Giter Club home page Giter Club logo

edge-detection's Introduction

EDGEDETECTION

Aim:

To perform edge detection using Sobel, Laplacian, and Canny edge detectors.

Software Required:

Anaconda - Python 3.7

Algorithm:

Step 1:

Import the required packages for further process.

Step 2:

Read the image and convert the bgr image to gray scale image.

Step 3:

Use any filters for smoothing the image to reduse the noise.

Step 4:

Apply the respective filters -Sobel,Laplacian edge dectector and Canny edge dector.

Step 5:

Display the filtered image using plot and imshow.

Program:

DEVELOPED BY : S Adithya Chowdary.
REF NO: 212221230100.

Import the packages

import cv2
import matplotlib.pyplot as plt

Load the image, Convert to grayscale and remove noise

import cv2
import matplotlib.pyplot as plt

img=cv2.imread("cycle.jpg",0)
gray=cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
gray = cv2.GaussianBlur(gray,(3,3),0)

SOBEL EDGE DETECTOR

SOBEL X AXIS:

sobelx = cv2.Sobel(gray,cv2.CV_64F,1,0,ksize=5)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(sobelx)
plt.title("Sobel X axis")
plt.axis("off")
plt.show()

SOBEL Y AXIS:

sobely = cv2.Sobel(gray,cv2.CV_64F,0,1,ksize=5)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(sobely)
plt.title("Sobel Y axis")
plt.axis("off")
plt.show()

SOBEL XY AXIS:

sobelxy = cv2.Sobel(gray,cv2.CV_64F,1,1,ksize=5)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(sobelxy)
plt.title("Sobel XY axis")
plt.axis("off")
plt.show()

LAPLACIAN EDGE DETECTOR


lap=cv2.Laplacian(gray,cv2.CV_64F)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(lap)
plt.title("Laplacian Edge Detector")
plt.axis("off")
plt.show()

CANNY EDGE DETECTOR


canny=cv2.Canny(gray,120,150)
plt.figure(figsize=(8,8))
plt.subplot(1,2,1)
plt.imshow(gray)
plt.title("Original Image")
plt.axis("off")
plt.subplot(1,2,2)
plt.imshow(canny)
plt.title("Canny Edge Detector")
plt.axis("off")
plt.show()


Output:

SOBEL EDGE DETECTOR

SOBEL X AXIS:

image

SOBEL Y AXIS:

image

SOBEL XY AXIS:

image

LAPLACIAN EDGE DETECTOR

image

CANNY EDGE DETECTOR

image

Result:

Thus the edges are detected using Sobel, Laplacian, and Canny edge detectors.

edge-detection's People

Contributors

swedha333 avatar adithya-siddam avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.