utils/retina_detector.py

import cv2
import numpy as np

def is_retina_image(image_path, min_circles=1, debug=False):
    """
    简单视网膜图片检测：
    1. 检查是否存在大致圆形（眼底）结构
    2. 检查颜色分布是否偏向红/黄/棕色
    """
    img = cv2.imread(image_path)
    if img is None:
        return False
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray = cv2.medianBlur(gray, 5)
    rows = gray.shape[0]
    # 霍夫圆检测
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp=1.2, minDist=rows/8,
                               param1=50, param2=30, minRadius=int(rows*0.3), maxRadius=int(rows*0.48))
    has_circle = circles is not None and len(circles[0]) >= min_circles
    # 颜色分布检测
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    # 视网膜常见色调范围（红/橙/黄/棕）
    mask1 = cv2.inRange(hsv, (5, 30, 30), (25, 255, 255))  # 橙黄
    mask2 = cv2.inRange(hsv, (0, 30, 30), (10, 255, 255))  # 红
    mask = cv2.bitwise_or(mask1, mask2)
    color_ratio = np.sum(mask > 0) / (img.shape[0] * img.shape[1])
    if debug:
        print(f"has_circle: {has_circle}, color_ratio: {color_ratio:.2f}")
    # 经验阈值：有圆且色块比例>0.2
    return has_circle and color_ratio > 0.2

def is_retina_image_fast(img_array, min_circles=1):
    """
    快速视网膜检测版本，直接使用numpy数组，避免重复读取文件
    """
    if img_array is None:
        return False

    # 转换为灰度图
    if len(img_array.shape) == 3:
        gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
    else:
        gray = img_array

    gray = cv2.medianBlur(gray, 5)
    rows = gray.shape[0]

    # 霍夫圆检测 - 使用更宽松的参数以加快检测
    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp=1.5, minDist=rows/6,
                               param1=40, param2=25, minRadius=int(rows*0.25), maxRadius=int(rows*0.55))

    has_circle = circles is not None and len(circles[0]) >= min_circles

    # 快速颜色检测 - 只检查红色通道
    if len(img_array.shape) == 3:
        # 转换为HSV进行颜色检测
        hsv = cv2.cvtColor(img_array, cv2.COLOR_RGB2HSV)
        # 视网膜常见色调范围（红/橙/黄/棕）
        mask1 = cv2.inRange(hsv, (5, 20, 20), (25, 255, 255))  # 橙黄
        mask2 = cv2.inRange(hsv, (0, 20, 20), (10, 255, 255))  # 红
        mask = cv2.bitwise_or(mask1, mask2)
        color_ratio = np.sum(mask > 0) / (img_array.shape[0] * img_array.shape[1])
    else:
        color_ratio = 0.5  # 灰度图默认通过颜色检测

    # 经验阈值：有圆且色块比例>0.15（稍微降低阈值以加快检测）
    return has_circle and color_ratio > 0.15