如何裁剪掉凸面缺陷? [英] How to crop away convexity defects?
问题描述
我正在尝试从轮廓中检测和精确定位图像中的某些对象.我得到的轮廓经常包含一些噪音(可能形成背景，我不知道).对象应类似于矩形或正方形，例如:
I'm trying to detect and fine-locate some objects in images from contours. The contours that I get often include some noise (maybe form the background, I don't know). The objects should look similar to rectangles or squares like:
我通过形状匹配 (cv::matchShapes) 来检测带有这些对象的轮廓，无论有无噪声，我都得到了很好的结果，但是我在精细定位方面存在问题，以防万一噪音.
I get very good results with shape matching (cv::matchShapes) to detect contours with those objects in them, with and without noise, but I have problems with the fine-location in case of noise.
噪音看起来像:
或 例如.
我的想法是找到凸面缺陷，如果它们变得太强，则以某种方式切除导致凹面的部分.检测缺陷是可以的，通常每个不需要的结构"都会有两个缺陷，但我一直纠结于如何决定应该从轮廓中删除哪些点以及在何处删除点.
My idea was to find convexity defects and if they become too strong, somehow crop away the part that leads to concavity. Detecting the defects is ok, typically I get two defects per "unwanted structure", but I'm stuck on how to decide what and where I should remove points from the contours.
以下是一些轮廓、它们的掩码(因此您可以轻松提取轮廓)和凸包，包括阈值凸度缺陷:
Here are some contours, their masks (so you can extract the contours easily) and the convex hull including thresholded convexity defects:
我可以只穿过轮廓并在本地决定轮廓是否执行左转"(如果顺时针行走)，如果是这样，删除轮廓点直到下一次左转?也许从凸面缺陷开始?
Could I just walk through the contour and locally decide whether a "left turn" is performed by the contour (if walking clockwise) and if so, remove contour points until the next left turn is taken? Maybe starting at a convexity defect?
我在找算法或者代码，编程语言应该不重要，算法更重要.
I'm looking for algorithms or code, programming language should not be important, algorithm is more important.
推荐答案
这种方法只对点有效.您无需为此创建蒙版.
This approach works only on points. You don't need to create masks for this.
主要思想是:
- 在轮廓上查找缺陷
- 如果我发现至少两个缺陷，请找出最接近的两个缺陷
- 从轮廓中移除两个最近缺陷之间的点
- 在新轮廓上从 1 重新开始
我得到以下结果.如您所见，它对于 平滑 缺陷(例如第 7 个图像)有一些缺点，但对于清晰可见的缺陷效果很好.我不知道这是否会解决您的问题，但可以作为一个起点.在实践中应该很快(你当然可以优化下面的代码，特别是 removeFromContour 函数).此外，这种方法的唯一参数是凸度缺陷的数量，因此它适用于小型和大型缺陷斑点.
I get the following results. As you can see, it has some drawbacks for smooth defects (e.g. 7th image), but works pretty good for clearly visible defects. I don't know if this will solve your problem, but can be a starting point. In practice should be quite fast (you can surely optimize the code below, specially the removeFromContour function). Also, the only parameter of this approach is the amount of the convexity defect, so it works well with both small and big defecting blobs.
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
int ed2(const Point& lhs, const Point& rhs)
{
return (lhs.x - rhs.x)*(lhs.x - rhs.x) + (lhs.y - rhs.y)*(lhs.y - rhs.y);
}
vector<Point> removeFromContour(const vector<Point>& contour, const vector<int>& defectsIdx)
{
int minDist = INT_MAX;
int startIdx;
int endIdx;
// Find nearest defects
for (int i = 0; i < defectsIdx.size(); ++i)
{
for (int j = i + 1; j < defectsIdx.size(); ++j)
{
float dist = ed2(contour[defectsIdx[i]], contour[defectsIdx[j]]);
if (minDist > dist)
{
minDist = dist;
startIdx = defectsIdx[i];
endIdx = defectsIdx[j];
}
}
}
// Check if intervals are swapped
if (startIdx <= endIdx)
{
int len1 = endIdx - startIdx;
int len2 = contour.size() - endIdx + startIdx;
if (len2 < len1)
{
swap(startIdx, endIdx);
}
}
else
{
int len1 = startIdx - endIdx;
int len2 = contour.size() - startIdx + endIdx;
if (len1 < len2)
{
swap(startIdx, endIdx);
}
}
// Remove unwanted points
vector<Point> out;
if (startIdx <= endIdx)
{
out.insert(out.end(), contour.begin(), contour.begin() + startIdx);
out.insert(out.end(), contour.begin() + endIdx, contour.end());
}
else
{
out.insert(out.end(), contour.begin() + endIdx, contour.begin() + startIdx);
}
return out;
}
int main()
{
Mat1b img = imread("path_to_mask", IMREAD_GRAYSCALE);
Mat3b out;
cvtColor(img, out, COLOR_GRAY2BGR);
vector<vector<Point>> contours;
findContours(img.clone(), contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);
vector<Point> pts = contours[0];
vector<int> hullIdx;
convexHull(pts, hullIdx, false);
vector<Vec4i> defects;
convexityDefects(pts, hullIdx, defects);
while (true)
{
// For debug
Mat3b dbg;
cvtColor(img, dbg, COLOR_GRAY2BGR);
vector<vector<Point>> tmp = {pts};
drawContours(dbg, tmp, 0, Scalar(255, 127, 0));
vector<int> defectsIdx;
for (const Vec4i& v : defects)
{
float depth = float(v[3]) / 256.f;
if (depth > 2) // filter defects by depth
{
// Defect found
defectsIdx.push_back(v[2]);
int startidx = v[0]; Point ptStart(pts[startidx]);
int endidx = v[1]; Point ptEnd(pts[endidx]);
int faridx = v[2]; Point ptFar(pts[faridx]);
line(dbg, ptStart, ptEnd, Scalar(255, 0, 0), 1);
line(dbg, ptStart, ptFar, Scalar(0, 255, 0), 1);
line(dbg, ptEnd, ptFar, Scalar(0, 0, 255), 1);
circle(dbg, ptFar, 4, Scalar(127, 127, 255), 2);
}
}
if (defectsIdx.size() < 2)
{
break;
}
// If I have more than two defects, remove the points between the two nearest defects
pts = removeFromContour(pts, defectsIdx);
convexHull(pts, hullIdx, false);
convexityDefects(pts, hullIdx, defects);
}
// Draw result contour
vector<vector<Point>> tmp = { pts };
drawContours(out, tmp, 0, Scalar(0, 0, 255), 1);
imshow("Result", out);
waitKey();
return 0;
}
<小时>
更新
处理近似轮廓(例如在 findContours 中使用 CHAIN_APPROX_SIMPLE)可能更快，但轮廓的长度必须使用 arcLength() 计算代码>.
Working on an approximated contour (e.g. using CHAIN_APPROX_SIMPLE in findContours) may be faster, but the length of contours must be computed using arcLength().
这是要在 removeFromContour 的 swapping 部分中替换的片段:
This is the snippet to replace in the swapping part of removeFromContour:
// Check if intervals are swapped
if (startIdx <= endIdx)
{
//int len11 = endIdx - startIdx;
vector<Point> inside(contour.begin() + startIdx, contour.begin() + endIdx);
int len1 = (inside.empty()) ? 0 : arcLength(inside, false);
//int len22 = contour.size() - endIdx + startIdx;
vector<Point> outside1(contour.begin(), contour.begin() + startIdx);
vector<Point> outside2(contour.begin() + endIdx, contour.end());
int len2 = (outside1.empty() ? 0 : arcLength(outside1, false)) + (outside2.empty() ? 0 : arcLength(outside2, false));
if (len2 < len1)
{
swap(startIdx, endIdx);
}
}
else
{
//int len1 = startIdx - endIdx;
vector<Point> inside(contour.begin() + endIdx, contour.begin() + startIdx);
int len1 = (inside.empty()) ? 0 : arcLength(inside, false);
//int len2 = contour.size() - startIdx + endIdx;
vector<Point> outside1(contour.begin(), contour.begin() + endIdx);
vector<Point> outside2(contour.begin() + startIdx, contour.end());
int len2 = (outside1.empty() ? 0 : arcLength(outside1, false)) + (outside2.empty() ? 0 : arcLength(outside2, false));
if (len1 < len2)
{
swap(startIdx, endIdx);
}
}
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