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c++ - 微调霍夫线功能参数OpenCV

我一直在尝试在正方形周围得到4条线，以便获得正方形的顶点。我会采用这种方法，而不是出于准确性而直接使用Harris或等高线方法来查找拐角。在opencv的内置函数中使用houghlines，我无法获得全长线来获取相交点，而且我也获得了太多不相关的线。我想知道是否可以对参数进行微调以达到我的要求？如果是，我该怎么办？我的问题与这个here.完全相同。但是即使更改了这些参数，我也没有得到这些行。我已经将原始图像以及代码和输出附加到了:

原始图片:

c++ - 微调霍夫线功能参数OpenCV-LMLPHP

代码:

#include <Windows.h>
#include "opencv2\highgui.hpp"
#include "opencv2\imgproc.hpp"
#include "opencv2/imgcodecs/imgcodecs.hpp"
#include "opencv2/videoio/videoio.hpp"

using namespace cv;
using namespace std;

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

    Mat image,src;
    image = imread("c:/pics/output2_1.bmp");
    src = image.clone();
    cvtColor(image, image, CV_BGR2GRAY);
    threshold(image, image, 0, 255, CV_THRESH_OTSU + CV_THRESH_BINARY_INV);

    namedWindow("thresh", WINDOW_NORMAL);
    resizeWindow("thresh", 600, 400);

    imshow("thresh", image);

    cv::Mat edges;

    cv::Canny(image, edges, 0, 255);

    vector<Vec2f> lines;
    HoughLines(edges, lines, 1, CV_PI / 180, 100, 0, 0);
    for (size_t i = 0; i < lines.size(); i++)
    {
        float rho = lines[i][0], theta = lines[i][1];
        Point pt1, pt2;
        double a = cos(theta), b = sin(theta);
        double x0 = a*rho, y0 = b*rho;
        pt1.x = cvRound(x0 + 1000 * (-b));
        pt1.y = cvRound(y0 + 1000 * (a));
        pt2.x = cvRound(x0 - 1000 * (-b));
        pt2.y = cvRound(y0 - 1000 * (a));
        line(src, pt1, pt2, Scalar(0, 0, 255), 3, CV_AA);
    }

namedWindow("Edges Structure", WINDOW_NORMAL);
resizeWindow("Edges Structure", 600, 400);

imshow("Edges Structure", src);
waitKey(0);

return(0);
}

输出图像:

c&#43;&#43; - 微调霍夫线功能参数OpenCV-LMLPHP

更新:此图像上有一个框架，因此我可以通过删除该框架来减少图像边框中不相关的线条，但是我仍然无法获得覆盖正方形的完整线条。

最佳答案

有很多方法可以做到这一点，我仅举一个例子。但是，我使用python最快，因此我的代码示例将使用该语言。不过，翻译起来应该不难(请为其他人完成文章后，请随时使用C++解决方案来编辑​​您的文章)。

对于预处理，我强烈建议 dilate() 添加您的边缘图像。这将使线变粗，从而有助于更好地适合霍夫线。霍夫线函数在摘要中的作用基本上是使经过数个角度和距离的线成为网格，并且如果这些线越过Canny的任何白色像素，则它会为该线给它经过的每个点打分。但是，Canny的直线不会很直，因此您将获得一些不同的直线得分。使这些Canny线更粗将意味着每条真正接近拟合度的线都有更高的得分机会。

c&#43;&#43; - 微调霍夫线功能参数OpenCV-LMLPHP

如果您要使用HoughLinesP，那么您的输出将是线段，您所拥有的只是线上的两个点。

由于线条大多是垂直和水平的，因此您可以根据其位置轻松分割线条。如果一条线的两个y坐标彼此靠近，则该线大部分为水平。如果两个x坐标彼此靠近，则该线大部分是垂直的。因此，您可以通过这种方式将线分为垂直线和水平线。

def segment_lines(lines, delta):
    h_lines = []
    v_lines = []
    for line in lines:
        for x1, y1, x2, y2 in line:
            if abs(x2-x1) < delta: # x-values are near; line is vertical
                v_lines.append(line)
            elif abs(y2-y1) < delta: # y-values are near; line is horizontal
                h_lines.append(line)
    return h_lines, v_lines

c&#43;&#43; - 微调霍夫线功能参数OpenCV-LMLPHP

然后，可以从它们的端点using determinants获得两个线段的交点。
def find_intersection(line1, line2):
    # extract points
    x1, y1, x2, y2 = line1[0]
    x3, y3, x4, y4 = line2[0]
    # compute determinant
    Px = ((x1*y2 - y1*x2)*(x3-x4) - (x1-x2)*(x3*y4 - y3*x4))/  \
        ((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
    Py = ((x1*y2 - y1*x2)*(y3-y4) - (y1-y2)*(x3*y4 - y3*x4))/  \
        ((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
    return Px, Py

因此，现在，如果您遍历所有线，则所有水平线和垂直线都将有交点，但是您有很多线，因此对于框的同一角，您将有很多交点。

c&#43;&#43; - 微调霍夫线功能参数OpenCV-LMLPHP

但是，这些都在一个 vector 中，因此，不仅需要平均每个角的点，还需要将它们实际分组在一起。您可以使用k-means聚类来实现此目的，该聚类在OpenCV中以 kmeans() 的形式实现。
def cluster_points(points, nclusters):
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    _, _, centers = cv2.kmeans(points, nclusters, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
    return centers

最后，我们可以使用 circle() 简单地绘制这些中心(确保我们首先舍入，因为到目前为止，所有内容都是浮点数)到图像上，以确保我们做对了。

c&#43;&#43; - 微调霍夫线功能参数OpenCV-LMLPHP

我们有它；四个角，在盒子的角落。

这是我在python中的完整代码，包括用于生成以上数字的代码:
import cv2
import numpy as np

def find_intersection(line1, line2):
    # extract points
    x1, y1, x2, y2 = line1[0]
    x3, y3, x4, y4 = line2[0]
    # compute determinant
    Px = ((x1*y2 - y1*x2)*(x3-x4) - (x1-x2)*(x3*y4 - y3*x4))/  \
        ((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
    Py = ((x1*y2 - y1*x2)*(y3-y4) - (y1-y2)*(x3*y4 - y3*x4))/  \
        ((x1-x2)*(y3-y4) - (y1-y2)*(x3-x4))
    return Px, Py

def segment_lines(lines, delta):
    h_lines = []
    v_lines = []
    for line in lines:
        for x1, y1, x2, y2 in line:
            if abs(x2-x1) < delta: # x-values are near; line is vertical
                v_lines.append(line)
            elif abs(y2-y1) < delta: # y-values are near; line is horizontal
                h_lines.append(line)
    return h_lines, v_lines

def cluster_points(points, nclusters):
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
    _, _, centers = cv2.kmeans(points, nclusters, None, criteria, 10, cv2.KMEANS_PP_CENTERS)
    return centers

img = cv2.imread('image.png')

# preprocessing
img = cv2.resize(img, None, fx=.5, fy=.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 150)
dilated = cv2.dilate(edges, np.ones((3,3), dtype=np.uint8))

cv2.imshow("Dilated", dilated)
cv2.waitKey(0)
cv2.imwrite('dilated.png', dilated)

# run the Hough transform
lines = cv2.HoughLinesP(dilated, rho=1, theta=np.pi/180, threshold=100, maxLineGap=20, minLineLength=50)

# segment the lines
delta = 10
h_lines, v_lines = segment_lines(lines, delta)

# draw the segmented lines
houghimg = img.copy()
for line in h_lines:
    for x1, y1, x2, y2 in line:
        color = [0,0,255] # color hoz lines red
        cv2.line(houghimg, (x1, y1), (x2, y2), color=color, thickness=1)
for line in v_lines:
    for x1, y1, x2, y2 in line:
        color = [255,0,0] # color vert lines blue
        cv2.line(houghimg, (x1, y1), (x2, y2), color=color, thickness=1)

cv2.imshow("Segmented Hough Lines", houghimg)
cv2.waitKey(0)
cv2.imwrite('hough.png', houghimg)

# find the line intersection points
Px = []
Py = []
for h_line in h_lines:
    for v_line in v_lines:
        px, py = find_intersection(h_line, v_line)
        Px.append(px)
        Py.append(py)

# draw the intersection points
intersectsimg = img.copy()
for cx, cy in zip(Px, Py):
    cx = np.round(cx).astype(int)
    cy = np.round(cy).astype(int)
    color = np.random.randint(0,255,3).tolist() # random colors
    cv2.circle(intersectsimg, (cx, cy), radius=2, color=color, thickness=-1) # -1: filled circle

cv2.imshow("Intersections", intersectsimg)
cv2.waitKey(0)
cv2.imwrite('intersections.png', intersectsimg)

# use clustering to find the centers of the data clusters
P = np.float32(np.column_stack((Px, Py)))
nclusters = 4
centers = cluster_points(P, nclusters)
print(centers)

# draw the center of the clusters
for cx, cy in centers:
    cx = np.round(cx).astype(int)
    cy = np.round(cy).astype(int)
    cv2.circle(img, (cx, cy), radius=4, color=[0,0,255], thickness=-1) # -1: filled circle

cv2.imshow("Center of intersection clusters", img)
cv2.waitKey(0)
cv2.imwrite('corners.png', img)

最后，只有一个问题...为什么不将OpenCV中实现的Harris corner detector用作 cornerHarris() ？因为它用很少的代码就能很好地工作。我对灰度图像进行了阈值处理，然后进行了一些模糊处理以去除伪角，然后...

c&#43;&#43; - 微调霍夫线功能参数OpenCV-LMLPHP

这是用以下代码生成的:
import cv2
import numpy as np

img = cv2.imread('image.png')

# preprocessing
img = cv2.resize(img, None, fx=.5, fy=.5)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
r, gray = cv2.threshold(gray, 120, 255, type=cv2.THRESH_BINARY)
gray = cv2.GaussianBlur(gray, (3,3), 3)

# run harris
gray = np.float32(gray)
dst = cv2.cornerHarris(gray,2,3,0.04)

# dilate the corner points for marking
dst = cv2.dilate(dst,None)
dst = cv2.dilate(dst,None)

# threshold
img[dst>0.01*dst.max()]=[0,0,255]

cv2.imshow('dst',img)
cv2.waitKey(0)
cv2.imwrite('harris.png', img)

我认为，通过一些小的调整，Harris拐角检测器可能比外推霍夫线相交点更为准确。

10-08 04:15
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