# Obtaining Edges using the Canny operator

Basic
Edge Detection
kornia.filters
In this tutorial we show how easily one can apply the typical canny edge detection using Kornia
Author

Pau Riba

Published

June 8, 2021

## Preparation

We first install Kornia.

``````%%capture
%matplotlib inline
!pip install kornia
!pip install kornia-rs``````
``````import kornia

kornia.__version__``````
``'0.6.12-dev'``

``````import io

import requests

filename = url.split("/")[-1] if len(filename) == 0 else filename
bytesio = io.BytesIO(requests.get(url).content)
# Save file
with open(filename, "wb") as outfile:
outfile.write(bytesio.getbuffer())

return filename

url = "https://ih1.redbubble.net/image.675644909.6235/flat,800x800,075,f.u3.jpg"
``'paranoia_agent.jpg'``

## Example

We first import the required libraries and load the data.

``````import kornia
import matplotlib.pyplot as plt
import torch

# read the image with Kornia
img_array = kornia.tensor_to_image(img_tensor)

plt.axis("off")
plt.imshow(img_array)
plt.show()``````

To apply a filter, we create the Canny operator object and apply it to the data. It will provide the magnitudes as well as the edges after the hysteresis process. Note that the edges are a binary image which is not differentiable!

``````# create the operator
canny = kornia.filters.Canny()

# blur the image
x_magnitude, x_canny = canny(img_tensor)``````

That’s it! We can compare the source image and the results from the magnitude as well as the edges:

``````# convert back to numpy
img_magnitude = kornia.tensor_to_image(x_magnitude.byte())
img_canny = kornia.tensor_to_image(x_canny.byte())

# Create the plot
fig, axs = plt.subplots(1, 3, figsize=(16, 16))
axs = axs.ravel()

axs[0].axis("off")
axs[0].set_title("image source")
axs[0].imshow(img_array)

axs[1].axis("off")
axs[1].set_title("canny magnitude")
axs[1].imshow(img_magnitude, cmap="Greys")

axs[2].axis("off")
axs[2].set_title("canny edges")
axs[2].imshow(img_canny, cmap="Greys")

plt.show()``````

Note that our final result still recovers some edges whose magnitude is quite low. Let us increase the thresholds and compare the final edges.

``````# create the operator
canny = kornia.filters.Canny(low_threshold=0.4, high_threshold=0.5)

# blur the image
_, x_canny_threshold = canny(img_tensor)``````
``````import torch.nn.functional as F

# convert back to numpy
img_canny_threshold = kornia.tensor_to_image(x_canny_threshold.byte())

# Create the plot
fig, axs = plt.subplots(1, 3, figsize=(16, 16))
axs = axs.ravel()

axs[0].axis("off")
axs[0].set_title("image source")
axs[0].imshow(img_array)

axs[1].axis("off")
axs[1].set_title("canny default")
axs[1].imshow(img_canny, cmap="Greys")

axs[2].axis("off")
axs[2].set_title("canny defined thresholds")
axs[2].imshow(img_canny_threshold, cmap="Greys")

plt.show()``````