問題描述

我有一個需要檢查相機焦點的應用程序.為此，我想在單個軸 (1D) 上的幾個預定義位置測量邊緣強度(梯度大小).圖像目標將是在一段時間背景上的黑色對象的簡單打印輸出.

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.

我在 Python 中使用 OpenCV.我知道 OpenCV 中有幾種邊緣檢測算法，例如 Canny、Sobel、laplace，但所有這些都是為了過濾圖像.我想實際測量邊緣的強度.OpenCV 中是否有任何算法可以提供此功能?還是我只是編寫自己的算法來測量邊緣強度?

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?

推薦答案

你可以像這樣計算量級:

You can compute the magnitude like:

1. 計算 dx 和 dy 導數(shù)(使用 cv::Sobel)
2. 計算幅度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)

這是一個計算梯度大小的簡單 C++ 代碼.您可以輕松移植到 Python，因為它只是對 OpenCV 函數(shù)的幾次調用:

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;
}

這篇關于在 OpenCV 中測量邊緣強度，梯度大小的文章就介紹到這了，希望我們推薦的答案對大家有所幫助，也希望大家多多支持html5模板網(wǎng)！