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import gradio as gr
import cv2
import numpy as np
from ultralytics import YOLO
import time
# Load YOLO model
model = YOLO("yolov8n.pt") # Updated to correct model name
# Define trapezoidal restricted area (top-left, top-right, bottom-right, bottom-left)
trapezoid_pts = np.array([[250, 150], [400, 150], [450, 300], [200, 300]], np.int32)
def is_inside_trapezoid(box, trapezoid_pts):
"""Check if the center of a detected object is inside the trapezoidal area."""
x1, y1, x2, y2 = box
cx, cy = int((x1 + x2) / 2), int((y1 + y2) / 2)
return cv2.pointPolygonTest(trapezoid_pts, (cx, cy), False) >= 0
def detect_objects(frame):
if frame is None:
return np.zeros((480, 640, 3), dtype=np.uint8), "No input frame"
results = model.predict(frame, conf=0.5)
annotated_frame = results[0].plot()
# Draw the trapezoid area
cv2.polylines(annotated_frame, [trapezoid_pts.reshape((-1, 1, 2))], isClosed=True, color=(0, 0, 255), thickness=2)
isAlert = {"alert": [False, ""], "personCount": 0}
classInIntrusion = ["person", "bicycle", "car", "motorcycle"]
for r in results:
for box, cls in zip(r.boxes.xyxy, r.boxes.cls):
class_id = int(cls.item())
if class_id == 0: # Person
isAlert["personCount"] += 1
if class_id in [0, 1, 2, 3]: # Person, bicycle, car, motorcycle
if is_inside_trapezoid(box.tolist(), trapezoid_pts):
isAlert["alert"] = [True, classInIntrusion[class_id]]
# Mark the intrusion with a red box
x1, y1, x2, y2 = map(int, box.tolist())
cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), (0, 0, 255), 3)
# Add alert text on the frame
alert_text = f"Intrusion Alert: {isAlert['alert'][0]}, Object: {isAlert['alert'][1]}, Persons: {isAlert['personCount']}"
cv2.putText(annotated_frame, alert_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
return annotated_frame, alert_text
def webcam_feed():
cap = cv2.VideoCapture(0)
# Check if the webcam opened successfully
if not cap.isOpened():
return np.zeros((480, 640, 3), dtype=np.uint8), "Failed to open webcam"
while True:
ret, frame = cap.read()
if not ret:
break
# Process frame
result_frame, alert_message = detect_objects(frame)
# Return the processed frame and alert message
yield result_frame, alert_message
# Create the Gradio interface with webcam
demo = gr.Interface(
fn=webcam_feed,
inputs=[],
outputs=[
gr.Image(label="Detection Output"),
gr.Textbox(label="Alert Status")
],
live=True,
title="YOLO Intrusion Detection",
description="Real-time detection of persons and vehicles inside a restricted trapezoidal area.",
allow_flagging="never"
)
if __name__ == "__main__":
demo.queue(max_size=1).launch() |