Python+OpenCV —— 全局二值化，局部二值化與自訂義二值化

資料來源: https://blog.csdn.net/weixin_43860783/article/details/110471280

code:

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


def global_binary(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    plt.hist(gray.ravel(), 256, [0, 256])
    plt.show()
    """
        src:输入图像
        thresh：设置阈值，当进行二值化时，大于该值的为1，小于归0
        maxval：maximum value to use with the #THRESH_BINARY and #THRESH_BINARY_INV thresholding types.最大像素值
        type：所采用的方法
        
        注意：当设置了自动搜索阈值的方法时（如cv.THRESH_OTSU或者cv.THRESH_TRIANGLE）,手动设置的阈值将不生效
    """
    thr, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
    print("threshold:{}".format(thr))
    cv.imshow("binary", binary)


def local_binary(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    """
        src:输入图像
        maxValue：同上
        adaptiveMethod：自适应方法，常用的有ADAPTIVE_THRESH_MEAN_C以及ADAPTIVE_THRESH_GAUSSIAN_C,分别为均值发育高斯均值法
                        The #BORDER_REPLICATE | #BORDER_ISOLATED is used to process boundaries.
        thresholdType: must be either #THRESH_BINARY or #THRESH_BINARY_INV，只能是二值化或INV（反二值化）
        blockSize：局部二值的参考块大小
        C : Constant subtracted from the mean or weighted mean. Normally, it is positive but may be zero or negative as well.
            对每个块，结算得到的阈值减去常数C再来进行二值化，用于减小特殊值带来的误差
    """
    dst = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 25, 10)
    cv.imshow("local_binary", dst)


def custom_binary(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    h, w = gray.shape[:2]
    print("h:{}, w:{}".format(h, w))
    mean = np.sum(gray.ravel()) / (h*w)
    thr, binary = cv.threshold(gray, mean, 255, cv.THRESH_BINARY)
    print("threshold:{}".format(thr))
    cv.imshow("custom_binary", binary)


src = cv.imread("data/lena.jpg")
cell = cv.imread("data/cell.jpg")
# cv.imshow("original", cell)
# global_binary(cell)
# local_binary(src)
custom_binary(src)

cv.waitKey(0)
cv.destroyAllWindows()