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preprocess.py
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preprocess.py
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# coding=utf8
import argparse, os, json, string
from collections import Counter
from Queue import Queue
from threading import Thread, Lock
from math import floor
import h5py
import numpy as np
from scipy.misc import imread, imresize
"""
This file expects a JSON file containing ground-truth regions and captions
in the same format as the region descriptions file from the Visual Genome
website. Concretely, this is a single large JSON file containing a list;
each element of the list describes a single image and has the following
format:
{
"id": [int], Unique identifier for this image,
"regions": [
{
"id": [int] Unique identifier for this region,
"image": [int] ID of the image to which this region belongs,
"height": [int] Height of the region in pixels,
"width": [int] Width of the region in pixels,
"phrase": [string] Caption for this region,
"x": [int] x-coordinate of the upper-left corner of the region,
"y": [int] y-coordinate of the upper-left corner of the region,
},
...
]
}
We assume that all images are on disk in a single folder, and that
the filename for each image is the same as its id with a .jpg extension.
This file will be preprocessed into an HDF5 file and a JSON file with
some auxiliary information. The captions will be tokenized with some
basic preprocessing (split by words, remove special characters).
Note, in general any indices anywhere in input/output of this file are 1-indexed.
The output JSON file is an object with the following elements:
- token_to_idx: Dictionary mapping strings to integers for encoding tokens,
in 1-indexed format.
- filename_to_idx: Dictionary mapping string filenames to indices.
- idx_to_token: Inverse of the above.
- idx_to_filename: Inverse of the above.
The output HDF5 file has the following format to describe N images with
M total regions:
- images: uint8 array of shape (N, 3, image_size, image_size) of pixel data,
in BDHW format. Images will be resized so their longest edge is image_size
pixels long, aligned to the upper left corner, and padded with zeros.
The actual size of each image is stored in the image_heights and image_widths
fields.
- image_heights: int32 array of shape (N,) giving the height of each image.
- image_widths: int32 array of shape (N,) giving the width of each image.
- original_heights: int32 array of shape (N,) giving the original height of
each image.
- original_widths: int32 array of shape (N,) giving the original width of
each image.
- boxes: int32 array of shape (M, 4) giving the coordinates of each bounding box.
Each row is (xc, yc, w, h) where yc and xc are center coordinates of the box,
and are one-indexed.
- lengths: int32 array of shape (M,) giving lengths of label sequence for each box
- captions: int32 array of shape (M, L) giving the captions for each region.
Captions in the input with more than L = --max_token_length tokens are
discarded. To recover a token from an integer in this matrix,
use idx_to_token from the JSON output file. Padded with zeros.
- img_to_first_box: int32 array of shape (N,). If img_to_first_box[i] = j then
captions[j] and boxes[j] give the first annotation for image i
(using one-indexing).
- img_to_last_box: int32 array of shape (N,). If img_to_last_box[i] = j then
captions[j] and boxes[j] give the last annotation for image i
(using one-indexing).
- box_to_img: int32 array of shape (M,). If box_to_img[i] = j then then
regions[i] and captions[i] refer to images[j] (using one-indexing).
"""
def build_vocab(data, min_token_instances, verbose=True):
""" Builds a set that contains the vocab. Filters infrequent tokens. """
token_counter = Counter()
for img in data:
for region in img['regions']:
if region['tokens'] is not None:
token_counter.update(region['tokens'])
vocab = set()
for token, count in token_counter.iteritems():
if count >= min_token_instances:
vocab.add(token)
if verbose:
print ('Keeping %d / %d tokens with enough instances'
% (len(vocab), len(token_counter)))
if len(vocab) < len(token_counter):
vocab.add('<UNK>')
if verbose:
print('adding special <UNK> token.')
else:
if verbose:
print('no <UNK> token needed.')
return vocab
def build_vocab_dict(vocab):
token_to_idx, idx_to_token = {}, {}
next_idx = 1
for token in vocab:
token_to_idx[token] = next_idx
idx_to_token[next_idx] = token
next_idx = next_idx + 1
return token_to_idx, idx_to_token
def encode_caption(tokens, token_to_idx, max_token_length):
encoded = np.zeros(max_token_length, dtype=np.int32)
for i, token in enumerate(tokens):
if token in token_to_idx:
encoded[i] = token_to_idx[token]
else:
encoded[i] = token_to_idx['<UNK>']
return encoded
def encode_captions(data, token_to_idx, max_token_length):
encoded_list = []
lengths = []
for img in data:
for region in img['regions']:
tokens = region['tokens']
if tokens is None: continue
tokens_encoded = encode_caption(tokens, token_to_idx, max_token_length)
encoded_list.append(tokens_encoded)
lengths.append(len(tokens))
return np.vstack(encoded_list), np.asarray(lengths, dtype=np.int32)
def encode_boxes(data, original_heights, original_widths, image_size):
all_boxes = []
xwasbad = 0
ywasbad = 0
wwasbad = 0
hwasbad = 0
for i, img in enumerate(data):
H, W = original_heights[i], original_widths[i]
scale = float(image_size) / max(H, W)
for region in img['regions']:
if region['tokens'] is None: continue
# recall: x,y are 1-indexed
x, y = round(scale*(region['x']-1)+1), round(scale*(region['y']-1)+1)
w, h = round(scale*region['width']), round(scale*region['height'])
# clamp to image
if x < 1: x = 1
if y < 1: y = 1
if x > image_size - 1:
x = image_size - 1
xwasbad += 1
if y > image_size - 1:
y = image_size - 1
ywasbad += 1
if x + w > image_size:
w = image_size - x
wwasbad += 1
if y + h > image_size:
h = image_size - y
hwasbad += 1
box = np.asarray([x+floor(w/2), y+floor(h/2), w, h], dtype=np.int32) # also convert to center-coord oriented
assert box[2]>=0 # width height should be positive numbers
assert box[3]>=0
all_boxes.append(box)
print 'number of bad x,y,w,h: ', xwasbad, ywasbad, wwasbad, hwasbad
return np.vstack(all_boxes)
def build_img_idx_to_box_idxs(data):
img_idx = 1
box_idx = 1
num_images = len(data)
img_to_first_box = np.zeros(num_images, dtype=np.int32)
img_to_last_box = np.zeros(num_images, dtype=np.int32)
for img in data:
img_to_first_box[img_idx - 1] = box_idx
for region in img['regions']:
if region['tokens'] is None: continue
box_idx += 1
img_to_last_box[img_idx - 1] = box_idx - 1 # -1 to make these inclusive limits
img_idx += 1
return img_to_first_box, img_to_last_box
def build_filename_dict(data):
# First make sure all filenames
filenames_list = ['%d.jpg' % img['id'] for img in data]
assert len(filenames_list) == len(set(filenames_list))
next_idx = 1
filename_to_idx, idx_to_filename = {}, {}
for img in data:
filename = '%d.jpg' % img['id']
filename_to_idx[filename] = next_idx
idx_to_filename[next_idx] = filename
next_idx += 1
return filename_to_idx, idx_to_filename
def encode_filenames(data, filename_to_idx):
filename_idxs = []
for img in data:
filename = '%d.jpg' % img['id']
idx = filename_to_idx[filename]
for region in img['regions']:
if region['tokens'] is None: continue
filename_idxs.append(idx)
return np.asarray(filename_idxs, dtype=np.int32)
def add_images(data, h5_file, args):
num_images = len(data)
shape = (num_images, 3, args.image_size, args.image_size)
image_dset = h5_file.create_dataset('images', shape, dtype=np.uint8)
original_heights = np.zeros(num_images, dtype=np.int32)
original_widths = np.zeros(num_images, dtype=np.int32)
image_heights = np.zeros(num_images, dtype=np.int32)
image_widths = np.zeros(num_images, dtype=np.int32)
lock = Lock()
q = Queue()
for i, img in enumerate(data):
filename = os.path.join(args.image_dir, '%s.jpg' % img['id'])
q.put((i, filename))
def worker():
while True:
i, filename = q.get()
img = imread(filename)
# handle grayscale
if img.ndim == 2:
img = img[:, :, None][:, :, [0, 0, 0]]
H0, W0 = img.shape[0], img.shape[1]
img = imresize(img, float(args.image_size) / max(H0, W0))
H, W = img.shape[0], img.shape[1]
# swap rgb to bgr. Is this the best way?
r = img[:,:,0].copy()
img[:,:,0] = img[:,:,2]
img[:,:,2] = r
lock.acquire()
if i % 1000 == 0:
print 'Writing image %d / %d' % (i, len(data))
original_heights[i] = H0
original_widths[i] = W0
image_heights[i] = H
image_widths[i] = W
image_dset[i, :, :H, :W] = img.transpose(2, 0, 1)
lock.release()
q.task_done()
print('adding images to hdf5.... (this might take a while)')
for i in xrange(args.num_workers):
t = Thread(target=worker)
t.daemon = True
t.start()
q.join()
h5_file.create_dataset('image_heights', data=image_heights)
h5_file.create_dataset('image_widths', data=image_widths)
h5_file.create_dataset('original_heights', data=original_heights)
h5_file.create_dataset('original_widths', data=original_widths)
def words_preprocess(phrase):
""" preprocess a sentence: lowercase, clean up weird chars, remove punctuation """
replacements = {
u'½': u'half',
u'—' : u'-',
u'™': u'',
u'¢': u'cent',
u'ç': u'c',
u'û': u'u',
u'é': u'e',
u'°': u' degree',
u'è': u'e',
u'…': u'',
}
for k, v in replacements.iteritems():
phrase = phrase.replace(k, v)
return str(phrase).lower().translate(None, string.punctuation).split()
def split_filter_captions(data, max_token_length, tokens_type, verbose=True):
"""
Modifies data in-place by adding a 'tokens' field to each region.
If the region's label is too long, 'tokens' will be None; otherwise
it will be a list of strings.
Splits by space when tokens_type = "words", or lists all chars when "chars"
"""
captions_kept = 0
captions_removed = 0
for i, img in enumerate(data):
if verbose and (i + 1) % 2000 == 0:
print 'Splitting tokens in image %d / %d' % (i + 1, len(data))
regions_per_image = 0
img_kept, img_removed = 0, 0
for region in img['regions']:
# create tokens array
if tokens_type == 'words':
tokens = words_preprocess(region['phrase'])
elif tokens_type == 'chars':
tokens = list(region['label'])
else:
assert False, 'tokens_type must be "words" or "chars"'
# filter by length
if max_token_length > 0 and len(tokens) <= max_token_length:
region['tokens'] = tokens
captions_kept += 1
img_kept += 1
regions_per_image = regions_per_image + 1
else:
region['tokens'] = None
captions_removed += 1
img_removed += 1
if regions_per_image == 0:
print 'kept %d, removed %d' % (img_kept, img_removed)
assert False, 'DANGER, some image has no valid regions. Not super sure this doesnt cause bugs. Think about more if it comes up'
if verbose:
print 'Keeping %d captions' % captions_kept
print 'Skipped %d captions for being too long' % captions_removed
def encode_splits(data, split_data):
""" Encode splits as intetgers and return the array. """
lookup = {'train': 0, 'val': 1, 'test': 2}
id_to_split = {}
split_array = np.zeros(len(data))
for split, idxs in split_data.iteritems():
for idx in idxs:
id_to_split[idx] = split
for i, img in enumerate(data):
split_array[i] = lookup[id_to_split[img['id']]]
return split_array
def filter_images(data, split_data):
""" Keep only images that are in some split and have some captions """
all_split_ids = set()
for split_name, ids in split_data.iteritems():
all_split_ids.update(ids)
new_data = []
for img in data:
keep = img['id'] in all_split_ids and len(img['regions']) > 0
if keep:
new_data.append(img)
return new_data
def main(args):
# read in the data
with open(args.region_data, 'r') as f:
data = json.load(f)
with open(args.split_json, 'r') as f:
split_data = json.load(f)
# Only keep images that are in a split
print 'There are %d images total' % len(data)
data = filter_images(data, split_data)
print 'After filtering for splits there are %d images' % len(data)
if args.max_images > 0:
data = data[:args.max_images]
# create the output hdf5 file handle
f = h5py.File(args.h5_output, 'w')
# add several fields to the file: images, and the original/resized widths/heights
add_images(data, f, args)
# add split information
split = encode_splits(data, split_data)
f.create_dataset('split', data=split)
# process "label" field in each region to a "tokens" field, and cap at some max length
split_filter_captions(data, args.max_token_length, args.tokens_type)
# build vocabulary
vocab = build_vocab(data, args.min_token_instances) # vocab is a set()
token_to_idx, idx_to_token = build_vocab_dict(vocab) # both mappings are dicts
# encode labels
captions_matrix, lengths_vector = encode_captions(data, token_to_idx, args.max_token_length)
f.create_dataset('labels', data=captions_matrix)
f.create_dataset('lengths', data=lengths_vector)
# encode boxes
original_heights = np.asarray(f['original_heights'])
original_widths = np.asarray(f['original_widths'])
boxes_matrix = encode_boxes(data, original_heights, original_widths, args.image_size)
f.create_dataset('boxes', data=boxes_matrix)
# integer mapping between image ids and box ids
img_to_first_box, img_to_last_box = build_img_idx_to_box_idxs(data)
f.create_dataset('img_to_first_box', data=img_to_first_box)
f.create_dataset('img_to_last_box', data=img_to_last_box)
filename_to_idx, idx_to_filename = build_filename_dict(data)
box_to_img = encode_filenames(data, filename_to_idx)
f.create_dataset('box_to_img', data=box_to_img)
f.close()
# and write the additional json file
json_struct = {
'token_to_idx': token_to_idx,
'idx_to_token': idx_to_token,
'filename_to_idx': filename_to_idx,
'idx_to_filename': idx_to_filename,
}
with open(args.json_output, 'w') as f:
json.dump(json_struct, f)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# INPUT settings
parser.add_argument('--region_data',
default='data/visual-genome/region_descriptions.json',
help='Input JSON file with regions and captions')
parser.add_argument('--image_dir',
default='data/visual-genome/images',
help='Directory containing all images')
parser.add_argument('--split_json',
default='info/densecap_splits.json',
help='JSON file of splits')
# OUTPUT settings
parser.add_argument('--json_output',
default='data/VG-regions-dicts.json',
help='Path to output JSON file')
parser.add_argument('--h5_output',
default='data/VG-regions.h5',
help='Path to output HDF5 file')
# OPTIONS
parser.add_argument('--image_size',
default=720, type=int,
help='Size of longest edge of preprocessed images')
parser.add_argument('--max_token_length',
default=15, type=int,
help="Set to 0 to disable filtering")
parser.add_argument('--min_token_instances',
default=15, type=int,
help="When token appears less than this times it will be mapped to <UNK>")
parser.add_argument('--tokens_type', default='words',
help="Words|chars for word or char split in captions")
parser.add_argument('--num_workers', default=5, type=int)
parser.add_argument('--max_images', default=-1, type=int,
help="Set to a positive number to limit the number of images we process")
args = parser.parse_args()
main(args)