最近、需要：紙文書やアップロードファイルを撮影するとき、あなたは自動的にセクションということだけで画像や文書ファイルを残して、バックグラウンドの除去をキャプチャする必要があります。
レンダリングに初
負荷によって提供さCvVideoCamera OpenCVのビデオストリーム使っファーストクラス
CvVideoCameraDelegateの-implemented方法：
- (void)processImage:(Mat &)mat；
カメラ入力の各フレームのリアルタイム画像を取得するプロキシ方法
- (void)processImage:(Mat &)mat
{
Mat src_gray, filtered, edges, dilated_edges;
//获取灰度图像
cvtColor(mat, src_gray, COLOR_BGR2GRAY);
//滤波，模糊处理，消除某些背景干扰信息
blur(src_gray, filtered, cv::Size(3, 3));
//腐蚀操作，消除某些背景干扰信息
erode(filtered, filtered, Mat(),cv::Point(-1, -1), 3, 1, 1);
int thresh = 35;
//边缘检测
Canny(filtered, edges, thresh, thresh*3, 3);
//膨胀操作，尽量使边缘闭合
dilate(edges, dilated_edges, Mat(), cv::Point(-1, -1), 3, 1, 1);
vector<vector<cv::Point> > contours, squares, hulls;
//寻找边框
findContours(dilated_edges, contours, RETR_LIST, CHAIN_APPROX_SIMPLE);
vector<cv::Point> hull, approx;
for (size_t i = 0; i < contours.size(); i++)
{
//边框的凸包
convexHull(contours[i], hull);
//多边形拟合凸包边框(此时的拟合的精度较低)
approxPolyDP(Mat(hull), approx, arcLength(Mat(approx), true)*0.02, true);
//筛选出面积大于某一阈值的，且四边形的各个角度都接近直角的凸四边形
if (approx.size() == 4 && fabs(contourArea(Mat(approx))) > 40000 &&
isContourConvex(Mat(approx)))
{
double maxCosine = 0;
for (int j = 2; j < 5; j++)
{
double cosine = fabs(getAngle(approx[j%4], approx[j-2], approx[j-1]));
maxCosine = MAX(maxCosine, cosine);
}
//角度大概72度
if (maxCosine < 0.3)
{
squares.push_back(approx);
hulls.push_back(hull);
}
}
}
vector<cv::Point> largest_square;
//找出外接矩形最大的四边形
int idex = findLargestSquare(squares, largest_square);
if (largest_square.size() == 0 || idex == -1) return;
//找到这个最大的四边形对应的凸边框，再次进行多边形拟合，此次精度较高，拟合的结果可能是大于4条边的多边形
//接下来的操作，主要是为了解决，证件有圆角时检测到的四个顶点的连线会有切边的问题
hull = hulls[idex];
approxPolyDP(Mat(hull), approx, 3, true);
vector<cv::Point> newApprox;
double maxL = arcLength(Mat(approx), true)*0.02;
//找到高精度拟合时得到的顶点中 距离小于 低精度拟合得到的四个顶点 maxL的顶点，排除部分顶点的干扰
for (cv::Point p : approx)
{
if (!(getSpacePointToPoint(p, largest_square[0]) > maxL &&
getSpacePointToPoint(p, largest_square[1]) > maxL &&
getSpacePointToPoint(p, largest_square[2]) > maxL &&
getSpacePointToPoint(p, largest_square[3]) > maxL))
{
newApprox.push_back(p);
}
}
//找到剩余顶点连线中，边长大于 2 * maxL的四条边作为四边形物体的四条边
vector<Vec4i> lines;
for (int i = 0; i < newApprox.size(); i++)
{
cv::Point p1 = newApprox[i];
cv::Point p2 = newApprox[(i+1)%newApprox.size()];
if (getSpacePointToPoint(p1, p2) > 2 * maxL)
{
lines.push_back(Vec4i(p1.x, p1.y, p2.x,p2.y));
}
}
//计算出这四条边中 相邻两条边的交点，即物体的四个顶点
vector<cv::Point> cornors1;
for (int i = 0; i < lines.size(); i++)
{
cv::Point cornor = computeIntersect(lines[i],lines[(i+1)%lines.size()]);
cornors1.push_back(cornor);
}
//绘制出四条边
for (int i = 0; i < cornors1.size(); i++)
{
line(mat, cornors1[i], cornors1[(i+1)%cornors1.size()], Scalar(0,0,255), 5);
}
}
関連カスタム関数：
#pragma mark =========== 寻找最大边框 ===========
int findLargestSquare(const vector<vector<cv::Point> >& squares, vector<cv::Point>& biggest_square)
{
if (!squares.size()) return -1;
int max_width = 0;
int max_height = 0;
int max_square_idx = 0;
for (int i = 0; i < squares.size(); i++)
{
cv::Rect rectangle = boundingRect(Mat(squares[i]));
if ((rectangle.width >= max_width) && (rectangle.height >= max_height))
{
max_width = rectangle.width;
max_height = rectangle.height;
max_square_idx = i;
}
}
biggest_square = squares[max_square_idx];
return max_square_idx;
}
/**
根据三个点计算中间那个点的夹角 pt1 pt0 pt2
*/
double getAngle(cv::Point pt1, cv::Point pt2, cv::Point pt0)
{
double dx1 = pt1.x - pt0.x;
double dy1 = pt1.y - pt0.y;
double dx2 = pt2.x - pt0.x;
double dy2 = pt2.y - pt0.y;
return (dx1*dx2 + dy1*dy2)/sqrt((dx1*dx1 + dy1*dy1)*(dx2*dx2 + dy2*dy2) + 1e-10);
}
/**
点到点的距离
@param p1 点1
@param p2 点2
@return 距离
*/
double getSpacePointToPoint(cv::Point p1, cv::Point p2)
{
int a = p1.x-p2.x;
int b = p1.y-p2.y;
return sqrt(a * a + b * b);
}
/**
两直线的交点
@param a 线段1
@param b 线段2
@return 交点
*/
cv::Point2f computeIntersect(cv::Vec4i a, cv::Vec4i b)
{
int x1 = a[0], y1 = a[1], x2 = a[2], y2 = a[3], x3 = b[0], y3 = b[1], x4 = b[2], y4 = b[3];
if (float d = ((float)(x1 - x2) * (y3 - y4)) - ((y1 - y2) * (x3 - x4)))
{
cv::Point2f pt;
pt.x = ((x1 * y2 - y1 * x2) * (x3 - x4) - (x1 - x2) * (x3 * y4 - y3 * x4)) / d;
pt.y = ((x1 * y2 - y1 * x2) * (y3 - y4) - (y1 - y2) * (x3 * y4 - y3 * x4)) / d;
return pt;
}
else
return cv::Point2f(-1, -1);
}
/**
对多个点按顺时针排序
@param corners 点的集合
*/
void sortCorners(std::vector<cv::Point2f>& corners)
{
if (corners.size() == 0) return;
//先延 X轴排列
cv::Point pl = corners[0];
int index = 0;
for (int i = 1; i < corners.size(); i++)
{
cv::Point point = corners[i];
if (pl.x > point.x)
{
pl = point;
index = i;
}
}
corners[index] = corners[0];
corners[0] = pl;
cv::Point lp = corners[0];
for (int i = 1; i < corners.size(); i++)
{
for (int j = i+1; j<corners.size(); j++)
{
cv::Point point1 = corners[i];
cv::Point point2 = corners[j];
if ((point1.y-lp.y*1.0)/(point1.x-lp.x)>(point2.y-lp.y*1.0)/(point2.x-lp.x))
{
cv::Point temp = point1;
corners[i] = corners[j];
corners[j] = temp;
}
}
}
}
四辺形の4つの頂点、対象画像を抽出します
//对顶点顺时针排序
sortCorners(_corners);
//计算目标图像的尺寸
cv::Point2f p0 = _corners[0];
cv::Point2f p1 = _corners[1];
cv::Point2f p2 = _corners[2];
cv::Point2f p3 = _corners[3];
float space0 = getSpacePointToPoint(p0, p1);
float space1 = getSpacePointToPoint(p1, p2);
float space2 = getSpacePointToPoint(p2, p3);
float space3 = getSpacePointToPoint(p3, p0);
float width = space1 > space3 ? space1 : space3;
float height = space0 > space2 ? space0 : space2;
cv::Mat quad = cv::Mat::zeros(height * 3, width * 3, CV_8UC3);
std::vector<cv::Point2f> quad_pts;
quad_pts.push_back(cv::Point2f(0, quad.rows));
quad_pts.push_back(cv::Point2f(0, 0));
quad_pts.push_back(cv::Point2f(quad.cols, 0));
quad_pts.push_back(cv::Point2f(quad.cols, quad.rows));
//提取图像
cv::Mat transmtx = cv::getPerspectiveTransform(_corners , quad_pts);
cv::warpPerspective(mat, quad, transmtx, quad.size());
あなたはgetPerspectiveTransformメソッドのクラッシュを呼び出すと、私の他の記事のOpenCVの関数呼び出しgetPerspectiveTransformエラーを参照してください。
最後に、ラプラス演算子を使用して部分画像のコントラストを向上させることができ、画像がより鮮明です
Mat imageMat;
Mat kernel = (Mat_<float>(3,3) << 0, -1, 0, -1, 5, -1, 0, -1, 0);
filter2D(quad, imageMat, quad.depth(), kernel);
//Mat --> UIImage
self.imageView.image = MatToUIImage(imageMat);
