def _write_image_level_labels(fname, image_ids, machine=False):
"""Writes CSV with 0-10 labels per image."""
lines = ['ImageID,Source,LabelName,Condidence']
all_class_label = ClassLabel(names_file=py_utils.get_tfds_path(
os.path.join('image', 'open_images_classes_all.txt')))
trainable_class_label = ClassLabel(names_file=py_utils.get_tfds_path(
os.path.join('image', 'open_images_classes_trainable.txt')))
for i, image_id in enumerate(image_ids):
if i < 1:
# Ensure that at least some image contains trainable classes.
labels = random.sample(trainable_class_label.names,
random.randint(0, 10))
else:
labels = random.sample(all_class_label.names,
random.randint(0, 10))
for label in labels:
source = random.choice(open_images.IMAGE_LEVEL_SOURCES)
confidence = random.choice((0, 1))
if machine:
confidence = '%.1f' % (random.randint(0, 10) / 10.)
else:
confidence = random.choice((0, 1))
lines.append('%s,%s,%s,%s' % (image_id, source, label, confidence))
path = os.path.join(_output_dir(), fname)
with open(path, 'w') as csv_f:
csv_f.write('\n'.join(lines))
def _write_bbox_labels(fname, image_ids):
"""Writes CSV with 0-10 labels per image."""
lines = [
'ImageID,Source,LabelName,Confidence,XMin,XMax,YMin,YMax,IsOccluded,'
'IsTruncated,IsGroupOf,IsDepiction,IsInside'
]
boxable_class_label = ClassLabel(names_file=py_utils.get_tfds_path(
os.path.join('image', 'open_images_classes_boxable.txt')))
for image_id in image_ids:
labels = random.sample(boxable_class_label.names,
random.randint(0, 10))
for label in labels:
source = random.choice(open_images.BBOX_SOURCES)
xmin = random.uniform(0, 1)
xmax = random.uniform(xmin, 1)
ymin = random.uniform(0, 1)
ymax = random.uniform(ymin, 1)
p1, p2, p3, p4, p5 = [
random.randint(-1, 1) for unused_i in range(5)
]
lines.append('%s,%s,%s,1,%.6f,%.6f,%.6f,%.6f,%s,%s,%s,%s,%s' %
(image_id, source, label, xmin, xmax, ymin, ymax, p1,
p2, p3, p4, p5))
path = os.path.join(_output_dir(), fname)
with open(path, 'w') as csv_f:
csv_f.write('\n'.join(lines))
def get_mako_template(tmpl_name):
"""Returns mako.lookup.Template object to use to render documentation.
Args:
tmpl_name: string, name of template to load.
Returns:
mako 'Template' instance that can be rendered.
"""
tmpl_path = py_utils.get_tfds_path("scripts/templates/%s.mako.md" % tmpl_name)
with tf.io.gfile.GFile(tmpl_path, "r") as tmpl_f:
tmpl_content = tmpl_f.read()
return mako.lookup.Template(tmpl_content, default_filters=["str", "trim"])
