OpenCV图像处理之图像的二值化解读

import cv2

img = cv2.imread('img/lena.jpg')
# 转为灰度图
new_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
height, width = new_img.shape[0:2]

# 设置阈值
thresh = 60

# 遍历每一个像素点
for row in range(height):
    for col in range(width):
        # 获取到灰度值
        gray = new_img[row, col]
        # 如果灰度值高于阈值 就等于255最大值
        if gray > thresh:
            new_img[row, col] = 255
        # 如果小于阈值，就直接改为0
        elif gray < thresh:
            new_img[row, col] = 0

cv2.imshow('img', new_img)
cv2.waitKey()

import cv2

img = cv2.imread('img/lena.jpg', cv2.IMREAD_GRAYSCALE)

thresh, new_img = cv2.threshold(img, 60, 255, cv2.THRESH_BINARY)

print(thresh)
cv2.imshow('img', img)
cv2.imshow('NEW_IMG', new_img)
cv2.waitKey()

import cv2

img = cv2.imread('img/lena.jpg', cv2.IMREAD_GRAYSCALE)

thresh_img = cv2.adaptiveThreshold(img, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 5)

cv2.imshow('thresh_img', thresh_img)
cv2.waitKey()

threshold(gaussian_img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

import cv2

img = cv2.imread('img/lena.jpg', cv2.IMREAD_GRAYSCALE)

# 使用255的阈值进行二值化
ret, thresh_img = cv2.threshold(img, 255, 255, cv2.THRESH_BINARY)
cv2.imshow('normal', thresh_img)

# 使用高斯滤波模糊图像  参数1： 图片矩阵  参数2：卷积核 参数3： 越大越模糊
gaussian_img = cv2.GaussianBlur(img, (5, 5), 0)
cv2.imshow('gaussian_img', gaussian_img)

# 使用大津算法0阈值二值化经过高斯滤波模糊后的图像
ret, thresh_img = cv2.threshold(gaussian_img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)

cv2.imshow('otsu', thresh_img)

cv2.imshow('img', img)
cv2.waitKey()