这是我在您的输入图像上尝试过的管道：
第一步：检测egdes
第 2 步：找到卡片的角
- 计算轮廓
-
按长度对轮廓进行排序，只保留最大的一个
- 生成此轮廓的凸包
- 从凸包中创建一个蒙版
- 使用
HoughLinesP 查找卡片的4 个面
- 计算 4 条边的交点
第 3 步：单应性
- 使用
findHomography 查找卡片的仿射变换（在步骤 2 找到 4 个交点）
-
使用计算出的单应矩阵扭曲输入图像
结果如下：
请注意，您必须找到一种方法来对 4 个交点进行排序，以便始终保持相同的顺序（否则 findHomography 将不起作用）。
我知道您没有要求提供代码，但我必须测试我的管道，所以它在这里... :)
Vec3f calcParams(Point2f p1, Point2f p2) // line's equation Params computation
{
float a, b, c;
if (p2.y - p1.y == 0)
{
a = 0.0f;
b = -1.0f;
}
else if (p2.x - p1.x == 0)
{
a = -1.0f;
b = 0.0f;
}
else
{
a = (p2.y - p1.y) / (p2.x - p1.x);
b = -1.0f;
}
c = (-a * p1.x) - b * p1.y;
return(Vec3f(a, b, c));
}
Point findIntersection(Vec3f params1, Vec3f params2)
{
float x = -1, y = -1;
float det = params1[0] * params2[1] - params2[0] * params1[1];
if (det < 0.5f && det > -0.5f) // lines are approximately parallel
{
return(Point(-1, -1));
}
else
{
x = (params2[1] * -params1[2] - params1[1] * -params2[2]) / det;
y = (params1[0] * -params2[2] - params2[0] * -params1[2]) / det;
}
return(Point(x, y));
}
vector<Point> getQuadrilateral(Mat & grayscale, Mat& output) // returns that 4 intersection points of the card
{
Mat convexHull_mask(grayscale.rows, grayscale.cols, CV_8UC1);
convexHull_mask = Scalar(0);
vector<vector<Point>> contours;
findContours(grayscale, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
vector<int> indices(contours.size());
iota(indices.begin(), indices.end(), 0);
sort(indices.begin(), indices.end(), [&contours](int lhs, int rhs) {
return contours[lhs].size() > contours[rhs].size();
});
/// Find the convex hull object
vector<vector<Point> >hull(1);
convexHull(Mat(contours[indices[0]]), hull[0], false);
vector<Vec4i> lines;
drawContours(convexHull_mask, hull, 0, Scalar(255));
imshow("convexHull_mask", convexHull_mask);
HoughLinesP(convexHull_mask, lines, 1, CV_PI / 200, 50, 50, 10);
cout << "lines size:" << lines.size() << endl;
if (lines.size() == 4) // we found the 4 sides
{
vector<Vec3f> params(4);
for (int l = 0; l < 4; l++)
{
params.push_back(calcParams(Point(lines[l][0], lines[l][1]), Point(lines[l][2], lines[l][3])));
}
vector<Point> corners;
for (int i = 0; i < params.size(); i++)
{
for (int j = i; j < params.size(); j++) // j starts at i so we don't have duplicated points
{
Point intersec = findIntersection(params[i], params[j]);
if ((intersec.x > 0) && (intersec.y > 0) && (intersec.x < grayscale.cols) && (intersec.y < grayscale.rows))
{
cout << "corner: " << intersec << endl;
corners.push_back(intersec);
}
}
}
for (int i = 0; i < corners.size(); i++)
{
circle(output, corners[i], 3, Scalar(0, 0, 255));
}
if (corners.size() == 4) // we have the 4 final corners
{
return(corners);
}
}
return(vector<Point>());
}
int main(int argc, char** argv)
{
Mat input = imread("playingcard_input.png");
Mat input_grey;
cvtColor(input, input_grey, CV_BGR2GRAY);
Mat threshold1;
Mat edges;
blur(input_grey, input_grey, Size(3, 3));
Canny(input_grey, edges, 30, 100);
vector<Point> card_corners = getQuadrilateral(edges, input);
Mat warpedCard(400, 300, CV_8UC3);
if (card_corners.size() == 4)
{
Mat homography = findHomography(card_corners, vector<Point>{Point(warpedCard.cols, 0), Point(warpedCard.cols, warpedCard.rows), Point(0,0) , Point(0, warpedCard.rows)});
warpPerspective(input, warpedCard, homography, Size(warpedCard.cols, warpedCard.rows));
}
imshow("warped card", warpedCard);
imshow("edges", edges);
imshow("input", input);
waitKey(0);
return 0;
}
编辑：我已经稍微调整了Canny 和HoughLinesP 函数的参数，以便更好地检测卡片（程序现在适用于两个输入样本）。