迭代法阈值选择算法是对双峰法的改进,他首先选择一个近似的阈值T,将图像分割成两个部分,R1和R2,计算出区域R1和R2的均值u1和u2,再选择新的
阈值T=(u1+u2)/2;
重复上面的过程,直到u1和u2不在变化为止,
实现前后对比:
实现代码:
import numpy as np import cv2 img = cv2.imread('./0.jpg', 0) #print(img[2, 4]) max_value = np.max(img) min_value = np.min(img) average_value = (max_value+min_value)/2 h, w = img.shape while True: fnum = 0 bnum = 0 fvalue = 0 bvalue = 0 faverage = 0 baverage = 0 b = 0 for i in range(h): for j in range(w): if img[i, j] > average_value: fnum += 1 fvalue += img[i, j] else: bnum += 1 bvalue += img[i, j] faverage = fvalue/fnum baverage = bvalue/bnum new_average = int((faverage + baverage)/2) if new_average == int(average_value): break else: average_value = new_average print(new_average) img = cv2.threshold(img, new_average, 255, cv2.THRESH_BINARY)[1] cv2.imshow('img', img) cv2.imwrite('9.jpg', img) cv2.waitKey(0) cv2.destroyAllWindows()