我在图像上使用 otsu threshold .

这是输入图像：

这是输出：

这是我正在使用的代码：

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/highgui/highgui.hpp"

#include

#include

#include

#include

#include

using namespace std;

using namespace cv;

int main(int argc, char const *argv[]) {

title("Text Extractor");

string win_name = "textextractor";

Mat img_a;

img_a = imread("../input/test_c.jpg");

Mat img_a_gray;

cvtColor(img_a, img_a_gray, CV_BGR2GRAY);

Mat img_a_blur;

GaussianBlur(img_a_gray, img_a_blur, Size(3, 3), 0, 0);

Mat img_a_thres;

// adaptiveThreshold(img_a_blur, img_a_thres, 255, ADAPTIVE_THRESH_MEAN_C, THRESH_BINARY, 5, 4);

threshold(img_a_blur, img_a_thres, 0, 255, THRESH_OTSU);

namedWindow(win_name + "_a", CV_WINDOW_AUTOSIZE);

imshow(win_name + "_a", img_a_thres);

imwrite("../output/output_a.jpg", img_a_thres);

waitKey(0);

return 0;

}

问题是输出在底部和左侧有一个黑色区域 . 我该怎么做才能最小化/删除它？

编辑：

我试过 equalizeHist() ，我得到了这个：

将尝试将图像分解成碎片并单独处理它们 .

对不起这是我的错 . 前一个是使用自适应过滤 . 使用 Otsu 我得到这个：

otsu的输出没有变化：/

编辑2：完成了Feng Tan算法，它提供了更好的结果，但是文本没有清晰度 .

码：

#include "opencv2/imgproc/imgproc.hpp"

#include "opencv2/highgui/highgui.hpp"

#include "opencv2/photo/photo.hpp"

#include

#include

#include

#include

#include

using namespace std;

using namespace cv;

int main(int argc, char const *argv[]) {

string win_name = "textextractor";

Mat img_c;

img_c = imread("../input/sample.jpg");

Mat img_c_gray;

cvtColor(img_c, img_c_gray, CV_BGR2GRAY);

Mat img_c_bin = Mat::zeros(img_c_gray.rows, img_c_gray.cols, CV_8UC1);

int s_win = 17;

int l_win = 35;

double min_tau = 10;

Rect roi_s = Rect(-s_win/2, -s_win/2, s_win, s_win);

Rect roi_l = Rect(-l_win/2, -l_win/2, l_win, l_win);

Rect img_c_roi = Rect(0, 0, img_c_gray.cols, img_c_gray.rows);

for (size_t r = 0; r < img_c_gray.rows; r++) {

for (size_t c = 0; c < img_c_gray.cols; c++) {

double pthres = 255;

Rect sROI = roi_s + Point(c, r);

sROI = sROI & img_c_roi;

if(sROI.width == 0 || sROI.height == 0) {

continue;

}

Rect lROI = roi_l + Point(c, r);

lROI = lROI & img_c_roi;

if(lROI.width == 0 || lROI.height == 0) {

continue;

}

Mat sROI_gray = img_c_gray(sROI);

Mat lROI_gray = img_c_gray(lROI);

double s_stdDev = 0;

double l_stdDev = 0;

double s_mean = 0;

double l_mean = 0;

double l_min = DBL_MAX;

for (size_t r = 0; r < sROI_gray.rows; r++) {

for (size_t c = 0; c < sROI_gray.cols; c++) {

s_mean += sROI_gray.at(r, c);

}

}

s_mean = s_mean / static_cast (sROI_gray.cols * sROI_gray.rows);

for (size_t r = 0; r < sROI_gray.rows; r++) {

for (size_t c = 0; c < sROI_gray.cols; c++) {

double diff = sROI_gray.at (r, c) - s_mean;

s_stdDev += diff * diff;

}

}

s_stdDev = sqrt(s_stdDev / static_cast (sROI_gray.cols * sROI_gray.rows));

for (size_t r = 0; r < lROI_gray.rows; r++) {

for (size_t c = 0; c < lROI_gray.cols; c++) {

l_mean += lROI_gray.at (c, r);

if(lROI_gray.at (r, c) < l_min) {

l_min = lROI_gray.at (r, c);

}

}

}

l_mean = l_mean / static_cast (lROI_gray.cols * lROI_gray.rows);

for (size_t r = 0; r < lROI_gray.rows; r++) {

for (size_t c = 0; c < lROI_gray.cols; c++) {

double diff = lROI_gray.at (r, c) - l_mean;

l_stdDev += diff * diff;

}

}

l_stdDev = sqrt(l_stdDev / static_cast (lROI_gray.cols * lROI_gray.rows));

double tau = ((s_mean - l_min) * (1 - s_stdDev / l_stdDev)) / 2.0;

if(tau < min_tau) {

tau = min_tau;

}

double threshold = s_mean - tau;

unsigned char pixel_val = img_c_gray.at(r, c);

if(pixel_val >= threshold) {

img_c_bin.at (r, c) = 255;

} else {

img_c_bin.at (r, c) = 0;

}

}

}

namedWindow(win_name + "_c", CV_WINDOW_AUTOSIZE);

imshow(win_name + "_c", img_c_bin);

imwrite("../output/output_c.jpg", img_c_bin);

waitKey(0);

return 0;

}

输出：