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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 = 80
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'))
filename = "res.png"
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 = 0.7,iou = 0.4,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 Accident 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 = 0.7,iou = 0.4,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 Accident 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)
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