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Real-time Object Detection with YOLO - Streamlit Application
Main application file for the web interface.
"""
import streamlit as st
import cv2
import numpy as np
import time
from PIL import Image
import sys
import os
# Add src directory to path
sys.path.append(os.path.join(os.path.dirname(__file__), 'src'))
from detection.yolo_detector import YOLODetector
from detection.webcam_capture import WebcamCapture, get_available_cameras
from utils.config import app_config
from utils.helpers import format_detection_info, log_detection_summary
# Page configuration
st.set_page_config(
page_title=app_config.ui.page_title,
page_icon="🎯",
layout="wide",
initial_sidebar_state="expanded"
)
# Initialize session state
if 'detector' not in st.session_state:
st.session_state.detector = None
if 'webcam' not in st.session_state:
st.session_state.webcam = None
if 'is_running' not in st.session_state:
st.session_state.is_running = False
if 'frame_count' not in st.session_state:
st.session_state.frame_count = 0
if 'detection_stats' not in st.session_state:
st.session_state.detection_stats = {}
def initialize_detector(model_name: str, confidence_threshold: float):
"""Initialize the YOLO detector."""
try:
with st.spinner(f"Loading {model_name} model..."):
detector = YOLODetector(model_name, confidence_threshold)
if detector.model is not None:
st.session_state.detector = detector
st.success(f"✅ Model {model_name} loaded successfully!")
return True
else:
st.error(f"❌ Failed to load model {model_name}")
return False
except Exception as e:
st.error(f"❌ Error loading model: {str(e)}")
return False
def initialize_webcam(camera_index: int):
"""Initialize the webcam."""
try:
webcam = WebcamCapture(
camera_index=camera_index,
width=app_config.camera.frame_width,
height=app_config.camera.frame_height
)
if webcam.start_capture():
st.session_state.webcam = webcam
st.success(f"✅ Camera {camera_index} initialized successfully!")
return True
else:
st.error(f"❌ Failed to initialize camera {camera_index}")
return False
except Exception as e:
st.error(f"❌ Error initializing camera: {str(e)}")
return False
def main():
"""Main application function."""
# Title and description
st.title("🎯 Real-time Object Detection with YOLO")
st.markdown("Detect objects in real-time using your webcam and YOLO models.")
# Sidebar for controls
with st.sidebar:
st.header("⚙️ Settings")
# Model selection
st.subheader("🤖 Model Configuration")
selected_model = st.selectbox(
"Select YOLO Model",
app_config.available_models,
index=app_config.available_models.index(app_config.detection.model_name),
help="Choose the YOLO model variant. Nano is fastest, X is most accurate."
)
# Show model description
model_info = app_config.get_model_info(selected_model)
st.info(model_info['description'])
# Confidence threshold
confidence_threshold = st.slider(
"Confidence Threshold",
min_value=0.1,
max_value=1.0,
value=app_config.detection.confidence_threshold,
step=0.05,
help="Minimum confidence score for detections"
)
# Camera selection
st.subheader("📹 Camera Configuration")
available_cameras = get_available_cameras()
if available_cameras:
camera_index = st.selectbox(
"Select Camera",
available_cameras,
index=0,
help="Choose the camera to use for detection"
)
else:
st.error("❌ No cameras detected!")
camera_index = 0
# Performance settings
st.subheader("⚡ Performance")
detection_interval = st.slider(
"Detection Interval",
min_value=1,
max_value=10,
value=app_config.performance.detection_interval,
help="Process every N frames (higher = faster but less frequent detection)"
)
# Display settings
st.subheader("🎨 Display Options")
show_fps = st.checkbox("Show FPS", value=app_config.ui.show_fps)
show_stats = st.checkbox("Show Statistics", value=app_config.ui.show_stats)
show_confidence = st.checkbox("Show Confidence Scores", value=app_config.ui.show_confidence)
# Control buttons
st.subheader("🎮 Controls")
col1, col2 = st.columns(2)
with col1:
if st.button("🚀 Start Detection", disabled=st.session_state.is_running):
# Initialize detector if needed
if (st.session_state.detector is None or
st.session_state.detector.model_name != selected_model or
st.session_state.detector.confidence_threshold != confidence_threshold):
if initialize_detector(selected_model, confidence_threshold):
st.session_state.detector.update_confidence_threshold(confidence_threshold)
else:
st.stop()
# Initialize webcam if needed
if (st.session_state.webcam is None or
not st.session_state.webcam.is_camera_available()):
if not initialize_webcam(camera_index):
st.stop()
st.session_state.is_running = True
st.session_state.frame_count = 0
st.rerun()
with col2:
if st.button("⏹️ Stop Detection", disabled=not st.session_state.is_running):
st.session_state.is_running = False
if st.session_state.webcam:
st.session_state.webcam.stop_capture()
st.session_state.webcam = None
st.rerun()
# Main content area
if st.session_state.is_running and st.session_state.webcam and st.session_state.detector:
# Create columns for video and stats
col1, col2 = st.columns([3, 1])
with col1:
st.subheader("📹 Live Video Feed")
video_placeholder = st.empty()
with col2:
if show_stats:
st.subheader("📊 Statistics")
stats_placeholder = st.empty()
# Performance metrics placeholders
if show_fps:
fps_placeholder = st.empty()
# Detection loop
while st.session_state.is_running:
frame = st.session_state.webcam.get_frame()
if frame is not None:
st.session_state.frame_count += 1
# Process frame for detection
if st.session_state.frame_count % detection_interval == 0:
detections = st.session_state.detector.detect_objects(frame)
annotated_frame = st.session_state.detector.draw_detections(frame, detections)
# Update detection stats
st.session_state.detection_stats = st.session_state.detector.get_detection_stats(detections)
else:
annotated_frame = frame
detections = []
# Convert BGR to RGB for display
display_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
# Display video
with video_placeholder.container():
st.image(display_frame, channels="RGB", use_column_width=True)
# Display FPS
if show_fps:
current_fps = st.session_state.webcam.get_fps()
fps_placeholder.metric("🎯 FPS", f"{current_fps:.1f}")
# Display statistics
if show_stats and st.session_state.detection_stats:
with stats_placeholder.container():
stats = st.session_state.detection_stats
st.metric("🎯 Objects Detected", stats['total_objects'])
if stats['total_objects'] > 0:
st.metric("📈 Avg Confidence", f"{stats['avg_confidence']:.2f}")
st.metric("🔝 Max Confidence", f"{stats['max_confidence']:.2f}")
# Class breakdown
st.write("**Object Classes:**")
for class_name, count in stats['class_counts'].items():
st.write(f"• {class_name}: {count}")
# Small delay to prevent overwhelming the interface
time.sleep(0.01)
else:
st.warning("⚠️ No frame received from camera")
time.sleep(0.1)
else:
# Show instructions when not running
st.info("👆 Configure your settings in the sidebar and click 'Start Detection' to begin!")
# Show system information
st.subheader("ℹ️ System Information")
col1, col2, col3 = st.columns(3)
with col1:
st.write("**Available Cameras:**")
cameras = get_available_cameras()
if cameras:
for cam in cameras:
st.write(f"• Camera {cam}")
else:
st.write("• No cameras detected")
with col2:
st.write("**Available Models:**")
for model in app_config.available_models:
st.write(f"• {model}")
with col3:
st.write("**Current Configuration:**")
st.write(f"• Model: {app_config.detection.model_name}")
st.write(f"• Confidence: {app_config.detection.confidence_threshold}")
st.write(f"• Camera: {app_config.camera.camera_index}")
if __name__ == "__main__":
main()
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