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.

This week, I’ve been exhibiting my ongoing project, word.camera, at IDFA DocLab in Amsterdam. My installation consists of four cameras:

1. The original word.camera physical prototype:
2. The sound camera physical prototype:
3. A new word.camera model that uses a context-free grammar to generate poems based on the images it captures:
4. A talking, pan-tilt-zoom surveillance camera that looks for faces in the hallway and then describes them aloud. (See also: this Motherboard video)
   
   
   

During the exhibition, I was also invited to deliver two lectures. Here are my slides from the first lecture:

Click here for my lecture slides

And here’s a video of the second one:

Visitors are able to reserve the portable cameras for half hour blocks by leaving their ID at the volunteer kiosk. I have really enjoyed watching people borrow and use my cameras.

I started by adding a shutter button to the top of the enclosure. I used a Cherry MX Blue mechanical keyboard switch that I had leftover from a project last year.

The battery and Raspberry Pi just barely fit into the enclosure:

The Raspberry Pi camera module is wedged snugly beneath the camera’s front plate:

In additional to playing the song, I added some functionality that provides a bit of context to the user. Using the pico2wave text-to-speech utility, the camera speaks the tags aloud before playing the song. Additionally, using SoX, the camera plays an initialization tone generated from the color histogram of the image before reading the tags.

Here’s the code that’s currently running on the Raspberry Pi:

from __future__ import unicode_literals

import os
import json
import uuid
import time
from random import choice as rc
from random import sample as rs
import re
import subprocess

import RPi.GPIO as GPIO
import picamera
from clarifai.client import ClarifaiApi
import requests
from PIL import Image

import sys
import threading

import spotify

import genius_token

# SPOTIFY STUFF

# Assuming a spotify_appkey.key in the current dir
session = spotify.Session()

# Process events in the background
loop = spotify.EventLoop(session)
loop.start()

# Connect an audio sink
audio = spotify.AlsaSink(session)

# Events for coordination
logged_in = threading.Event()
logged_out = threading.Event()
end_of_track = threading.Event()

logged_out.set()


def on_connection_state_updated(session):
    if session.connection.state is spotify.ConnectionState.LOGGED_IN:
        logged_in.set()
        logged_out.clear()
    elif session.connection.state is spotify.ConnectionState.LOGGED_OUT:
        logged_in.clear()
        logged_out.set()


def on_end_of_track(self):
    end_of_track.set()

# Register event listeners
session.on(
    spotify.SessionEvent.CONNECTION_STATE_UPDATED, on_connection_state_updated)
session.on(spotify.SessionEvent.END_OF_TRACK, on_end_of_track)

# Assuming a previous login with remember_me=True and a proper logout
# session.relogin()
# session.login(genius_token.spotify_un, genius_token.spotify_pwd, remember_me=True)

# logged_in.wait()

# CAMERA STUFF

# Init Camera
camera = picamera.PiCamera()

# Init GPIO
GPIO.setmode(GPIO.BCM)

# Button Pin
GPIO.setup(18, GPIO.IN, pull_up_down=GPIO.PUD_UP)

IMGPATH = '/home/pi/soundcamera/img/'

clarifai_api = ClarifaiApi()

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in xrange(0, len(l), n):
        yield l[i:i+n]

def take_photo():
    fn = str(int(time.time()))+'.jpg' # TODO: Change to timestamp hash
    fp = IMGPATH+fn
    camera.capture(fp)
    return fp

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in xrange(0, len(l), n):
        yield l[i:i+n]

def get_tags(fp):
    fileObj = open(fp)
    result = clarifai_api.tag_images(fileObj)
    resultObj = result['results'][0]
    tags = resultObj['result']['tag']['classes']
    return tags

def genius_search(tags):
    access_token = genius_token.token
    payload = {
        'q': ' '.join(tags),
        'access_token': access_token
    }
    endpt = 'http://api.genius.com/search'
    response = requests.get(endpt, params=payload)
    results = response.json()
    hits = results['response']['hits']
    
    artists_titles = []
    
    for h in hits:
        hit_result = h['result']
        if hit_result['url'].endswith('lyrics'):
            artists_titles.append(
                (hit_result['primary_artist']['name'], hit_result['title'])
            )
    
    return artists_titles

def spotify_search(query):
    endpt = "https://api.spotify.com/v1/search"
    payload = {
        'q': query,
        'type': 'track'
    }
    response = requests.get(endpt, params=payload)
    result = response.json()
    result_zero = result['tracks']['items'][0]
    
    return result_zero['uri']

def main(fn):
    tags = get_tags(fn)
    for tag_chunk in chunks(tags,3):
        artists_titles = genius_search(tag_chunk)
        for artist, title in artists_titles:
            try:
                result_uri = spotify_search(artist+' '+title)
            except IndexError:
                pass
            else:
                print tag_chunk
                byline = "%s by %s" % (title, artist)
                print byline
                to_read = ', '.join(tag_chunk) + ". " + byline
                return to_read, result_uri

def play_uri(track_uri):
    # Play a track
    # audio = spotify.AlsaSink(session)
    session.login(genius_token.spotify_un, genius_token.spotify_pwd, remember_me=True)
    logged_in.wait()
    track = session.get_track(track_uri).load()
    session.player.load(track)
    session.player.play()


def stop_track():
    session.player.play(False)
    session.player.unload()
    session.logout()
    logged_out.wait()
    audio._close()

def talk(msg):
    proc = subprocess.Popen(
        ['bash', '/home/pi/soundcamera/play_text.sh', msg]
    )
    proc.communicate()

def play_tone(freqs):
    freq1, freq2 = freqs
    proc = subprocess.Popen(
        ['play', '-n', 'synth', '0.25', 'saw', "%i-%i" % (freq1, freq2)]
    )
    proc.communicate()

def histo_tone(fp):
    im = Image.open(fp)
    hist = im.histogram()
    vals = map(sum, chunks(hist, 64)) # list of 12 values
    print vals
    map(play_tone, chunks(vals,2))

if __name__ == "__main__":
    input_state = True
    new_state = True
    hold_counter = 0
    while 1:
        input_state = GPIO.input(18)
        if not (input_state and new_state):
            talk("capturing")

            # Hold for 15 seconds to turn off
            while not GPIO.input(18):
                time.sleep(0.1)
                hold_counter += 1
                if hold_counter > 150:
                    os.system('shutdown now -h')
                    sys.exit()

            # Reset hold counter
            hold_counter = 0

            # Else take photo
            try:
                img_fp = take_photo()
                msg, uri = main(img_fp)
                histo_tone(img_fp)
                talk(msg)
                play_uri(uri)
            except:
                print sys.exc_info()

            # Wait for playback to complete or Ctrl+C
            try:
                while not end_of_track.wait(0.1):
                    # If new photo, play new song
                    new_state = GPIO.input(18)
                    if not new_state:
                        stop_track()
                        # time.sleep(2)
                        break
            except KeyboardInterrupt:
                pass

The front-end JavaScript code is available on GitHub. Here is the primary back-end Python code:

import os
import json
import uuid
from base64 import decodestring
import time
from random import choice as rc
from random import sample as rs
import re

import PIL
from PIL import Image
import requests
import exifread

from flask import Flask, request, abort, jsonify
from flask.ext.cors import CORS
from werkzeug import secure_filename

from clarifai.client import ClarifaiApi

app = Flask(__name__)
CORS(app)

app.config['UPLOAD_FOLDER'] = '/var/www/SoundCamera/SoundCamera/static/img'
IMGPATH = '/var/www/SoundCamera/SoundCamera/static/img/'

clarifai_api = ClarifaiApi()

@app.route("/")
def index():
    return "These aren't the droids you're looking for."

@app.route("/img", methods=["POST"])
def img():
	request.get_data()
	if request.method == "POST":
		f = request.files['file']
		if f:
			filename = secure_filename(f.filename)
			f.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
			new_filename = resize_image(filename)
			return jsonify(uri=main(new_filename))
		else:
			abort(501)

@app.route("/b64", methods=["POST"])
def base64():
	if request.method == "POST":
		fstring = request.form['base64str']
		filename = str(uuid.uuid4())+'.jpg'
		file_obj = open(IMGPATH+filename, 'w')
		file_obj.write(fstring.decode('base64'))
		file_obj.close()
		return jsonify(uri=main(filename))

@app.route("/url")
def url():
	img_url = request.args.get('url')
	response = requests.get(img_url, stream=True)
	orig_filename = img_url.split('/')[-1]
	if response.status_code == 200:
		with open(IMGPATH+orig_filename, 'wb') as f:
			for chunk in response.iter_content(1024):
				f.write(chunk)
		new_filename = resize_image(orig_filename)
		return jsonify(uri=main(new_filename))
	else:
		abort(500)


# def allowed_img_file(filename):
#     return '.' in filename and \
# 		filename.rsplit('.', 1)[1].lower() in set(['.jpg', '.jpeg', '.png'])

def resize_image(fn):
    longedge = 640
    orientDict = {
        1: (0, 1),
        2: (0, PIL.Image.FLIP_LEFT_RIGHT),
        3: (-180, 1),
        4: (0, PIL.Image.FLIP_TOP_BOTTOM),
        5: (-90, PIL.Image.FLIP_LEFT_RIGHT),
        6: (-90, 1),
        7: (90, PIL.Image.FLIP_LEFT_RIGHT),
        8: (90, 1)
    }

    imgOriList = []
    try:
        f = open(IMGPATH+fn, "rb")
        exifTags = exifread.process_file(f, details=False, stop_tag='Image Orientation')
        if 'Image Orientation' in exifTags:
            imgOriList.extend(exifTags['Image Orientation'].values)
    except:
        pass

    img = Image.open(IMGPATH+fn)
    w, h = img.size
    newName = str(uuid.uuid4())+'.jpeg'
    if w >= h:
        wpercent = (longedge/float(w))
        hsize = int((float(h)*float(wpercent)))
        img = img.resize((longedge,hsize), PIL.Image.ANTIALIAS)
    else:
        hpercent = (longedge/float(h))
        wsize = int((float(w)*float(hpercent)))
        img = img.resize((wsize,longedge), PIL.Image.ANTIALIAS)

    for val in imgOriList:
        if val in orientDict:
            deg, flip = orientDict[val]
            img = img.rotate(deg)
            if flip != 1:
                img = img.transpose(flip)

    img.save(IMGPATH+newName, format='JPEG')
    os.remove(IMGPATH+fn)
    
    return newName

def chunks(l, n):
    """Yield successive n-sized chunks from l."""
    for i in xrange(0, len(l), n):
        yield l[i:i+n]

def get_tags(fp):
    fileObj = open(fp)
    result = clarifai_api.tag_images(fileObj)
    resultObj = result['results'][0]
    tags = resultObj['result']['tag']['classes']
    return tags

def genius_search(tags):
    access_token = 'd2IuV9fGKzYEWVnzmLVtFnm-EYvBQKR8Uh3I1cfZOdr8j-BGVTPThDES532dym5a'
    payload = {
        'q': ' '.join(tags),
        'access_token': access_token
    }
    endpt = 'http://api.genius.com/search'
    response = requests.get(endpt, params=payload)
    results = response.json()
    hits = results['response']['hits']
    
    artists_titles = []
    
    for h in hits:
        hit_result = h['result']
        if hit_result['url'].endswith('lyrics'):
            artists_titles.append(
                (hit_result['primary_artist']['name'], hit_result['title'])
            )
    
    return artists_titles

def spotify_search(query):
    endpt = "https://api.spotify.com/v1/search"
    payload = {
        'q': query,
        'type': 'track'
    }
    response = requests.get(endpt, params=payload)
    result = response.json()
    result_zero = result['tracks']['items'][0]
    
    return result_zero['uri']

def main(fn):
    tags = get_tags(IMGPATH+fn)
    for tag_chunk in chunks(tags,3):
        artists_titles = genius_search(tag_chunk)
        for artist, title in artists_titles:
            try:
                result_uri = spotify_search(artist+' '+title)
            except IndexError:
                pass
            else:
                return result_uri


if __name__ == "__main__":
    app.run()

It uses the same algorithm discussed in my prior post. Now that I have the opportunity to test it more, I am not quite satisfied with the results it is providing. First of all, they are not entirely deterministic (you can upload the same photo twice and end up with two different songs in some cases). Moreover, the results from a human face — which I expect to be a common use case — are not very personal. For the next steps in this project, I plan to integrate additional data including GPS, weather, time of day, and possibly even facial expressions in order to improve the output.

The broken cameras I ordered from eBay have arrived, and I have been considering how to use them as cases for the new models. I also purchased a GPS module for my Raspberry Pi, so the next Sound Camera prototype, with new features integrated, will likely be a physical version. I’m planning to use this Kodak Brownie camera (c. 1916):

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.

I have not yet chosen the authors I plan to embed in these cameras, or how I plan to present the extracted text. I also have tentative plans for a new iteration of the talking surveillance camera I developed last semester, but more on that will be provided in future posts.

This week, I spent some time on eBay finding a few broken medium- and large-format cameras to use as cases. Here’s what I bought (for $5 to $25 each):

I am current waiting to receive them so that I can start planning the builds. Below is a list of the additional parts that will be required for each camera:

Raspberry Pi 2 ($40)
85.60mm x 56mm x 21mm (or roughly 3.37″ x 2.21″ x 0.83″)

Raspberry Pi Camera Board ($30)
25mm x 20mm x 9mm

Buck Converter ($10)
51 * 26.3 * 14 (L * W * H) (mm)

7.4V LiIon Battery Pack ($90)
22mm (0.9″) x 104mm (4.1″) x 107mm (4.2″)
OR two USB batteries ($40)

Thermal Printer ($25 from China or $50 from U.S.)
~4 1/8″ (105mm) x 2 1/4″ (58mm) for rectangular hole
~58mm deep

On/Off Switch ($1)
18.60mm x 12.40mm rectangular hole
13.9mm deep

LED Button ($5)
Shutter button, user will hold for 3 seconds to turn off Raspberry Pi
16mm round hole
~1.5″ deep

1/4-size permaproto board ($3)

1/4″ Acrylic ($12) or Broken Medium Format TLR ($30-69)

Jumper Wires ($2)