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# app.py
import cv2
import gradio as gr
from ultralytics import YOLO
# ── Config ─────────────────────────────────────────────
MODEL_PATH = "yolov8n.pt" # peso pre-entrenado (COCO)
CONF_THRES = 0.3 # confianza mínima
LINE_RATIO = 0.5 # línea virtual: mitad de la altura
# ───────────────────────────────────────────────────────
model = YOLO(MODEL_PATH)
# Estado global
memory = {} # {track_id: previous_cy}
in_count = 0
out_count = 0
def count_people(frame):
global memory, in_count, out_count
h, w = frame.shape[:2]
line_y = int(h * LINE_RATIO)
# Detección + tracking ByteTrack interno
results = model.track(frame, classes=[0], conf=CONF_THRES,
persist=True, verbose=False) # solo clase “person”
annotated = frame.copy()
cv2.line(annotated, (0, line_y), (w, line_y), (0, 255, 255), 2)
if results:
for box in results[0].boxes:
x1, y1, x2, y2 = map(int, box.xyxy[0])
cx, cy = int((x1 + x2) / 2), int((y1 + y2) / 2)
tid = int(box.id[0]) if box.id is not None else -1
# Dibujo
cv2.rectangle(annotated, (x1, y1), (x2, y2), (0, 255, 0), 1)
cv2.circle(annotated, (cx, cy), 3, (0, 0, 255), -1)
cv2.putText(annotated, str(tid), (x1, y1 - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
# Lógica de cruce
prev_cy = memory.get(tid, cy)
if prev_cy < line_y <= cy: # entra
in_count += 1
elif prev_cy > line_y >= cy: # sale
out_count += 1
memory[tid] = cy
# Overlay de números
total = in_count - out_count
label = f"In: {in_count} | Out: {out_count} | Ocupación: {total}"
cv2.putText(annotated, label, (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
return annotated, label
demo = gr.Interface(
fn=count_people,
inputs=gr.Image(source="webcam", streaming=True),
outputs=[gr.Image(), gr.Text()],
title="Contador de personas (entrada única)",
live=True,
)
if __name__ == "__main__":
demo.launch()
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