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import streamlit as st
import cv2
import numpy as np
import torch
import yolov5
from yolov5 import load
# Load YOLOv5 model
model = yolov5.load('best.pt') # Replace with your model path
def detect_number_plate(frame):
# Convert frame to RGB
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Perform inference
results = model(img)
# Parse results
detections = results.pandas().xyxy[0]
plates = []
for _, row in detections.iterrows():
if row['name'] == 'number_plate': # Adjust based on your model�s class names
plates.append({
'class': row['name'],
'confidence': row['confidence'],
'x_min': row['xmin'],
'y_min': row['ymin'],
'x_max': row['xmax'],
'y_max': row['ymax']
})
return plates
def detect_smoke(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (21, 21), 0)
_, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
smoke_intensity = np.sum(thresh) / (thresh.shape[0] * thresh.shape[1])
smoke_detected = smoke_intensity > 0.1 # Adjust this threshold
return smoke_detected, smoke_intensity
def process_frame(frame):
plates = detect_number_plate(frame)
smoke_detected, smoke_intensity = detect_smoke(frame)
return {
'smoke_detected': smoke_detected,
'smoke_intensity': smoke_intensity,
'number_plates': plates
}
# Streamlit app
st.title("Vehicle Number Plate and Smoke Detection")
uploaded_file = st.file_uploader("Choose an image...", type="jpg")
if uploaded_file is not None:
# Convert file to image
in_memory_file = uploaded_file.read()
np_arr = np.frombuffer(in_memory_file, np.uint8)
frame = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
# Process the frame
results = process_frame(frame)
st.subheader("Results")
st.write(f"Smoke Detected: {results['smoke_detected']}")
st.write(f"Smoke Intensity: {results['smoke_intensity']:.2f}")
st.subheader("Number Plates Detected")
for plate in results['number_plates']:
st.write(f"Class: {plate['class']}, Confidence: {plate['confidence']:.2f}")
st.write(f"Bounding Box: ({plate['x_min']}, {plate['y_min']}) to ({plate['x_max']}, {plate['y_max']})")
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