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h5tool.py
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h5tool.py
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# Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
# This file is not part of the Pioneer Networks, but it is necessary to produce the CelebA-HQ dataset properly
# for use outside Tensorflow.
# The file was adapted by Ari Heljakka to allow for creating training and testing datasets in separate H5 files
# and updated to support the Tensorflow version 1.8.0.
# All rights remain with the original copyright holder, NVIDIA CORPORATION.
import os
import sys
import io
import glob
import pickle
import argparse
import threading
import queue
import traceback
import numpy as np
import scipy.ndimage
import PIL.Image
import h5py # conda install h5py
#----------------------------------------------------------------------------
class HDF5Exporter:
def __init__(self, h5_filename, resolution, channels=3):
rlog2 = int(np.floor(np.log2(resolution)))
assert resolution == 2 ** rlog2
self.resolution = resolution
self.channels = channels
self.h5_file = h5py.File(h5_filename, 'w')
self.h5_lods = []
self.buffers = []
self.buffer_sizes = []
for lod in range(rlog2, -1, -1):
r = 2 ** lod; c = channels
bytes_per_item = c * (r ** 2)
chunk_size = int(np.ceil(128.0 / bytes_per_item))
buffer_size = int(np.ceil(512.0 * np.exp2(20) / bytes_per_item))
lod = self.h5_file.create_dataset('data%dx%d' % (r,r), shape=(0,c,r,r), dtype=np.uint8,
maxshape=(None,c,r,r), chunks=(chunk_size,c,r,r), compression='gzip', compression_opts=4)
self.h5_lods.append(lod)
self.buffers.append(np.zeros((buffer_size,c,r,r), dtype=np.uint8))
self.buffer_sizes.append(0)
def close(self):
for lod in range(len(self.h5_lods)):
self.flush_lod(lod)
self.h5_file.close()
def add_images(self, img):
assert img.ndim == 4 and img.shape[1] == self.channels and img.shape[2] == img.shape[3]
assert img.shape[2] >= self.resolution and img.shape[2] == 2 ** int(np.floor(np.log2(img.shape[2])))
for lod in range(len(self.h5_lods)):
while img.shape[2] > self.resolution / (2 ** 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.uint8(np.clip(np.round(img), 0, 255))
ofs = 0
while ofs < quant.shape[0]:
num = min(quant.shape[0] - ofs, self.buffers[lod].shape[0] - self.buffer_sizes[lod])
self.buffers[lod][self.buffer_sizes[lod] : self.buffer_sizes[lod] + num] = quant[ofs : ofs + num]
self.buffer_sizes[lod] += num
if self.buffer_sizes[lod] == self.buffers[lod].shape[0]:
self.flush_lod(lod)
ofs += num
def num_images(self):
return self.h5_lods[0].shape[0] + self.buffer_sizes[0]
def flush_lod(self, lod):
num = self.buffer_sizes[lod]
if num > 0:
self.h5_lods[lod].resize(self.h5_lods[lod].shape[0] + num, axis=0)
self.h5_lods[lod][-num:] = self.buffers[lod][:num]
self.buffer_sizes[lod] = 0
#----------------------------------------------------------------------------
class ExceptionInfo(object):
def __init__(self):
self.type, self.value = sys.exc_info()[:2]
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, verbose_exceptions=True): # returns (result, args)
result, args = self.result_queues[func].get()
if isinstance(result, ExceptionInfo):
if verbose_exceptions:
print(('\n\nWorker thread caught an exception:\n' + result.traceback + '\n'))
raise Exception('%s, %s' % (result.type, 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 inspect(h5_filename):
print(('%-20s%s' % ('HDF5 filename', h5_filename)))
file_size = os.stat(h5_filename).st_size
print(('%-20s%.2f GB' % ('Total size', float(file_size) / np.exp2(30))))
h5 = h5py.File(h5_filename, 'r')
lods = sorted([value for key, value in h5.items() if key.startswith('data')], key=lambda lod: -lod.shape[3])
shapes = [lod.shape for lod in lods]
shape = shapes[0]
h5.close()
print(('%-20s%d' % ('Total images', shape[0])))
print(('%-20s%dx%d' % ('Resolution', shape[3], shape[2])))
print(('%-20s%d' % ('Color channels', shape[1])))
print(('%-20s%.2f KB' % ('Size per image', float(file_size) / shape[0] / np.exp2(10))))
if len(lods) != int(np.log2(shape[3])) + 1:
print('Warning: The HDF5 file contains incorrect number of LODs')
if any(s[0] != shape[0] for s in shapes):
print('Warning: The HDF5 file contains inconsistent number of images in different LODs')
print('Perhaps the dataset creation script was terminated abruptly?')
#----------------------------------------------------------------------------
def compare(first_h5, second_h5):
print(('Comparing %s vs. %s' % (first_h5, second_h5)))
h5_a = h5py.File(first_h5, 'r')
h5_b = h5py.File(second_h5, 'r')
lods_a = sorted([value for key, value in h5_a.items() if key.startswith('data')], key=lambda lod: -lod.shape[3])
lods_b = sorted([value for key, value in h5_b.items() if key.startswith('data')], key=lambda lod: -lod.shape[3])
shape_a = lods_a[0].shape
shape_b = lods_b[0].shape
if shape_a[1] != shape_b[1]:
print(('The datasets have different number of color channels: %d vs. %d' % (shape_a[1], shape_b[1])))
elif shape_a[3] != shape_b[3] or shape_a[2] != shape_b[2]:
print(('The datasets have different resolution: %dx%d vs. %dx%d' % (shape_a[3], shape_a[2], shape_b[3], shape_b[2])))
else:
min_images = min(shape_a[0], shape_b[0])
num_diffs = 0
for idx in range(min_images):
print(('%d / %d\r' % (idx, min_images)))
if np.any(lods_a[0][idx] != lods_b[0][idx]):
print(('%-40s\r' % ''))
print(('Different image: %d' % idx))
num_diffs += 1
if shape_a[0] != shape_b[0]:
print(('The datasets contain different number of images: %d vs. %d' % (shape_a[0], shape_b[0])))
if num_diffs == 0:
print(('All %d images are identical.' % min_images))
else:
print(('%d images out of %d are different.' % (num_diffs, min_images)))
h5_a.close()
h5_b.close()
#----------------------------------------------------------------------------
def display(h5_filename, start=None, stop=None, step=None):
print(('Displaying images from %s' % h5_filename))
h5 = h5py.File(h5_filename, 'r')
lods = sorted([value for key, value in h5.items() if key.startswith('data')], key=lambda lod: -lod.shape[3])
indices = list(range(lods[0].shape[0]))
indices = indices[start : stop : step]
import cv2 # pip install opencv-python
window_name = 'h5tool'
cv2.namedWindow(window_name)
print('Press SPACE or ENTER to advance, ESC to exit.')
for idx in indices:
print(('%d / %d\r' % (idx, lods[0].shape[0])))
img = lods[0][idx]
img = img.transpose(1, 2, 0) # CHW => HWC
img = img[:, :, ::-1] # RGB => BGR
cv2.imshow(window_name, img)
c = cv2.waitKey()
if c == 27:
break
h5.close()
print(('%-40s\r' % ''))
print('Done.')
#----------------------------------------------------------------------------
def extract(h5_filename, output_dir, start=None, stop=None, step=None):
print(('Extracting images from %s to %s' % (h5_filename, output_dir)))
h5 = h5py.File(h5_filename, 'r')
lods = sorted([value for key, value in h5.items() if key.startswith('data')], key=lambda lod: -lod.shape[3])
shape = lods[0].shape
indices = list(range(shape[0]))[start : stop : step]
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
for idx in indices:
print(('%d / %d\r' % (idx, shape[0])))
img = lods[0][idx]
if img.shape[0] == 1:
img = PIL.Image.fromarray(img[0], 'L')
else:
img = PIL.Image.fromarray(img.transpose(1, 2, 0), 'RGB')
img.save(os.path.join(output_dir, 'img%08d.png' % idx))
h5.close()
print(('%-40s\r' % ''))
print(('Extracted %d images.' % len(indices)))
#----------------------------------------------------------------------------
def create_custom(h5_filename, image_dir):
print(('Creating custom dataset %s from %s' % (h5_filename, image_dir)))
glob_pattern = os.path.join(image_dir, '*')
image_filenames = sorted(glob.glob(glob_pattern))
if len(image_filenames) == 0:
print(('Error: No input images found in %s' % glob_pattern))
return
img = np.asarray(PIL.Image.open(image_filenames[0]))
resolution = img.shape[0]
channels = img.shape[2] if img.ndim == 3 else 1
if img.shape[1] != resolution:
print('Error: Input images must have the same width and height')
return
if resolution != 2 ** int(np.floor(np.log2(resolution))):
print('Error: Input image resolution must be a power-of-two')
return
if channels not in [1, 3]:
print('Error: Input images must be stored as RGB or grayscale')
h5 = HDF5Exporter(h5_filename, resolution, channels)
for idx in range(len(image_filenames)):
print(('%d / %d\r' % (idx, len(image_filenames))))
img = np.asarray(PIL.Image.open(image_filenames[idx]))
if channels == 1:
img = img[np.newaxis, :, :] # HW => CHW
else:
img = img.transpose(2, 0, 1) # HWC => CHW
h5.add_images(img[np.newaxis])
print(('%-40s\r' % 'Flushing data...'))
h5.close()
print(('%-40s\r' % ''))
print(('Added %d images.' % len(image_filenames)))
#----------------------------------------------------------------------------
def create_mnist(h5_filename, mnist_dir, export_labels=False):
print(('Loading MNIST data 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
print(('Creating %s' % h5_filename))
h5 = HDF5Exporter(h5_filename, 32, 1)
h5.add_images(images)
h5.close()
if export_labels:
npy_filename = os.path.splitext(h5_filename)[0] + '-labels.npy'
print(('Creating %s' % npy_filename))
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
np.save(npy_filename, onehot)
print(('Added %d images.' % images.shape[0]))
#----------------------------------------------------------------------------
def create_mnist_rgb(h5_filename, mnist_dir, num_images=1000000, random_seed=123):
print(('Loading MNIST data 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
print(('Creating %s' % h5_filename))
h5 = HDF5Exporter(h5_filename, 32, 3)
np.random.seed(random_seed)
for idx in range(num_images):
if idx % 100 == 0:
print(('%d / %d\r' % (idx, num_images)))
h5.add_images(images[np.newaxis, np.random.randint(images.shape[0], size=3)])
print(('%-40s\r' % 'Flushing data...'))
h5.close()
print(('%-40s\r' % ''))
print(('Added %d images.' % num_images))
#----------------------------------------------------------------------------
def create_cifar10(h5_filename, cifar10_dir, export_labels=False):
print(('Loading CIFAR-10 data from %s' % cifar10_dir))
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)
images.append(data['data'].reshape(-1, 3, 32, 32))
labels.append(np.uint8(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.uint8
assert np.min(images) == 0 and np.max(images) == 255
assert np.min(labels) == 0 and np.max(labels) == 9
print(('Creating %s' % h5_filename))
h5 = HDF5Exporter(h5_filename, 32, 3)
h5.add_images(images)
h5.close()
if export_labels:
npy_filename = os.path.splitext(h5_filename)[0] + '-labels.npy'
print(('Creating %s' % npy_filename))
onehot = np.zeros((labels.size, np.max(labels) + 1), dtype=np.float32)
onehot[np.arange(labels.size), labels] = 1.0
np.save(npy_filename, onehot)
print(('Added %d images.' % images.shape[0]))
#----------------------------------------------------------------------------
def create_lsun(h5_filename, lmdb_dir, resolution=256, max_images=None):
print(('Creating LSUN dataset %s from %s' % (h5_filename, lmdb_dir)))
import lmdb # pip install lmdb
import cv2 # pip install opencv-python
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
h5 = HDF5Exporter(h5_filename, resolution, 3)
for idx, (key, value) in enumerate(txn.cursor()):
print(('%d / %d\r' % (h5.num_images(), min(h5.num_images() + total_images - idx, max_images))))
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
h5.add_images(img[np.newaxis])
except:
print(('%-40s\r' % ''))
print((sys.exc_info()[1]))
raise
if h5.num_images() == max_images:
break
print(('%-40s\r' % 'Flushing data...'))
num_added = h5.num_images()
h5.close()
print(('%-40s\r' % ''))
print(('Added %d images.' % num_added))
#----------------------------------------------------------------------------
def create_celeba(h5_filename, celeba_dir, cx=89, cy=121):
print(('Creating CelebA dataset %s from %s' % (h5_filename, celeba_dir)))
glob_pattern = os.path.join(celeba_dir, 'img_align_celeba_png', '*.png')
image_filenames = sorted(glob.glob(glob_pattern))
num_images = 202599
if len(image_filenames) < num_images:
print(('Error: Expected to find %d images in %s' % (num_images, glob_pattern)))
return
h5 = HDF5Exporter(h5_filename, 128, 3)
for idx in range(num_images):
print(('%d / %d\r' % (idx, num_images)))
img = np.asarray(PIL.Image.open(image_filenames[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
h5.add_images(img[np.newaxis])
print(('%-40s\r' % 'Flushing data...'))
h5.close()
print(('%-40s\r' % ''))
print(('Added %d images.' % num_images))
#----------------------------------------------------------------------------
ignored = 0
def create_celeba_hq(h5_filename, celeba_dir, delta_dir, data_split, num_threads=4, num_tasks=100):
print(('Loading CelebA data from %s' % celeba_dir))
glob_pattern = os.path.join(celeba_dir, 'img_celeba', '*.jpg')
glob_expected = 202599
if len(glob.glob(glob_pattern)) < glob_expected:
print(('Error: Expected to find %d images in %s' % (glob_expected, glob_pattern)))
return
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)
if os.path.exists(h5_filename):
print("ERROR! That H5 file exists. Will not overwrite by default since this takes a day to generate, yo.")
return
print(('Loading CelebA-HQ deltas from %s' % delta_dir))
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
glob_pattern = os.path.join(delta_dir, 'delta*.zip')
glob_expected = 30
min_idx = 0
max_idx = 300000
if data_split == 'train':
max_idx = 182636 # The validation data starts from file name 182638.jpg which corresponds to idx 182637
elif data_split == 'test':
min_idx = 182637
else:
assert(False)
print("Only including idx {} to {}".format(min_idx, max_idx))
if len(glob.glob(glob_pattern)) != glob_expected:
print(('Error: Expected to find %d zips in %s' % (glob_expected, glob_pattern)))
return
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:]]
def rot90(v):
return np.array([-v[1], v[0]])
def process_func(idx):
global ignored
# Load original image.
orig_idx = fields['orig_idx'][idx]
if (orig_idx > max_idx or orig_idx < min_idx):
ignored += 1
print("Ignore {}".format(fields['orig_file'][idx]))
return -1, None
orig_file = fields['orig_file'][idx]
orig_path = os.path.join(celeba_dir, 'img_celeba', 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] # disable md5 verify
# 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('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] # disable md5 verify
return idx, img
print(('Creating %s' % h5_filename))
h5 = HDF5Exporter(h5_filename, 1024, 3)
with ThreadPool(num_threads) as pool:
print(('%d / %d\r' % (0, len(fields['idx']))))
for idx, img in pool.process_items_concurrently(fields['idx'], process_func=process_func, max_items_in_flight=num_tasks):
if idx >= 0:
h5.add_images(img[np.newaxis])
print(('%d / %d\r' % (idx + 1, len(fields['idx']))))
print(('%-40s\r' % 'Flushing data...'))
h5.close()
print(('%-40s\r' % ''))
print(('Added %d images.' % len(fields['idx'])))
print('Ignored: {}'.format(ignored))
#----------------------------------------------------------------------------
def execute_cmdline(argv):
prog = argv[0]
parser = argparse.ArgumentParser(
prog = prog,
description = 'Tool for creating, extracting, and visualizing HDF5 datasets.',
epilog = 'Type "%s <command> -h" for more information.' % prog)
subparsers = parser.add_subparsers(dest='command')
def add_command(cmd, desc, example=None):
epilog = 'Example: %s %s' % (prog, example) if example is not None else None
return subparsers.add_parser(cmd, description=desc, help=desc, epilog=epilog)
p = add_command( 'inspect', 'Print information about HDF5 dataset.',
'inspect mnist-32x32.h5')
p.add_argument( 'h5_filename', help='HDF5 file to inspect')
p = add_command( 'compare', 'Compare two HDF5 datasets.',
'compare mydataset.h5 mnist-32x32.h5')
p.add_argument( 'first_h5', help='First HDF5 file to compare')
p.add_argument( 'second_h5', help='Second HDF5 file to compare')
p = add_command( 'display', 'Display images in HDF5 dataset.',
'display mnist-32x32.h5')
p.add_argument( 'h5_filename', help='HDF5 file to visualize')
p.add_argument( '--start', help='Start index (inclusive)', type=int, default=None)
p.add_argument( '--stop', help='Stop index (exclusive)', type=int, default=None)
p.add_argument( '--step', help='Step between consecutive indices', type=int, default=None)
p = add_command( 'extract', 'Extract images from HDF5 dataset.',
'extract mnist-32x32.h5 cifar10-images')
p.add_argument( 'h5_filename', help='HDF5 file to extract')
p.add_argument( 'output_dir', help='Directory to extract the images into')
p.add_argument( '--start', help='Start index (inclusive)', type=int, default=None)
p.add_argument( '--stop', help='Stop index (exclusive)', type=int, default=None)
p.add_argument( '--step', help='Step between consecutive indices', type=int, default=None)
p = add_command( 'create_custom', 'Create HDF5 dataset for custom images.',
'create_custom mydataset.h5 myimagedir')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'image_dir', help='Directory to read the images from')
p = add_command( 'create_mnist', 'Create HDF5 dataset for MNIST.',
'create_mnist mnist-32x32.h5 ~/mnist --export_labels')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'mnist_dir', help='Directory to read MNIST data from')
p.add_argument( '--export_labels', help='Create *-labels.npy alongside the HDF5', action='store_true')
p = add_command( 'create_mnist_rgb', 'Create HDF5 dataset for MNIST-RGB.',
'create_mnist_rgb mnist-rgb-32x32.h5 ~/mnist')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'mnist_dir', help='Directory to read MNIST data from')
p.add_argument( '--num_images', help='Number of composite images to create (default: 1000000)', type=int, default=1000000)
p.add_argument( '--random_seed', help='Random seed (default: 123)', type=int, default=123)
p = add_command( 'create_cifar10', 'Create HDF5 dataset for CIFAR-10.',
'create_cifar10 cifar-10-32x32.h5 ~/cifar10 --export_labels')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'cifar10_dir', help='Directory to read CIFAR-10 data from')
p.add_argument( '--export_labels', help='Create *-labels.npy alongside the HDF5', action='store_true')
p = add_command( 'create_lsun', 'Create HDF5 dataset for single LSUN category.',
'create_lsun lsun-airplane-256x256-100k.h5 ~/lsun/airplane_lmdb --resolution 256 --max_images 100000')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'lmdb_dir', help='Directory to read LMDB database from')
p.add_argument( '--resolution', help='Output resolution (default: 256)', type=int, default=256)
p.add_argument( '--max_images', help='Maximum number of images (default: none)', type=int, default=None)
p = add_command( 'create_celeba', 'Create HDF5 dataset for CelebA.',
'create_celeba celeba-128x128.h5 ~/celeba')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'celeba_dir', help='Directory to read CelebA data from')
p.add_argument( '--cx', help='Center X coordinate (default: 89)', type=int, default=89)
p.add_argument( '--cy', help='Center Y coordinate (default: 121)', type=int, default=121)
p = add_command( 'create_celeba_hq', 'Create HDF5 dataset for CelebA-HQ.',
'create_celeba_hq celeba-hq-1024x1024.h5 ~/celeba ~/celeba-hq-deltas')
p.add_argument( 'h5_filename', help='HDF5 file to create')
p.add_argument( 'celeba_dir', help='Directory to read CelebA data from')
p.add_argument( 'delta_dir', help='Directory to read CelebA-HQ deltas from')
p.add_argument( 'data_split', help='[train | test] dataset type to create')
p.add_argument( '--num_threads', help='Number of concurrent threads (default: 4)', type=int, default=4)
p.add_argument( '--num_tasks', help='Number of concurrent processing tasks (default: 100)', type=int, default=100)
args = parser.parse_args(argv[1:])
func = globals()[args.command]
del args.command
func(**vars(args))
#----------------------------------------------------------------------------
if __name__ == "__main__":
execute_cmdline(sys.argv)
#----------------------------------------------------------------------------