pb.py

# Adapted from github.com/ProGamerGov
# wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
# unzip -d model inception5h.zip

from skimage.io import imread, imsave
import numpy as np
import os
import tensorflow as tf
import time
import urllib
import random
from PIL import Image
from os.path import expanduser


# Adapted from https://github.com/ProGamerGov/Protobuf-Dreamer

from mycroft.skills.core import MycroftSkill, Message, dig_for_message
from imgurpython import ImgurClient
import urllib2
from mycroft.util.log import LOG
import json
from bs4 import BeautifulSoup
from os.path import join
import tensorflow as tf
from pypexels import PyPexels
try:
    import yagmail
except ImportError:
    yagmail = None

__author__ = 'jarbas'


class DreamSkill(MycroftSkill):
    def __init__(self):
        super(DreamSkill, self).__init__()

        # free keys for the people
        if "use_pexels" not in self.settings:
            self.settings["use_pexels"] = False
        if "pexels" not in self.settings:
            self.settings["pexels"] = "563492ad6f91700001000001c7b3c09baae746f648a8cdfceef1362c"
        if "client_id" not in self.settings:
            self.settings["client_id"] = "0357b39cbf22749"
        if "client_secret" not in self.settings:
            self.settings["client_secret"] = "15f679a7147c12407913aeb0ff893e6156fba16b"

        client_id = self.settings.get("client_id")
        client_secret = self.settings.get("client_secret")

        self.client = ImgurClient(client_id, client_secret)

        # TODO test all layers, not sure they all can be dreamed on
        self.layers = ['conv2d0_w',
                       'conv2d0_b',
                       'conv2d1_w',
                       'conv2d1_b',
                       'conv2d2_w',
                       'conv2d2_b',
                       'mixed3a_1x1_w',
                       'mixed3a_1x1_b',
                       'mixed3a_3x3_bottleneck_w',
                       'mixed3a_3x3_bottleneck_b',
                       'mixed3a_3x3_w',
                       'mixed3a_3x3_b',
                       'mixed3a_5x5_bottleneck_w',
                       'mixed3a_5x5_bottleneck_b',
                       'mixed3a_5x5_w',
                       'mixed3a_5x5_b',
                       'mixed3a_pool_reduce_w',
                       'mixed3a_pool_reduce_b',
                       'mixed3b_1x1_w',
                       'mixed3b_1x1_b',
                       'mixed3b_3x3_bottleneck_w',
                       'mixed3b_3x3_bottleneck_b',
                       'mixed3b_3x3_w',
                       'mixed3b_3x3_b',
                       'mixed3b_5x5_bottleneck_w',
                       'mixed3b_5x5_bottleneck_b',
                       'mixed3b_5x5_w',
                       'mixed3b_5x5_b',
                       'mixed3b_pool_reduce_w',
                       'mixed3b_pool_reduce_b',
                       'mixed4a_1x1_w',
                       'mixed4a_1x1_b',
                       'mixed4a_3x3_bottleneck_w',
                       'mixed4a_3x3_bottleneck_b',
                       'mixed4a_3x3_w',
                       'mixed4a_3x3_b',
                       'mixed4a_5x5_bottleneck_w',
                       'mixed4a_5x5_bottleneck_b',
                       'mixed4a_5x5_w',
                       'mixed4a_5x5_b',
                       'mixed4a_pool_reduce_w',
                       'mixed4a_pool_reduce_b',
                       'mixed4b_1x1_w',
                       'mixed4b_1x1_b',
                       'mixed4b_3x3_bottleneck_w',
                       'mixed4b_3x3_bottleneck_b',
                       'mixed4b_3x3_w',
                       'mixed4b_3x3_b',
                       'mixed4b_5x5_bottleneck_w',
                       'mixed4b_5x5_bottleneck_b',
                       'mixed4b_5x5_w',
                       'mixed4b_5x5_b',
                       'mixed4b_pool_reduce_w',
                       'mixed4b_pool_reduce_b',
                       'mixed4c_1x1_w',
                       'mixed4c_1x1_b',
                       'mixed4c_3x3_bottleneck_w',
                       'mixed4c_3x3_bottleneck_b',
                       'mixed4c_3x3_w',
                       'mixed4c_3x3_b',
                       'mixed4c_5x5_bottleneck_w',
                       'mixed4c_5x5_bottleneck_b',
                       'mixed4c_5x5_w',
                       'mixed4c_5x5_b',
                       'mixed4c_pool_reduce_w',
                       'mixed4c_pool_reduce_b',
                       'mixed4d_1x1_w',
                       'mixed4d_1x1_b',
                       'mixed4d_3x3_bottleneck_w',
                       'mixed4d_3x3_bottleneck_b',
                       'mixed4d_3x3_w',
                       'mixed4d_3x3_b',
                       'mixed4d_5x5_bottleneck_w',
                       'mixed4d_5x5_bottleneck_b',
                       'mixed4d_5x5_w',
                       'mixed4d_5x5_b',
                       'mixed4d_pool_reduce_w',
                       'mixed4d_pool_reduce_b',
                       'mixed4e_1x1_w',
                       'mixed4e_1x1_b',
                       'mixed4e_3x3_bottleneck_w',
                       'mixed4e_3x3_bottleneck_b',
                       'mixed4e_3x3_w',
                       'mixed4e_3x3_b',
                       'mixed4e_5x5_bottleneck_w',
                       'mixed4e_5x5_bottleneck_b',
                       'mixed4e_5x5_w',
                       'mixed4e_5x5_b',
                       'mixed4e_pool_reduce_w',
                       'mixed4e_pool_reduce_b',
                       'mixed5a_1x1_w',
                       'mixed5a_1x1_b',
                       'mixed5a_3x3_bottleneck_w',
                       'mixed5a_3x3_bottleneck_b',
                       'mixed5a_3x3_w',
                       'mixed5a_3x3_b',
                       'mixed5a_5x5_bottleneck_w',
                       'mixed5a_5x5_bottleneck_b',
                       'mixed5a_5x5_w',
                       'mixed5a_5x5_b',
                       'mixed5a_pool_reduce_w',
                       'mixed5a_pool_reduce_b',
                       'mixed5b_1x1_w',
                       'mixed5b_1x1_b',
                       'mixed5b_3x3_bottleneck_w',
                       'mixed5b_3x3_bottleneck_b',
                       'mixed5b_3x3_w',
                       'mixed5b_3x3_b',
                       'mixed5b_5x5_bottleneck_w',
                       'mixed5b_5x5_bottleneck_b',
                       'mixed5b_5x5_w',
                       'mixed5b_5x5_b',
                       'mixed5b_pool_reduce_w',
                       'mixed5b_pool_reduce_b',
                       'head0_bottleneck_w',
                       'head0_bottleneck_b',
                       'nn0_w',
                       'nn0_b',
                       'softmax0_w',
                       'softmax0_b',
                       'head1_bottleneck_w',
                       'head1_bottleneck_b',
                       'nn1_w',
                       'nn1_b',
                       'softmax1_w',
                       'softmax1_b',
                       'softmax2_w',
                       'softmax2_b',
                       'conv2d0_pre_relu/conv',
                       'conv2d0_pre_relu',
                       'conv2d0',
                       'maxpool0',
                       'localresponsenorm0',
                       'conv2d1_pre_relu/conv',
                       'conv2d1_pre_relu',
                       'conv2d1',
                       'conv2d2_pre_relu/conv',
                       'conv2d2_pre_relu',
                       'conv2d2',
                       'localresponsenorm1',
                       'maxpool1',
                       'mixed3a_1x1_pre_relu/conv',
                       'mixed3a_1x1_pre_relu',
                       'mixed3a_1x1',
                       'mixed3a_3x3_bottleneck_pre_relu/conv',
                       'mixed3a_3x3_bottleneck_pre_relu',
                       'mixed3a_3x3_bottleneck',
                       'mixed3a_3x3_pre_relu/conv',
                       'mixed3a_3x3_pre_relu',
                       'mixed3a_3x3',
                       'mixed3a_5x5_bottleneck_pre_relu/conv',
                       'mixed3a_5x5_bottleneck_pre_relu',
                       'mixed3a_5x5_bottleneck',
                       'mixed3a_5x5_pre_relu/conv',
                       'mixed3a_5x5_pre_relu',
                       'mixed3a_5x5',
                       'mixed3a_pool',
                       'mixed3a_pool_reduce_pre_relu/conv',
                       'mixed3a_pool_reduce_pre_relu',
                       'mixed3a_pool_reduce',
                       'mixed3a/concat_dim',
                       'mixed3a',
                       'mixed3b_1x1_pre_relu/conv',
                       'mixed3b_1x1_pre_relu',
                       'mixed3b_1x1',
                       'mixed3b_3x3_bottleneck_pre_relu/conv',
                       'mixed3b_3x3_bottleneck_pre_relu',
                       'mixed3b_3x3_bottleneck',
                       'mixed3b_3x3_pre_relu/conv',
                       'mixed3b_3x3_pre_relu',
                       'mixed3b_3x3',
                       'mixed3b_5x5_bottleneck_pre_relu/conv',
                       'mixed3b_5x5_bottleneck_pre_relu',
                       'mixed3b_5x5_bottleneck',
                       'mixed3b_5x5_pre_relu/conv',
                       'mixed3b_5x5_pre_relu',
                       'mixed3b_5x5',
                       'mixed3b_pool',
                       'mixed3b_pool_reduce_pre_relu/conv',
                       'mixed3b_pool_reduce_pre_relu',
                       'mixed3b_pool_reduce',
                       'mixed3b/concat_dim',
                       'mixed3b',
                       'maxpool4',
                       'mixed4a_1x1_pre_relu/conv',
                       'mixed4a_1x1_pre_relu',
                       'mixed4a_1x1',
                       'mixed4a_3x3_bottleneck_pre_relu/conv',
                       'mixed4a_3x3_bottleneck_pre_relu',
                       'mixed4a_3x3_bottleneck',
                       'mixed4a_3x3_pre_relu/conv',
                       'mixed4a_3x3_pre_relu',
                       'mixed4a_3x3',
                       'mixed4a_5x5_bottleneck_pre_relu/conv',
                       'mixed4a_5x5_bottleneck_pre_relu',
                       'mixed4a_5x5_bottleneck',
                       'mixed4a_5x5_pre_relu/conv',
                       'mixed4a_5x5_pre_relu',
                       'mixed4a_5x5',
                       'mixed4a_pool',
                       'mixed4a_pool_reduce_pre_relu/conv',
                       'mixed4a_pool_reduce_pre_relu',
                       'mixed4a_pool_reduce',
                       'mixed4a/concat_dim',
                       'mixed4a',
                       'mixed4b_1x1_pre_relu/conv',
                       'mixed4b_1x1_pre_relu',
                       'mixed4b_1x1',
                       'mixed4b_3x3_bottleneck_pre_relu/conv',
                       'mixed4b_3x3_bottleneck_pre_relu',
                       'mixed4b_3x3_bottleneck',
                       'mixed4b_3x3_pre_relu/conv',
                       'mixed4b_3x3_pre_relu',
                       'mixed4b_3x3',
                       'mixed4b_5x5_bottleneck_pre_relu/conv',
                       'mixed4b_5x5_bottleneck_pre_relu',
                       'mixed4b_5x5_bottleneck',
                       'mixed4b_5x5_pre_relu/conv',
                       'mixed4b_5x5_pre_relu',
                       'mixed4b_5x5',
                       'mixed4b_pool',
                       'mixed4b_pool_reduce_pre_relu/conv',
                       'mixed4b_pool_reduce_pre_relu',
                       'mixed4b_pool_reduce',
                       'mixed4b/concat_dim',
                       'mixed4b',
                       'mixed4c_1x1_pre_relu/conv',
                       'mixed4c_1x1_pre_relu',
                       'mixed4c_1x1',
                       'mixed4c_3x3_bottleneck_pre_relu/conv',
                       'mixed4c_3x3_bottleneck_pre_relu',
                       'mixed4c_3x3_bottleneck',
                       'mixed4c_3x3_pre_relu/conv',
                       'mixed4c_3x3_pre_relu',
                       'mixed4c_3x3',
                       'mixed4c_5x5_bottleneck_pre_relu/conv',
                       'mixed4c_5x5_bottleneck_pre_relu',
                       'mixed4c_5x5_bottleneck',
                       'mixed4c_5x5_pre_relu/conv',
                       'mixed4c_5x5_pre_relu',
                       'mixed4c_5x5',
                       'mixed4c_pool',
                       'mixed4c_pool_reduce_pre_relu/conv',
                       'mixed4c_pool_reduce_pre_relu',
                       'mixed4c_pool_reduce',
                       'mixed4c/concat_dim',
                       'mixed4c',
                       'mixed4d_1x1_pre_relu/conv',
                       'mixed4d_1x1_pre_relu',
                       'mixed4d_1x1',
                       'mixed4d_3x3_bottleneck_pre_relu/conv',
                       'mixed4d_3x3_bottleneck_pre_relu',
                       'mixed4d_3x3_bottleneck',
                       'mixed4d_3x3_pre_relu/conv',
                       'mixed4d_3x3_pre_relu',
                       'mixed4d_3x3',
                       'mixed4d_5x5_bottleneck_pre_relu/conv',
                       'mixed4d_5x5_bottleneck_pre_relu',
                       'mixed4d_5x5_bottleneck',
                       'mixed4d_5x5_pre_relu/conv',
                       'mixed4d_5x5_pre_relu',
                       'mixed4d_5x5',
                       'mixed4d_pool',
                       'mixed4d_pool_reduce_pre_relu/conv',
                       'mixed4d_pool_reduce_pre_relu',
                       'mixed4d_pool_reduce',
                       'mixed4d/concat_dim',
                       'mixed4d',
                       'mixed4e_1x1_pre_relu/conv',
                       'mixed4e_1x1_pre_relu',
                       'mixed4e_1x1',
                       'mixed4e_3x3_bottleneck_pre_relu/conv',
                       'mixed4e_3x3_bottleneck_pre_relu',
                       'mixed4e_3x3_bottleneck',
                       'mixed4e_3x3_pre_relu/conv',
                       'mixed4e_3x3_pre_relu',
                       'mixed4e_3x3',
                       'mixed4e_5x5_bottleneck_pre_relu/conv',
                       'mixed4e_5x5_bottleneck_pre_relu',
                       'mixed4e_5x5_bottleneck',
                       'mixed4e_5x5_pre_relu/conv',
                       'mixed4e_5x5_pre_relu',
                       'mixed4e_5x5',
                       'mixed4e_pool',
                       'mixed4e_pool_reduce_pre_relu/conv',
                       'mixed4e_pool_reduce',
                       'mixed4e/concat_dim',
                       'mixed4e',
                       'maxpool10',
                       'mixed5a_1x1_pre_relu/conv',
                       'mixed5a_1x1_pre_relu',
                       'mixed5a_1x1',
                       'mixed5a_3x3_bottleneck_pre_relu/conv',
                       'mixed5a_3x3_bottleneck_pre_relu',
                       'mixed5a_3x3_bottleneck',
                       'mixed5a_3x3_pre_relu/conv',
                       'mixed5a_3x3_pre_relu',
                       'mixed5a_3x3',
                       'mixed5a_5x5_bottleneck_pre_relu/conv',
                       'mixed5a_5x5_bottleneck_pre_relu',
                       'mixed5a_5x5_bottleneck',
                       'mixed5a_5x5_pre_relu/conv',
                       'mixed5a_5x5_pre_relu',
                       'mixed5a_5x5',
                       'mixed5a_pool',
                       'mixed5a_pool_reduce_pre_relu/conv',
                       'mixed5a_pool_reduce_pre_relu',
                       'mixed5a_pool_reduce',
                       'mixed5a/concat_dim',
                       'mixed5a',
                       'mixed5b_1x1_pre_relu/conv',
                       'mixed5b_1x1_pre_relu',
                       'mixed5b_1x1',
                       'mixed5b_3x3_bottleneck_pre_relu/conv',
                       'mixed5b_3x3_bottleneck_pre_relu',
                       'mixed5b_3x3_bottleneck',
                       'mixed5b_3x3_pre_relu/conv',
                       'mixed5b_3x3_pre_relu',
                       'mixed5b_3x3',
                       'mixed5b_5x5_bottleneck_pre_relu/conv',
                       'mixed5b_5x5_bottleneck_pre_relu',
                       'mixed5b_5x5_bottleneck',
                       'mixed5b_5x5_pre_relu/conv',
                       'mixed5b_5x5_pre_relu',
                       'mixed5b_5x5',
                       'mixed5b_pool',
                       'mixed5b_pool_reduce_pre_relu/conv',
                       'mixed5b_pool_reduce_pre_relu',
                       'mixed5b_pool_reduce',
                       'mixed5b/concat_dim',
                       'mixed5b',
                       'avgpool0',
                       'head0_pool',
                       'head0_bottleneck_pre_relu/conv',
                       'head0_bottleneck_pre_relu',
                       'head0_bottleneck',
                       'head0_bottleneck/reshape/shape',
                       'head0_bottleneck/reshape',
                       'nn0_pre_relu/matmul',
                       'nn0_pre_relu',
                       'nn0',
                       'nn0/reshape/shape',
                       'nn0/reshape',
                       'softmax0_pre_activation/matmul',
                       'softmax0_pre_activation',
                       'softmax0',
                       'head1_pool',
                       'head1_bottleneck_pre_relu/conv',
                       'head1_bottleneck_pre_relu',
                       'head1_bottleneck',
                       'head1_bottleneck/reshape/shape',
                       'head1_bottleneck/reshape',
                       'nn1_pre_relu/matmul',
                       'nn1_pre_relu',
                       'nn1',
                       'nn1/reshape/shape',
                       'nn1/reshape',
                       'softmax1_pre_activation/matmul',
                       'softmax1_pre_activation',
                       'softmax1',
                       'avgpool0/reshape/shape',
                       'avgpool0/reshape',
                       'softmax2_pre_activation/matmul',
                       'softmax2_pre_activation',
                       'softmax2']
        self.layer_nicknames = {
            "plants": ["mixed4a_3x3_bottleneck_pre_relu", 84],
            "fractals": ["mixed4a_3x3_bottleneck_pre_relu", 83],
            "snakes and lizards": ["mixed4c_pool_reduce", 7],
            "feathers": ["mixed4c_pool_reduce", 14],
            "rodents": ["mixed4c_pool_reduce", 23],
            "spirals": ["mixed4c_pool_reduce", 53],
            "3d": ["mixed4c_pool_reduce", 54],
            "shiny": ["mixed4c_pool_reduce", 56],
            "houses": ["mixed4c_pool_reduce", 61],
            "fish": ["mixed5a_1x1", 158],
            "balls": ["mixed5a_1x1", 9],
            "bark": ["mixed5a_1x1", 107],
            "clocks": ["mixed5a_1x1", 134],
            "flowers on metal": ["mixed5a_1x1", 198],
            "quadrilaterals": ["mixed4c", 56],
            "letters": ["mixed4c", 87],
            "squares": ["mixed4a_3x3_bottleneck_pre_relu", 51],
            "wool": ["mixed4e", 62],
            "arches": ["mixed4c", 477],
            "fluffy dogs": ["mixed4c", 111],
            "flowers": ["mixed4c_3x3_bottleneck", 30],
        }

        if "iter_value" not in self.settings:
            self.settings["iter_value"] = 20
        if "octave_value" not in self.settings:
            self.settings["octave_value"] = 4
        if "octave_scale_value" not in self.settings:
            self.settings["octave_scale_value"] = 1.4
        if "step_size" not in self.settings:
            self.settings["step_size"] = 1.5
        if "tile_size" not in self.settings:
            self.settings["tile_size"] = 512
        if "model_folder" not in self.settings:
            self.settings["model_folder"] = \
                expanduser("~/tensorflow_models/inception5h")
        if "model" not in self.settings:
            self.settings["model"] = join(self.settings["model_folder"], "tensorflow_inception_graph.pb")
        if "print_model" not in self.settings:
            self.settings["print_model"] = True
        if "verbose" not in self.settings:
            self.settings["verbose"] = True
        if "output_dir" not in self.settings:
            self.settings["output_dir"] = expanduser("~/dreams")

        if "layers" not in self.settings:
            self.settings["layers"] = ['conv2d2_pre_relu/conv', 'conv2d2_pre_relu',
                        'conv2d2', 'localresponsenorm1', 'maxpool1', 'mixed3a_pool', 'mixed3a', 'mixed3b_3x3_pre_relu/conv', 'mixed3b_3x3_pre_relu', 'mixed3b_3x3', 'mixed3b_5x5_bottleneck_pre_relu/conv', 'mixed3b_5x5_bottleneck_pre_relu', 'mixed3b_5x5_bottleneck', 'mixed3b_5x5_pre_relu/conv', 'mixed3b_5x5_pre_relu', 'mixed3b_5x5', 'mixed3b_pool', 'mixed3b_pool_reduce_pre_relu/conv', 'mixed3b_pool_reduce_pre_relu', 'mixed3b_pool_reduce', 'mixed3b/concat_dim', 'mixed3b', 'maxpool4', 'mixed4a_1x1_pre_relu/conv', 'mixed4a_1x1_pre_relu', 'mixed4a_1x1', 'mixed4a_3x3_bottleneck_pre_relu/conv', 'mixed4a_3x3_bottleneck_pre_relu', 'mixed4a_3x3_bottleneck', 'mixed4a_3x3_pre_relu/conv', 'mixed4a_3x3_pre_relu', 'mixed4a_3x3', 'mixed4a_5x5_bottleneck_pre_relu/conv', 'mixed4a_5x5_bottleneck_pre_relu', 'mixed4a_5x5_bottleneck', 'mixed4a_5x5_pre_relu/conv', 'mixed4a_5x5_pre_relu', 'mixed4a_5x5', 'mixed4a_pool', 'mixed4a_pool_reduce_pre_relu/conv', 'mixed4a_pool_reduce_pre_relu', 'mixed4a_pool_reduce', 'mixed4a/concat_dim', 'mixed4a', 'mixed4b_1x1_pre_relu/conv', 'mixed4b_1x1_pre_relu', 'mixed4b_1x1', 'mixed4b_3x3_bottleneck_pre_relu/conv', 'mixed4b_3x3_bottleneck_pre_relu', 'mixed4b_3x3_bottleneck', 'mixed4b_3x3_pre_relu/conv', 'mixed4b_3x3_pre_relu', 'mixed4b_3x3', 'mixed4b_5x5_bottleneck_pre_relu/conv', 'mixed4b_5x5_bottleneck_pre_relu', 'mixed4b_5x5_bottleneck', 'mixed4b_5x5_pre_relu/conv', 'mixed4b_5x5_pre_relu', 'mixed4b_5x5', 'mixed4b_pool', 'mixed4b_pool_reduce_pre_relu/conv', 'mixed4b_pool_reduce_pre_relu', 'mixed4b_pool_reduce', 'mixed4b/concat_dim', 'mixed4b', 'mixed4c_1x1_pre_relu/conv', 'mixed4c_1x1_pre_relu', 'mixed4c_1x1', 'mixed4c_3x3_bottleneck_pre_relu/conv', 'mixed4c_3x3_bottleneck_pre_relu', 'mixed4c_3x3_bottleneck', 'mixed4c_3x3_pre_relu/conv', 'mixed4c_3x3_pre_relu', 'mixed4c_3x3', 'mixed4c_5x5_bottleneck_pre_relu/conv', 'mixed4c_5x5_bottleneck_pre_relu', 'mixed4c_5x5_bottleneck', 'mixed4c_5x5_pre_relu/conv', 'mixed4c_5x5_pre_relu', 'mixed4c_5x5', 'mixed4c_pool', 'mixed4c_pool_reduce_pre_relu/conv', 'mixed4c_pool_reduce_pre_relu', 'mixed4c_pool_reduce', 'mixed4c/concat_dim', 'mixed4c', 'mixed4d_1x1_pre_relu/conv', 'mixed4d_1x1_pre_relu', 'mixed4d_1x1', 'mixed4d_3x3_bottleneck_pre_relu/conv', 'mixed4d_3x3_bottleneck_pre_relu', 'mixed4d_3x3_bottleneck', 'mixed4d_3x3_pre_relu/conv', 'mixed4d_3x3_pre_relu', 'mixed4d_3x3', 'mixed4d_5x5_bottleneck_pre_relu/conv', 'mixed4d_5x5_bottleneck_pre_relu', 'mixed4d_5x5_bottleneck', 'mixed4d_5x5_pre_relu/conv', 'mixed4d_5x5_pre_relu', 'mixed4d_5x5', 'mixed4d_pool', 'mixed4d_pool_reduce_pre_relu/conv', 'mixed4d_pool_reduce_pre_relu', 'mixed4d_pool_reduce', 'mixed4d/concat_dim', 'mixed4d', 'mixed4e_1x1_pre_relu/conv', 'mixed4e_1x1_pre_relu', 'mixed4e_1x1', 'mixed4e_3x3_bottleneck_pre_relu/conv', 'mixed4e_3x3_bottleneck_pre_relu', 'mixed4e_3x3_bottleneck', 'mixed4e_3x3_pre_relu/conv', 'mixed4e_3x3_pre_relu', 'mixed4e_3x3', 'mixed4e_5x5_bottleneck_pre_relu/conv', 'mixed4e_5x5_bottleneck_pre_relu', 'mixed4e_5x5_bottleneck', 'mixed4e_5x5_pre_relu/conv', 'mixed4e_5x5_pre_relu', 'mixed4e_5x5', 'mixed4e_pool', 'mixed4e_pool_reduce_pre_relu/conv', 'mixed4e_pool_reduce', 'mixed4e/concat_dim', 'mixed4e', 'maxpool10', 'mixed5a_1x1_pre_relu/conv', 'mixed5a_1x1_pre_relu', 'mixed5a_1x1', 'mixed5a_3x3_bottleneck_pre_relu/conv', 'mixed5a_3x3_bottleneck_pre_relu', 'mixed5a_3x3_bottleneck', 'mixed5a_3x3_pre_relu/conv', 'mixed5a_3x3_pre_relu', 'mixed5a_3x3', 'mixed5a_5x5_bottleneck_pre_relu/conv', 'mixed5a_5x5_bottleneck_pre_relu', 'mixed5a_5x5_bottleneck', 'mixed5a_5x5_pre_relu/conv', 'mixed5a_5x5_pre_relu', 'mixed5a_5x5', 'mixed5a_pool', 'mixed5a_pool_reduce_pre_relu/conv', 'mixed5a_pool_reduce_pre_relu', 'mixed5a_pool_reduce', 'mixed5a/concat_dim', 'mixed5a', 'mixed5b_1x1_pre_relu/conv', 'mixed5b_1x1_pre_relu', 'mixed5b_1x1', 'mixed5b_3x3_bottleneck_pre_relu/conv', 'mixed5b_3x3_bottleneck_pre_relu', 'mixed5b_3x3_bottleneck', 'mixed5b_3x3_pre_relu/conv', 'mixed5b_3x3_pre_relu', 'mixed5b_3x3', 'mixed5b_5x5_bottleneck_pre_relu/conv', 'mixed5b_5x5_bottleneck_pre_relu', 'mixed5b_5x5_bottleneck', 'mixed5b_5x5_pre_relu/conv', 'mixed5b_5x5_pre_relu', 'mixed5b_5x5', 'mixed5b_pool', 'mixed5b_pool_reduce_pre_relu/conv', 'mixed5b_pool_reduce_pre_relu', 'mixed5b_pool_reduce', 'mixed5b/concat_dim', 'mixed5b', 'avgpool0', 'head0_pool', 'head0_bottleneck_pre_relu/conv', 'head0_bottleneck_pre_relu', 'head0_bottleneck', 'head0_bottleneck/reshape/shape', 'head0_bottleneck/reshape', 'nn0_pre_relu/matmul', 'nn0_pre_relu', 'nn0', 'nn0/reshape/shape', 'nn0/reshape', 'softmax0_pre_activation/matmul', 'softmax0_pre_activation', 'softmax0', 'head1_pool', 'head1_bottleneck_pre_relu/conv', 'head1_bottleneck_pre_relu', 'head1_bottleneck', 'head1_bottleneck/reshape/shape', 'head1_bottleneck/reshape', 'nn1_pre_relu/matmul', 'nn1_pre_relu', 'nn1', 'nn1/reshape/shape', 'nn1/reshape', 'softmax1_pre_activation/matmul', 'softmax1_pre_activation', 'softmax1', 'avgpool0/reshape/shape', 'avgpool0/reshape', 'softmax2_pre_activation/matmul', 'softmax2_pre_activation', 'softmax2']


        # check if folders
        if not os.path.exists(self.settings["output_dir"]):
            os.makedirs(self.settings["output_dir"])

        # private email
        if yagmail is not None:
            mail_config = self.config_core.get("email", {})
            self.email = mail_config.get("email")
            self.password = mail_config.get("password")
            self.target_mail = mail_config.get("destinatary", self.email)

    def send(self, body):
        title = "Mycroft DeepDream Skill"
        # try private sending
        if yagmail is not None and self.email and self.password:
            with yagmail.SMTP(self.email, self.password) as yag:
                yag.send(self.target_email, title, body)
        else:
            # else use mycroft home
            self.send_email(title, body)

    def speak(self, utterance, expect_response=False, metadata=None):
        """
            Speak a sentence.

                   Args:
                       utterance:          sentence mycroft should speak
                       expect_response:    set to True if Mycroft should expect
                                           a response from the user and start
                                           listening for response.
                       metadata:           Extra data to be transmitted
                                           together with speech
               """
        metadata = metadata or {}
        # registers the skill as being active
        self.enclosure.register(self.name)
        data = {'utterance': utterance,
                'expect_response': expect_response,
                "metadata": metadata}
        message = dig_for_message()
        if message:
            self.emitter.emit(message.reply("speak", data))
        else:
            self.emitter.emit(Message("speak", data))

    def initialize(self):
        # check if model exists, if not download!
        DeepDreamer.maybe_download_and_extract(self.settings["model_folder"])
        self.register_intent_file("dream.intent", self.handle_dream_intent)
        self.emitter.on("dream.request", self.handle_dream_request)

    def handle_dream_intent(self, message):
        search = message.data.get("AboutKeyword")
        data = {}
        filepath = expanduser("~/dream_seed.jpg")
        if search:
            # collect dream entropy
            self.speak("dreaming about " + search)
            pics = self.search_pic(search)
            url = random.choice(pics)
            urllib.urlretrieve(url, filepath)
            data["dream_name"] = search + "_" + time.asctime()
        elif self.settings["use_pexels"]:
            url = random.choice(self.popular_pic_urls())
            req = urllib2.Request(url, None, {
                'User-agent': 'Mozilla/5.0 (Windows; U; Windows NT 5.1; de; rv:1.9.1.5) Gecko/20091102 Firefox/3.5.5'})
            resp = urllib2.urlopen(req)
            content = resp.read()
            with open(filepath, "wb") as f:
                f.write(content)
        else:
            url = "https://unsplash.it/640/480/?random"
            urllib.urlretrieve(url, filepath)
        data["dream_source"] = filepath
        self.emitter.emit(message.reply("dream.request", data))

    def handle_dream_request(self, message):
        self.speak("dream started")
        channel_value = message.data.get("channel_value", 58)
        seed = message.data.get("dream_source")
        output_name = message.data.get("dream_name")
        layer_name = message.data.get("layer_name", "mixed4c_3x3_bottleneck_pre_relu")
        iter_value = message.data.get("iter_value", self.settings[
            "iter_value"])
        step_size = message.data.get("step_size", self.settings["step_size"])
        octave_value = message.data.get("octave_value",
                                        self.settings["octave_value"])
        octave_scale_value = message.data.get("octave_scale_value",
                                              self.settings["octave_scale_value"])
        DD = DeepDreamer(self.settings["model"],
                              self.settings["print_model"],
                              self.settings["verbose"])

        filepath = DD.dream(output_name, seed, channel_value,
                                 layer_name, iter_value, step_size,
                                 octave_value, octave_scale_value)
        if filepath:
            data = self.client.upload_from_path(filepath)
            metadata = message.data
            metadata["url"] = data["link"]
            self.speak("here is what i dreamed " + data["link"], metadata=metadata)

            mail = "url: " + data["link"] + "\nlayer: " + layer_name + \
                   "\nchannel: " + str(channel_value) + "\niter_num: " + str(iter_value)
            self.send(mail)
            self.emitter.emit(message.reply("dream.response", metadata))
        else:
            if layer_name in self.settings["layers"]:
                self.settings["layers"].remove(layer_name)
            self.speak("I could not dream this time")

    ## pic search
    def popular_pic_urls(self, num_pages=1):
        # instantiate PyPexels object
        api_key = self.settings["pexels"]
        urls = []
        try:
            py_pexels = PyPexels(api_key=api_key)
            popular_photos = py_pexels.popular(per_page=10)
            i = 0
            while popular_photos.has_next and i <= num_pages:
                for photo in popular_photos.entries:
                    urls.append(photo.src["original"])
                # no need to specify per_page: will take from original object
                popular_photos = popular_photos.get_next_page()
                i += 1
        except Exception as e:
            LOG.error(e)
        return urls

    def get_soup(self, url, header):
        return BeautifulSoup(
            urllib2.urlopen(urllib2.Request(url, headers=header)),
            'html.parser')

    def search_pic(self, searchkey, dlnum=5):
        query = searchkey  # raw_input("query image")# you can change the query for the image  here
        query = query.split()
        query = '+'.join(query)
        url = "https://www.google.co.in/search?q=" + query + "&source=lnms&tbm=isch"
        header = {
            'User-Agent': "Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36"
        }
        soup = self.get_soup(url, header)
        i = 0
        ActualImages = []  # contains the link for Large original images, type of  image
        for a in soup.find_all("div", {"class": "rg_meta"}):
            link = json.loads(a.text)["ou"]
            ActualImages.append(link)
            i += 1
            if i >= dlnum:
                break
        return ActualImages

    def shutdown(self):
        """
            Remove all registered handlers and perform skill shutdown.
        """
        self.emitter.remove("dream.request", self.handle_dream_request)
        super(DreamSkill, self).shutdown()


def create_skill():
    return DreamSkill()


from skimage.io import imread, imsave
import numpy as np
import os
import time
import urllib
import random
from PIL import Image
from os.path import expanduser


class DeepDreamer(object):
    def __init__(self, model_path, print_model=False, verbose=True):

        self.model_path = model_path
        self.verbose = verbose
        self.print_model = print_model
        self.model_fn = os.path.join(os.path.dirname(os.path.realpath(
            __file__)), model_path)

        self.tile_size = 512
        # creating TensorFlow session and loading the model
        self.graph = tf.Graph()
        self.sess = tf.InteractiveSession(graph=self.graph)
        with tf.gfile.FastGFile(self.model_fn, 'rb') as f:
            self.graph_def = tf.GraphDef()
            self.graph_def.ParseFromString(f.read())
        self.t_input = tf.placeholder(np.float32,
                                      name='input')  # define the input tensor
        imagenet_mean = 117.0
        t_preprocessed = tf.expand_dims(self.t_input - imagenet_mean, 0)
        tf.import_graph_def(self.graph_def, {'input': t_preprocessed})

        # Optionally print the inputs and layers of the specified graph.
        if self.print_model:
            print(self.graph.get_operations())

        self.last_layer = None
        self.last_grad = None
        self.last_channel = None
        # TODO test all layers, not sure they all can be dreamed on
        self.layers = ['conv2d2_pre_relu/conv', 'conv2d2_pre_relu', 'conv2d2', 'localresponsenorm1', 'maxpool1', 'mixed3a_pool', 'mixed3a', 'mixed3b_3x3_pre_relu/conv', 'mixed3b_3x3_pre_relu', 'mixed3b_3x3', 'mixed3b_5x5_bottleneck_pre_relu/conv', 'mixed3b_5x5_bottleneck_pre_relu', 'mixed3b_5x5_bottleneck', 'mixed3b_5x5_pre_relu/conv', 'mixed3b_5x5_pre_relu', 'mixed3b_5x5', 'mixed3b_pool', 'mixed3b_pool_reduce_pre_relu/conv', 'mixed3b_pool_reduce_pre_relu', 'mixed3b_pool_reduce', 'mixed3b/concat_dim', 'mixed3b', 'maxpool4', 'mixed4a_1x1_pre_relu/conv', 'mixed4a_1x1_pre_relu', 'mixed4a_1x1', 'mixed4a_3x3_bottleneck_pre_relu/conv', 'mixed4a_3x3_bottleneck_pre_relu', 'mixed4a_3x3_bottleneck', 'mixed4a_3x3_pre_relu/conv', 'mixed4a_3x3_pre_relu', 'mixed4a_3x3', 'mixed4a_5x5_bottleneck_pre_relu/conv', 'mixed4a_5x5_bottleneck_pre_relu', 'mixed4a_5x5_bottleneck', 'mixed4a_5x5_pre_relu/conv', 'mixed4a_5x5_pre_relu', 'mixed4a_5x5', 'mixed4a_pool', 'mixed4a_pool_reduce_pre_relu/conv', 'mixed4a_pool_reduce_pre_relu', 'mixed4a_pool_reduce', 'mixed4a/concat_dim', 'mixed4a', 'mixed4b_1x1_pre_relu/conv', 'mixed4b_1x1_pre_relu', 'mixed4b_1x1', 'mixed4b_3x3_bottleneck_pre_relu/conv', 'mixed4b_3x3_bottleneck_pre_relu', 'mixed4b_3x3_bottleneck', 'mixed4b_3x3_pre_relu/conv', 'mixed4b_3x3_pre_relu', 'mixed4b_3x3', 'mixed4b_5x5_bottleneck_pre_relu/conv', 'mixed4b_5x5_bottleneck_pre_relu', 'mixed4b_5x5_bottleneck', 'mixed4b_5x5_pre_relu/conv', 'mixed4b_5x5_pre_relu', 'mixed4b_5x5', 'mixed4b_pool', 'mixed4b_pool_reduce_pre_relu/conv', 'mixed4b_pool_reduce_pre_relu', 'mixed4b_pool_reduce', 'mixed4b/concat_dim', 'mixed4b', 'mixed4c_1x1_pre_relu/conv', 'mixed4c_1x1_pre_relu', 'mixed4c_1x1', 'mixed4c_3x3_bottleneck_pre_relu/conv', 'mixed4c_3x3_bottleneck_pre_relu', 'mixed4c_3x3_bottleneck', 'mixed4c_3x3_pre_relu/conv', 'mixed4c_3x3_pre_relu', 'mixed4c_3x3', 'mixed4c_5x5_bottleneck_pre_relu/conv', 'mixed4c_5x5_bottleneck_pre_relu', 'mixed4c_5x5_bottleneck', 'mixed4c_5x5_pre_relu/conv', 'mixed4c_5x5_pre_relu', 'mixed4c_5x5', 'mixed4c_pool', 'mixed4c_pool_reduce_pre_relu/conv', 'mixed4c_pool_reduce_pre_relu', 'mixed4c_pool_reduce', 'mixed4c/concat_dim', 'mixed4c', 'mixed4d_1x1_pre_relu/conv', 'mixed4d_1x1_pre_relu', 'mixed4d_1x1', 'mixed4d_3x3_bottleneck_pre_relu/conv', 'mixed4d_3x3_bottleneck_pre_relu', 'mixed4d_3x3_bottleneck', 'mixed4d_3x3_pre_relu/conv', 'mixed4d_3x3_pre_relu', 'mixed4d_3x3', 'mixed4d_5x5_bottleneck_pre_relu/conv', 'mixed4d_5x5_bottleneck_pre_relu', 'mixed4d_5x5_bottleneck', 'mixed4d_5x5_pre_relu/conv', 'mixed4d_5x5_pre_relu', 'mixed4d_5x5', 'mixed4d_pool', 'mixed4d_pool_reduce_pre_relu/conv', 'mixed4d_pool_reduce_pre_relu', 'mixed4d_pool_reduce', 'mixed4d/concat_dim', 'mixed4d', 'mixed4e_1x1_pre_relu/conv', 'mixed4e_1x1_pre_relu', 'mixed4e_1x1', 'mixed4e_3x3_bottleneck_pre_relu/conv', 'mixed4e_3x3_bottleneck_pre_relu', 'mixed4e_3x3_bottleneck', 'mixed4e_3x3_pre_relu/conv', 'mixed4e_3x3_pre_relu', 'mixed4e_3x3', 'mixed4e_5x5_bottleneck_pre_relu/conv', 'mixed4e_5x5_bottleneck_pre_relu', 'mixed4e_5x5_bottleneck', 'mixed4e_5x5_pre_relu/conv', 'mixed4e_5x5_pre_relu', 'mixed4e_5x5', 'mixed4e_pool', 'mixed4e_pool_reduce_pre_relu/conv', 'mixed4e_pool_reduce', 'mixed4e/concat_dim', 'mixed4e', 'maxpool10', 'mixed5a_1x1_pre_relu/conv', 'mixed5a_1x1_pre_relu', 'mixed5a_1x1', 'mixed5a_3x3_bottleneck_pre_relu/conv', 'mixed5a_3x3_bottleneck_pre_relu', 'mixed5a_3x3_bottleneck', 'mixed5a_3x3_pre_relu/conv', 'mixed5a_3x3_pre_relu', 'mixed5a_3x3', 'mixed5a_5x5_bottleneck_pre_relu/conv', 'mixed5a_5x5_bottleneck_pre_relu', 'mixed5a_5x5_bottleneck', 'mixed5a_5x5_pre_relu/conv', 'mixed5a_5x5_pre_relu', 'mixed5a_5x5', 'mixed5a_pool', 'mixed5a_pool_reduce_pre_relu/conv', 'mixed5a_pool_reduce_pre_relu', 'mixed5a_pool_reduce', 'mixed5a/concat_dim', 'mixed5a', 'mixed5b_1x1_pre_relu/conv', 'mixed5b_1x1_pre_relu', 'mixed5b_1x1', 'mixed5b_3x3_bottleneck_pre_relu/conv', 'mixed5b_3x3_bottleneck_pre_relu', 'mixed5b_3x3_bottleneck', 'mixed5b_3x3_pre_relu/conv', 'mixed5b_3x3_pre_relu', 'mixed5b_3x3', 'mixed5b_5x5_bottleneck_pre_relu/conv', 'mixed5b_5x5_bottleneck_pre_relu', 'mixed5b_5x5_bottleneck', 'mixed5b_5x5_pre_relu/conv', 'mixed5b_5x5_pre_relu', 'mixed5b_5x5', 'mixed5b_pool', 'mixed5b_pool_reduce_pre_relu/conv', 'mixed5b_pool_reduce_pre_relu', 'mixed5b_pool_reduce', 'mixed5b/concat_dim', 'mixed5b', 'avgpool0', 'head0_pool', 'head0_bottleneck_pre_relu/conv', 'head0_bottleneck_pre_relu', 'head0_bottleneck', 'head0_bottleneck/reshape/shape', 'head0_bottleneck/reshape', 'nn0_pre_relu/matmul', 'nn0_pre_relu', 'nn0', 'nn0/reshape/shape', 'nn0/reshape', 'softmax0_pre_activation/matmul', 'softmax0_pre_activation', 'softmax0', 'head1_pool', 'head1_bottleneck_pre_relu/conv', 'head1_bottleneck_pre_relu', 'head1_bottleneck', 'head1_bottleneck/reshape/shape', 'head1_bottleneck/reshape', 'nn1_pre_relu/matmul', 'nn1_pre_relu', 'nn1', 'nn1/reshape/shape', 'nn1/reshape', 'softmax1_pre_activation/matmul', 'softmax1_pre_activation', 'softmax1', 'avgpool0/reshape/shape', 'avgpool0/reshape', 'softmax2_pre_activation/matmul', 'softmax2_pre_activation', 'softmax2']
        self.resize = self.tffunc(np.float32, np.int32)(self._resize)

    # Helper function that uses TF to resize an image
    @classmethod
    def _resize(cls, img, size):
        img = tf.expand_dims(img, 0)
        return tf.image.resize_bilinear(img, size)[0, :, :, :]

    def T(self, layer):
        '''Helper for getting layer output tensor'''
        return self.graph.get_tensor_by_name("import/%s:0" % layer)

    def tffunc(self, *argtypes):
        '''Helper that transforms TF-graph generating function into a regular one.
        See "resize" function below.
        '''
        placeholders = list(map(tf.placeholder, argtypes))

        def wrap(f):
            out = f(*placeholders)

            def wrapper(*args, **kw):
                return out.eval(dict(zip(placeholders, args)),
                                session=self.sess)

            return wrapper

        return wrap

    def get_random_pic(self, filepath=None):
        filepath = filepath or expanduser("~/dream_seed.jpg")
        url = "https://unsplash.it/640/480/?random"
        urllib.urlretrieve(url, filepath)
        return filepath

    def calc_grad_tiled(self, img, t_grad, tile_size=512):
        '''Compute the value of tensor t_grad over the image in a tiled way.
        Random shifts are applied to the image to blur tile boundaries over
        multiple iterations.'''
        sz = tile_size
        h, w = img.shape[:2]
        sx, sy = np.random.randint(sz, size=2)
        img_shift = np.roll(np.roll(img, sx, 1), sy, 0)
        grad = np.zeros_like(img)
        for y in range(0, max(h - sz // 2, sz), sz):
            for x in range(0, max(w - sz // 2, sz), sz):
                sub = img_shift[y:y + sz, x:x + sz]
                g = self.sess.run(t_grad, {self.t_input: sub})
                grad[y:y + sz, x:x + sz] = g
        return np.roll(np.roll(grad, -sx, 1), -sy, 0)

    def render_deepdream(self, t_grad, img0, iter_n=10, step=1.5, octave_n=4,
                         octave_scale=1.4):
        # split the image into a number of octaves
        img = img0
        octaves = []
        for i in range(octave_n - 1):
            hw = img.shape[:2]
            lo = self.resize(img, np.int32(np.float32(hw) / octave_scale))
            hi = img - self.resize(lo, hw)
            img = lo
            octaves.append(hi)

        # generate details octave by octave
        for octave in range(octave_n):
            if octave > 0:
                hi = octaves[-octave]
                img = self.resize(img, hi.shape[:2]) + hi
            for i in range(iter_n):
                g = self.calc_grad_tiled(img, t_grad, self.tile_size)
                img += g * (step / (np.abs(g).mean() + 1e-7))
                if self.verbose:
                    print "Iteration Number: %d" % i
            if self.verbose:
                print "Octave Number: %d" % octave

        return Image.fromarray(np.uint8(np.clip(img / 255.0, 0, 1) * 255))

    def render(self, img, layer='mixed4d_3x3_bottleneck_pre_relu',
               channel=139, iter_n=10, step=1.5, octave_n=4,
               octave_scale=1.4):
        if self.last_layer == layer and self.last_channel == channel:
            t_grad = self.last_grad
        else:
            if channel == 4242:
                t_obj = tf.square(self.graph, self.T(layer))
            else:
                t_obj = self.T(layer)[:, :, :, channel]
            t_score = tf.reduce_mean(t_obj)  # defining the optimization objective
            t_grad = tf.gradients(t_score, self.t_input)[
                0]  # behold the power of automatic differentiation!
            self.last_layer = layer
            self.last_grad = t_grad
            self.last_channel = channel
        img0 = np.float32(img)
        return self.render_deepdream(t_grad, img0, iter_n, step, octave_n,
                                octave_scale)

    def dream(self, output_name=None, seed=None, channel_value=None,
              layer_name=None,
              iter_value=10,
              step_size=1.5,
              octave_value=4, octave_scale_value=1.5):

        self.last_layer = None
        self.last_grad = None
        self.last_channel = None
        seed = seed or self.get_random_pic()
        input_img = imread(seed)
        if layer_name not in self.layers:
            layer_name = None
        channel_value = channel_value or random.randint(0,300)
        layer_name = layer_name or random.choice(self.layers)
        print layer_name, channel_value
        try:
            output_img = self.render(input_img, layer=layer_name,
                             channel=channel_value,
                            iter_n=iter_value, step=step_size, octave_n=octave_value,
                            octave_scale=octave_scale_value)
            output_name = output_name or time.asctime().strip() +"_" + layer_name.replace("/", "")+"_"+str(channel_value)
            if ".jpg" not in output_name:
                output_name += ".jpg"
            print output_name
            imsave(output_name, output_img)
            return output_name
        except Exception as e:
            print e
            return False

    def dream_all(self, seed=None, channel_value=None,
              iter_value=10,
              step_size=1.5,
              octave_value=4, octave_scale_value=1.5):
        layers = list(self.layers)
        random.shuffle(layers)
        for layer_name in layers:
            try:
                self.dream(layer_name=layer_name, seed=seed,
                           channel_value=channel_value,
                            iter_value=iter_value, step_size=step_size,
                           octave_value=octave_value,
                            octave_scale_value=octave_scale_value)
            except Exception as e:
                print e

    @classmethod
    def maybe_download_and_extract(cls, model_folder):
        # """Download and extract model zip file."""
        # wget https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip
        # unzip -d model inception5h.zip
        url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip"
        if not os.path.exists(model_folder):
            os.makedirs(model_folder)
        filename = url.split('/')[-1]
        filepath = os.path.join(model_folder, filename)
        if not os.path.exists(filepath):
            print("Model is not in folder, downloading")
            urllib.urlretrieve(url, filepath)
            statinfo = os.stat(filepath)
            print('Successfully downloaded '+ filename +
                          "\n"+str(statinfo.st_size) + ' bytes.')
            # unzip
            import zipfile
            zip_ref = zipfile.ZipFile(filepath, 'r')
            zip_ref.extractall(model_folder)
            zip_ref.close()


d = DeepDreamer("/home/user/tensorflow_models/inception5h"
                "/tensorflow_inception_graph.pb")
d.dream()