Creating more filters with OpenCV and Python

Introduction

Hello! 😀

In this tutorial I will be showcasing some more filters using OpenCV and Python! This is a continuation of my previous example which can be found here: dev.to/ethand91/creating-various-filters-wi..

I've already discussed how to create the virtual environment in previous tutorials so I will skip that part.

Well lets get started creating some more filters! 🥳

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Vignette Filter

First we will create the vignette filter. The vignette filter is achieved by creating a broad 2D Gaussian kernel.

def vignette(image, level = 2): 
    height, width = image.shape[:2]

    x_resultant_kernel = cv2.getGaussianKernel(width, width/level)
    y_resultant_kernel = cv2.getGaussianKernel(height, height/level)

    kernel = y_resultant_kernel * x_resultant_kernel.T
    mask = kernel / kernel.max()

    image_vignette = np.copy(image)

    for i in range(3):
        image_vignette[:,:,i] = image_vignette[:,:,i] * mask

    return image_vignette

Here we generate the vignette mask using Gaussian kernels, we then generate the result matrix and then apply the mask to each of the image's color channels.

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Embossed Filter

The next filter is the embossed filter:

def embossed_edges(image):

    kernel = np.array([[0, -3, -3], [3, 0, -3], [3, 3, 0]])

    image_emboss = cv2.filter2D(image, -1, kernel = kernel)

    return image_emboss

Here we create an array for each of the channels and then apply it to the image via filter2D.

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Outline Filter

The next filter is the outline filter:

def outline(image, k = 9): 
    k = max(k, 9)
    kernel = np.array([[-1, -1, -1], [-1, k, -1], [-1, -1, -1]])

    image_outline = cv2.filter2D(image, ddepth = -1, kernel = kernel)

    return image_outline

Similar to the embossed filter but this time we increase the quality of the outlines.

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Style Filter

The final filter is one of my personal favorites, the style filter.

def style(image):
    image_blur = cv2.GaussianBlur(image, (5, 5), 0, 0)
    image_style = cv2.stylization(image_blur, sigma_s = 40, sigma_r = 0.1)

    return image_style

This filter is really cool IMO. Before calling stylization it's best to blur the image a bit for better results.

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Conclusion

Here I have shown how to create more various filters with opencv/python. I hope this tutorial was useful to you.

If you have any cool filters please share them. 😎

The source code and the original image can be found via: github.com/ethand91/python-opencv-filters

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