opencv
Détection de bord
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Syntaxe
- bords = cv2. Canny (image, seuil1, seuil2 [, bords [, apertureSize [, L2gradient]]])
- void Canny (image InputArray, bords OutputArray, double threshold1, double threshold2, int apertureSize = 3, bool L2gradient = false
Paramètres
Paramètre | Détails |
---|---|
image | Image d'entrée |
bords | Image de sortie |
seuil1 | Premier seuil pour la procédure d'hystérésis |
seuil2 | Deuxième seuil pour la procédure d'hystérésis |
ouvertureTaille | Taille d'ouverture pour l'opérateur Sobel |
L2gradient | Indicateur indiquant si un algorithme plus précis pour le dégradé d'image doit être utilisé |
Algorithme Canny
L'algorithme de Canny est un détecteur de bord plus récent conçu comme un problème de traitement du signal. Dans OpenCV, il génère une image binaire marquant les arêtes détectées.
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 Algorithm - C ++
Vous trouverez ci-dessous une utilisation de l'algorithme canny en c ++. Notez que l'image est d'abord convertie en image en niveaux de gris, puis que le filtre gaussien est utilisé pour réduire le bruit dans l'image. Ensuite, l'algorithme de Canny est utilisé pour la détection des contours.
// 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;
}
Calcul des seuils de Canny
Calcul automatique des seuils bas et haut pour l'opération Canny en ouverture
Canny Edge Video de Webcam Capture - Python
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()
Prototypage de Canny Edge Thresholds à l'aide de trackbars
"""
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)
Modified text is an extract of the original Stack Overflow Documentation
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