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import os
import time
from datetime import datetime
import gradio as gr
import cv2
import numpy as np
from detect_people import detect_people_yolo
from table_occupancy import estimate_table_occupancy
from face_utils import recognize_faces
from db import get_table_status, log_customer_visit, get_alerts, db_enabled
# ---------- Tunables via env ----------
CONF_THRESH = float(os.getenv("PEOPLE_CONF_THRESH", "0.55"))
NMS_THRESH = float(os.getenv("PEOPLE_NMS_THRESH", "0.45"))
MAX_SIDE = int(os.getenv("PEOPLE_MAX_SIDE", "1280")) # resize safeguard
FACES_DIR = os.getenv("FACES_DIR", "faces_db") # faces gallery path
TOPK = int(os.getenv("FACE_TOPK", "3")) # max matches to show
ENABLE_FACES = os.getenv("ENABLE_FACES", "1") == "1"
# --------------------------------------
def _safe_resize(frame, max_side=1280):
h, w = frame.shape[:2]
m = max(h, w)
if m <= max_side:
return frame
scale = max_side / m
return cv2.resize(frame, (int(w*scale), int(h*scale)))
def process_frame(image):
"""
image: np.ndarray (H, W, 3) from gr.Image
Returns: (annotated_frame, text_status)
"""
if image is None:
return None, "No frame."
frame = _safe_resize(image, MAX_SIDE)
# 1) People detection (OpenCV DNN YOLOv4-tiny)
people, boxes, confs, annotated = detect_people_yolo(
frame, conf_thresh=CONF_THRESH, nms_thresh=NMS_THRESH, draw=True
)
people_count = len(people)
# 2) Table occupancy (simple heuristic; customize as needed)
seated = estimate_table_occupancy(people_count)
# 3) Face recognition (optional & robust)
face_text = "Disabled"
if ENABLE_FACES:
try:
matches = recognize_faces(frame, FACES_DIR, topk=TOPK)
if isinstance(matches, str):
face_text = matches # a friendly warning string
else:
# pretty print topk names with avg distance
if len(matches) == 0:
face_text = "No known faces"
else:
face_text = ", ".join(
[f"{m['name']} ({m['score']:.2f})" for m in matches[:TOPK]]
)
except Exception as e:
face_text = f"Error: {e}"
# 4) DB (optional / won’t crash if missing)
db_text = "DB disabled"
if db_enabled():
try:
# log a single event to show it works (you can wire to your business logic)
log_customer_visit(face_text if isinstance(face_text, str) else "faces", datetime.utcnow(), table_id=1)
alerts = get_alerts()
if alerts:
db_text = f"Alerts: {alerts}"
else:
db_text = "Alerts: none"
except Exception as e:
db_text = f"DB error: {e}"
status = f"People: {people_count}, Seated: {bool(seated)}, Face Match: {face_text}, {db_text}"
return annotated[:, :, ::-1], status # convert BGR->RGB for display
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("## CCTV Backend – People Count, Table Occupancy & Face Match (CPU-only)")
with gr.Row():
inp = gr.Image(type="numpy", label="Frame (image or snapshot)")
with gr.Row():
out_img = gr.Image(type="numpy", label="Detections", interactive=False)
out_txt = gr.Textbox(label="Status", interactive=False)
btn = gr.Button("Process")
btn.click(process_frame, inputs=[inp], outputs=[out_img, out_txt])
# Also auto-run when user drops an image
inp.change(process_frame, inputs=[inp], outputs=[out_img, out_txt])
if __name__ == "__main__":
# Gradio SSR sometimes logs warnings; keep defaults minimal for Spaces
demo.launch(server_name="0.0.0.0", server_port=7860)
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