OpenCV3入門（五）圖像的閾值

時間 2020-02-13

標籤 opencv3 opencv 入門圖像閾值简体版

原文原文鏈接

1、圖像閾值與二值化

閾值是一種簡單的圖像分割方法，一幅圖像包括目標物體（前景）、背景還有噪聲，要想從數字圖像中直接提取出目標物體，能夠設定一個像素值即閾值，而後用圖像的每個像素點和閾值作比較，給出斷定結果。html

二值化是特殊的閾值分割方法，把圖像分爲兩部分，以閾值T爲分割線，大於T的像素羣和小於T的像素羣，這樣圖像就變爲黑白二色圖像。經過設定一個標準若是大於這個標準就設爲白，若是小於這個標準就設爲黑，而這個標準就是閾值。算法

2、OpenCV閾值threshold

double threshold(InputArray src, OutputArray dst, double thresh, double maxval, int type);函數

（1）第一個參數，InputArray 類型的 src，源圖像。單通道，8 或 32位浮點數類型的深度。學習

（2）第二個參數，OutputArray 類型的 dst，輸出圖像。spa

（3）第三個參數，double 類型的 thresh，選取的閾值。.net

（4）第四個參數，double 類型的 maxval。3d

（5）第五個參數，int 類型的 type。閾值類型。以下所示：code

type類型以下：orm

enum  cv::ThresholdTypes { 
  cv::THRESH_BINARY = 0, 
  cv::THRESH_BINARY_INV = 1, 
  cv::THRESH_TRUNC = 2, 
  cv::THRESH_TOZERO = 3, 
  cv::THRESH_TOZERO_INV = 4, 
  cv::THRESH_MASK = 7, 
  cv::THRESH_OTSU = 8, 
  cv::THRESH_TRIANGLE = 16 
}

不一樣的閾值方法生成關係以下圖。htm

Mat img = Mat::zeros(6, 6, CV_8UC1);
randu(img, 0, 255);

int th = 100;
Mat threshold1, threshold2, threshold3, threshold4, threshold5, threshold6, threshold7, threshold8;
threshold(img, threshold1, th, 200, THRESH_BINARY);
threshold(img, threshold2, th, 200, THRESH_BINARY_INV);
threshold(img, threshold3, th, 200, THRESH_TRUNC);

cout << "raw=\r\n"<<img << "\r\n" << endl;
cout << "THRESH_BINARY=\r\n" << threshold1 << "\r\n" << endl;
cout << "THRESH_BINARY_INV=\r\n" << threshold2 << "\r\n" << endl;
cout << "THRESH_TRUNC=\r\n" << threshold3 << "\r\n" << endl;

上面代碼中randu(img, 0, 255)做用是產出隨機數填充img矩陣。輸出結果以下。

raw=
[ 91,   2,  79, 179,  52, 205;
 236,   8, 181, 239,  26, 248;
 207, 218,  45, 183, 158, 101;
 102,  18, 118,  68, 210, 139;
 198, 207, 211, 181, 162, 197;
 191, 196,  40,   7, 243, 230]

THRESH_BINARY=
[  0,   0,   0, 200,   0, 200;
 200,   0, 200, 200,   0, 200;
 200, 200,   0, 200, 200, 200;
 200,   0, 200,   0, 200, 200;
 200, 200, 200, 200, 200, 200;
 200, 200,   0,   0, 200, 200]

THRESH_BINARY_INV=
[200, 200, 200,   0, 200,   0;
   0, 200,   0,   0, 200,   0;
   0,   0, 200,   0,   0,   0;
   0, 200,   0, 200,   0,   0;
   0,   0,   0,   0,   0,   0;
   0,   0, 200, 200,   0,   0]

THRESH_TRUNC=
[ 91,   2,  79, 100,  52, 100;
 100,   8, 100, 100,  26, 100;
 100, 100,  45, 100, 100, 100;
 100,  18, 100,  68, 100, 100;
 100, 100, 100, 100, 100, 100;
 100, 100,  40,   7, 100, 100]

THRESH_BINARY，thresh=100，maxval=200，大於閾值限定爲200，小於閾值清零。

THRESH_BINARY_INV的做用和THRESH_BINARY 相反，小於閾值置200，大於閾值清。

THRESH_TRUNC的做用是對大於閾值的數據進行截斷，其他值保留原值不變。

圖像閾值例子以下。

Mat img = imread("D:/WORK/5.OpenCV/LeanOpenCV/pic_src/pic6.bmp", IMREAD_GRAYSCALE);
int th = 100;
Mat threshold1, threshold2, threshold3, threshold4, threshold5, threshold6, threshold7, threshold8;
threshold(img, threshold1, th, 200, THRESH_BINARY);
threshold(img, threshold2, th, 200, THRESH_BINARY_INV);
threshold(img, threshold3, th, 200, THRESH_TRUNC);

imshow("raw pic",img);
imshow("THRESH_BINARY", threshold1);
imshow("THRESH_BINARY_INV", threshold2);
imshow("THRESH_TRUNC", threshold3);

3、自動閾值—大津法OTSU

最大類間方差是由日本學者大津(Nobuyuki Otsu)於1979年提出，是一種自適應的閾值肯定方法。算法假設圖像像素可以根據閾值，被分紅背景[background]和目標[objects]兩部分。而後，計算該最佳閾值來區分這兩類像素，使得兩類像素區分度最大。

算法原理爲：

設圖像Img長寬尺寸爲M*N, T爲二值化的閾值；

N0爲灰度小於T的像素的個數，N0的平均灰度爲μ0。

N1 爲灰度大於T的像素的個數，N1的平均灰度爲μ1。

ω0=N0/ M×N                   (1)   //落在N0的機率

ω1=N1/ M×N                   (2) 　//落在N1的機率

N0+N1=M×N                    (3) 　

ω0+ω1=1 (4) 　　　　　　

μ=ω0*μ0+ω1*μ1              (5) 　//平均灰度乘以機率再相加

g=ω0(μ0-μ)^2+ω1(μ1-μ)^2     (6)   //類間方差

將式(5)代入式(6),獲得等價公式: g=ω0ω1(μ0-μ1)^2    (7)

OpenCV自帶了OSTU算法。

Mat img = imread("D:/WORK/5.OpenCV/LeanOpenCV/pic_src/pic2.bmp", IMREAD_GRAYSCALE);
int th = 100;
Mat threshold1, threshold2, threshold3;
threshold(img, threshold1, th, 255, THRESH_BINARY);
threshold(img, threshold2, th, 255, THRESH_TRUNC);
threshold(img, threshold3, th, 255, THRESH_OTSU); // 閾值隨意設置便可

imshow("raw pic",img);
imshow("THRESH_BINARY", threshold1);
imshow("THRESH_TRUNC",  threshold2);
imshow("THRESH_OTSU",   threshold3);