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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
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