Noise filter with labels

In [2]:

    

import sdm as sdmlib
import matplotlib.pyplot as plt
from PIL import Image, ImageDraw, ImageFont
import urllib, cStringIO
import random
from IPython.core.display import display, Image as IPythonImage
%matplotlib inline


    

In [3]:

    

width = 30
height = 30
noise_flip = True


    

In [16]:

    

def gen_img(letter='A'):
    img = Image.new('RGBA', (width, height), (255, 255, 255))
    font = ImageFont.truetype('Arial.ttf', 30)
    draw = ImageDraw.Draw(img)
    w, h = draw.textsize(letter, font=font)
    top = (height-w)//2
    left = (width-h)//2 if h <= 30 else 30-2-h
    draw.text((top, left), letter, (0, 0, 0), font=font)
    return img


    

In [17]:

    

def gen_noise_add(img, p=0.15, flip=False):
    img2 = img.copy()
    draw = ImageDraw.Draw(img2)
    for py in xrange(height):
        for px in xrange(width):
            if random.random() < p:
                if flip:
                    pixel = img.getpixel((px, py))
                    value = sum([int(x/255+0.5) for x in pixel[:3]])//3
                    assert value == 0 or value == 1
                    value = (1 - value)*255
                    draw.point((px, py), fill=(value, value, value))
                else:
                    draw.point((px, py), fill=(0, 0, 0))
    return img2


    

In [18]:

    

img = gen_img();
img2 = gen_noise_add(img, flip=noise_flip)
plt.subplot(1, 2, 1)
plt.imshow(img)
plt.subplot(1, 2, 2)
plt.imshow(img2);


    

In [7]:

    

def to_bitstring(img):
    v = []
    bs = sdmlib.Bitstring.init_ones(1000)
    for py in xrange(height):
        for px in xrange(width):
            pixel = img.getpixel((px, py))
            value = sum([int(x/255+0.5) for x in pixel[:3]])//3
            assert value == 0 or value == 1
            idx = px+width*py
            assert idx >= 0 and idx < 1000, 'Ops {} {} {}'.format(x, y, idx)
            bs.set_bit(idx, value)
            v.append(value)
    v2 = [bs.get_bit(i) for i in xrange(height*width)]
    assert v == v2
    return bs


    

In [8]:

    

def to_img(bs):
    img = Image.new('RGBA', (30, 30), (255, 255, 255))
    draw = ImageDraw.Draw(img)
    for py in xrange(height):
        for px in xrange(width):
            idx = px+width*py
            assert idx >= 0 and idx < 1000, 'Ops {} {} {}'.format(x, y, idx)
            x = 255*bs.get_bit(idx)
            draw.point((px, py), fill=(x, x, x))
    return img


    

In [9]:

    

bits = 1000
sample = 1000000
scanner_type = sdmlib.SDM_SCANNER_OPENCL


    

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address_space = sdmlib.AddressSpace.init_random(bits, sample)
counter = sdmlib.Counter.init_zero(bits, sample)
sdm = sdmlib.SDM(address_space, counter, 451, scanner_type)


    

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sdm.write_random_bitstrings(500)


    

In [12]:

    

def fill_memory(letter, p=0.15, n=100):
    cols = 15
    rows = n//cols + 1
    plt.figure(figsize=(20,10))
    for i in xrange(n):
        img = gen_img(letter=letter);
        img2 = gen_noise_add(img, flip=noise_flip)
        #display(img2)
        plt.subplot(rows, cols, i+1)
        plt.imshow(img2)
        bs = to_bitstring(img2)
        sdm.write(bs, bs)


    

In [13]:

    

fill_memory('A')


    

In [14]:

    

def read(letter, n=6, p=0.25):
    n = 6
    cols = 7
    rows = n//cols + 1
    plt.figure(figsize=(20,10))

    img = gen_img(letter=letter);
    img2 = gen_noise_add(img, p=p, flip=noise_flip)
    plt.subplot(rows, cols, 1)
    plt.imshow(img2)

    for i in xrange(n):
        bs2 = to_bitstring(img2)
        bs3 = sdm.read(bs2)
        img3 = to_img(bs3)
        plt.subplot(rows, cols, i+2)
        plt.imshow(img3)
        img2 = img3


    

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read('W', p=0.)


    

In [15]:

    

read('I', p=0.42)


    

In [12]:

    

v = []
for p in [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45]:
    img = gen_img(letter='A');
    img2 = gen_noise_add(img, p=p, flip=noise_flip)
    v.append(img2)

fig = plt.figure(figsize=(8, 6), dpi=200)
for i, img in enumerate(v):
    plt.subplot(1, 10, i+1)
    plt.imshow(img, label=str(p))
plt.subplots_adjust(bottom=0.1, right=2.0, top=0.9)


    

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