opencv
エッジ検出

構文

• エッジ= cv2。 Canny （image、threshold1、threshold2 [、edges [、apertureSize [、L2gradient]]）
• void Canny （InputArray image、OutputArray edges、double threshold1、double threshold2、int apertureSize = 3、ブール値L2gradient = false）

パラメーター

キャニーアルゴリズム

Cannyアルゴリズムは、信号処理の問題として設計された、より最近のエッジ検出器です。 OpenCVでは、検出されたエッジをマークするバイナリイメージを出力します。

Python：

import cv2
import sys

# Load the image file
image = cv2.imread('image.png')

# Check if image was loaded improperly and exit if so
if image is None:
    sys.exit('Failed to load image')

# Detect edges in the image. The parameters control the thresholds
edges = cv2.Canny(image, 100, 2500, apertureSize=5)

# Display the output in a window
cv2.imshow('output', edges)
cv2.waitKey()

Cannyアルゴリズム - C ++

以下はC ++のcannyアルゴリズムの使用法です。画像はまずグレースケール画像に変換され、次にガウスフィルタが画像のノイズを低減するために使用されることに注意してください。次に、Cannyアルゴリズムをエッジ検出に使用します。

// CannyTutorial.cpp : Defines the entry point for the console application. 
// Environment: Visual studio 2015, Windows 10
// Assumptions: Opecv is installed configured in the visual studio project
// Opencv version: OpenCV 3.1

#include "stdafx.h"
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<string>
#include<iostream>



int main()
{

    //Modified from source: https://github.com/MicrocontrollersAndMore/OpenCV_3_Windows_10_Installation_Tutorial
    cv::Mat imgOriginal;        // input image
    cv::Mat imgGrayscale;        // grayscale of input image
    cv::Mat imgBlurred;            // intermediate blured image
    cv::Mat imgCanny;            // Canny edge image

    std::cout << "Please enter an image filename : ";
    std::string img_addr;
    std::cin >> img_addr;

    std::cout << "Searching for " + img_addr << std::endl;

    imgOriginal = cv::imread(img_addr);            // open image

    if (imgOriginal.empty()) {                                    // if unable to open image
        std::cout << "error: image not read from file\n\n";        // show error message on command line
        return(0);                                                // and exit program
    }

    cv::cvtColor(imgOriginal, imgGrayscale, CV_BGR2GRAY);        // convert to grayscale

    cv::GaussianBlur(imgGrayscale,            // input image
        imgBlurred,                            // output image
        cv::Size(5, 5),                        // smoothing window width and height in pixels
        1.5);                                // sigma value, determines how much the image will be blurred

    cv::Canny(imgBlurred,            // input image
        imgCanny,                    // output image
        100,                        // low threshold
        200);                        // high threshold


    // Declare windows
    // Note: you can use CV_WINDOW_NORMAL which allows resizing the window
    // or CV_WINDOW_AUTOSIZE for a fixed size window matching the resolution of the image
    // CV_WINDOW_AUTOSIZE is the default
    cv::namedWindow("imgOriginal", CV_WINDOW_AUTOSIZE);        
    cv::namedWindow("imgCanny", CV_WINDOW_AUTOSIZE);

    //Show windows
    cv::imshow("imgOriginal", imgOriginal);        
    cv::imshow("imgCanny", imgCanny);

    cv::waitKey(0);                    // hold windows open until user presses a key
    return 0;
}

キャニーのしきい値の計算

opencvでのCanny演算の低しきい値と高しきい値の自動計算

Webcam Capture - CathyのCanny Edgeビデオ

import cv2


def canny_webcam():
    "Live capture frames from webcam and show the canny edge image of the captured frames."

    cap = cv2.VideoCapture(0)

    while True:
        ret, frame = cap.read()  # ret gets a boolean value. True if reading is successful (I think). frame is an
        # uint8 numpy.ndarray

        frame = cv2.GaussianBlur(frame, (7, 7), 1.41)
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

        edge = cv2.Canny(frame, 25, 75)

        cv2.imshow('Canny Edge', edge)

        if cv2.waitKey(20) == ord('q'):  # Introduce 20 milisecond delay. press q to exit.
            break

canny_webcam()

Canny Edge Thresholdsトラックバーを使用したプロトタイピング

""" 
CannyTrackbar function allows for a better understanding of 
the mechanisms behind Canny Edge detection algorithm and rapid
prototyping. The example includes basic use case.

2 of the trackbars allow for tuning of the Canny function and
the other 2 help with understanding how basic filtering affects it.
"""
import cv2

def empty_function(*args):
    pass

def CannyTrackbar(img):
    win_name = "CannyTrackbars"

    cv2.namedWindow(win_name)
    cv2.resizeWindow(win_name, 500,100)

    cv2.createTrackbar("canny_th1", win_name, 0, 255, empty_function)
    cv2.createTrackbar("canny_th2", win_name, 0, 255, empty_function)
    cv2.createTrackbar("blur_size", win_name, 0, 255, empty_function)
    cv2.createTrackbar("blur_amp", win_name, 0, 255, empty_function)

    while True:
        cth1_pos = cv2.getTrackbarPos("canny_th1", win_name)
        cth2_pos = cv2.getTrackbarPos("canny_th2", win_name)
        bsize_pos = cv2.getTrackbarPos("blur_size", win_name)
        bamp_pos = cv2.getTrackbarPos("blur_amp", win_name)

        img_blurred = cv2.GaussianBlur(img.copy(), (trackbar_pos3 * 2 + 1, trackbar_pos3 * 2 + 1), bamp_pos)
        canny = cv2.Canny(img_blurred, cth1_pos, cth2_pos)
        cv2.imshow(win_name, canny)

        key = cv2.waitKey(1) & 0xFF
        if key == ord("c"):
            break

    cv2.destroyAllWindows()
    return canny

img = cv2.imread("image.jpg")
canny = CannyTrackbar(img)
cv2.imwrite("result.jpg", canny)