python实现超大图像的二值化方法

一，分块处理超大图像的二值化问题

  (1) 全局阈值处理
  (2) 局部阈值

二，空白区域过滤

三，先缩放进行二值化，然后还原大小

 

np.mean() 返回数组元素的平均值

np.std() 返回数组元素的标准差

一，分块处理超大图像的二值化问题

 (1) 全局阈值处理 

 (2) 局部阈值

 1 import cv2 as cv
import numpy as np

"""
def big_image_binary(image):
    print(image.shape)  #(4208, 2368, 3)　　#超大图像，屏幕无法显示完整
    cw,ch = 256,256
    h,w = image.shape[:2]
    gray = cv.cvtColor(image,cv.COLOR_RGB2GRAY)   #要二值化图像，要先进行灰度化处理
    for row in range(0,h,ch):
        for col in range(0,w,cw):
            roi = gray[row:row+ch,col:col+cw]   #获取分块
            #全局阈值
            # ret,binary = cv.threshold(roi,0,255,cv.THRESH_BINARY|cv.THRESH_OTSU)
           

            #局部阈值　　
            binary = cv.adaptiveThreshold(roi,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,127,20)　　
            gray[row:row + ch, col:col + cw] = binary　　#分块覆盖
            print(np.std(binary),np.mean(binary))

    cv.imwrite("binary2.jpg",gray)

 二，空白区域过滤

#空白区域过滤
def big_image_binary(image):
    print(image.shape)
    # 分成小块，每一块的宽高
    cw = 256
    ch = 256
    #整个图像的宽高
    h, w = image.shape[:2]
    gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
    #步长 ch cw
    for row in range(0, h, ch):
        for col in range(0, w, cw):
            #获取分块（感兴趣区域）
            roi = gray[row:row+ch, col:cw+col]
            print(np.std(roi), np.mean(roi))
            dev = np.std(roi)
            if dev < 15:
                gray[row:row + ch, col:cw + col] = 255   #dev < 15的让它变白
            else:
                ret, dst = cv.threshold(roi, 0, 255, cv.THRESH_BINARY | cv.THRESH_OTSU)
                gray[row:row + ch, col:cw + col] = dst
    cv.imwrite("D:/vcprojects/result_binary.png", gray)


print("--------- Python OpenCV Tutorial ---------")
src = cv.imread("D:/vcprojects/images/red_text2.png")
#cv.namedWindow("input image", cv.WINDOW_AUTOSIZE)
#cv.imshow("input image", src)
big_image_binary(src)
cv.waitKey(0)

cv.destroyAllWindows()
big_image_binary(src)
cv.waitKey(0)

cv.destroyAllWindows()

 参考：

https://blog.csdn.net/weixin_41722450/article/details/104265595