Mixing
Image Compression on Python using sklearn K-Means
I'm doing the Machine Learnirg course on Coursera and after implementing it on Octave I'm doing it on Python. I'm on a exercise that takes a 128x128 image with RGB colors, apply K-means with 16 clusters to it (to find the most significant 16 colors) and then print the image again. The problem is that when I run it on Octave the image looks similar to the original but on Python the images doesn't look very alike (images).
I tried to change the parameters of the clusterization but nothing solved the problem. The code that I'm doing is as follows:
import imageio
from sklearn.cluster import KMeans
image = imageio.imread('bird_small.png')
extended_image = image.reshape(128*128, 3)
extended_image = extended_image/255
image_km = KMeans(n_clusters=16, n_init=100)
image_km.fit(extended_image)
clusters = image_km.predict(extended_image)
centroids = image_km.cluster_centers_
reduced_extended_image = extended_image
# Here I substitue each data color point for the centroid
for i in range(0, len(extended_image)):
color = clusters[i]
reduced_extended_image[i, :] = centroids[color, :]
compressed_image = reduced_extended_image.reshape(128, 128, 3)
plt.imshow(compressed_image)
If any of you tried to run the code, the 'bird_small.png' image is here
Do you know where the problem could be? Thanks
python scikit-learn k-means
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
add a comment |
I'm doing the Machine Learnirg course on Coursera and after implementing it on Octave I'm doing it on Python. I'm on a exercise that takes a 128x128 image with RGB colors, apply K-means with 16 clusters to it (to find the most significant 16 colors) and then print the image again. The problem is that when I run it on Octave the image looks similar to the original but on Python the images doesn't look very alike (images).
I tried to change the parameters of the clusterization but nothing solved the problem. The code that I'm doing is as follows:
import imageio
from sklearn.cluster import KMeans
image = imageio.imread('bird_small.png')
extended_image = image.reshape(128*128, 3)
extended_image = extended_image/255
image_km = KMeans(n_clusters=16, n_init=100)
image_km.fit(extended_image)
clusters = image_km.predict(extended_image)
centroids = image_km.cluster_centers_
reduced_extended_image = extended_image
# Here I substitue each data color point for the centroid
for i in range(0, len(extended_image)):
color = clusters[i]
reduced_extended_image[i, :] = centroids[color, :]
compressed_image = reduced_extended_image.reshape(128, 128, 3)
plt.imshow(compressed_image)
If any of you tried to run the code, the 'bird_small.png' image is here
Do you know where the problem could be? Thanks
python scikit-learn k-means
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
add a comment |
I'm doing the Machine Learnirg course on Coursera and after implementing it on Octave I'm doing it on Python. I'm on a exercise that takes a 128x128 image with RGB colors, apply K-means with 16 clusters to it (to find the most significant 16 colors) and then print the image again. The problem is that when I run it on Octave the image looks similar to the original but on Python the images doesn't look very alike (images).
I tried to change the parameters of the clusterization but nothing solved the problem. The code that I'm doing is as follows:
import imageio
from sklearn.cluster import KMeans
image = imageio.imread('bird_small.png')
extended_image = image.reshape(128*128, 3)
extended_image = extended_image/255
image_km = KMeans(n_clusters=16, n_init=100)
image_km.fit(extended_image)
clusters = image_km.predict(extended_image)
centroids = image_km.cluster_centers_
reduced_extended_image = extended_image
# Here I substitue each data color point for the centroid
for i in range(0, len(extended_image)):
color = clusters[i]
reduced_extended_image[i, :] = centroids[color, :]
compressed_image = reduced_extended_image.reshape(128, 128, 3)
plt.imshow(compressed_image)
If any of you tried to run the code, the 'bird_small.png' image is here
Do you know where the problem could be? Thanks
python scikit-learn k-means
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
I'm doing the Machine Learnirg course on Coursera and after implementing it on Octave I'm doing it on Python. I'm on a exercise that takes a 128x128 image with RGB colors, apply K-means with 16 clusters to it (to find the most significant 16 colors) and then print the image again. The problem is that when I run it on Octave the image looks similar to the original but on Python the images doesn't look very alike (images).
I tried to change the parameters of the clusterization but nothing solved the problem. The code that I'm doing is as follows:
import imageio
from sklearn.cluster import KMeans
image = imageio.imread('bird_small.png')
extended_image = image.reshape(128*128, 3)
extended_image = extended_image/255
image_km = KMeans(n_clusters=16, n_init=100)
image_km.fit(extended_image)
clusters = image_km.predict(extended_image)
centroids = image_km.cluster_centers_
reduced_extended_image = extended_image
# Here I substitue each data color point for the centroid
for i in range(0, len(extended_image)):
color = clusters[i]
reduced_extended_image[i, :] = centroids[color, :]
compressed_image = reduced_extended_image.reshape(128, 128, 3)
plt.imshow(compressed_image)
If any of you tried to run the code, the 'bird_small.png' image is here
Do you know where the problem could be? Thanks
python scikit-learn k-means
python scikit-learn k-means
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
asked Nov 19 '18 at 22:45
Luiz NonenmacherLuiz Nonenmacher
11
11
add a comment |
add a comment |
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