ascii_conv.py

from pybrain.structure import FeedForwardNetwork
from pybrain.structure import LinearLayer, SigmoidLayer
from pybrain.structure import FullConnection
from pybrain.rl.agents import LearningAgent
from PIL import Image, ImageDraw, ImageFont, ImageOps
import numpy as np
import copy

def mse(imageA, imageB):
    # the 'Mean Squared Error' between the two images is the
    # sum of the squared difference between the two images;
    # NOTE: the two images must have the same dimension
    err = np.sum((imageA - imageB) ** 2)
    err /= float(imageA.shape[0])
    
    # return the MSE, the lower the error, the more "similar"
    # the two images are
    return err

def render(input_arr):
    output = input_arr
    out_im = Image.new("RGBA", (128,128), color = (0xFF,0xFF,0xFF,0xFF))
    d = ImageDraw.Draw(out_im)
    fnt = ImageFont.truetype('unifont-10.0.06.ttf', 8, encoding="utf-32")
    for charac, i in zip(output, range(len(output))):
        x = 8 * int(i %16)
        y = 8 * int(i/16)
        try:
            d.text((x,y),unichr(max(0,min(0xffffffff,int(charac)))),font=fnt,fill=(0x00,0x00,0x00,0xff))
        except:
            pass
    out_arr = np.array(out_im.convert("L").resize((32,32))).flatten()
    return out_arr, out_im

def main():
    in_layer = LinearLayer(32*32)
    hidden_layer = SigmoidLayer(32*32)
    out_layer = LinearLayer(16*16)

    n = FeedForwardNetwork()

    n.addInputModule(in_layer)
    n.addModule(hidden_layer)
    n.addOutputModule(out_layer)

    in_to_hidden = FullConnection(in_layer, hidden_layer)
    hidden_to_out = FullConnection(hidden_layer, out_layer)
    n.addConnection(in_to_hidden)
    n.addConnection(hidden_to_out)
    n.sortModules()

    im = Image.open("7ae.jpg").convert("L").resize((32,32))
    im_array = np.array(im).flatten()
    print(n.activate(im_array))
    print(n.activate(im_array))
    m = n
    n.mutate()
    print(n.activate(im_array))
    print(m.activate(im_array))

    print(n.activate(im_array))
    output = n.activate(im_array)
    out_arr, out_im = render(output)
    old_comp_val = mse(out_arr,im_array)
    print(old_comp_val)
    curr_out_array = out_arr
    curr_out_im = out_im
    for i in range(200):
        if i % 20 == 0:
            print(old_comp_val)
        m = n.copy()
        m.mutate()
        output = m.activate(im_array)
        out_arr, out_im = render(output)
        comp_val = mse(out_arr,im_array)
        #print(comp_val)
        if comp_val <= old_comp_val:
            n = m.copy()
            old_comp_val = comp_val
            curr_out_array = out_arr
            curr_out_im = out_im
        
    print(comp_val)
    
    # d = ImageDraw.Draw(curr_out_im)
    # fnt = ImageFont.truetype('unifont-10.0.06.ttf', 20, encoding="utf-32")
    # d.text((0,0),unichr(0x2307),font=fnt,fill=(0x00,0x00,0x00,0xff))
    curr_out_im.convert("L").resize((32,32)).show()
    im.show()
if __name__ == "__main__":
    main()