LSD 直线检测C++
时间: 2023-08-16 16:10:57 浏览: 41
lsd直线检测是一种基于OpenCV-Python的图像处理技术,用于检测图像中的直线。该技术使用LSD算法(Line Segment Detection)来检测图像中的直线,可以应用于许多领域,如机器人视觉、自动驾驶、医学图像处理等。LSD直线检测可以快速准确地检测出图像中的直线,具有很高的实用价值。
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lsd直线检测c++
LSD直线检测是一种基于边缘检测的直线检测算法,它可以在图像中检测出直线的位置和方向。在C++中,可以使用OpenCV库中的LSD算法实现直线检测。以下是一个简单的示例代码:
```c++
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace cv;
using namespace std;
int main()
{
Mat img = imread("test.jpg", IMREAD_GRAYSCALE);
if (img.empty())
{
cout << "Failed to load image!" << endl;
return -1;
}
vector<Vec4f> lines;
Ptr<LineSegmentDetector> ls = createLineSegmentDetector(LSD_REFINE_STD);
ls->detect(img, lines);
for (size_t i = 0; i < lines.size(); i++)
{
Vec4f line = lines[i];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
float theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[2]);
line[3] = round(line[3]);
line[0] = max(0.0f, min(line[0], (float)img.cols - 1));
line[1] = max(0.0f, min(line[1], (float)img.rows - 1));
line[2] = max(0.0f, min(line[2], (float)img.cols - 1));
line[3] = max(0.0f, min(line[3], (float)img.rows - 1));
line[2] -= line[0];
line[3] -= line[1];
theta = atan2(line[3], line[2]) * 180 / CV_PI;
if (theta < 0)
theta += 180;
line[2] += line[0];
line[3] += line[1];
line[0] = round(line[0]);
line[1] = round(line[1]);
line[2] = round(line[
LSD直线检测python
要在Python中实现LSD直线检测,首先需要安装OpenCV库。OpenCV是一个开源的计算机视觉库,提供了许多用于图像处理的功能,其中包括LSD直线检测。
在Python代码中导入OpenCV库:
import cv2
读入图像并转换为灰度图:
# 读入图像
image = cv2.imread('image.jpg')
# 转换为灰度图
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
调用cv2.createLineSegmentDetector函数创建一个LSD对象,并使用该对象的detect方法进行直线检测:
# 创建LSD对象
lsd = cv2.createLineSegmentDetector(0)
# 进行直线检测
lines, width, prec, nfa = lsd.detect(gray)
检测到的直线段将以二维数组的形式返回,每一行表示一条直线,列分别表示该直线的起点和终点坐标。可以使用如下代码在图像上绘制检测到的直线:
# 在图像上绘制直线
for line in lines:
x1, y1, x2, y2 = line
cv2.line(image, (x1, y1), (x2, y2), (0, 0, 255), 2)