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 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()
### shifting kernel
fc_x = 0.5
fc_y = 0.5
im_size = 128
kernel_loc = 2.*np.pi*fc_x * np.arange(im_size).reshape((1, im_size, 1)) + \
2.*np.pi*fc_y * np.arange(im_size).reshape((im_size, 1, 1))
kernel_cos = np.cos(kernel_loc)
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')
### shifting the image
#img = np.asarray(img)
#img_t = 2.* (img.astype(np.float32) / 255.) - 1.
#img_sh = img_t * kernel_cos
#img = (img_sh + 1.) / 2. * 255.
#img = np.rint(img).clip(0, 255).astype(np.uint8)
#img = PIL.Image.fromarray(img)
img.save(os.path.join(output_dir, 'img%08d.png' % idx))
idx += 1
print('Extracted %d images.' % idx)
