python-opencv 图像处理基础 （七）图像二值化+超大图像二值化

图像二值化

全局阈值二值化
局部阈值二值化

import cv2 as cv
import numpy as np

#全局阈值
def threshold_demo(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY|cv.THRESH_OTSU)
    #ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)#小于127黑色
    #ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_BINARY_INV)#大于127黑色
    #ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_TRUNC)#大于127显示127，小于127黑色
    #ret, binary = cv.threshold(gray, 127, 255, cv.THRESH_TOZERO)#小于127为0，显示黑色
    print("threshold value %s"%ret)
    cv.imshow("binary", binary)

#局部阈值  自适应阈值
def local_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    #binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 25, 10)
    binary = cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 25, 10)
    cv.imshow("binary", binary)

#自己计算均值进行二值化分割
def custom_threshold(image):
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    h, w = gray.shape[:2]
    m = np.reshape(gray, [1, w*h])
    print(m)
    mean = m.sum() / (w*h)
    print("mean : ", mean)
    ret, binary = cv.threshold(gray, mean, 255, cv.THRESH_BINARY)
    cv.imshow("binary", binary)

if __name__== "__main__":
    print("--------- Python OpenCV Tutorial ---------")
    src = cv.imread("../opencv-python-img/lena.png")
    cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
    cv.imshow("input image", src)
    #threshold_demo(src)
    #local_threshold(src)
    custom_threshold(src)
    cv.waitKey(0)

    cv.destroyAllWindows()

超大图像二值化

图像大小获取
空白图像过滤

import cv2 as cv
import numpy as np


def big_image_binary(image):
    print(image.shape)
    cw = 256
    ch = 256
    h, w = image.shape[:2]
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    #第一种 过滤空白图像实现图像二值化  如果不过滤直接二值化会导致一些噪点显示黑色
    for row in range(0, h, ch):
        for col in range(0, w, cw):
            roi = gray[row:row+ch, col:cw+col]
            print(np.std(roi), np.mean(roi))
            dev = np.std(roi)
            if dev < 15:#空白图像过滤
                gray[row:row + ch, col:cw + col] = 255
            else:
                ret, dst = cv.threshold(roi, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)#效果不好
                #dst=cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 127,20)#超大图像使用局部阈值二值化效果好。
                gray[row:row + ch, col:cw + col] = dst
    #第二种 使用自适应阈值进行二值化 即局部阈值二值化
    # for row in range(0, h, ch):
    #     for col in range(0, w, cw):
    #         roi = gray[row:row+ch, col:cw+col]
    #         dst=cv.adaptiveThreshold(gray, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 127,20)#超大图像使用局部阈值二值化效果好。
    #          gray[row:row + ch, col:cw + col] = dst
    # cv.imwrite("../result_binary.png", gray)


if __name__=="__main__":

    print("--------- Python OpenCV Tutorial ---------")
    src = cv.imread("../opencv-python-img/text1.png")
    #cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
    #cv.imshow("input image", src)
    big_image_binary(src)
    cv.waitKey(0)

    cv.destroyAllWindows()