"""
vCDR (vertical Cup-to-Disc Ratio) Computation
Computes vCDR from fundus images using Hough Circle Transform
"""

import cv2
import numpy as np

def compute_robust_vcdr(image_path):
    """
    Compute vCDR from fundus image
    Returns: vCDR value (float between 0 and 1)
    """
    try:
        # Read image
        img = cv2.imread(str(image_path))
        if img is None:
            return 0.5  # Default value
        
        # Resize for processing
        process_size = 512
        img_resized = cv2.resize(img, (process_size, process_size))
        gray = cv2.cvtColor(img_resized, cv2.COLOR_BGR2GRAY)
        blurred = cv2.GaussianBlur(gray, (9, 9), 2)
        
        scale_factor = process_size / 512.0
        
        # Detect optic disc (larger circle)
        circles_disc = cv2.HoughCircles(
            blurred, cv2.HOUGH_GRADIENT, dp=1,
            minDist=int(100 * scale_factor),
            param1=50, param2=30,
            minRadius=int(30 * scale_factor),
            maxRadius=int(250 * scale_factor)
        )
        
        # Detect optic cup (smaller circle)
        circles_cup = cv2.HoughCircles(
            blurred, cv2.HOUGH_GRADIENT, dp=1,
            minDist=int(50 * scale_factor),
            param1=50, param2=15,
            minRadius=int(10 * scale_factor),
            maxRadius=int(120 * scale_factor)
        )
        
        if circles_disc is not None:
            disc_r = circles_disc[0][0][2]
            cup_r = circles_cup[0][0][2] if circles_cup is not None else disc_r * 0.3
            vcdr = cup_r / disc_r
            return min(max(vcdr, 0.1), 0.9)  # Clamp between 0.1 and 0.9
        
        return 0.5  # Default if detection fails
    
    except Exception as e:
        print(f"Error computing vCDR: {e}")
        return 0.5