utils/object_detection.py

import cv2
import numpy as np
from ultralytics import YOLO

# Model load (Tiny version for faster real-time performance)
model = YOLO('yolov8n.pt') 

def detect_objects(frame, confidence=0.45): 
    """
    Optimized Detection for Blind Assistance
    - imgsz=320: High FPS (Smooth video)
    - focal_length adjusted for calibrated distance
    """
    
    # Run Prediction
    results = model.predict(
        frame, 
        conf=confidence, 
        imgsz=320, 
        verbose=False,
        half=False # CPU ke liye False, agar GPU ho toh True kar dein
    ) 
    
    detections = []
    
    # Calibration: Realistic object widths (meters)
    # In widths ko accurate rakhna 100% result ke liye zaroori hai
    REAL_WIDTHS = {
        "person": 0.50, 
        "cell phone": 0.08, 
        "laptop": 0.35, 
        "bottle": 0.07,
        "cup": 0.10,
        "chair": 0.55,
        "tv": 0.80,
        "keyboard": 0.40,
        "mouse": 0.06
    }
    
    # Optimized Focal Length for standard webcams
    # (Distance = RealWidth * Focal / PixelWidth)
    FOCAL_LENGTH = 650 

    for r in results:
        # Image dimensions for coordinate normalization
        img_h, img_w = frame.shape[:2]
        
        for box in r.boxes:
            # Bounding box coordinates
            coords = box.xyxy[0].tolist()
            x1, y1, x2, y2 = map(int, coords)
            
            w_px = x2 - x1
            h_px = y2 - y1
            
            # Label selection
            cls_id = int(box.cls[0])
            label = model.names[cls_id]
            
            # Smart Distance Calculation
            real_w = REAL_WIDTHS.get(label, 0.25) # Default 0.25m if unknown
            distance = (real_w * FOCAL_LENGTH) / w_px if w_px > 0 else 0
            
            # Final Clean Object
            detections.append({
                "label": label,
                "x": x1, 
                "y": y1, 
                "w": w_px, 
                "h": h_px,
                "distance": round(distance, 2)
            })
            
    # Sirf top objects (distance wise) bhejein taake JS overload na ho
    detections = sorted(detections, key=lambda x: x['distance'])[:5]
            
    return detections