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# app.py
import streamlit as st
from processor import process_image, process_video, process_frame
import os
from PIL import Image
import tempfile
from streamlit_webrtc import VideoTransformerBase, webrtc_streamer, RTCConfiguration
import cv2
# Set page configuration
st.set_page_config(page_title="Traffic Violation Detection", layout="wide")
st.title("🚦 Motorbike Violation Detection App")
# Sidebar options
option = st.sidebar.radio("Select Option:", ("Image", "Video", "Live Camera"))
if option == "Image":
st.header("🖼️ Upload and Process Image")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, caption='Uploaded Image.', use_column_width=True)
if st.button("Process Image"):
with st.spinner("Processing..."):
with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tmp:
image.save(tmp.name)
frame = cv2.imread(tmp.name)
processed = process_image(tmp.name, "alfont_com_arial-1.ttf")
if processed is not None:
st.image(processed, caption='Processed Image.', use_column_width=True)
# Save processed image to temporary file for download
_, img_encoded = cv2.imencode('.jpg', processed)
st.download_button(
label="📥 Download Image",
data=img_encoded.tobytes(),
file_name="processed_image.jpg",
mime="image/jpeg"
)
else:
st.error("Failed to process the image.")
elif option == "Video":
st.header("🎥 Upload and Process Video")
uploaded_video = st.file_uploader("Choose a video...", type=["mp4", "avi", "mov"])
if uploaded_video is not None:
# Save the uploaded video to a temporary file
with open("temp_video.mov", "wb") as f:
f.write(uploaded_video.read())
# Display the uploaded video
st.video("temp_video.mov")
if st.button("Process Video"):
with st.spinner("Processing Video..."):
processed_path = process_video("temp_video.mov")
if processed_path and os.path.exists(processed_path):
st.success("Video processed successfully!")
st.video(processed_path)
with open(processed_path, "rb") as file:
st.download_button(
label="📥 Download Processed Video",
data=file,
file_name="processed_video.mov",
mime="video/mov"
)
else:
st.error("Failed to process the video.")
elif option == "Live Camera":
st.header("📷 Live Camera Feed")
st.info("Live processing is active. Detected violations will be annotated on the video feed.")
# RTC Configuration for streamlit-webrtc
RTC_CONFIGURATION = RTCConfiguration({"iceServers": [{"urls": ["stun:stun.l.google.com:19302"]}]})
class VideoTransformer(VideoTransformerBase):
def __init__(self):
self.font_path = "alfont_com_arial-1.ttf"
def transform(self, frame):
img = frame.to_ndarray(format="bgr24") # Convert the frame to a numpy array
processed_img = process_frame(img, self.font_path) # Process the image using your custom function
return processed_img
# Start the WebRTC streaming
webrtc_ctx = webrtc_streamer(
key="live-camera",
rtc_configuration=RTC_CONFIGURATION,
video_transformer_factory=VideoTransformer,
media_stream_constraints={"video": True, "audio": False},
async_transform=True
)
# Optional: Provide a stop button to end the live stream
if webrtc_ctx.video_receiver:
st.button("Stop Live Camera", on_click=webrtc_ctx.stop_stream)
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