Canny Edge Detection OpenCV Python With Source Code
The Canny Edge Detection OpenCV Python Code was developed using Python OpenCV, This Canny Edge Detector is a multi-step algorithm used to detect a wide range of edges in images.
The Canny edge detector is arguably the most well known and the most used edge detector in all of computer vision and image processing.
A Canny Edge Detection uses a filter based on the derivative of a Gaussian in order to compute the intensity of the gradients. The Gaussian reduces the effect of noise present in the image.
In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
What is OpenCV?
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library.
OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.
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To start executing Canny Edge Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.
Canny Edge Detection OpenCV Python With Source Code : Steps on how to run the project
Time needed: 5 minutes
These are the steps on how to run Canny Edge Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
Installed Libraries
import cv2 as cv import numpy as np from matplotlib import pyplot as plt
Complete Source Code
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
def canny():
'''
The Canny edge detection algorithm is composed
of 5 steps:
1. Noise Reduction
2. Gradient Calculation
3. Non-maximum Suppressions
4. Double threshold
5. Edge Tracking by Hysteresis
'''
img = cv.imread("./img/sunflower.jpg", 0)
canny = cv.Canny(img, 150, 200)
title = ["Original Image", "Canny"]
images = [img, canny]
for i in range(len(images)):
plt.subplot(2, 2, i+1), plt.imshow(images[i], 'gray')
plt.title(title[i])
plt.xticks([]), plt.yticks([])
plt.show()
if __name__ == '__main__':
canny()
Output
Download Source Code below
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Summary
In this Python OpenCV Project With Source Code, we learned how to use image gradients, one of the most fundamental building blocks of computer vision and image processing, to create an edge detector.
Specifically, we focused on the Canny edge detector, the most well known and most used edge detector in the computer vision community.
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Inquiries
If you have any questions or suggestions about Canny Edge Detection OpenCV Python With Source Code, please feel free to leave a comment below.
Frequently Asked Questions
How does Canny edge detection work?
Five steps internally: noise reduction (Gaussian blur), gradient calculation (Sobel), non-maximum suppression (thin edges), double thresholding (keep strong edges, conditionally keep weak), edge tracking by hysteresis. In OpenCV: cv2.Canny(img, lower, upper). Lower threshold around 50, upper around 150 work for most images, tune with a trackbar for your scene.
What OpenCV version do I need to run this project?
Use OpenCV 4.5 or newer. Install with pip install opencv-python (the standard build for desktop projects). Some projects also need opencv-contrib-python which adds extra modules (SIFT, SURF, advanced trackers). The pip install command auto-downloads pre-built wheels so no compilation is needed on Windows, Mac, or Linux.
How do I install OpenCV and the dependencies for this project?
Open a terminal, then: pip install opencv-python numpy. Most projects also need one of these: mediapipe (for face / hand / pose detection), pyzbar (for barcode and QR), pytesseract (for OCR), Pillow (for image manipulation), pyautogui (for screen capture). Pin Python version to 3.10, 3.11, or 3.12 for maximum library compatibility.
Can I use this OpenCV project for a BSIT or CSE capstone?
Yes, but extend it. A single OpenCV demo (face detection alone, lane detection alone) is too narrow for full capstone scope. Combine it with a real domain (attendance system using face recognition, traffic monitoring system using lane detection, fitness coach app using pose detection), add a database to log results, build a simple Tkinter or Streamlit UI, and document the whole pipeline in Chapter 3.
Why am I getting AttributeError or ImportError when running this code?
Three most common causes: (1) You installed opencv-python but the code needs opencv-contrib-python (extra modules like xfeatures2d). Reinstall with pip install opencv-contrib-python. (2) You are on Python 3.13 but some wheels (mediapipe) lag behind, downgrade to Python 3.11 or 3.12. (3) NumPy version mismatch, pin numpy to a version your other libraries support.
Where do I find more OpenCV and Machine Learning project ideas?
Browse our Machine Learning Projects hub for 23+ OpenCV demos with source code. For capstone-scale AI ideas (RAG, NLP, recommendation systems), see 100+ AI Capstone Project Ideas. For broader Python project ideas, our Python Projects library has 250+ working capstones.