except Exception:
face_crops = []
if not face_crops:
return jsonify({"status": "no_face"})
# Use latest frame for identity
try:
img = decode_image(frames[-1])
except Exception:
return jsonify({"status": "no_face"})
embedding, _ = get_face_embedding(img)
if embedding is None:
return jsonify({"status": "no_face"})
liveness = check_liveness_sequence(face_crops)
else:
if not frame:
return jsonify({"status": "no_face"})
try:
img = decode_image(frame)
except Exception:
return jsonify({"status": "no_face"})
embedding, face_crop = get_face_embedding(img)
if embedding is None:
return jsonify({"status": "no_face"})
if face_crop is not None:
liveness = check_liveness(face_crop)
else:
liveness = {"is_live": True}
if not liveness.get("is_live", True):
logger.info(f"Spoof detected (score={liveness.get('score', 0):.4f}, reason={liveness.get('reason', '')})")
return jsonify({
"status": "spoof",
"reason": liveness.get("reason", "Liveness check failed"),
"scores": liveness.get("scores", {}),
"composite": liveness.get("score", 0.0),
})
# Identity search
employee_id, score = store.search(embedding)
if employee_id is None:
return jsonify({"status": "unknown"})
employee = get_employee(employee_id)
name = employee["name"] if employee else employee_id
result = mark_attendance(employee_id)
if result["status"] == "cooldown":
return jsonify({
"status": "cooldown",
"name": name,
"message": "Please wait 1 minute before punching again.",
})
punch_type = result.get("punch_type", "in")
logger.info(f"Punch {punch_type}: {employee_id} ({name}) at {result['timestamp']}")
return jsonify({
"status": "success",
"name": name,
"employee_id": employee_id,
"timestamp": result["timestamp"],
"punch_type": punch_type,
"confidence": round(score, 4),
})
@app.route("/api/verify-pin", methods=["POST"])
def api_verify_pin():
"""Verify PIN to unlock Register form for this page. PIN must match REGISTER_PIN (default 3620)."""
data = request.get_json(force=True)
pin = (data.get("pin") or "").strip()
if pin == REGISTER_PIN:
return jsonify({"status": "ok", "message": "Verified"})
return jsonify({"status": "error", "message": "Incorrect PIN"}), 403
@app.route("/api/employees", methods=["GET"])
def api_employees_list():
employees = get_all_employees()
return jsonify({"status": "ok", "employees": employees, "count": len(employees)})
@app.route("/api/attendance/today", methods=["GET"])
def api_today_attendance():
records = get_today_attendance()
return jsonify({"status": "ok", "records": records, "count": len(records)})
@app.route("/api/health", methods=["GET"])
def health():
return jsonify({
"status": "ok",
"total_employees_indexed": store.total_vectors,
})
# ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
if __name__ == "__main__":
port = int(os.environ.get("PORT", 5000))
debug = os.environ.get("FLASK_DEBUG", "0") == "1"
logger.info(f"One Step Greener starting on http://localhost:{port}")
app.run(host="0.0.0.0", port=port, debug=debug, threaded=True)
|