注:此教程是对贾志刚老师的opencv课程学习的一个记录,在此表示对贾老师的感谢.
需求: 扫描仪扫描到的法律文件，需要切边，去掉边缘空白
例如下图,左边是要处理的图像,右边是处理过后的图像.
解决思路: 边缘检测----> 轮廓发现或者直线检测最大外接矩形---->截图最大外接矩阵所在的区域
#include <opencv2/opencv.hpp>#include <iostream>#include <math.h>using namespace cv;using namespace std;Mat src, gray_src, dst;int threshold_value = 100;int max_level = 255;const char *output_win = "Contours Result";const char *roi_win = "Final Result";void FindROI(int, void *);int main(int argc, char **argv) {src = imread("/home/fuhong/code/cpp/opencv_learning/src/小案例/imgs/case1.png");if (src.empty()) {printf("could not load image...\n");return -1;}namedWindow("input image", CV_WINDOW_AUTOSIZE);imshow("input image", src);namedWindow(output_win, CV_WINDOW_AUTOSIZE);// namedWindow(roi_win, CV_WINDOW_AUTOSIZE);//createTrackbar("Threshold:", output_win, &threshold_value, max_level, FindROI);FindROI(0, 0);waitKey(0);return 0;}void FindROI(int, void *) {cvtColor(src, gray_src, COLOR_BGR2GRAY); //先将图像转化为灰度图Mat canny_output;Canny(gray_src, canny_output, threshold_value, threshold_value * 2, 3, false); //canny边缘检测vector<vector<Point>> contours;vector<Vec4i> hireachy;findContours(canny_output, contours, hireachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0)); //查找轮廓/** // 在二值图像上发现轮廓使用cv::findContours(InputOutputArray binImg, // 输入图像，非0的像素被看成1,0的像素值保持不变，8-bitOutputArrayOfArrays contours,// 全部发现的轮廓对象OutputArray, hierachy// 图该的拓扑结构，可选，该轮廓发现算法正是基于图像拓扑结构实现。int mode, // 轮廓返回的模式int method,// 发现方法Point offset=Point()// 轮廓像素的位移，默认（0, 0）没有位移)*/int minw = src.cols * 0.75;int minh = src.rows * 0.75;RNG rng(12345);Mat drawImage = Mat::zeros(src.size(), CV_8UC3);Rect bbox;for (size_t t = 0; t < contours.size(); t++) {//找到轮廓的最小外接斜矩形 参考:/u013925378/article/details/84563011RotatedRect minRect = minAreaRect(contours[t]);float degree = abs(minRect.angle);// 如果矩阵轮廓大于0.75倍的图像长度和宽度,并且不是图像的最外层轮廓,则是要寻找的轮廓if (minRect.size.width > minw && minRect.size.height > minh && minRect.size.width < (src.cols - 5)) {printf("current angle : %f\n", degree);Point2f pts[4];minRect.points(pts);bbox = minRect.boundingRect();Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));for (int i = 0; i < 4; i++) {line(drawImage, pts[i], pts[(i + 1) % 4], color, 2, 8, 0);}}}imshow(output_win, drawImage);if (bbox.width > 0 && bbox.height > 0) {Mat roiImg = src(bbox);imshow(roi_win, roiImg);}return;}
效果:
进阶需求：有时候图像不是正的，有一定的倾斜角度，这种情况下，将图像旋正，并且去除白边．
解决思路: 边缘检测----> 轮廓发现或者直线检测最大外接矩形---->计算需旋转的角度----->将图像旋正----->边缘检测----> 轮廓发现或者直线检测最大外接矩形---->截取最大外接矩阵所在的区域
代码实现：
#include <opencv2/opencv.hpp>#include <iostream>#include <math.h>using namespace cv;using namespace std;Mat src, gray_src, dst;int threshold_value = 100;int max_level = 255;const char* output_win = "Contours Result";const char* roi_win = "Final Result";void FindROI(int, void*);void Check_Skew(int, void*);int main(int argc, char** argv) {src = imread("D:/gloomyfish/case1r.png");if (src.empty()) {printf("could not load image...\n");return -1;}namedWindow("input image", CV_WINDOW_AUTOSIZE);imshow("input image", src);namedWindow(output_win, CV_WINDOW_AUTOSIZE);Check_Skew(0, 0);// namedWindow(roi_win, CV_WINDOW_AUTOSIZE);//createTrackbar("Threshold:", output_win, &threshold_value, max_level, FindROI);// FindROI(0, 0);waitKey(0);return 0;}void Check_Skew(int, void*) {Mat canny_output;cvtColor(src, gray_src, COLOR_BGR2GRAY);Canny(gray_src, canny_output, threshold_value, threshold_value * 2, 3, false);vector<vector<Point>> contours;vector<Vec4i> hireachy;findContours(canny_output, contours, hireachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));Mat drawImg = Mat::zeros(src.size(), CV_8UC3);float maxw = 0;float maxh = 0;double degree = 0;for (size_t t = 0; t < contours.size(); t++) {RotatedRect minRect = minAreaRect(contours[t]);degree = abs(minRect.angle);if (degree > 0) {maxw = max(maxw, minRect.size.width);maxh = max(maxh, minRect.size.height);}}RNG rng(12345);for (size_t t = 0; t < contours.size(); t++) {RotatedRect minRect = minAreaRect(contours[t]);if (maxw == minRect.size.width && maxh == minRect.size.height) {degree = minRect.angle;Point2f pts[4];minRect.points(pts);Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));for (int i = 0; i < 4; i++) {line(drawImg, pts[i], pts[(i + 1) % 4], color, 2, 8, 0);}}}printf("max contours width : %f\n", maxw);printf("max contours height : %f\n", maxh);printf("max contours angle : %f\n", degree);imshow(output_win, drawImg);Point2f center(src.cols / 2, src.rows / 2);Mat rotm = getRotationMatrix2D(center, degree, 1.0);Mat dst;warpAffine(src, dst, rotm, src.size(), INTER_LINEAR, 0, Scalar(255, 255, 255));imshow("Correct Image", dst);}void FindROI(int, void*) {cvtColor(src, gray_src, COLOR_BGR2GRAY);Mat canny_output;Canny(gray_src, canny_output, threshold_value, threshold_value * 2, 3, false);vector<vector<Point>> contours;vector<Vec4i> hireachy;findContours(canny_output, contours, hireachy, RETR_TREE, CHAIN_APPROX_SIMPLE, Point(0, 0));int minw = src.cols*0.75;int minh = src.rows*0.75;RNG rng(12345);Mat drawImage = Mat::zeros(src.size(), CV_8UC3);Rect bbox;for (size_t t = 0; t < contours.size(); t++) {RotatedRect minRect = minAreaRect(contours[t]);float degree = abs(minRect.angle);if (minRect.size.width > minw && minRect.size.height > minh && minRect.size.width < (src.cols-5)) {printf("current angle : %f\n", degree);Point2f pts[4];minRect.points(pts);bbox = minRect.boundingRect();Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));for (int i = 0; i < 4; i++) {line(drawImage, pts[i], pts[(i + 1)%4], color, 2, 8, 0);}}}imshow(output_win, drawImage);if (bbox.width > 0 && bbox.height > 0) {Mat roiImg = src(bbox);imshow(roi_win, roiImg);}return;}
