meta_data = {}

ic_file = join(data_dir, 'annotations/captions_{}2014.json'.format(split))

with open(ic_file) as f:

ic_data = json.loads(f.read())

meta_data['data_length'] = len(ic_data['annotations'])

with open(join(data_dir, 'meta_{}.pkl'.format(split)), 'wb') as f:

pickle.dump(meta_data, f)

model = skipthoughts.load_model()

batch_no = 0

print "Total Batches", len(ic_data['annotations'])/batch_size

while batch_no*batch_size < len(ic_data['annotations']):

captions = []

image_ids = []

idx = batch_no

for i in range(batch_no*batch_size, (batch_no+1)*batch_size):

idx = i%len(ic_data['annotations'])

captions.append(ic_data['annotations'][idx]['caption'])

image_ids.append(ic_data['annotations'][idx]['image_id'])

print captions

print image_ids

# Thought Vectors

tv_batch = skipthoughts.encode(model, captions)

h5f_tv_batch = h5py.File( join(data_dir, 'tvs/'+split + '_tvs_' + str(batch_no)), 'w')

h5f_tv_batch.create_dataset('tv', data=tv_batch)

h5f_tv_batch.close()

h5f_tv_batch_image_ids = h5py.File( join(data_dir, 'tvs/'+split + '_tv_image_id_' + str(batch_no)), 'w')

h5f_tv_batch_image_ids.create_dataset('tv', data=image_ids)

h5f_tv_batch_image_ids.close()

print "Batches Done", batch_no, len(ic_data['annotations'])/batch_size

import time

img_dir = join(data_dir, 'flowers/jpg')

image_files = [f for f in os.listdir(img_dir) if 'jpg' in f]

print image_files[300:400]

print len(image_files)

image_captions = { img_file : [] for img_file in image_files }

caption_dir = join(data_dir, 'flowers/text_c10')

class_dirs = []

for i in range(1, 103):

class_dir_name = 'class_%.5d'%(i)

class_dirs.append( join(caption_dir, class_dir_name))

for class_dir in class_dirs:

caption_files = [f for f in os.listdir(class_dir) if 'txt' in f]

for cap_file in caption_files:

with open(join(class_dir,cap_file)) as f:

captions = f.read().split('\n')

img_file = cap_file[0:11] + ".jpg"

# 5 captions per image

image_captions[img_file] += [cap for cap in captions if len(cap) > 0][0:5]

print len(image_captions)

model = skipthoughts.load_model()

encoded_captions = {}

for i, img in enumerate(image_captions):

st = time.time()

encoded_captions[img] = skipthoughts.encode(model, image_captions[img])

print i, len(image_captions), img

print "Seconds", time.time() - st

h = h5py.File(join(data_dir, 'flower_tv.hdf5'))

for key in encoded_captions:

h.create_dataset(key, data=encoded_captions[key])

import time

img_dir = join(data_dir, 'shapes/images')

image_files = [f for f in os.listdir(img_dir) if 'png' in f]

print image_files[300:400]

print len(image_files)

image_captions = { img_file : [] for img_file in image_files }

caption_dir = join(data_dir, 'shapes/texts')

caption_files = [f for f in os.listdir(caption_dir) if 'txt' in f]

for cap_file in caption_files:

with open(join(caption_dir,cap_file)) as f:

captions = f.read().split('\n')

img_file = cap_file[0:5] + ".png"

# 5 captions per image

image_captions[img_file] += [cap for cap in captions if len(cap) > 0][0:5]

print len(image_captions)

model = skipthoughts.load_model()

encoded_captions = {}

for i, img in enumerate(image_captions):

st = time.time()

encoded_captions[img] = skipthoughts.encode(model, image_captions[img])

print i, len(image_captions), img

print "Seconds", time.time() - st

h = h5py.File(join(data_dir, 'shapes_tv.hdf5'))

for key in encoded_captions:

h.create_dataset(key, data=encoded_captions[key])

import time

print("BEGIN LOADING FLICKR")

img_dir = join('/home/hhl028/WINNtranslation/flickr', 'cropped_images')

def get_images_path_in_directory(path):

'''

Get path of all images recursively in directory filtered by extension list.

path: Path of directory contains images.

Return path of images in selected directory.

'''

images_path_in_directory = []

image_extensions = ['.png', '.jpg']

for root_path, directory_names, file_names in os.walk(path):

for file_name in file_names:

lower_file_name = file_name.lower()

if any(map(lambda image_extension:

lower_file_name.endswith(image_extension),

image_extensions)):

images_path_in_directory.append(os.path.join(root_path, file_name))

return images_path_in_directory

# # read the labels into some data structure

img_to_text = {}

with open("/home/hhl028/WINNtranslation/flickr/image_labels.txt", 'r') as file:

i = 0

for line in file:

tmp = line.split("|")

label = '_'.join(tmp[:2])

img_to_text[label] = tmp[2:]

if i % 25000 == 0:

print("Finished iteration %d" % (i))

i += 1

def remove_invalid_paths(pos_all_images_path, img_to_text):

'''

Some images don't have text. Don't use those.

'''

res = []

for path in pos_all_images_path:

# get image name from path

img_name = path.split('/')[-1].replace('.png','').replace('.jpg','')

label = '_'.join((img_name).split("_")[:2])

# don't use image unless we have phrases for it

if label in img_to_text:

res.append(path)

return res

# remove images with invalid paths

image_files = remove_invalid_paths(image_files, img_to_text)

image_captions = { img_file : [] for img_file in image_files }

# remove images without one of the target terms in it

def get_image_texts(pos_all_images_path, target_phrases, img_to_text):

'''

Given a list of positive ground truth images/text, return two lists,

one of image paths and one of texts.

At least one of the phrases for the given image must contain the

target text.

'''

return_labels = []

return_texts = []

for image_path in pos_all_images_path:

img_name = image_path.split('/')[-1].replace('.png','').replace('.jpg','')

label = '_'.join((img_name).split("_")[:2])

for phrase in img_to_text[label]:

for target_phrase in target_phrases:

if target_phrase in phrase + '\n':

return_labels.append(label)

return_texts.append(phrase.strip())

return zip(return_labels, return_texts)

# class 1: man

parser = argparse.ArgumentParser()

parser.add_argument('--split', type=str, default='train',

help='train/val')

parser.add_argument('--data_dir', type=str, default='Data',

help='Data directory')

parser.add_argument('--batch_size', type=int, default=64,

help='Batch Size')

parser.add_argument('--data_set', type=str, default='flowers',

help='Data Set : Flowers, MS-COCO')

args = parser.parse_args()

if args.data_set == 'flowers':

save_caption_vectors_flowers(args.data_dir)

elif args.data_set == 'shapes':

save_caption_vectors_shapes(args.data_dir)

elif args.data_set == 'flickr':

save_caption_vectors_flickr(args.data_dir)

else: