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import streamlit as st
import cv2
import numpy as np
from ultralytics import YOLO
# Load the YOLO model
model = YOLO('yolov8_Medium.pt') # Ensure the model file is in the root directory of your Space
def run_yolo(image):
# Run the model on the image and get results
results = model(image)
return results
def process_results(results, image):
# Draw bounding boxes and labels on the image
boxes = results[0].boxes # Get boxes from results
for box in boxes:
# Get the box coordinates and label
x1, y1, x2, y2 = map(int, box.xyxy[0]) # Convert to integer coordinates
conf = box.conf[0] # Confidence score
cls = int(box.cls[0]) # Class index
label = model.names[cls] # Get class name from index
# Draw rectangle and label on the image
cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2) # Blue box
cv2.putText(image, f"{label} {conf:.2f}", (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
return image
def process_video(uploaded_file):
# Read the video file
video = cv2.VideoCapture(uploaded_file)
frames = []
while True:
ret, frame = video.read()
if not ret:
break # Break the loop if there are no frames left
# Run YOLO model on the current frame
results = run_yolo(frame)
# Process the results and draw boxes on the current frame
processed_frame = process_results(results, frame)
frames.append(processed_frame) # Save the processed frame
video.release()
# Create a video writer to save the processed frames
height, width, _ = frames[0].shape
out = cv2.VideoWriter('processed_video.mp4', cv2.VideoWriter_fourcc(*'mp4v'), 30, (width, height))
for frame in frames:
out.write(frame) # Write each processed frame to the video
out.release()
def main():
st.title("Motorbike Violation Detection")
# Upload file
uploaded_file = st.file_uploader("Choose an image or video...", type=["jpg", "jpeg", "png", "mp4"])
if uploaded_file is not None:
if uploaded_file.type in ["image/jpeg", "image/png", "image/jpg"]:
# Process the image
image = np.array(cv2.imdecode(np.frombuffer(uploaded_file.read(), np.uint8), 1))
results = run_yolo(image)
# Process the results and draw boxes on the image
processed_image = process_results(results, image)
# Display the processed image
st.image(processed_image, caption='Detected Image', use_column_width=True)
elif uploaded_file.type == "video/mp4":
# Process the video
process_video(uploaded_file) # Process the video and save the output
st.video('processed_video.mp4') # Display the processed video
if __name__ == "__main__":
main()
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