Cartonization

Writing a program that converts an image to a cartoon!

K-means Clustering

This code performs image cartoonization using the K-means clustering algorithm. Here's a breakdown of what the code does:

1. The required libraries are imported, including cv2 for image processing, numpy for numerical operations, matplotlib for visualization, and sklearn for K-means clustering.

2. The convert_to_cartoon function is defined, which takes an image path as input.

3. The image specified by the image path is read using cv2.imread.

4. The image is converted from the default BGR color space to the RGB color space using cv2.cvtColor.

5. Canny edge detection is applied to the RGB image using cv2.Canny, which detects the edges in the image.

6. The RGB image is reshaped into a 2D array of pixel values using image_rgb.reshape((-1, 3)).

7. The pixel values are converted to float32 data type.

8. The K-means clustering algorithm is applied to the pixel values using cv2.kmeans. The number of clusters is specified as 10, but you can change this value. The algorithm assigns each pixel to one of the clusters based on its color similarity.

9. The centers of the clusters are extracted.

10. The pixel values are replaced with the values of the cluster centers to obtain the cartoonized image.

11. The cartoon image is reshaped back to the original shape of the RGB image.

12. The edges image is converted to the BGR color space using cv2.cvtColor.

13. The cartoon image is combined with the edges image to create a cartoonized version of the original image.

14. The brightness of the cartoonized image is adjusted using cv2.convertScaleAbs.

15. The original image and the cartoonized image are displayed side by side using matplotlib.pyplot.imshow.

16. The function convert_to_cartoon is called with the provided image path.

Overall, the code reads an image, applies edge detection and K-means clustering to create a cartoonized version of the image, and then displays the original and cartoonized images.

Average Filter

This code performs image cartoonization using the average filter and edge detection. Here's a breakdown of what the code does:

1. The required libraries are imported, including cv2 for image processing, numpy for numerical operations, and matplotlib for visualization.

2. The convert_to_cartoon function is defined, which takes an image path as input.

3. The image specified by the image path is read using cv2.imread.

4. The image is converted from the default BGR color space to the RGB color space using cv2.cvtColor.

5. An average filter is applied to the RGB image using cv2.blur. The size of the filter kernel is specified as (10, 10), but you can change this value. The average filtering operation reduces noise and smooths the image.

6. The smoothed image is converted to grayscale using cv2.cvtColor.

7. Canny edge detection is applied to the grayscale image using cv2.Canny, which detects the edges in the image.

8. The edges image is converted to the BGR color space using cv2.cvtColor.

9. The edges image is combined with the smoothed image to create a cartoonized version of the original image. This combines the edges with the smoothed regions to create a cartoon-like appearance.

10. The original image and the cartoonized image are displayed side by side using matplotlib.pyplot.imshow.

11. The function convert_to_cartoon is called with the provided image path.

Overall, the code reads an image, applies average filtering, edge detection, and combines the edges with the smoothed image to create a cartoonized version of the image. The original and cartoonized images are then displayed.

Bilateral Filter

This code performs image cartoonization using the bilateral filter and Canny edge detection. Here's a breakdown of what the code does:

1. The required libraries are imported, including cv2 for image processing, numpy for numerical operations, and matplotlib for visualization.

2. The convert_to_cartoon function is defined, which takes an image path as input.

3. The image specified by the image path is read using cv2.imread.

4. The image is converted from the default BGR color space to the RGB color space using cv2.cvtColor.

5. The bilateral filter is applied to the RGB image using cv2.bilateralFilter. The bilateral filter reduces noise while preserving edges. The parameters used for the filter are a diameter of 31, and sigma values of 250 for color space and intensity space. You can adjust these values for different effects.

6. The smoothed image is converted to grayscale using cv2.cvtColor.

7. Canny edge detection is applied to the grayscale image using cv2.Canny, which detects the edges in the image.

8. The edges image is converted to the BGR color space using cv2.cvtColor.

9. A black image of the same size as the original image is created using np.zeros_like.

10. The edges image is combined with the smoothed image to create a cartoonized version of the original image. This combines the edges with the smoothed regions to create a cartoon-like appearance.

11. The original image and the cartoonized image are displayed side by side using matplotlib.pyplot.imshow.

12. The function convert_to_cartoon is called with the provided image path.

Overall, the code reads an image, applies the bilateral filter to reduce noise while preserving edges, performs Canny edge detection, and combines the edges with the smoothed image to create a cartoonized version of the image. The original and cartoonized images are then displayed.

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