在OpenCV中测量边缘强度，梯度大小 [英] Measure edge strength in OpenCV, magnitude of gradient

问题描述

我有一个需要检查相机焦点的应用程序.为此，我想在单个轴(1D)上的几个预定义位置中测量边缘强度(梯度的大小).图像目标将是背景上黑色物体的简单打印输出.

I have an application where I need to check the focus of a camera. For this, I want to measure edge strength (magnitude of gradient) in several predefined locations on a single axis (1D). The image target will be a simple printout of black objects on a while background.

我正在将OpenCV与Python结合使用.我知道OpenCV中有几种边缘检测算法，例如Canny，Sobel，laplace，但所有这些算法都是为了过滤图像.我想实际测量边缘的强度. OpenCV中是否有可以提供此功能的算法?还是我只是编写自己的算法来测量边缘强度?

I am using OpenCV with Python. I know there are several edge detection algorithms within OpenCV like Canny, Sobel, laplace but all of these are to filter the image. I want to actually measure the strength of an edge. Are there any algorithms within OpenCV that can provide this? Or do I just write my own algorithm to measure edge strength?

推荐答案

您可以像这样计算幅度:

You can compute the magnitude like:

1. 计算dx和dy导数(使用cv::Sobel)
2. 计算幅度sqrt(dx^2 + dy^2)(使用cv::magnitude)

1. Compute dx and dy derivatives (using cv::Sobel)
2. Compute the magnitude sqrt(dx^2 + dy^2) (using cv::magnitude)

这是一个简单的C ++代码，用于计算梯度的大小.您可以轻松移植到Python，因为它只是对OpenCV函数的一些调用:

This is a simple C++ code that compute the magnitude of the gradient. You can easily port to Python, since it's just a few calls to OpenCV functions:

#include <opencv2/opencv.hpp>
using namespace cv;

int main()
{
    //Load image
    Mat3b img = imread("path_to_image");

    //Convert to grayscale
    Mat1b gray;
    cvtColor(img, gray, COLOR_BGR2GRAY);

    //Compute dx and dy derivatives
    Mat1f dx, dy;
    Sobel(gray, dx, CV_32F, 1, 0);
    Sobel(gray, dy, CV_32F, 0, 1);

    //Compute gradient
    Mat1f magn;
    magnitude(dx, dy, magn);

    //Show gradient
    imshow("Magnitude", magn);
    waitKey();

    return 0;
}

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