Oil Paint Effect With OpenCV Python

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This is Day 25 of the #100DaysOfPython challenge.

This post will use the OpenCV Python library to apply an oil painting effect to an image.

Prerequisites

1. Familiarity with Pipenv. See here for my post on Pipenv.
2. An image to use with the OpenCV library.

Getting started

Let's create the oil-paint-effect-with-open-cv-python directory and install Pillow.

# Make the `oil-paint-effect-with-open-cv-python` directory
$ mkdir oil-paint-effect-with-open-cv-python
$ cd oil-paint-effect-with-open-cv-python
$ touch main.py

# Init the virtual environment
$ pipenv --three
$ pipenv install opencv-python opencv-contrib-python


# if you have issues with a hanging lockfile, try add the --skip-lock option

At this stage, you will need to add an image to the root of your directory. In my case, I will add base_img.jpg to the directory (which will be an image from Unsplash).

We are now ready to start coding!

Applying the oil painting effect

This section simply loads the image in var base_img (assuming you are following the directory structure where the notebook is in the docs folder).

import cv2
img = cv2.imread('./base_img.jpg')

Once that is complete, we can apply the oil paiting effect with one liner of code:

res = cv2.xphoto.oilPainting(img, 7, 1)

We can now compare by displaying the images:

cv2.imshow("original", img)
cv2.imshow("res", res)
cv2.waitKey(0)
cv2.destroyAllWindows()

This will display the images in a window.

The original image:

After applying the effect:

When you are finished with viewing, hit escape to exit.

Summary

Today's post demonstrated how to use the OpenCV package to programmatically apply an oil painting effect to an image.

Resources and further reading

1. The ABCs of Pipenv
2. OpenCV Python library
3. Pipenv

Photo credit: dancristianp

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Comments

[NITYANAND Chandrakar]

# Retry the oil painting effect using an alternative OpenCV approach

The cv2.xphoto module might not be available; using bilateral filters as a workaround for the effect

def apply_oil_painting_alternative(image_path, output_path):
# Read the input image
image = cv2.imread(image_path)

# Apply bilateral filters repeatedly to achieve a painterly effect

oil_painting = image.copy()

for _ in range(3):  # Repeating for a stronger effect

    oil_painting = cv2.bilateralFilter(oil_painting, d=9, sigmaColor=75, sigmaSpace=75)

  
  
  Save the result


cv2.imwrite(output_path, oil_painting)

return output_path

Apply the effect and save the result

output_image_path = "/mnt/data/oil_painting_effect_alternative.png"
apply_oil_painting_alternative(input_image_path, output_image_path)