Synesthesia-1

Synesthesia

Takes in an image and replace the colors with words semantically related to the color.

So, if there’s a red patch in your image, in that area in the resulting image, you might find the words “cherry”, “communism”, “anger”, and “balloon”

Concept: Natan Last, Execution: Jenna Zeigen (me!)

I’ve blogged about this project and more how each of the methods work at http://linkguistics.blogspot.com/2012/12/project-synesthesia.html

Components:

1. cooccurrence.py – This is the module that makes the lists of words are associated with the colors.
2. imagesimpler.py – This module reduces the color space so that the words can be properly associated with the colors
3. synesthesizer.py – This module takes the words and image and maps them, creating the final piece
4. synserver.py – Flask-powered; Makes synesthesizer a web app with a pretty GUI with the help of syn.js and syn.css

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GUI

While synthesizer.py also works as a command line tool, it also has a web app interface powered by Flask. You upload your image, which will them be previewed, and then choose all the conditions to modify the parameters.

When you submit your image and choices, the result will appear and you can then click on the preview to download the image.

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API

cooccurrence.py

• Finds words within a corpus that cooccur significantly with a target word
• Dependencies: BeautifulSoup4 and numpy

CooccurrenceFinder()

corpus_scraper(self, word, numdocs, redo=False)

• Purpose: Scrapes Wikipedia search results for a word and compiles all text to a single text file
• Inputs: Words to be searched for, number of documents to be scraped (the more the better the results), if corpuses for the word should be re-scraped due to changes in parameters, etc
• Outputs: The file path of the written file

find_relateds(self, corpus, word, distance, extra_stops, stdevs)

• Purpose: To divine a list of words associated with a provided word as determined by a threshold of co-occurrence
• Inputs: The corpus to be analyzed, the words to be analyzed for, the max distance that would satisfy a co-occurrence, extra stop words (domain specific?), standard deviation threshold for significance
• Outputs: Significant co-occurrences

find_close_words(self, dist, text, word)

• Purpose: Finds all the words in a corpus within a specified distance of a target word
• Inputs: Desired distance, text corpus, target word
• Outputs: Dictionary of distances to words that distance from the target

tally_occurrences(self, word, pair_set, stopwords)

• Purpose: Create a frequency distribution for the area surrounding the target word, but not including stop words or derivatives of the target
• Inputs: Target word, dictionary of distances to words, stopwords
• Outputs: Nothing explicitly

find_significant_cooccurrences(self, counts, SDs)

• Purpose: The find the significant co-occurrences from a frequency distribution
• Inputs: Frequency distribution, number of standard deviations for signficance
• Outputs: List of significant cooccurrences

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imagesimpler.py

• Simplifies the color space of an image to a specified space
• Dependencies: pillow and webcolors

ImageSimpler():

simplify(self, imagefile, colors, percentsize)

• Purpose: To take an image, scale it as specified, and “round” all colors to one of a specified list of colors and then return the mutated image. Only takes jpeg and png. Otherwise, throws an Exception.
• Inputs: Image to be manipulated, colors to be “rounded to”, scaled size of the image
• Outputs: The mutated image

scale_by_percent(self, im, percent)

• Purpose: Scale an image to a specified size
• Inputs: Image, desired percent size
• Outputs: Scaled image

map_RGB_to_color_word(self, colors)

• Purpose: Creates a mapping of rgb colors to “css colors”
• Inputs: List of css colors
• Outputs: Dictionary of rgbs to css colors

reduce_to_requested_color_space(self, pix_obj, im, color_mapping)

• Purpose: Creates an image in which colors are changed to be whatever they are closest to from a list of colors
• Inputs: PIL pixel object, image, dictionary of rgbs to css colors
• Outputs: Color-simplified image

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synesthesizer.py

• The main attraction: Takes the image and makes its colors words. Works at the command line as well as through the web GUI powered by synserver.py
• Dependencies: pillow, webcolors, pygame, cooccurrence.py, and imagesimpler.py

Synesthesizer():

combinatorics(self)

• Purpose: Creates a file containing a pickled dictionary, keyed by color words, of strings containing associates that are up to 25 characters long
• Inputs: None
• Outputs: Nothing explicit

reconstitute(self)

• Purpose: Unpickles the dictionary created by combinatorics() and saves it as an attribue of the Synesthesizer
• Inputs: None
• Outputs: Nothing explicit

synesthesize(self, image, colors, fontname)

• Purpose: Take an image, replace all the colors with words associated with the colors, saves the image to disk
• Inputs: Image to be adulterated, colors to be used

• Outputs: Filename of created image

• Contains three helper methods: check_fit(), get_space(), and paint_picture()