【py脚本】批量二值化处理图像

参考：python实现批量图像二值化

注意上面博客中阈值类型表的介绍：

cv2.THRESH_BINARY
cv2.THRESH_BINARY_INV
cv2.THRESH_TRUNC
cv2.THRESH_TOZERO
cv2.THRESH_TOZERO_INV

单个图片处理：

import cv2
img = cv2.imread("166dian.jpg")
print(img)
# 先进行灰度化处理，再进行二值化
Grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 127是二值化阈值，大于255的像素值都置为0
ret, thresh = cv2.threshold(Grayimg, 127, 255, cv2.THRESH_BINARY)
cv2.imwrite('166dian1.jpg', thresh)

输入一个输出六个结果：

import cv2
import numpy as np
from matplotlib import pyplot as plt
img=cv2.imread('166dian.jpg')
# 中值滤波
# img = cv2.medianBlur(img, 5)
GrayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret,thresh1=cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY)
ret,thresh2=cv2.threshold(GrayImage,127,255,cv2.THRESH_BINARY_INV)
ret,thresh3=cv2.threshold(GrayImage,127,255,cv2.THRESH_TRUNC)
ret,thresh4=cv2.threshold(GrayImage,127,255,cv2.THRESH_TOZERO)
ret,thresh5=cv2.threshold(GrayImage,127,255,cv2.THRESH_TOZERO_INV)
titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']
images = [GrayImage, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in range(6):
   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray') #两行，三列，序号 出图
   plt.title(titles[i])
   plt.xticks([]),plt.yticks([])
plt.show()

某文件夹中图片批量处理：

注意：

1.路径最好要全英文
2. 根据原博客稍微改动

import os
import cv2
from PIL import Image
def binarization():
    # 获取目录下所有图片名
    filename = os.listdir(r"F:\lianxi\lianxi\py\input")#F:\python_Demo\DeepLearning\tools3\shapes\cmutestGT
    print(filename)
    # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表。
    base_dir = r"F:\lianxi\lianxi\py\input"  # input
    new_dir = r"F:\lianxi\lianxi\py\output"  # output
    for img in filename:
        name = img
        path1 = os.path.join(base_dir, img)
        print(name)
        img = cv2.imread(path1)
        #print(img)
        Grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        ret, thresh = cv2.threshold(Grayimg, 127, 255, cv2.THRESH_BINARY)
        cv2.imwrite('name.jpg', thresh)
        image = Image.open('name.jpg')
        # 有需要可对图像进行大小调整
        # image = image.resize((350, 350),Image.ANTIALIAS)
        path = os.path.join(new_dir, name)
        image.save(path)


binarization()