图像阈值处理
Python
简单阈值处理
OpenCV的cv.threshold用于简单阈值处理，它的第一个参数是灰度源图像src；第二个参数是阈值thresh；第三个参数是赋值给超过阈值的像素的最大值maxval；第四个参数则是阈值处理的类型：
cv.threshold返回两个输出。第一个是使用的阈值，第二个输出是阈值图像dst。下面比较不同类型的阈值处理方法：
import cv2 as cv
from matplotlib import pyplot as plt
import numpy as np
img = cv.imread('threshold.jpg', 0)
ret, thresh1 = cv.threshold(img, 127, 255, cv.THRESH_BINARY)
ret, thresh2 = cv.threshold(img, 127, 255, cv.THRESH_BINARY_INV)
ret, thresh3 = cv.threshold(img, 127, 255, cv.THRESH_TRUNC)
ret, thresh4 = cv.threshold(img, 127, 255, cv.THRESH_TOZERO)
ret, thresh5 = cv.threshold(img, 127, 255, cv.THRESH_TOZERO_INV)
titles = ['Original Image', 'BINARY', 'BINARY_INV', 'TRUNC', 'TOZERO', 'TOZERO_INV']
images = [img, thresh1, thresh2, thresh3, thresh4, thresh5]
for i in range(6):
plt.subplot(2, 3, i + 1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
plt.show()
自适应阈值处理
简单阈值处理在图像全局都使用同一个阈值，如果图像在不同区域有不同的照明条件，这可能就不适用了。在这种情况下，自适应阈值处理更适合。自适应阈值处理根据像素周围的一个小区域来确定阈值。可以使用OpenCV的cv.adaptiveThreshold实现这个功能。
cv.adaptiveThreshold的第一个参数是灰度源图像src；第二个参数是赋值给超过阈值的像素的最大值maxval；
第三个参数adaptiveMethod决定如何计算阈值:
cv.ADAPTIVE_THRESH_MEAN_C：阈值是邻域内像素的平均值减去常数C。cv.ADAPTIVE_THRESH_GAUSSIAN_C：阈值是邻域内像素的高斯加权和减去常数C。
第四个参数则是阈值处理的类型；第五个参数 blockSize决定邻域区域的大小；第六个参数是常数C。
下面对比一下简单阈值处理和自适应阈值处理：
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('adaptiveThreshold.jpg', 0)
ret, th1 = cv.threshold(img, 127, 255, cv.THRESH_BINARY)
th2 = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY, 11, 2)
th3 = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 11, 2)
titles = ['Original Image', 'BINARY(v=127)',
'ADAPTIVE_THRESH_MEAN_C', 'ADAPTIVE_THRESH_GAUSSIAN_C']
images = [img, th1, th2, th3]
for i in range(4):
plt.subplot(2, 2, i + 1), plt.imshow(images[i], 'gray')
plt.title(titles[i])
plt.xticks([]), plt.yticks([])
plt.show()
大津(Otsu)法
使用cv.threshold()函数，阈值处理的类型可以任意选择，THRESH_OTSU作为一个额外的flag即可使用大津法。下面以一个直方图包含两个峰的图像(双峰图像)为例：
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('coins.png', 0)
# 普通二值化
ret1, th1 = cv.threshold(img, 127, 255, cv.THRESH_BINARY)
# Otsu法二值化
ret2, th2 = cv.threshold(img, 0, 255, cv.THRESH_BINARY + cv.THRESH_OTSU) # 随便给个0为阈值
images = [img, [], th1,
img, [], th2]
titles = ['Original Image', 'Histogram', 'BINARY (v=127)',
'Original Image', 'Histogram', 'OTSU(v=0)']
for i in range(2):
plt.subplot(2, 3, i * 3 + 1), plt.imshow(images[i * 3], 'gray')
plt.title(titles[i * 3]), plt.xticks([]), plt.yticks([])
plt.subplot(2, 3, i * 3 + 2), plt.hist(images[i * 3].ravel(), 256)
plt.title(titles[i * 3 + 1]), plt.xticks([]), plt.yticks([])
plt.subplot(2, 3, i * 3 + 3), plt.imshow(images[i * 3 + 2], 'gray')
plt.title(titles[i * 3 + 2]), plt.xticks([]), plt.yticks([])
plt.show()
大津法无需人工指定阈值即可达到较为理想的效果。
C++
简单阈值处理
#include <opencv2\opencv.hpp>
using namespace cv;
int main()
{
Mat img = imread("threshold.jpg",0);
Mat thresh1, thresh2, thresh3, thresh4, thresh5;
threshold(img, thresh1, 127, 255, THRESH_BINARY);
threshold(img, thresh2, 127, 255, THRESH_BINARY_INV);
threshold(img, thresh3, 127, 255, THRESH_TRUNC);
threshold(img, thresh4, 127, 255, THRESH_TOZERO);
threshold(img, thresh5, 127, 255, THRESH_TOZERO_INV);
imshow("Original Image", img);
imshow("BINARY", thresh1);
imshow("BINARY_INV", thresh2);
imshow("TRUNC", thresh3);
imshow("TOZERO", thresh4);
imshow("TOZERO_INV", thresh5);
waitKey(0);
return 0;
}
自适应阈值处理
#include <opencv2\opencv.hpp>
using namespace cv;
int main()
{
Mat img = imread("adaptiveThreshold.jpg",0);
Mat thresh1, thresh2, thresh3;
threshold(img, thresh1, 127, 255, THRESH_BINARY);
adaptiveThreshold(img, thresh2,255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 11, 2);
adaptiveThreshold(img, thresh3,255, ADAPTIVE_THRESH_GAUSSIAN_C, THRESH_BINARY, 11, 2);
imshow("Original Image", img);
imshow("BINARY", thresh1);
imshow("ADAPTIVE_THRESH_MEAN_C", thresh2);
imshow("ADAPTIVE_THRESH_GAUSSIAN_C", thresh3);
waitKey(0);
return 0;
}
大津(Otsu)法
#include <opencv2\opencv.hpp>
using namespace cv;
int main()
{
Mat img = imread("coins.png",0);
Mat thresh1, thresh2;
threshold(img, thresh1, 127, 255, THRESH_BINARY);
threshold(img, thresh2, 0, 255, THRESH_BINARY + THRESH_OTSU);
imshow("Original Image", img);
imshow("BINARY", thresh1);
imshow("OTSU", thresh2);
waitKey(0);
return 0;
}
代码：
https://gitee.com/BinaryAI/open-cv-c--and-python
参考：
[1]https://docs.opencv.org/4.6.0/
[2]https://zhuanlan.zhihu.com/p/384457101
[3]数字图像处理（MATLAB版）（第2版）,张德丰, 人民邮电出版社
