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:
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! 🥳
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.
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.
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.
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.
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:
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Latest comments (2)
Interesting. Very informative!
Thank you :)