app.py

import os
import cv2
import smtplib
import gradio as gr
from email import encoders
from ultralytics import YOLO
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart

sender_email = os.environ.get("sender_email")
receiver_email = os.environ.get("receiver_email")
sender_password = os.environ.get("sender_password")
smtp_port = 8080
smtp_server = "smtp.gmail.com"
subject = "Accident detected"


def send_email(accident_type,image):
    body = accident_type

    msg = MIMEMultipart()
    msg['From'] = sender_email
    msg['To'] = receiver_email
    msg['Subject'] = subject

    msg.attach(MIMEText(body, 'plain'))

    is_success, buffer = cv2.imencode(".jpg", image)
    attachment = buffer.tobytes()

    attachment_package = MIMEBase('application', 'octet-stream')
    attachment_package.set_payload((attachment)
    encoders.encode_base64(attachment_package)
    attachment_package.add_header('Content-Disposition', "attachment; filename= " + filename)
    msg.attach(attachment_package)

    text = msg.as_string()

    print("Connecting to server")
    gmail_server = smtplib.SMTP(smtp_server, smtp_port)
    gmail_server.starttls()
    gmail_server.login(sender_email, sender_password)
    print("Successfully Connected to Server")

    print("Sending email to ", receiver_email)
    gmail_server.sendmail(sender_email, receiver_email, text)
    print("Email sent to ", receiver_email)

    gmail_server.quit()

def check_acc(box):
    res_index_list = box.cls.tolist()
    result = ""

    for index in res_index_list:
        if index == 1:
            result = "Bike Bike Accident Detected"
            break
        elif index == 2:
            result = "Bike Object Accident Detected"
            break
        elif index == 3:
            result = "Bike Person Accident Detected"
            break
        elif index == 5:
            result = "Car Bike Accident Detected"
            break
        elif index == 6:
            result = "Car Car Accident Detected"
            break
        elif index == 7:
            result = "Car Object Accident Detected"
            break
        elif index == 8:
            result = "Car Person Accident Detected"
            break
    
    return result

def image_predict(image):
    res = ""
    model_path = "best.pt"
    model = YOLO(model_path)
    results = model.predict(image,conf = 0.6,iou = 0.3,imgsz = 512)
    box = results[0].boxes
    res = check_acc(box)
    annotated_frame = results[0].plot()
    if len(res) >0:
        annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
        send_email(res, annotated_frame_bgr)
        return (res, annotated_frame)

    return ("No Class Detected", None)

def extract_frames(video):
    vidcap = cv2.VideoCapture(video)
    vidcap = cv2.VideoCapture(video)
    fps = vidcap.get(cv2.CAP_PROP_FPS)
    nof = 4
    frame_no = 0
    while vidcap.isOpened():
        res = ""
        render = None
        success, image = vidcap.read()
        
        if success ==False:
            break
        
        # Check if it's time to process the frame based on the desired interval
        if (frame_no % (int(fps / nof))) == 0:
            model_path = "best.pt"
            model = YOLO(model_path)
            results = model.predict(image,conf = 0.6,iou = 0.3,imgsz = 512)  
            box = results[0].boxes
            res = check_acc(box) 
            annotated_frame = results[0].plot()

            if len(res) >0:
                annotated_frame_bgr = cv2.cvtColor(annotated_frame, cv2.COLOR_RGB2BGR)
                send_email(res, annotated_frame_bgr)
                return (res, annotated_frame)
            
        frame_no += 1  # Increment frame number
        
    return ("No Class Detected", None)


def take_input(image, video):
    if(video != None):
        res = extract_frames(video)
    else:
        res = image_predict(image)
    return res


with gr.Blocks(title="YOLOS Object Detection", css=".gradio-container {background:lightyellow;}") as demo:
    gr.HTML('<h1>Accident Detection Using Yolov8</h1>')
    gr.HTML("<br>")
    with gr.Row():
        input_image = gr.Image(label="Input image")
        input_video = gr.Video(label="Input video")
        output_label = gr.Text(label="output label")
        output_image = gr.Image(label="Output image")
    gr.HTML("<br>")
    send_btn = gr.Button("Detect")
    gr.HTML("<br>")

    send_btn.click(fn=take_input, inputs=[input_image, input_video], outputs=[output_label, output_image])

demo.launch(debug=True)