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de06ea6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | import streamlit as st
import cv2
import numpy as np
from ultralytics import YOLO
from PIL import Image
import tempfile
import os
# ---------------- PAGE CONFIG ----------------
st.set_page_config(
page_title="Helmet Detection",
page_icon="🪖",
layout="wide"
)
st.title("🪖 Helmet Detection System")
st.write("Upload an image or video to detect helmet usage")
# ---------------- LOAD MODEL ----------------
@st.cache_resource
def load_model():
return YOLO("best.pt") # your trained model path
model = load_model()
# ---------------- TABS ----------------
tab1, tab2 = st.tabs([" Image Detection", " Video Detection"])
# ==================================================
# IMAGE HELMET DETECTION
# ==================================================
with tab1:
st.header("Image Helmet Detection")
image_file = st.file_uploader(
"Upload an Image",
type=["jpg", "jpeg", "png"]
)
if image_file is not None:
image = Image.open(image_file)
image_np = np.array(image)
# YOLO inference
results = model(image_np, conf=0.4)
annotated_img = results[0].plot()
# ---------- DISPLAY IN SMALL WINDOWS ----------
col1, col2 = st.columns(2)
with col1:
st.subheader("Original Image")
st.image(image, width=350)
with col2:
st.subheader("Detection Result")
st.image(annotated_img, channels="BGR", width=350)
# ==================================================
# VIDEO HELMET DETECTION
# ==================================================
with tab2:
st.header("Video Helmet Detection")
video_file = st.file_uploader(
"Upload a Video",
type=["mp4", "avi", "mov"]
)
if video_file is not None:
temp_video = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4")
temp_video.write(video_file.read())
temp_video.close()
cap = cv2.VideoCapture(temp_video.name)
stframe = st.empty()
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# YOLO inference
results = model(frame, conf=0.4)
annotated_frame = results[0].plot()
# ---------- SMALL VIDEO WINDOW ----------
stframe.image(
annotated_frame,
channels="BGR",
width=450
)
cap.release()
os.remove(temp_video.name)
st.success(" Video processing completed")
st.markdown("""
<br>
<div style='text-align:center; padding:12px; background-color:#111111; border-radius:10px;'>
<span style='color:#AAAAAA; font-size:16px;'>
Designed & Developed by <b style='color:#CCCCCC;'>Yedeedya Injeti</b><br>
Under <b style='color:#B8860B;'>Innomatics Research Labs</b>
</span>
</div>
<br>
""", unsafe_allow_html=True)
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