Inside the antique camera is a Raspberry Pi with a camera module behind the lens. The flow of passages is controlled by a single, handwritten JSON file. When there is overlap between the tags detected in an image by Clarifai and the tags assigned to a passage, and the candidate passage occurs next in a storyline that has already begun, that passage is printed out. If no passage can be found, the camera prints poetry enabled by a recursive context-free grammar and constructed from words detected in the image.

This week, I am planning to add a back end component that will allow photos taken to be preserved as albums, and passages printed to be read later online. For now, here is the JSON file that controls the order of output:

{
    "zero": {
        "tags": ["moon", "swamp", "marble", "north america", "insect", "street"],
        "order": 0,
        "next": ["story"]
    },
    "guam_zero": {
    	"tags": ["computer", "technology", "future", "keyboard", "politics"],
    	"order": 0,
    	"next": ["guam_one"]
    },
    "guam_one": {
    	"tags": ["computer", "technology", "future", "keyboard", "politics"],
    	"order": 1,
    	"next": []
    },
    "dream_zero": {
    	"tags": ["dream", "dark", "night", "sleep", "bed", "bedroom", "indoors"],
    	"order": 0,
    	"next": ["chess_board"]
    },
    "chess_board": {
    	"tags": ["dream", "dark", "night", "sleep", "bed", "bedroom", "indoors"],
    	"order": 2,
    	"next": ["black_queen", "black_pawn", "black_king", "black_rook", "white_king", "white_knight"]
    },
    "black_queen": {
    	"tags": ["dream", "dark", "black", "night", "sleep", "bed", "bedroom", "indoors", "chess", "game", "queen"],
    	"order": 3,
    	"next": ["wake_up"]
    },
    "black_pawn": {
    	"tags": ["dream", "dark", "black", "night", "sleep", "bed", "bedroom", "indoors", "chess", "game", "pawn"],
    	"order": 3,
    	"next": ["wake_up"]
    },
    "black_king": {
    	"tags": ["dream", "dark", "black", "night", "sleep", "bed", "bedroom", "indoors", "chess", "game", "king"],
    	"order": 3,
    	"next": ["wake_up"]
    },
    "black_rook": {
    	"tags": ["dream", "dark", "black", "night", "sleep", "bed", "bedroom", "indoors", "chess", "game", "rook", "castle"],
    	"order": 3,
    	"next": ["wake_up"]
    },
    "white_king": {
    	"tags": ["dream", "dark", "white", "night", "sleep", "bed", "bedroom", "indoors", "chess", "game", "king"],
    	"order": 3,
    	"next": ["wake_up"]
    },
    "white_knight": {
    	"tags": ["dream", "dark", "white", "night", "sleep", "bed", "bedroom", "indoors", "chess", "game", "knight"],
    	"order": 3,
    	"next": ["wake_up"]
    },
    "wake_up": {
    	"tags": ["dream", "dark", "night", "sleep", "bed", "bedroom", "indoors"],
    	"order": 4,
    	"next": []
    },
    "forget": {
    	"tags": ["man", "men", "boy"],
    	"order": 0,
    	"next": []
    },    
    "story": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "street", "woman", "women", "girl"],
    	"order": 1,
    	"next": ["miss_vest", "forget"]
    },
    "miss_vest": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "street", "woman", "women", "girl"],
    	"order": 2,
    	"next": ["envelope", "forget"]
    },
    "envelope": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "street", "woman", "women", "girl", "paper", "envelope", "mail"],
    	"order": 3,
    	"next": ["apartment", "forget"]
    },
    "apartment": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "street", "woman", "women", "girl", "paper", "envelope", "mail"],
    	"order": 4,
    	"next": ["email"]
    },
    "email": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "woman", "women", "girl", "paper", "envelope", "mail", "computer", "technology"],
    	"order": 5,
    	"next": ["match"]
    },
    "match": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "man", "men", "boy", "paper", "envelope", "mail", "computer", "technology"],
    	"order": 5,
    	"next": ["smithpoint", "morning"]
    },
    "morning": {
    	"tags": ["day", "sun", "bedroom", "bed", "breakfast", "morning", "dream", "dark", "night"],
    	"order": 6,
    	"next": ["call"]
    },
    "call": {
    	"tags": ["phone", "telephone", "technology", "computer"],
    	"order": 7,
    	"next": ["smithpoint"]
    },
    "smithpoint": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "man", "men", "boy", "bar", "drink", "alcohol", "wine", "beer"],
    	"order": 8,
    	"next": ["drive", "forget"]
    },
    "drive": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "man", "men", "boy", "bar", "drink", "alcohol", "wine", "beer"],
    	"order": 9,
    	"next": ["take_pill", "toss_pill"]
    },
    "take_pill": {
    	"tags": ["drug", "pill", "man", "men", "boy", "bar", "night", "drink", "alcohol", "wine", "beer"],
    	"order": 10,
    	"next": ["meet_stranger_drugs", "john_home"]
    },
    "toss_pill": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "girl", "street", "woman", "women"],
    	"order": 10,
    	"next": ["meet_stranger_no_drugs"]
    },
    "meet_stranger_drugs": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "man", "men", "boy", "bar", "drink", "alcohol", "wine", "beer"],
    	"order": 11,
    	"next": ["john_home"]
    },
    "meet_stranger_no_drugs": {
    	"tags": ["moon", "swamp", "marble", "north america", "insect", "night", "man", "men", "boy", "bar", "drink", "alcohol", "wine", "beer"],
    	"order": 11,
    	"next": ["painting"]
    },
    "painting": {
    	"tags": ["painting", "art", "moon", "swamp", "marble", "north america", "insect", "night", "man", "men", "boy", "bar", "drink", "alcohol", "wine", "beer"],
    	"order": 12,
    	"next": []
    },
    "john_home": {
    	"tags": ["drug", "pill", "man", "men", "boy", "bar", "night", "drink", "alcohol", "wine", "beer"],
    	"order": 13,
    	"next": []
    }

}

And here is the code that’s currently running on the Raspberry Pi:

import RPi.GPIO as GPIO
from Adafruit_Thermal import *
import time
import os
import sys
import json
import picamera
from clarifai.client import ClarifaiApi
from pattern.en import referenced

import gen

# Init Clarifai
os.environ["CLARIFAI_APP_ID"] = "nAT8dW6B0Oc5qA6JQfFcdIEr-CajukVSOZ6u_IsN"
os.environ["CLARIFAI_APP_SECRET"] = "BnETdY6wtp8DmXIWCBZf8nE4XNPtlHMdtK0ISNJQ"
clarifai_api = ClarifaiApi() # Assumes Env Vars Set

# Init System Paths
APP_PATH = os.path.dirname(os.path.realpath(__file__))
IMG_PATH = os.path.join(APP_PATH, 'img')
TALE_PATH = os.path.join(APP_PATH, 'tales')

# Init tale_dict
with open(os.path.join(APP_PATH, 'tales_dict.json'), 'r') as infile:
    tale_dict = json.load(infile)

# Seen tales
seen_tales = list()

# Init Camera
camera = picamera.PiCamera()

# Init Printer
printer = Adafruit_Thermal("/dev/ttyAMA0", 9600, timeout=5)
printer.boldOn()

# Init GPIO
# With camera pointed forward...
# LEFT:  11 (button), 15 (led)
# RIGHT: 13 (button), 16 (led)
GPIO.setmode(GPIO.BOARD)
ledPins = (15,16)
butPins = (11,13)

for pinNo in ledPins:
    GPIO.setup(pinNo, GPIO.OUT)

for pinNo in butPins:
    GPIO.setup(pinNo, GPIO.IN, pull_up_down=GPIO.PUD_UP)

# Open Grammar Dict
with open(os.path.join(APP_PATH, 'weird_grammar.json'), 'r') as infile:
    grammar_dict = json.load(infile)

def blink_left_right(count):
    ledLeft, ledRight = ledPins
    for _ in range(count):
        GPIO.output(ledRight, False)
        GPIO.output(ledLeft, True)
        time.sleep(0.2)
        GPIO.output(ledRight, True)
        GPIO.output(ledLeft, False)
        time.sleep(0.2)
    GPIO.output(ledRight, False)

def to_lines(sentences):
    def sentence_to_lines(text):
        LL = 32
        tokens = text.split(' ')
        lines = list()
        curLine = list()
        charCount = 0
        for t in tokens:
            charCount += (len(t)+1)
            if charCount > LL:
                lines.append(' '.join(curLine))
                curLine = [t]
                charCount = len(t)+1
            else:
                curLine.append(t)
        lines.append(' '.join(curLine))
        return '\n'.join(lines)
    sentence_lines = map(sentence_to_lines, sentences)
    return '\n\n'.join(sentence_lines)

def open_tale(tale_name):
    with open(os.path.join(TALE_PATH, tale_name), 'r') as infile:
        tale_text = to_lines(
            filter(lambda x: x.strip(), infile.read().strip().split('\n'))
        )
    return tale_text

def pick_tale(tags, next_tales):
    choice = str()
    record = 0
    for tale in tale_dict:
        if tale in next_tales or tale_dict[tale]['order'] == 0:
            score = len(set(tale_dict[tale]['tags']) & set(tags))
            if tale in next_tales and score > 0 and not tale in seen_tales:
                score += 100
            if score > record:
                choice = tale
                record = score
    return choice


blink_left_right(5)
imgCount = 1
cur_tale = str()


while True:
    inputLeft, inputRight = map(GPIO.input, butPins)
    if inputLeft != inputRight:
        try:
            img_fn = str(int(time.time()*100))+'.jpg'
            img_fp = os.path.join(IMG_PATH, img_fn)

            camera.capture(img_fp)

            blink_left_right(3)

            result = clarifai_api.tag_images(open(img_fp))
            tags = result['results'][0]['result']['tag']['classes']

            if cur_tale:
                next_tales = tale_dict[cur_tale]['next']
            else:
                next_tales = list()

            tale_name = pick_tale(tags, next_tales)
            cur_tale = tale_name

            if tale_name:
                lines_to_print = open_tale(tale_name)
                seen_tales.append(tale_name)

            else:
                grammar_dict["N"].extend(tags)

                if not inputLeft:
                    sentences = [gen.make_polar(grammar_dict, 10, sent=0) for _ in range(10)]
                elif not inputRight:
                    sentences = [gen.make_polar(grammar_dict, 10) for _ in range(10)]
                else:
                    sentences = gen.main(grammar_dict, 10)

                lines_to_print = to_lines(sentences)

            prefix = '\n\n\nNo. %i\n\n'%imgCount

            printer.println(prefix+lines_to_print+'\n\n\n')

            grammar_dict["N"] = list()
            imgCount += 1
        except:
            blink_left_right(15)
            print sys.exc_info()

    elif (not inputLeft) and (not inputRight):
        offCounter = 0
        for _ in range(100):
            inputLeft, inputRight = map(GPIO.input, butPins)
            if (not inputLeft) and (not inputRight):
                time.sleep(0.1)
                offCounter += 1
                if offCounter > 50:
                    os.system('sudo shutdown -h now')
            else:
                break

Click here for a Google Drive folder with all the passages from the novel.

My robot will take the form of a benevolent computer virus. Using tools like pyautogui and the python webbrowser library, it will respond to user inquiries by opening documents, typing, and displaying web pages. It will also talk back to users using Apple’s text-to-speech utility.

I am building this robot using Wit.ai, a deep learning tool for making voice interfaces. Using the tool’s dashboard, I have been training my robot to respond to various user intents.

The core of the functionality will be a therapy bot similar to ELIZA, but with some additional functionality. When this project is complete, I believe it will provide an interesting take on artificial intelligence. Using AI tools for different purposes than they were designed, I hope to make users question whether the tool they are using is in fact sentient and aware of their presence.

My code:
iPython Notebook

The script I wrote gets concept words from the image via Clarifai, then searches song lyrics for those words on Genius, then finds the song on Spotify. Below are some images I put through the algorithm. You can click on each one to hear the song that resulted, though you will need to login to Spotify to do so.

The next step will be to get this code working on a Raspberry Pi inside one of the film camera bodies I just received via eBay.

Last week, I launched a web application and a concept for photographic text generation that I have been working on for a few months. The idea came to me while working on another project, a computer generated screenplay, and I will discuss the connection in this post.

word.camera is responsive — it works on desktop, tablet, and mobile devices running recent versions of iOS or Android. The code behind it is open source and available on GitHub, because lexography is for everyone.

Users can share their lexographs using unique URLs. Of all this lexographs I’ve seen generated by users since the site launched (there are now almost 7,000), this one, shared on reddit’s /r/creativecoding, stuck with me the most: http://word.camera/i/7KZPPaqdP

I was surprised when the software noticed and commented on the singer in the painting behind me: http://word.camera/i/ypQvqJr6L

I was inspired to create this project while working on another project. This semester, I received a grant from the Future of Storytelling Initiative at NYU to produce a computer generated screenplay, and I had been thinking about how to generate text that’s more cohesive and realistically descriptive, meaning that it would transition between related topics in a logical fashion and describe a scene that could realistically exist (no “colorless green ideas sleeping furiously”) in order to making filming the screenplay possible . After playing with the Clarifai API, which uses convolutional neural networks to tag images, it occurred to me that including photographs in my input corpus, rather than relying on text alone, could provide those qualities. word.camera is my first attempt at producing that type of generative text.

At the moment, the results are not nearly as grammatical as I would like them to be, and I’m working on that. The algorithm extracts tags from images using Clarifai’s convolutional neural networks, then expands those tags into paragraphs using ConceptNet (a lexical relations database developed at MIT) and a flexible template system. The template system enables the code to build sentences that connect concepts together.

This project is about augmenting our creativity and presenting images in a different format, but it’s also about creative applications of artificial intelligence technology. I think that when we think about the type of artificial intelligence we’ll have in the future, based on what we’ve read in science fiction novels, we think of a robot that can describe and interact with its environment with natural language. I think that creating the type of AI we imagine in our wildest sci-fi fantasies is not only an engineering problem, but also a design problem that requires a creative approach.

I hope lexography eventually becomes accepted as a new form of photography. As a writer and a photographer, I love the idea that I could look at a scene and photograph it because it might generate an interesting poem or short story, rather than just an interesting image. And I’m not trying to suggest that word.camera is the final or the only possible implementation of that new art form. I made the code behind word.camera open source because I want others to help improve it and make their own versions — provided they also make their code available under the same terms, which is required under the GNU GPLv3 open source license I’m using. As the technology gets better, the results will get better, and lexography will make more sense to people as a worthy artistic pursuit.

I’m thrilled that the project has received worldwide attention from photography blogs and a few media outlets, and I hope users around the world continue enjoying word.camera as I keep working to improve it. Along with improving the language, I plan to expand the project by offering a mobile app and generated downloadable ebooks so that users can enjoy their lexographs offline.

Click Here for Part II