[Python] How to vectorize an image using python

Is there a way to convert an image into a vectorized form such as as follows:

I have looked this up searching for CNN, Pillow and CV2 methods however I didn't find any available resource online to implement this image transformation.

Appreciating your help on this matter.

Normal Image:



Vectorized Image:



Update:

I have implemented image segmentation using SLIC method, the following is new code and Resultant Image. As can be seen, the new image is close to the vectorized image(desired outcome) however the quality/detail difference is still alot.

Any further ideas?

from skimage import data, segmentation, color
from skimage.future import graph
from matplotlib import pyplot as plt
from PIL import Image
from skimage import io

path='/home/user/Desktop/Image1.jpeg'
img = io.imread(path)

labels1 = segmentation.slic(img, compactness=50, n_segments=5000,
start_label=1)
out1 = color.label2rgb(labels1, img, kind='avg', bg_label=0)
out1 = Image.fromarray(out1, 'RGB')
out1.save('/home/user/Desktop/1.png')
g = graph.rag_mean_color(img, labels1, mode='similarity')
labels2 = graph.cut_normalized(labels1, g)
out2 = color.label2rgb(labels2, img, kind='avg', bg_label=0)
out2 = Image.fromarray(out2, 'RGB')
out2.save('/home/user/Desktop/2.png')

fig, ax = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(6, 8))

ax[0].imshow(out1)
ax[1].imshow(out2)

for a in ax:
a.axis('off')

plt.tight_layout()
plt.show()


New Image(N_Segments=2000)



New Image (N_segments=5000, took 5 minutes of computational time)

Continue reading...