sampler.py

'''
Implementation of Compositional Pattern Producing Networks in Tensorflow

https://en.wikipedia.org/wiki/Compositional_pattern-producing_network

@hardmaru, 2016
@w4nderlust, 2017

Sampler Class

This file is meant to be run inside an IPython session, as it is meant
to be used interacively for experimentation.

It shouldn't be that hard to take bits of this code into a normal
command line environment though if you want to use outside of IPython.

usage:

%run -i sampler.py

sampler = Sampler(z_dim = 4, c_dim = 1, scale = 8.0, net_size = 32)

'''
from __future__ import absolute_import, division, print_function, unicode_literals

import numpy as np
import tensorflow as tf
import math
import random
import PIL
from PIL import Image
import pylab
from cppn import CPPN
from rppn import RPPN
import matplotlib
import matplotlib.pyplot as plt
import imageio

try:
    mgc = get_ipython().magic
    mgc(u'matplotlib inline')
    pylab.rcParams['figure.figsize'] = (10.0, 10.0)
except NameError:
    pass


class Sampler():
    def __init__(self, model_type='CPPN', z_dim=8, c_dim=1, scale=10.0, net_size=32, act='tanh'):
        if model_type.lower() == 'cppn':
            self.model = CPPN(z_dim=z_dim, c_dim=c_dim, scale=scale, net_size=net_size)
        elif model_type.lower() == 'rppn':
            self.model = RPPN(z_dim=z_dim, c_dim=c_dim, scale=scale, net_size=net_size, act=act)
        else:
            self.model = CPPN(z_dim=z_dim, c_dim=c_dim, scale=scale, net_size=net_size)
        self.z = self.generate_z()  # saves most recent z here, in case we find a nice image and want the z-vec

    def reinit(self):
        self.model.reinit()

    def generate_z(self):
        z = np.random.uniform(-1.0, 1.0, size=(1, self.model.z_dim)).astype(np.float32)
        return z

    def generate(self, z=None, x_dim=1080, y_dim=1060, scale=10.0, k=3, act=None):
        if z is None:
            z = self.generate_z()
        else:
            z = np.reshape(z, (1, self.model.z_dim))
        self.z = z
        return self.model.generate(z, x_dim, y_dim, scale, k=k, act=act)[0]

    def show_image(self, image_data):
        '''
        image_data is a tensor, in [height width depth]
        image_data is NOT an instance of the PIL.Image class
        '''
        matplotlib.interactive(False)
        # plt.subplot(1, 1, 1)
        y_dim = image_data.shape[0]
        x_dim = image_data.shape[1]
        c_dim = self.model.c_dim
        if c_dim > 1:
            plt.imshow(image_data, interpolation='nearest')
        else:
            plt.imshow(image_data.reshape(y_dim, x_dim), cmap='Greys', interpolation='nearest')
        # plt.axis('off')
        plt.show(block=True)

    def to_np_image(self, image_data):
        # convert to PIL.Image format from np array (0, 1)
        img_data = np.array(1 - image_data)
        y_dim = image_data.shape[0]
        x_dim = image_data.shape[1]
        c_dim = self.model.c_dim
        if c_dim > 1:
            img_data = np.array(img_data.reshape((y_dim, x_dim, c_dim)) * 255.0, dtype=np.uint8)
        else:
            img_data = np.array(img_data.reshape((y_dim, x_dim)) * 255.0, dtype=np.uint8)
        return img_data

    def to_image(self, image_data):
        # convert to PIL.Image format from np array (0, 1)
        return Image.fromarray(self.to_np_image(image_data))

    def save_png(self, image_data, filename):
        if not filename.endswith(".png"):
            filename += ".png"
        self.to_image(image_data).save(filename)

    def save_anim_gif(self, z1, z2, filename, n_frame=10, duration1=0.5, duration2=1.0, duration=0.1, x_dim=512,
                      y_dim=512, scale=10.0, k1=3, k2=3, act=None, reverse=True):
        '''
        this saves an animated gif from two latent states z1 and z2
        n_frame: number of states in between z1 and z2 morphing effect, exclusive of z1 and z2
        duration1, duration2, control how long z1 and z2 are shown.  duration controls frame speed, in seconds
        '''
        delta_z = (z2 - z1) / (n_frame + 1)
        delta_k = (k2 - k1) / (n_frame + 1)
        total_frames = n_frame + 2
        images = []
        for i in range(total_frames):
            z = z1 + delta_z * float(i)
            k = int(k1 + delta_k * float(i))
            images.append(self.to_np_image(self.generate(z, x_dim, y_dim, scale, k=k, act=act)))
            print("processing image ", i)
        durations = [duration1] + [duration] * n_frame + [duration2]
        if reverse == True:  # go backwards in time back to the first state
            revImages = list(images)
            revImages.reverse()
            revImages = revImages[1:]
            images = images + revImages
            durations = durations + [duration] * n_frame + [duration1]
        print("writing gif file...")
        if not filename.endswith(".gif"):
            filename += ".gif"
        imageio.mimsave(filename, images, duration=durations)

    def save_anim_mp4(self, z1, z2, filename, n_frame=10, fps=10, x_dim=512, y_dim=512, scale=10.0, k1=3, k2=3,
                      act=None, reverse=True):
        '''
        this saves an animated mp4 from two latent states z1 and z2
        n_frame: number of states in between z1 and z2 morphing effect, exclusive of z1 and z2
        fps: number of frames displayed in a second
        '''
        delta_z = (z2 - z1) / (n_frame + 1)
        delta_k = (k2 - k1) / (n_frame + 1)
        total_frames = n_frame + 2
        images = []
        for i in range(total_frames):
            z = z1 + delta_z * float(i)
            k = int(k1 + delta_k * float(i))
            images.append(self.to_np_image(self.generate(z, x_dim, y_dim, scale, k=k, act=act)))
            print("processing image ", i)
        if reverse == True:  # go backwards in time back to the first state
            revImages = list(images)
            revImages.reverse()
            revImages = revImages[1:]
            images = images + revImages
        if not filename.endswith(".mp4"):
            filename += ".mp4"
        imageio.mimsave(filename, images, fps=fps)