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dataset_tool.py
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dataset_tool.py
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import os
import sys
import glob
import argparse
import threading
import six.moves.queue as Queue
import traceback
import numpy as np
import tensorflow as tf
import PIL.Image
import csv
import pdb
import tfutil
import dataset
import pickle
#----------------------------------------------------------------------------
def error(msg):
print('Error: ' + msg)
exit(1)
#----------------------------------------------------------------------------
class TFRecordExporter:
def __init__(self, tfrecord_dir, expected_images, print_progress=True, progress_interval=10):
self.tfrecord_dir = tfrecord_dir
self.tfr_prefix = os.path.join(self.tfrecord_dir, os.path.basename(self.tfrecord_dir))
self.expected_images = expected_images
self.cur_images = 0
self.shape = None
self.resolution_log2 = None
self.tfr_writers = []
self.print_progress = print_progress
self.progress_interval = progress_interval
if self.print_progress:
print('Creating dataset "%s"' % tfrecord_dir)
if not os.path.isdir(self.tfrecord_dir):
os.makedirs(self.tfrecord_dir)
assert(os.path.isdir(self.tfrecord_dir))
def close(self):
if self.print_progress:
print('%-40s\r' % 'Flushing data...', end='', flush=True)
for tfr_writer in self.tfr_writers:
tfr_writer.close()
self.tfr_writers = []
if self.print_progress:
print('%-40s\r' % '', end='', flush=True)
print('Added %d images.' % self.cur_images)
def choose_shuffled_order(self): # Note: Images and labels must be added in shuffled order.
order = np.arange(self.expected_images)
np.random.RandomState(123).shuffle(order)
return order
def add_image(self, img):
if self.print_progress and self.cur_images % self.progress_interval == 0:
print('%d / %d\r' % (self.cur_images, self.expected_images), end='', flush=True)
if self.shape is None:
self.shape = img.shape
self.resolution_log2 = int(np.log2(self.shape[1]))
assert self.shape[0] in [1, 3]
assert self.shape[1] == self.shape[2]
assert self.shape[1] == 2**self.resolution_log2
tfr_opt = tf.python_io.TFRecordOptions(tf.python_io.TFRecordCompressionType.NONE)
for lod in range(self.resolution_log2 - 1):
tfr_file = self.tfr_prefix + '-r%02d.tfrecords' % (self.resolution_log2 - lod)
self.tfr_writers.append(tf.python_io.TFRecordWriter(tfr_file, tfr_opt))
assert img.shape == self.shape
for lod, tfr_writer in enumerate(self.tfr_writers):
if lod:
img = img.astype(np.float32)
img = (img[:, 0::2, 0::2] + img[:, 0::2, 1::2] + img[:, 1::2, 0::2] + img[:, 1::2, 1::2]) * 0.25
quant = np.rint(img).clip(0, 255).astype(np.uint8)
ex = tf.train.Example(features=tf.train.Features(feature={
'shape': tf.train.Feature(int64_list=tf.train.Int64List(value=quant.shape)),
'data': tf.train.Feature(bytes_list=tf.train.BytesList(value=[quant.tostring()]))}))
tfr_writer.write(ex.SerializeToString())
self.cur_images += 1
def add_labels(self, labels):
if self.print_progress:
print('%-40s\r' % 'Saving labels...', end='', flush=True)
assert labels.shape[0] == self.cur_images
with open(self.tfr_prefix + '-rxx.labels', 'wb') as f:
np.save(f, labels.astype(np.float32))
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
#----------------------------------------------------------------------------
class ExceptionInfo(object):
def __init__(self):
self.value = sys.exc_info()[1]
self.traceback = traceback.format_exc()
#----------------------------------------------------------------------------
class WorkerThread(threading.Thread):
def __init__(self, task_queue):
threading.Thread.__init__(self)
self.task_queue = task_queue
def run(self):
while True:
func, args, result_queue = self.task_queue.get()
if func is None:
break
try:
result = func(*args)
except:
result = ExceptionInfo()
result_queue.put((result, args))
#----------------------------------------------------------------------------
class ThreadPool(object):
def __init__(self, num_threads):
assert num_threads >= 1
self.task_queue = Queue.Queue()
self.result_queues = dict()
self.num_threads = num_threads
for idx in range(self.num_threads):
thread = WorkerThread(self.task_queue)
thread.daemon = True
thread.start()
def add_task(self, func, args=()):
assert hasattr(func, '__call__') # must be a function
if func not in self.result_queues:
self.result_queues[func] = Queue.Queue()
self.task_queue.put((func, args, self.result_queues[func]))
def get_result(self, func): # returns (result, args)
result, args = self.result_queues[func].get()
if isinstance(result, ExceptionInfo):
print('\n\nWorker thread caught an exception:\n' + result.traceback)
raise result.value
return result, args
def finish(self):
for idx in range(self.num_threads):
self.task_queue.put((None, (), None))
def __enter__(self): # for 'with' statement
return self
def __exit__(self, *excinfo):
self.finish()
def process_items_concurrently(self, item_iterator, process_func=lambda x: x, pre_func=lambda x: x, post_func=lambda x: x, max_items_in_flight=None):
if max_items_in_flight is None: max_items_in_flight = self.num_threads * 4
assert max_items_in_flight >= 1
results = []
retire_idx = [0]
def task_func(prepared, idx):
return process_func(prepared)
def retire_result():
processed, (prepared, idx) = self.get_result(task_func)
results[idx] = processed
while retire_idx[0] < len(results) and results[retire_idx[0]] is not None:
yield post_func(results[retire_idx[0]])
results[retire_idx[0]] = None
retire_idx[0] += 1
for idx, item in enumerate(item_iterator):
prepared = pre_func(item)
results.append(None)
self.add_task(func=task_func, args=(prepared, idx))
while retire_idx[0] < idx - max_items_in_flight + 2:
for res in retire_result(): yield res
while retire_idx[0] < len(results):
for res in retire_result(): yield res
#----------------------------------------------------------------------------
def display(tfrecord_dir):
print('Loading dataset "%s"' % tfrecord_dir)
tfutil.init_tf({'gpu_options.allow_growth': True})
dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size='full', repeat=False, shuffle_mb=0)
tfutil.init_uninited_vars()
idx = 0
while True:
try:
images, labels = dset.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
break
if idx == 0:
print('Displaying images')
import cv2 # pip install opencv-python
cv2.namedWindow('dataset_tool')
print('Press SPACE or ENTER to advance, ESC to exit')
print('\nidx = %-8d\nlabel = %s' % (idx, labels[0].tolist()))
cv2.imshow('dataset_tool', images[0].transpose(1, 2, 0)[:, :, ::-1]) # CHW => HWC, RGB => BGR
idx += 1
if cv2.waitKey() == 27:
break
print('\nDisplayed %d images.' % idx)
#----------------------------------------------------------------------------
def extract(tfrecord_dir, output_dir):
print('Loading dataset "%s"' % tfrecord_dir)
tfutil.init_tf({'gpu_options.allow_growth': True})
dset = dataset.TFRecordDataset(tfrecord_dir, max_label_size=0, repeat=False, shuffle_mb=0)
tfutil.init_uninited_vars()
print('Extracting images to "%s"' % output_dir)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
idx = 0
while True:
if idx % 10 == 0:
print('%d\r' % idx, end='', flush=True)
try:
images, labels = dset.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
break
if images.shape[1] == 1:
img = PIL.Image.fromarray(images[0][0], 'L')
else:
img = PIL.Image.fromarray(images[0].transpose(1, 2, 0), 'RGB')
img.save(os.path.join(output_dir, 'img%08d.png' % idx))
idx += 1
print('Extracted %d images.' % idx)
#----------------------------------------------------------------------------
def compare(tfrecord_dir_a, tfrecord_dir_b, ignore_labels):
max_label_size = 0 if ignore_labels else 'full'
print('Loading dataset "%s"' % tfrecord_dir_a)
tfutil.init_tf({'gpu_options.allow_growth': True})
dset_a = dataset.TFRecordDataset(tfrecord_dir_a, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
print('Loading dataset "%s"' % tfrecord_dir_b)
dset_b = dataset.TFRecordDataset(tfrecord_dir_b, max_label_size=max_label_size, repeat=False, shuffle_mb=0)
tfutil.init_uninited_vars()
print('Comparing datasets')
idx = 0
identical_images = 0
identical_labels = 0
while True:
if idx % 100 == 0:
print('%d\r' % idx, end='', flush=True)
try:
images_a, labels_a = dset_a.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
images_a, labels_a = None, None
try:
images_b, labels_b = dset_b.get_minibatch_np(1)
except tf.errors.OutOfRangeError:
images_b, labels_b = None, None
if images_a is None or images_b is None:
if images_a is not None or images_b is not None:
print('Datasets contain different number of images')
break
if images_a.shape == images_b.shape and np.all(images_a == images_b):
identical_images += 1
else:
print('Image %d is different' % idx)
if labels_a.shape == labels_b.shape and np.all(labels_a == labels_b):
identical_labels += 1
else:
print('Label %d is different' % idx)
idx += 1
print('Identical images: %d / %d' % (identical_images, idx))
if not ignore_labels:
print('Identical labels: %d / %d' % (identical_labels, idx))
#----------------------------------------------------------------------------
def create_mnist(tfrecord_dir, mnist_dir):
print('Loading MNIST from "%s"' % mnist_dir)
import gzip
with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
images = np.frombuffer(file.read(), np.uint8, offset=16)
with gzip.open(os.path.join(mnist_dir, 'train-labels-idx1-ubyte.gz'), 'rb') as file:
labels = np.frombuffer(file.read(), np.uint8, offset=8)
images = images.reshape(-1, 1, 28, 28)
images = np.pad(images, [(0,0), (0,0), (2,2), (2,2)], 'constant', constant_values=0)
assert images.shape == (60000, 1, 32, 32) and images.dtype == np.uint8
assert labels.shape == (60000,) and labels.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_mnistrgb(tfrecord_dir, mnist_dir, num_images=1000000, random_seed=123):
print('Loading MNIST from "%s"' % mnist_dir)
import gzip
with gzip.open(os.path.join(mnist_dir, 'train-images-idx3-ubyte.gz'), 'rb') as file:
images = np.frombuffer(file.read(), np.uint8, offset=16)
images = images.reshape(-1, 28, 28)
images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
with TFRecordExporter(tfrecord_dir, num_images) as tfr:
rnd = np.random.RandomState(random_seed)
for idx in range(num_images):
tfr.add_image(images[rnd.randint(images.shape[0], size=3)])
#----------------------------------------------------------------------------
def create_cifar10(tfrecord_dir, cifar10_dir):
print('Loading CIFAR-10 from "%s"' % cifar10_dir)
import pickle
images = []
labels = []
for batch in range(1, 6):
with open(os.path.join(cifar10_dir, 'data_batch_%d' % batch), 'rb') as file:
data = pickle.load(file, encoding='latin1')
images.append(data['data'].reshape(-1, 3, 32, 32))
labels.append(data['labels'])
images = np.concatenate(images)
labels = np.concatenate(labels)
assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
assert labels.shape == (50000,) and labels.dtype == np.int32
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_cifar100(tfrecord_dir, cifar100_dir):
print('Loading CIFAR-100 from "%s"' % cifar100_dir)
import pickle
with open(os.path.join(cifar100_dir, 'train'), 'rb') as file:
data = pickle.load(file, encoding='latin1')
images = data['data'].reshape(-1, 3, 32, 32)
labels = np.array(data['fine_labels'])
assert images.shape == (50000, 3, 32, 32) and images.dtype == np.uint8
assert labels.shape == (50000,) and labels.dtype == np.int32
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 99
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_svhn(tfrecord_dir, svhn_dir):
print('Loading SVHN from "%s"' % svhn_dir)
import pickle
images = []
labels = []
for batch in range(1, 4):
with open(os.path.join(svhn_dir, 'train_%d.pkl' % batch), 'rb') as file:
data = pickle.load(file, encoding='latin1')
images.append(data[0])
labels.append(data[1])
images = np.concatenate(images)
labels = np.concatenate(labels)
assert images.shape == (73257, 3, 32, 32) and images.dtype == np.uint8
assert labels.shape == (73257,) and labels.dtype == np.uint8
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
with TFRecordExporter(tfrecord_dir, images.shape[0]) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
tfr.add_image(images[order[idx]])
tfr.add_labels(onehot[order])
#----------------------------------------------------------------------------
def create_lsun(tfrecord_dir, lmdb_dir, resolution=256, max_images=None):
print('Loading LSUN dataset from "%s"' % lmdb_dir)
import lmdb # pip install lmdb
import cv2 # pip install opencv-python
import io
with lmdb.open(lmdb_dir, readonly=True).begin(write=False) as txn:
total_images = txn.stat()['entries']
if max_images is None:
max_images = total_images
with TFRecordExporter(tfrecord_dir, max_images) as tfr:
for idx, (key, value) in enumerate(txn.cursor()):
try:
try:
img = cv2.imdecode(np.fromstring(value, dtype=np.uint8), 1)
if img is None:
raise IOError('cv2.imdecode failed')
img = img[:, :, ::-1] # BGR => RGB
except IOError:
img = np.asarray(PIL.Image.open(io.BytesIO(value)))
crop = np.min(img.shape[:2])
img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
img = PIL.Image.fromarray(img, 'RGB')
img = img.resize((resolution, resolution), PIL.Image.ANTIALIAS)
img = np.asarray(img)
img = img.transpose(2, 0, 1) # HWC => CHW
tfr.add_image(img)
except:
print(sys.exc_info()[1])
if tfr.cur_images == max_images:
break
#----------------------------------------------------------------------------
def create_celeba(tfrecord_dir, celeba_dir, cx=89, cy=121):
print('Loading CelebA from "%s"' % celeba_dir)
glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
image_filenames = sorted(glob.glob(glob_pattern))
expected_images = 202599
if len(image_filenames) != expected_images:
error('Expected to find %d images' % expected_images)
with TFRecordExporter(tfrecord_dir, len(image_filenames)) as tfr:
order = tfr.choose_shuffled_order()
for idx in range(order.size):
img = np.asarray(PIL.Image.open(image_filenames[order[idx]]))
assert img.shape == (218, 178, 3)
img = img[cy - 64 : cy + 64, cx - 64 : cx + 64]
img = img.transpose(2, 0, 1) # HWC => CHW
tfr.add_image(img)
#----------------------------------------------------------------------------
def create_celebahq(tfrecord_dir, celeba_dir, delta_dir, num_threads=4, num_tasks=100):
print('Loading CelebA from "%s"' % celeba_dir)
expected_images = 202599
if len(glob.glob(os.path.join(celeba_dir, 'img_celeba','img', '*.jpg'))) != expected_images:
error('Expected to find %d images' % expected_images)
with open(os.path.join(celeba_dir, 'Anno', 'list_landmarks_celeba.txt'), 'rt') as file:
landmarks = [[float(value) for value in line.split()[1:]] for line in file.readlines()[2:]]
landmarks = np.float32(landmarks).reshape(-1, 5, 2)
print('Loading CelebA-HQ deltas from "%s"' % delta_dir)
import scipy.ndimage
import hashlib
import bz2
import zipfile
import base64
import cryptography.hazmat.primitives.hashes
import cryptography.hazmat.backends
import cryptography.hazmat.primitives.kdf.pbkdf2
import cryptography.fernet
expected_zips = 30
if len(glob.glob(os.path.join(delta_dir, 'deltas*.zip'))) != expected_zips:
error('Expected to find %d zips' % expected_zips)
with open(os.path.join(delta_dir, 'image_list.txt'), 'rt') as file:
lines = [line.split() for line in file]
fields = dict()
for idx, field in enumerate(lines[0]):
type = int if field.endswith('idx') else str
fields[field] = [type(line[idx]) for line in lines[1:]]
indices = np.array(fields['idx'])
# Must use pillow version 3.1.1 for everything to work correctly.
if getattr(PIL, 'PILLOW_VERSION', '') != '3.1.1':
error('create_celebahq requires pillow version 3.1.1') # conda install pillow=3.1.1
# Must use libjpeg version 8d for everything to work correctly.
img = np.array(PIL.Image.open(os.path.join(celeba_dir, 'img_celeba','img', '000001.jpg')))
md5 = hashlib.md5()
md5.update(img.tobytes())
if md5.hexdigest() != '9cad8178d6cb0196b36f7b34bc5eb6d3':
error('create_celebahq requires libjpeg version 8d') # conda install jpeg=8d
def rot90(v):
return np.array([-v[1], v[0]])
def process_func(idx):
# Load original image.
orig_idx = fields['orig_idx'][idx]
orig_file = fields['orig_file'][idx]
orig_path = os.path.join(celeba_dir, 'img_celeba','img', orig_file)
img = PIL.Image.open(orig_path)
# Choose oriented crop rectangle.
lm = landmarks[orig_idx]
eye_avg = (lm[0] + lm[1]) * 0.5 + 0.5
mouth_avg = (lm[3] + lm[4]) * 0.5 + 0.5
eye_to_eye = lm[1] - lm[0]
eye_to_mouth = mouth_avg - eye_avg
x = eye_to_eye - rot90(eye_to_mouth)
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = rot90(x)
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
zoom = 1024 / (np.hypot(*x) * 2)
# Shrink.
shrink = int(np.floor(0.5 / zoom))
if shrink > 1:
size = (int(np.round(float(img.size[0]) / shrink)), int(np.round(float(img.size[1]) / shrink)))
img = img.resize(size, PIL.Image.ANTIALIAS)
quad /= shrink
zoom *= shrink
# Crop.
border = max(int(np.round(1024 * 0.1 / zoom)), 3)
crop = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
# Simulate super-resolution.
superres = int(np.exp2(np.ceil(np.log2(zoom))))
if superres > 1:
img = img.resize((img.size[0] * superres, img.size[1] * superres), PIL.Image.ANTIALIAS)
quad *= superres
zoom /= superres
# Pad.
pad = (int(np.floor(min(quad[:,0]))), int(np.floor(min(quad[:,1]))), int(np.ceil(max(quad[:,0]))), int(np.ceil(max(quad[:,1]))))
pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
if max(pad) > border - 4:
pad = np.maximum(pad, int(np.round(1024 * 0.3 / zoom)))
img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
h, w, _ = img.shape
y, x, _ = np.mgrid[:h, :w, :1]
mask = 1.0 - np.minimum(np.minimum(np.float32(x) / pad[0], np.float32(y) / pad[1]), np.minimum(np.float32(w-1-x) / pad[2], np.float32(h-1-y) / pad[3]))
blur = 1024 * 0.02 / zoom
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0,1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.round(img), 0, 255)), 'RGB')
quad += pad[0:2]
# Transform.
img = img.transform((4096, 4096), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
img = img.resize((1024, 1024), PIL.Image.ANTIALIAS)
img = np.asarray(img).transpose(2, 0, 1)
# Verify MD5.
md5 = hashlib.md5()
md5.update(img.tobytes())
assert md5.hexdigest() == fields['proc_md5'][idx]
# Load delta image and original JPG.
with zipfile.ZipFile(os.path.join(delta_dir, 'deltas%05d.zip' % (idx - idx % 1000)), 'r') as zip:
delta_bytes = zip.read(os.path.join('deltas%05d' % (idx - idx % 1000) ,'delta%05d.dat' % idx))
with open(orig_path, 'rb') as file:
orig_bytes = file.read()
# Decrypt delta image, using original JPG data as decryption key.
algorithm = cryptography.hazmat.primitives.hashes.SHA256()
backend = cryptography.hazmat.backends.default_backend()
salt = bytes(orig_file, 'ascii')
kdf = cryptography.hazmat.primitives.kdf.pbkdf2.PBKDF2HMAC(algorithm=algorithm, length=32, salt=salt, iterations=100000, backend=backend)
key = base64.urlsafe_b64encode(kdf.derive(orig_bytes))
delta = np.frombuffer(bz2.decompress(cryptography.fernet.Fernet(key).decrypt(delta_bytes)), dtype=np.uint8).reshape(3, 1024, 1024)
# Apply delta image.
img = img + delta
# Verify MD5.
md5 = hashlib.md5()
md5.update(img.tobytes())
assert md5.hexdigest() == fields['final_md5'][idx]
return img
with TFRecordExporter(tfrecord_dir, indices.size) as tfr:
order = tfr.choose_shuffled_order()
with ThreadPool(num_threads) as pool: