def get_data(z, filename):
import generate
output = generate.story(
z,
'/Users/xinzhang/Document/courses/671project/flask/app/static/img/' +
filename)
return output
def get_story(file_path):
#return 'test'
return generate.story(z, file_path)
def tell_a_story(image):
passage = generate.story(z, image.to_stream(), bw=1)
return {'passage': passage}
import generate z = generate.load_all() generate.story(z, './images/ex1.jpg')
# -*- coding: utf-8 -*- # # AIWriter.py # # Copyright 2016 Franz Habison <*****@*****.**> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, # MA 02110-1301, USA. # # # Info from # https://www.youtube.com/watch?v=x24VEUEph0Q&feature=youtu.be import generate z = generate.load_all() generate.story(z, "myImage.jpg")
#generates stories for a folfer of images --useful when evaluating
#sj2842
import glob
import generate
z = generate.load_all()
# from IPython.core.display import Image, display
# from PIL import Image as Img
for filename in glob.iglob('/Users/shreyajain/storyteller/neural-storyteller/images2/*'):
print('%s' % filename)
# display(Image(filename))
generate.story(z, filename)
print("########## Parameters set k = 200 and bw=50 ###############")
print(generate.story(z, filename,k=200, bw=50))
# print("########## Parameters set k = 300 and bw=100 ###############")
# print(generate.story(z, filename, k=300, bw=50))
# print("########## Parameters set k = 400 and bw=100 ###############")
# print(generate.story(z, filename, k=400, bw=50))
import sys,time
import argparse
import config
import generate
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model_cache_path', help = 'model cache path')
parser.add_argument('--type', help = 'train or inference',default='inference')
parser.add_argument('--input', help = 'input file')
parser.add_argument('--style', help = 'use style')
parser.add_argument('--condition_count', type=int,default=100)
parser.add_argument('--beamwidth', type=int,default=50)
args = parser.parse_args()
print args
if args.type == 'inference':
config.init(args.model_cache_path)
z = generate.load_all()
if args.style:
s = generate.story(z, args.input,args.condition_count,args.beamwidth,lyric=True)
else:
s = generate.story(z, args.input,args.condition_count,args.beamwidth)
#s = generate.story(z, args.input)
output_file = '/data/output/{}.txt'.format(str(int(time.time())));
with open(output_file, "w") as f:
f.write('{}'.format(s))
print output_file
parser.add_argument('--model_cache_path', help='model cache path')
parser.add_argument('--type',
help='train or inference',
default='inference')
parser.add_argument('--input', help='input file')
parser.add_argument('--style', help='use style')
parser.add_argument('--condition_count', type=int, default=100)
parser.add_argument('--beamwidth', type=int, default=50)
args = parser.parse_args()
print args
if args.type == 'inference':
config.init(args.model_cache_path)
z = generate.load_all()
if args.style:
s = generate.story(z,
args.input,
args.condition_count,
args.beamwidth,
lyric=True)
else:
s = generate.story(z, args.input, args.condition_count,
args.beamwidth)
#s = generate.story(z, args.input)
output_file = '/data/output/{}.txt'.format(str(int(time.time())))
with open(output_file, "w") as f:
f.write('{}'.format(s))
print output_file
import keras
import numpy
import imagenet_utils
import generate
from keras.preprocessing import image
def load_image(file_name):
img = image.load_img(file_name, target_size=(224, 224))
im = image.img_to_array(img)
im = numpy.expand_dims(im, axis=0)
im = imagenet_utils.preprocess_input(im)
return im
image = load_image('./images/ex1.jpg')
z = generate.load_all()
generate.story(z, image)
#as5446
#making files for langauge check
import glob
import generate
z = generate.load_all()
import generate2
y = generate2.load_all()
import generate3
w = generate3.load_all()
fil = open('/Users/shreyajain/Downloads/grammar.txt', 'w')
for filename in glob.iglob('/Users/shreyajain/Downloads/eval/*.jpg'):
story = generate.story(z, filename, k=100, bw=50)
story2 = generate2.story(y, filename, k=20, bw=5)
story3 = generate3.story(w, filename, k=20, bw=5)
print(story, story2, story3)
fil.write(story + '\t' + story2 + '\t' + story3)
fil.write('\n')
def get(filename):
z = generate.load_all()
output = generate.story(z, filename)
return output