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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+asset/ezgif-5-12682faad5.gif filter=lfs diff=lfs merge=lfs -text
+asset/ezgif-5-28a1705b9b.gif filter=lfs diff=lfs merge=lfs -text

LICENSE

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README copy.md

@@ -0,0 +1,165 @@
+# Real-Time Object Detection with YOLOv8
+
+A Streamlit-based web application for real-time object detection in videos using YOLOv8. This application supports multiple YOLO models, real-time detection, object tracking, and video processing with annotated output.
+
+## Demo
+![me](https://github.com/damndeepesh/QuickTrack/blob/main/asset/ezgif-5-12682faad5.gif)
+![me](https://github.com/damndeepesh/QuickTrack/blob/main/asset/ezgif-5-28a1705b9b.gif)
+
+## Features
+
+- Multiple YOLOv8 model support (Nano to XLarge)
+- Real-time object detection and tracking
+- Support for video files and live streams
+- Unique ID tracking for detected objects
+- Customizable detection confidence
+- Color-coded object categories
+- Downloadable processed videos
+- Interactive web interface
+
+## Installation
+
+### Prerequisites
+- Python 3.8 or higher
+- CUDA-compatible GPU (optional, but recommended for better performance)
+
+### Step 1: Clone the Repository 
+- git clone <repository-url>
+- cd <repository-name>
+
+### Step 2: Create a Virtual Environment (Recommended)
+
+## Windows
+- python -m venv venv
+- venv\Scripts\activate
+
+## Linux/Mac
+- python3 -m venv venv
+- source venv/bin/activate
+
+### Step 3: Install Dependencies
+- pip install -r requirements.txt
+
+## Usage
+
+### Starting the Application
+- streamlit run app.py
+
+
+### Step-by-Step Guide
+
+1. **Select a Model**:
+   - Choose from available YOLOv8 models in the sidebar
+   - Models range from Nano (fastest) to XLarge (most accurate)
+   - Review model details in the expandable section
+   - Click "Load Selected Model" to download and initialize
+
+2. **Configure Settings**:
+   - Adjust detection confidence using the slider
+   - Lower values detect more objects but may increase false positives
+   - Higher values are more selective but might miss some objects
+
+3. **Input Selection**:
+   - Choose between "Video File" or "Live Stream URL"
+   - For video files: Upload MP4 or AVI format
+   - For streams: Enter a valid stream URL
+
+4. **Start Detection**:
+   - Click "Start Detection" in the sidebar
+   - Watch real-time detection with bounding boxes
+   - Each object gets a unique tracking ID
+
+5. **Download Results**:
+   - Stop detection when finished
+   - Download button appears automatically
+   - Processed video includes all annotations
+
+## About YOLO Models
+
+### Available Models
+
+1. **YOLOv8n (Nano)**:
+   - Size: 6.7 MB
+   - Best for: Real-time applications on CPU
+   - Speed: ⚡⚡⚡⚡⚡
+   - Accuracy: ⭐⭐
+
+2. **YOLOv8s (Small)**:
+   - Size: 22.4 MB
+   - Best for: Balanced performance
+   - Speed: ⚡⚡⚡⚡
+   - Accuracy: ⭐⭐⭐
+
+3. **YOLOv8m (Medium)**:
+   - Size: 52.2 MB
+   - Best for: Standard detection tasks
+   - Speed: ⚡⚡⚡
+   - Accuracy: ⭐⭐⭐⭐
+
+4. **YOLOv8l (Large)**:
+   - Size: 87.7 MB
+   - Best for: High accuracy needs
+   - Speed: ⚡⚡
+   - Accuracy: ⭐⭐⭐⭐⭐
+
+5. **YOLOv8x (XLarge)**:
+   - Size: 131.7 MB
+   - Best for: Maximum accuracy
+   - Speed: ⚡
+   - Accuracy: ⭐⭐⭐⭐⭐⭐
+
+### Model Selection Guide
+
+- **CPU Only**: Use Nano or Small models
+- **GPU Available**: Medium to XLarge models recommended
+- **Real-time Needs**: Nano or Small models
+- **Accuracy Priority**: Large or XLarge models
+- **Balanced**: Medium model
+
+## Technical Details
+
+- Built with Streamlit and OpenCV
+- Uses Ultralytics YOLOv8 implementation
+- Supports multiple video codecs
+- Real-time frame processing and buffering
+- Unique object tracking with IoU
+- Color-coded object categories
+- Frame buffer for smooth video writing
+
+## Troubleshooting
+
+1. **Video Not Loading**:
+   - Check file format (MP4/AVI supported)
+   - Ensure file isn't corrupted
+   - Try a different video codec
+
+2. **Slow Performance**:
+   - Use a smaller YOLO model
+   - Reduce input video resolution
+   - Check GPU availability
+
+3. **Detection Issues**:
+   - Adjust confidence threshold
+   - Try a larger YOLO model
+   - Ensure good lighting in video
+
+4. **Download Issues**:
+   - Wait for processing to complete
+   - Check available disk space
+   - Try a different browser
+
+## Requirements
+
+- streamlit>=1.24.0
+- opencv-python-headless>=4.7.0
+- torch>=2.0.0
+- torchvision>=0.15.0
+- numpy>=1.24.0
+- ultralytics>=8.0.0
+- python-dateutil>=2.8.2
+
+## Acknowledgments
+
+- YOLOv8 by Ultralytics - https://docs.ultralytics.com/models/yolov8/
+- Streamlit Framework - https://streamlit.io
+- OpenCV Project - https://docs.opencv.org/4.x/index.html

app.py

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+import streamlit as st
+import cv2
+import torch
+import numpy as np
+import time
+import tempfile
+from pathlib import Path
+
+# Import detection utilities
+from detection_utils import load_model, detect_objects, draw_boxes, ObjectTracker
+
+def initialize_video_capture(input_source, video_file=None, url=None):
+    """Initialize video capture and writer"""
+    cap = None
+    out = None
+    output_path = None
+    
+    if input_source == "Video File" and video_file is not None:
+        # Save uploaded file to temp location
+        tfile = tempfile.NamedTemporaryFile(delete=False, suffix='.mp4')
+        tfile.write(video_file.read())
+        tfile.flush()
+        video_path = tfile.name
+        
+        # Open video capture
+        cap = cv2.VideoCapture(video_path)
+        
+        if cap.isOpened():
+            # Get video properties
+            width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+            height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+            fps = int(cap.get(cv2.CAP_PROP_FPS))
+            
+            # Ensure valid FPS
+            if fps <= 0:
+                fps = 30
+            
+            # Create output path in a temporary directory
+            temp_dir = tempfile.gettempdir()
+            output_path = str(Path(temp_dir) / 'detected_output.mp4')
+            
+            # Try different codecs in order of preference
+            codecs = [
+                ('avc1', '.mp4'),
+                ('mp4v', '.mp4'),
+                ('XVID', '.avi')
+            ]
+            
+            for codec, ext in codecs:
+                try:
+                    output_path = str(Path(temp_dir) / f'detected_output{ext}')
+                    fourcc = cv2.VideoWriter_fourcc(*codec)
+                    out = cv2.VideoWriter(
+                        output_path,
+                        fourcc,
+                        fps,
+                        (width, height),
+                        isColor=True
+                    )
+                    
+                    # Test if writer is working
+                    if out.isOpened():
+                        break
+                except Exception:
+                    continue
+            
+            if out is None or not out.isOpened():
+                st.error("Failed to create video writer")
+                return None, None, None
+    
+    elif input_source == "Live Stream URL" and url:
+        cap = cv2.VideoCapture(url)
+    
+    return cap, out, output_path
+
+def get_model_info():
+    """Return information about available YOLO models"""
+    return {
+        'yolov8n.pt': {
+            'name': 'YOLOv8 Nano',
+            'description': 'Smallest and fastest model. Best for CPU or low-power devices.',
+            'speed': '⚡⚡⚡⚡⚡',
+            'accuracy': '⭐⭐',
+            'size': '6.7 MB',
+            'details': 'Ideal for real-time applications with limited computing power.'
+        },
+        'yolov8s.pt': {
+            'name': 'YOLOv8 Small',
+            'description': 'Small model balancing speed and accuracy.',
+            'speed': '⚡⚡⚡⚡',
+            'accuracy': '⭐⭐⭐',
+            'size': '22.4 MB',
+            'details': 'Good for general purpose detection with decent performance.'
+        },
+        'yolov8m.pt': {
+            'name': 'YOLOv8 Medium',
+            'description': 'Medium-sized model with good balance.',
+            'speed': '⚡⚡⚡',
+            'accuracy': '⭐⭐⭐⭐',
+            'size': '52.2 MB',
+            'details': 'Recommended for standard detection tasks with good GPU.'
+        },
+        'yolov8l.pt': {
+            'name': 'YOLOv8 Large',
+            'description': 'Large model with high accuracy.',
+            'speed': '⚡⚡',
+            'accuracy': '⭐⭐⭐⭐⭐',
+            'size': '87.7 MB',
+            'details': 'Best for high-accuracy requirements with good computing power.'
+        },
+        'yolov8x.pt': {
+            'name': 'YOLOv8 XLarge',
+            'description': 'Extra large model with highest accuracy.',
+            'speed': '⚡',
+            'accuracy': '⭐⭐⭐⭐⭐⭐',
+            'size': '131.7 MB',
+            'details': 'Best for tasks requiring maximum accuracy, requires powerful GPU.'
+        }
+    }
+
+def main():
+    st.title("Real-Time Object Detection")
+    
+    # Initialize session state
+    if 'tracker' not in st.session_state:
+        st.session_state.tracker = ObjectTracker()
+    if 'cap' not in st.session_state:
+        st.session_state.cap = None
+    if 'out' not in st.session_state:
+        st.session_state.out = None
+    if 'output_path' not in st.session_state:
+        st.session_state.output_path = None
+    if 'processed_frames' not in st.session_state:
+        st.session_state.processed_frames = 0
+    if 'selected_model' not in st.session_state:
+        st.session_state.selected_model = 'yolov8x.pt'
+    if 'model' not in st.session_state:
+        st.session_state.model = None
+    
+    # Sidebar settings
+    st.sidebar.title("Settings")
+    
+    # Model selection
+    st.sidebar.subheader("Model Selection")
+    model_info = get_model_info()
+    selected_model = st.sidebar.selectbox(
+        "Choose YOLO Model",
+        options=list(model_info.keys()),
+        format_func=lambda x: model_info[x]['name'],
+        index=list(model_info.keys()).index(st.session_state.selected_model)
+    )
+    
+    # Display model information
+    with st.sidebar.expander("Model Details", expanded=True):
+        st.markdown(f"**{model_info[selected_model]['name']}**")
+        st.write(model_info[selected_model]['description'])
+        st.write(f"Speed: {model_info[selected_model]['speed']}")
+        st.write(f"Accuracy: {model_info[selected_model]['accuracy']}")
+        st.write(f"Size: {model_info[selected_model]['size']}")
+        st.write(f"Details: {model_info[selected_model]['details']}")
+    
+    # Add Load Model button
+    if st.sidebar.button("Load Selected Model"):
+        with st.spinner(f"Loading {model_info[selected_model]['name']}..."):
+            st.session_state.model = load_model(selected_model)
+            st.session_state.selected_model = selected_model
+            st.sidebar.success("Model loaded successfully!")
+    
+    # Detection confidence
+    detection_confidence = st.sidebar.slider("Detection Confidence", 0.0, 1.0, 0.5)
+    
+    # Input selection
+    input_source = st.radio("Select Input Source", ["Video File", "Live Stream URL"])
+    
+    try:
+        # Handle video input
+        if input_source == "Video File":
+            video_file = st.file_uploader("Upload Video", type=['mp4', 'avi'])
+            if video_file is not None:
+                st.session_state.cap, st.session_state.out, st.session_state.output_path = initialize_video_capture(input_source, video_file=video_file)
+        else:
+            url = st.text_input("Enter Stream URL")
+            if url:
+                st.session_state.cap, st.session_state.out, st.session_state.output_path = initialize_video_capture(input_source, url=url)
+        
+        if st.session_state.cap is not None and not st.session_state.cap.isOpened():
+            st.error("Error: Could not open video source")
+            st.stop()
+        
+        # Create placeholder for video display
+        video_placeholder = st.empty()
+        
+        # Initialize frame buffer in session state
+        if 'frame_buffer' not in st.session_state:
+            st.session_state.frame_buffer = []
+        
+        # Control buttons - Move them to sidebar to avoid duplication
+        st.sidebar.markdown("---")
+        st.sidebar.subheader("Controls")
+        start_button = st.sidebar.button("Start Detection")
+        stop_button = st.sidebar.button("Stop Detection")
+        
+        if start_button:
+            if st.session_state.model is None:
+                st.error("Please load a model first using the 'Load Selected Model' button")
+                st.stop()
+            if st.session_state.cap is None:
+                st.error("Please upload a video or provide a stream URL first")
+                st.stop()
+            st.session_state.run_detection = True
+            st.session_state.processed_frames = 0
+            st.session_state.frame_buffer = []  # Clear buffer on start
+        if stop_button:
+            st.session_state.run_detection = False
+        
+        # Detection loop
+        while (hasattr(st.session_state, 'run_detection') and 
+               st.session_state.run_detection and 
+               st.session_state.cap is not None):
+            
+            ret, frame = st.session_state.cap.read()
+            if not ret:
+                break
+            
+            # Perform detection
+            detections = detect_objects(st.session_state.model, frame, detection_confidence)
+            
+            # Draw boxes on frame
+            annotated_frame = draw_boxes(frame, detections, st.session_state.tracker)
+            
+            # Add frame to buffer
+            st.session_state.frame_buffer.append(annotated_frame)
+            
+            # Write frames to video periodically
+            if len(st.session_state.frame_buffer) >= 30:  # Write every 30 frames
+                for buffered_frame in st.session_state.frame_buffer:
+                    if st.session_state.out is not None:
+                        st.session_state.out.write(buffered_frame)
+                        st.session_state.processed_frames += 1
+                st.session_state.frame_buffer.clear()
+            
+            # Update display every 3rd frame
+            if st.session_state.processed_frames % 3 == 0:
+                video_placeholder.image(annotated_frame, channels="BGR")
+            
+            # Minimal sleep to prevent UI freezing
+            time.sleep(0.001)
+        
+        # Write remaining frames in buffer
+        if st.session_state.frame_buffer and st.session_state.out is not None:
+            for buffered_frame in st.session_state.frame_buffer:
+                st.session_state.out.write(buffered_frame)
+                st.session_state.processed_frames += 1
+            st.session_state.frame_buffer.clear()
+        
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+        raise e
+    
+    finally:
+        # Ensure proper cleanup and save remaining frames
+        if hasattr(st.session_state, 'frame_buffer') and st.session_state.frame_buffer and hasattr(st.session_state, 'out') and st.session_state.out is not None:
+            for buffered_frame in st.session_state.frame_buffer:
+                st.session_state.out.write(buffered_frame)
+                st.session_state.processed_frames += 1
+            st.session_state.frame_buffer.clear()
+        
+        # Release resources
+        if hasattr(st.session_state, 'cap') and st.session_state.cap is not None:
+            st.session_state.cap.release()
+        
+        if hasattr(st.session_state, 'out') and st.session_state.out is not None:
+            st.session_state.out.release()
+            cv2.destroyAllWindows()
+        
+        # Add a separator
+        st.markdown("---")
+        
+        # Download section
+        if st.session_state.processed_frames > 0:
+            st.subheader("Download Processed Video")
+            
+            # Force flush and wait
+            time.sleep(3)  # Increased wait time
+            
+            if (st.session_state.output_path and 
+                Path(st.session_state.output_path).exists()):
+                
+                try:
+                    with open(st.session_state.output_path, 'rb') as f:
+                        video_data = f.read()
+                        if len(video_data) > 1000:
+                            st.success(f"Successfully processed {st.session_state.processed_frames} frames")
+                            # Make download button more prominent
+                            st.download_button(
+                                label="📥 Download Processed Video",
+                                data=video_data,
+                                file_name=f"detected_video_{time.strftime('%Y%m%d_%H%M%S')}.mp4",
+                                mime="video/mp4",
+                                key="download_button"
+                            )
+                        else:
+                            st.error("Error: Video file is empty or corrupted")
+                            st.info("Try processing the video again with different settings")
+                except Exception as e:
+                    st.error(f"Error preparing download: {str(e)}")
+                    st.info("Please try processing the video again")
+            else:
+                st.error("Output video file not found")
+                st.info("Make sure to complete the video processing before downloading")
+
+if __name__ == "__main__":
+    main()

detection_utils.py

@@ -0,0 +1,245 @@
+import torch
+import cv2
+import numpy as np
+from collections import defaultdict
+import streamlit as st
+
+# Add this color_map dictionary before the draw_boxes function
+# Extended color map for different classes
+color_map = {
+    # People and animals
+    'person': (0, 0, 255),      # Red
+    'dog': (0, 255, 255),       # Cyan
+    'cat': (255, 0, 255),       # Magenta
+    'bird': (165, 42, 42),      # Brown
+    'horse': (128, 0, 0),       # Maroon
+    'sheep': (230, 216, 173),   # Beige
+    'cow': (112, 128, 144),     # Slate
+    
+    # Vehicles
+    'car': (255, 0, 0),         # Blue
+    'truck': (255, 165, 0),     # Orange
+    'bicycle': (128, 0, 128),   # Purple
+    'motorcycle': (255, 192, 203), # Pink
+    'bus': (255, 255, 0),       # Yellow
+    'train': (0, 128, 0),       # Dark Green
+    'airplane': (70, 130, 180),  # Steel Blue
+    'boat': (0, 165, 255),      # Orange-Red
+    
+    # Objects
+    'traffic light': (0, 255, 127),  # Spring Green
+    'fire hydrant': (255, 69, 0),    # Red-Orange
+    'stop sign': (220, 20, 60),      # Crimson
+    'bench': (107, 142, 35),         # Olive
+    'chair': (0, 128, 128),          # Teal
+    'dining table': (255, 215, 0),   # Gold
+    'cell phone': (138, 43, 226),    # Blue Violet
+    'laptop': (0, 191, 255),         # Deep Sky Blue
+    'keyboard': (255, 127, 80),      # Coral
+    'book': (218, 112, 214),         # Orchid
+    'clock': (240, 230, 140),        # Khaki
+    
+    # Sports
+    'sports ball': (0, 250, 154),    # Medium Spring Green
+    'kite': (255, 240, 245),         # Lavender
+    'baseball bat': (188, 143, 143), # Rosy Brown
+    'baseball glove': (46, 139, 87), # Sea Green
+    
+    # Food
+    'bottle': (0, 206, 209),         # Turquoise
+    'wine glass': (255, 248, 220),   # Cornsilk
+    'cup': (147, 112, 219),          # Medium Purple
+    'fork': (218, 165, 32),          # Goldenrod
+    'sandwich': (210, 105, 30),      # Chocolate
+    'pizza': (188, 143, 143),        # Rosy Brown
+    
+    # Additional objects
+    'backpack': (0, 100, 0),         # Dark Green
+    'umbrella': (255, 182, 193),     # Light Pink
+    'handbag': (219, 112, 147),      # Pale Violet Red
+    'tie': (106, 90, 205),           # Slate Blue
+    'suitcase': (72, 61, 139),       # Dark Slate Blue
+    'frisbee': (32, 178, 170),       # Light Sea Green
+    'skis': (135, 206, 250),         # Light Sky Blue
+    'snowboard': (176, 224, 230),    # Powder Blue
+    'tennis racket': (218, 112, 214),# Orchid
+    'surfboard': (0, 139, 139),      # Dark Cyan
+    'remote': (143, 188, 143),       # Dark Sea Green
+    'mouse': (216, 191, 216),        # Thistle
+    'toaster': (255, 222, 173),      # Navajo White
+    'sink': (112, 128, 144),         # Slate Gray
+    'refrigerator': (47, 79, 79),    # Dark Slate Gray
+    'tv': (25, 25, 112),             # Midnight Blue
+    'microwave': (0, 139, 139),      # Dark Cyan
+    'oven': (160, 82, 45),           # Sienna
+    'toothbrush': (199, 21, 133),    # Medium Violet Red
+    'scissors': (176, 196, 222),     # Light Steel Blue
+}
+
+def load_model(model_path='yolov8x.pt'):
+    """Load YOLOv8 model"""
+    try:
+        from ultralytics import YOLO
+        import os
+        os.environ['NNPACK_FAST_MATH'] = 'OFF'
+        
+        # Load the selected model
+        model = YOLO(model_path)
+        
+        # Warmup the model
+        model.predict(np.zeros((640, 640, 3)), verbose=False)
+        
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {str(e)}")
+        st.stop()
+
+def detect_objects(model, frame, conf_threshold=0.5):
+    """
+    Detect objects in a frame using YOLO with optimized processing
+    """
+    # Convert frame to RGB
+    frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+    
+    # Perform detection with optimized settings
+    results = model.predict(
+        frame_rgb,
+        conf=conf_threshold,
+        verbose=False,
+        device='0' if torch.cuda.is_available() else 'cpu',
+        imgsz=1280,  # Increased size for better detection
+        iou=0.4,    # Adjusted IOU threshold
+        max_det=300,  # Increase maximum detections
+        agnostic_nms=True,  # Better handling of objects of different sizes
+    )
+    
+    result = results[0]
+    detections = []
+    
+    if hasattr(result, 'boxes'):
+        boxes = result.boxes.cpu().numpy()
+        for box in boxes:
+            try:
+                x1, y1, x2, y2 = map(int, box.xyxy[0])
+                class_id = int(box.cls[0])
+                confidence = float(box.conf[0])
+                class_name = result.names[class_id]
+                
+                detection = {
+                    'bbox': [x1, y1, x2, y2],
+                    'class': class_name,
+                    'confidence': confidence
+                }
+                detections.append(detection)
+            except Exception as e:
+                continue
+    
+    return detections
+
+class ObjectTracker:
+    def __init__(self):
+        self.next_id = 1
+        self.object_ids = {}
+        self.id_timeout = 30
+        self.last_positions = {}
+    
+    def get_object_id(self, bbox, class_name):
+        """Assign or retrieve ID for detected object based on position and IoU"""
+        center = ((bbox[0] + bbox[2]) // 2, (bbox[1] + bbox[3]) // 2)
+        
+        # Calculate box area
+        box_area = (bbox[2] - bbox[0]) * (bbox[3] - bbox[1])
+        
+        best_iou = 0
+        best_id = None
+        
+        # Check existing objects
+        for obj_id, (old_bbox, old_class, last_seen) in list(self.last_positions.items()):
+            if last_seen > self.id_timeout:
+                del self.last_positions[obj_id]
+                continue
+            
+            # Calculate IoU
+            x1 = max(bbox[0], old_bbox[0])
+            y1 = max(bbox[1], old_bbox[1])
+            x2 = min(bbox[2], old_bbox[2])
+            y2 = min(bbox[3], old_bbox[3])
+            
+            if x2 > x1 and y2 > y1:
+                intersection = (x2 - x1) * (y2 - y1)
+                old_area = (old_bbox[2] - old_bbox[0]) * (old_bbox[3] - old_bbox[1])
+                union = box_area + old_area - intersection
+                iou = intersection / union
+                
+                if iou > best_iou and iou > 0.3 and class_name == old_class:
+                    best_iou = iou
+                    best_id = obj_id
+        
+        if best_id is not None:
+            self.last_positions[best_id] = (bbox, class_name, 0)
+            return best_id
+        
+        # If no match found, assign new ID
+        new_id = self.next_id
+        self.next_id += 1
+        self.last_positions[new_id] = (bbox, class_name, 0)
+        return new_id
+    
+    def update_timeouts(self):
+        """Update timeout counters for all tracked objects"""
+        for obj_id in self.last_positions:
+            bbox, class_name, timeout = self.last_positions[obj_id]
+            self.last_positions[obj_id] = (bbox, class_name, timeout + 1)
+
+def draw_boxes(frame, detections, tracker):
+    """
+    Optimized version of drawing bounding boxes and labels with improved visibility
+    """
+    annotated_frame = frame.copy()
+    tracker.update_timeouts()
+    
+    # Thicker lines and larger text for better visibility
+    box_thickness = 3
+    text_scale = 0.7
+    text_thickness = 2
+    
+    for det in detections:
+        x1, y1, x2, y2 = det['bbox']
+        obj_id = tracker.get_object_id(det['bbox'], det['class'])
+        
+        # Get color with default
+        color = color_map.get(det['class'].lower(), (0, 255, 0))
+        
+        # Create label with better formatting
+        label = f"#{obj_id} {det['class']} {det['confidence']:.2f}"
+        
+        # Draw box with thicker lines
+        cv2.rectangle(annotated_frame, (x1, y1), (x2, y2), color, box_thickness)
+        
+        # Improve text background
+        (text_width, text_height), baseline = cv2.getTextSize(
+            label, cv2.FONT_HERSHEY_SIMPLEX, text_scale, text_thickness)
+        
+        # Make background rectangle slightly larger
+        padding = 5
+        cv2.rectangle(annotated_frame, 
+                     (x1, y1 - text_height - baseline - padding * 2),
+                     (x1 + text_width + padding * 2, y1),
+                     color, -1)
+        
+        # Add white border around the text for better visibility
+        for dx, dy in [(-1,-1), (-1,1), (1,-1), (1,1)]:
+            cv2.putText(annotated_frame, label,
+                       (x1 + padding + dx, y1 - padding + dy),
+                       cv2.FONT_HERSHEY_SIMPLEX, text_scale,
+                       (0, 0, 0), text_thickness + 1)
+        
+        # Draw main text
+        cv2.putText(annotated_frame, label,
+                    (x1 + padding, y1 - padding),
+                    cv2.FONT_HERSHEY_SIMPLEX, text_scale,
+                    (255, 255, 255), text_thickness)
+        
+        det['id'] = obj_id
+    
+    return annotated_frame

requirements.txt

@@ -0,0 +1,7 @@
+opencv-python-headless>=4.7.0
+torch>=2.0.0
+torchvision>=0.15.0
+numpy>=1.24.0
+streamlit>=1.24.0
+ultralytics>=8.0.0
+python-dateutil>=2.8.2