Increase resolution with word-cloud and remove empty border

Question

I am using word cloud with some txt files. How do I change this example if I wanted to 1) increase resolution and 2) remove empty border.

#!/usr/bin/env pyt         


        

Answer 1

It is very simple, plt.tight_layout(pad=0) does the job, reduces the space in the background, removing the excess padding.

Answer 2

You can't increase the resolution of the image in plt.show() since that is determined by your screen, but you can increase the size. This allows it to scale, zoom, etc. without blurring. To do this pass dimensions to WordCloud, e.g.

wordcloud = WordCloud(width=800, height=400).generate(text)

However, this just determines the size of the image created by WordCloud. When you display this using matplotlib it is scaled to the size of the plot canvas, which is (by default) around 800x600 and you again lose quality. To fix this you need to specify the size of the figure before you call imshow, e.g.

plt.figure( figsize=(20,10) )
plt.imshow(wordcloud)

By doing this I can successfully create a 2000x1000 high resolution word cloud.

For your second question (removing the border) first we could set the border to black, so it is less apparent, e.g.

plt.figure( figsize=(20,10), facecolor='k' )

You can also shrink the size of the border by using tight_layout, e.g.

plt.tight_layout(pad=0)

The final code:

# Read the whole text.
text = open(path.join(d, 'constitution.txt')).read()
wordcloud = WordCloud(width=1600, height=800).generate(text)
# Open a plot of the generated image.

plt.figure( figsize=(20,10), facecolor='k')
plt.imshow(wordcloud)
plt.axis("off")
plt.tight_layout(pad=0)
plt.show()

By replacing the last two lines with the following you can get the final output shown below:

plt.savefig('wordcloud.png', facecolor='k', bbox_inches='tight')

Answer 3

Blurry wordclouds - I've been wrestling with this. For my use, I found that too large a differential in the between the most frequent word occurrences and those with few occurrences left the lower-count words unreadable. When I scaled the more frequent counts to reduce the differential, all the lower-frequency words were much more readable.

Answer 4

If you are trying to use an image as a mask, make sure to use a big image to get better image quality.. I spent hours figuring this out.

Heres an example of a code snippet I used

mask = np.array(Image.open('path_to_your_image'))
image_colors = ImageColorGenerator(mask)
wordcloud = WordCloud(width=1600, height=800, background_color="rgba(255, 255, 255, 0)", mask=mask
                     ,color_func = image_colors).generate_from_frequencies(x)

# Display the generated image:
plt.figure( figsize=(20,10) )
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")