我需要检测以下显示的图像是否为不良图像。有谁能提供一种方法或算法来检测眩光，并对不良图像和良好图像进行分类？我尝试过模板匹配/特征匹配，但它不适用于我的情况。此外，如果可能，算法应独立于环境工作。
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我尝试过这个算法（模板匹配）：

import numpy as np
import cv2
from matplotlib import pyplot as plt

MIN_MATCH_COUNT = 35

img1 = cv2.imread('C:/Users/LB-185/Downloads/imgs/after_thresh/19_12_53_964057.png',0)          # queryImage
img2 = cv2.imread('C:/Users/LB-185/Downloads/imgs/after_thresh/19_12_54_355454.png',0)          # trainImage

sift = cv2.SIFT_create()

kp1, des1 = sift.detectAndCompute(img1,None)       #finding keypoints and descriptors from img 1
kp2, des2 = sift.detectAndCompute(img2,None)       #finding keypoints and descriptors from img 2

FLANN_INDEX_KDTREE = 0
index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)
search_params = dict(checks = 50)

flann = cv2.FlannBasedMatcher(index_params, search_params)

matches = flann.knnMatch(des1,des2,k=2)

good = []
for m,n in matches:
    if m.distance < 0.7*n.distance:
        good.append(m)
if len(good)>MIN_MATCH_COUNT:
    src_pts = np.float32([ kp1[m.queryIdx].pt for m in good ]).reshape(-1,1,2)
    dst_pts = np.float32([ kp2[m.trainIdx].pt for m in good ]).reshape(-1,1,2)

    M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC,5.0)
    matchesMask = mask.ravel().tolist()

    h,w = img1.shape
    pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)
    dst = cv2.perspectiveTransform(pts,M)

    img2 = cv2.polylines(img2,[np.int32(dst)],True,255,3, cv2.LINE_AA)

else:
    print ("Not enough matches are found - {}{}".format(len(good),MIN_MATCH_COUNT))
    matchesMask = None
    
draw_params = dict(matchColor = (0,255,0), singlePointColor = None, matchesMask = matchesMask, flags = 2)# # draw only inliers draw matches in green color

img3 = cv2.drawMatches(img1,kp1,img2,kp2,good,None,**draw_params)
plt.figure(figsize=(20, 20))
plt.imshow(img3, 'gray'),plt.show()

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我不得不把下面给出的图像分类为好图像和上面提到的图像为坏图像。