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app.py

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+import os
+import cv2
+import tempfile
+import requests
+import base64
+import numpy as np
+import logging
+from dataclasses import dataclass
+from typing import Optional, Union, Tuple
+from PIL import Image
+from io import BytesIO
+from ultralytics import YOLO
+import streamlit as st
+import yt_dlp as youtube_dl
+from config import Config
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+@dataclass
+class DetectionResult:
+    """Data class to store detection results"""
+    success: bool
+    image: Optional[np.ndarray] = None
+    error_message: Optional[str] = None
+
+class YOLOModel:
+    """Class to handle YOLO model operations"""
+    def __init__(self, model_name: str = Config.DEFAULT_MODEL):
+        self.model = self._load_model(model_name)
+    
+    def _load_model(self, model_name: str) -> Optional[YOLO]:
+        """Load YOLO model with error handling"""
+        try:
+            return YOLO(model_name)
+        except Exception as e:
+            logger.error(f"Error loading model: {e}")
+            return None
+    
+    def detect_objects(self, image: np.ndarray) -> DetectionResult:
+        """Perform object detection on the input image"""
+        if self.model is None:
+            return DetectionResult(False, error_message="Model not loaded")
+        
+        try:
+            results = self.model(image)
+            annotated_image = image.copy()
+            
+            for result in results[0].boxes:
+                x1, y1, x2, y2 = map(int, result.xyxy[0])
+                label = self.model.names[int(result.cls)]
+                confidence = result.conf.item()
+                
+                if confidence < Config.CONFIDENCE_THRESHOLD:
+                    continue
+                    
+                cv2.rectangle(
+                    annotated_image, 
+                    (x1, y1), 
+                    (x2, y2), 
+                    Config.BBOX_COLOR, 
+                    2
+                )
+                label_text = f'{label} {confidence:.2f}'
+                cv2.putText(
+                    annotated_image,
+                    label_text,
+                    (x1, y1 - 10),
+                    cv2.FONT_HERSHEY_SIMPLEX,
+                    Config.FONT_SCALE,
+                    Config.BBOX_COLOR,
+                    Config.FONT_THICKNESS
+                )
+            
+            return DetectionResult(True, annotated_image)
+        except Exception as e:
+            logger.error(f"Error during object detection: {e}")
+            return DetectionResult(False, error_message=str(e))
+
+class ImageProcessor:
+    """Class to handle image processing operations"""
+    def __init__(self, model: YOLOModel):
+        self.model = model
+    
+    def process_image(self, image: Union[Image.Image, str]) -> DetectionResult:
+        """Process image from various sources (PIL Image or URL)"""
+        try:
+            if isinstance(image, str):
+                image = self._load_image_from_url(image)
+            
+            if image is None:
+                return DetectionResult(False, error_message="Failed to load image")
+                
+            np_image = np.array(image)
+            return self.model.detect_objects(np_image)
+        except Exception as e:
+            logger.error(f"Error processing image: {e}")
+            return DetectionResult(False, error_message=str(e))
+    
+    def _load_image_from_url(self, url: str) -> Optional[Image.Image]:
+        """Load image from URL with support for base64"""
+        try:
+            if url.startswith('data:image'):
+                header, encoded = url.split(',', 1)
+                image_data = base64.b64decode(encoded)
+                return Image.open(BytesIO(image_data))
+            else:
+                response = requests.get(url)
+                response.raise_for_status()
+                return Image.open(BytesIO(response.content))
+        except Exception as e:
+            logger.error(f"Error loading image from URL: {e}")
+            return None
+
+class VideoProcessor:
+    """Class to handle video processing operations"""
+    def __init__(self, model: YOLOModel):
+        self.model = model
+        os.makedirs(Config.TEMP_DIR, exist_ok=True)
+    
+    def process_video(self, input_path: str) -> Tuple[bool, Optional[str]]:
+        """Process video file and return path to processed video"""
+        try:
+            cap = cv2.VideoCapture(input_path)
+            if not cap.isOpened():
+                return False, "Cannot open video file"
+            
+            output_path = os.path.join(Config.TEMP_DIR, "processed_video.mp4")
+            self._setup_video_writer(cap, output_path)
+            
+            while True:
+                ret, frame = cap.read()
+                if not ret:
+                    break
+                    
+                result = self.model.detect_objects(frame)
+                if result.success:
+                    self.writer.write(result.image)
+            
+            cap.release()
+            self.writer.release()
+            return True, output_path
+        except Exception as e:
+            logger.error(f"Error processing video: {e}")
+            return False, str(e)
+    
+    def _setup_video_writer(self, cap: cv2.VideoCapture, output_path: str):
+        """Set up video writer with input video properties"""
+        frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        fourcc = cv2.VideoWriter_fourcc(*Config.VIDEO_OUTPUT_FORMAT)
+        self.writer = cv2.VideoWriter(
+            output_path, 
+            fourcc, 
+            fps, 
+            (frame_width, frame_height)
+        )
+
+def download_youtube_video(youtube_url: str) -> Optional[str]:
+    """Download YouTube video and return path to downloaded file"""
+    try:
+        temp_dir = tempfile.gettempdir()
+        output_path = os.path.join(temp_dir, 'downloaded_video.mp4')
+        ydl_opts = {
+            'format': 'best',
+            'outtmpl': output_path
+        }
+        with youtube_dl.YoutubeDL(ydl_opts) as ydl:
+            ydl.download([youtube_url])
+        return output_path
+    except Exception as e:
+        logger.error(f"Failed to retrieve video from YouTube: {e}")
+        return None
+
+def main():
+    """Main application function"""
+    st.title("MULTIMEDIA OBJECT DETECTION USING YOLO")
+    
+    # Model selection with description
+    st.subheader("Model Selection")
+    model_choice = st.selectbox(
+        "Select YOLO Model",
+        options=Config.AVAILABLE_MODELS,
+        index=Config.AVAILABLE_MODELS.index(Config.DEFAULT_MODEL),
+        format_func=lambda x: f"{x} - {Config.YOLO_MODELS[x]}"
+    )
+    
+    # Display model capabilities
+    model_type = "Detection"
+    if "pose" in model_choice:
+        model_type = "Pose Estimation"
+        st.info("This model will detect and estimate human poses in the image/video.")
+    elif "seg" in model_choice:
+        model_type = "Instance Segmentation"
+        st.info("This model will perform instance segmentation, creating precise masks for detected objects.")
+    else:
+        st.info("This model will detect and classify objects with bounding boxes.")
+    
+    # Initialize model and processors
+    model = YOLOModel(model_choice)
+    image_processor = ImageProcessor(model)
+    video_processor = VideoProcessor(model)
+    
+    tabs = st.tabs(["Image Detection", "Video Detection"])
+    
+    with tabs[0]:
+        st.header("Image Detection")
+        input_choice = st.radio("Select Input Method", ["Upload", "URL"])
+        
+        if input_choice == "Upload":
+            uploaded_image = st.file_uploader(
+                "Upload Image", 
+                type=Config.ALLOWED_IMAGE_TYPES
+            )
+            if uploaded_image is not None:
+                image = Image.open(uploaded_image)
+                result = image_processor.process_image(image)
+                if result.success:
+                    st.image(result.image, caption="Processed Image", use_container_width=True)
+                else:
+                    st.error(result.error_message)
+        
+        elif input_choice == "URL":
+            image_url = st.text_input("Image URL")
+            if image_url:
+                result = image_processor.process_image(image_url)
+                if result.success:
+                    st.image(result.image, caption="Processed Image", use_container_width=True)
+                else:
+                    st.error(result.error_message)
+    
+    with tabs[1]:
+        st.header("Video Detection")
+        video_choice = st.radio("Select Input Method", ["Upload", "YouTube"])
+        
+        if video_choice == "Upload":
+            uploaded_video = st.file_uploader(
+                "Upload Local Video", 
+                type=Config.ALLOWED_VIDEO_TYPES
+            )
+            if uploaded_video is not None:
+                input_video_path = os.path.join(Config.TEMP_DIR, uploaded_video.name)
+                with open(input_video_path, "wb") as f:
+                    f.write(uploaded_video.read())
+                
+                success, result = video_processor.process_video(input_video_path)
+                if success:
+                    st.video(result)
+                else:
+                    st.error(result)
+        
+        elif video_choice == "YouTube":
+            video_url = st.text_input("YouTube Video URL")
+            if video_url:
+                input_video_path = download_youtube_video(video_url)
+                if input_video_path:
+                    success, result = video_processor.process_video(input_video_path)
+                    if success:
+                        st.video(result)
+                    else:
+                        st.error(result)
+
+if __name__ == "__main__":
+    main()

config.py

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+from typing import List, Dict
+
+class Config:
+    # Model configurations with descriptions
+    YOLO_MODELS = {
+        "yolov8n.pt": "YOLOv8 Nano - Fastest and smallest model, best for CPU/edge devices",
+        "yolov8s.pt": "YOLOv8 Small - Good balance of speed and accuracy",
+        "yolov8m.pt": "YOLOv8 Medium - Better accuracy, still reasonable speed",
+        "yolov8l.pt": "YOLOv8 Large - High accuracy, slower speed",
+        "yolov8x.pt": "YOLOv8 XLarge - Highest accuracy, slowest speed",
+        # Pose estimation models
+        "yolov8n-pose.pt": "YOLOv8 Nano Pose - Fast pose estimation",
+        "yolov8s-pose.pt": "YOLOv8 Small Pose - Balanced pose estimation",
+        "yolov8m-pose.pt": "YOLOv8 Medium Pose - Accurate pose estimation",
+        "yolov8l-pose.pt": "YOLOv8 Large Pose - High accuracy pose estimation",
+        "yolov8x-pose.pt": "YOLOv8 XLarge Pose - Most accurate pose estimation",
+        # Segmentation models
+        "yolov8n-seg.pt": "YOLOv8 Nano Segmentation - Fast instance segmentation",
+        "yolov8s-seg.pt": "YOLOv8 Small Segmentation - Balanced segmentation",
+        "yolov8m-seg.pt": "YOLOv8 Medium Segmentation - Accurate segmentation",
+        "yolov8l-seg.pt": "YOLOv8 Large Segmentation - High accuracy segmentation",
+        "yolov8x-seg.pt": "YOLOv8 XLarge Segmentation - Most accurate segmentation"
+    }
+    
+    AVAILABLE_MODELS: List[str] = list(YOLO_MODELS.keys())
+    DEFAULT_MODEL: str = "yolov8s.pt"
+    
+    # File configurations
+    ALLOWED_IMAGE_TYPES: List[str] = ["jpg", "jpeg", "png"]
+    ALLOWED_VIDEO_TYPES: List[str] = ["mp4", "mov", "avi"]
+    
+    # Video processing
+    TEMP_DIR: str = "temp"
+    VIDEO_OUTPUT_FORMAT: str = "mp4v"
+    
+    # UI configurations
+    CONFIDENCE_THRESHOLD: float = 0.25  # Lowered for better detection
+    BBOX_COLOR: tuple = (0, 255, 0)
+    FONT_SCALE: float = 0.5
+    FONT_THICKNESS: int = 2

requirements.txt

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+opencv-python>=4.7.0
+pillow>=9.5.0
+requests>=2.31.0
+numpy>=1.24.3
+ultralytics>=8.0.0
+streamlit>=1.24.0
+yt-dlp>=2023.3.4