def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'datumaro', env=dm_env)
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'label_me', env=dm_env)
dataset.transform('masks_to_polygons')
import_dm_annotations(dataset, instance_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
image_info = {}
frames = [
YoloExtractor.name_from_path(osp.relpath(p, tmp_dir))
for p in glob(osp.join(tmp_dir, '**', '*.txt'), recursive=True)
]
root_hint = find_dataset_root(
[DatasetItem(id=frame) for frame in frames], task_data)
for frame in frames:
frame_info = None
try:
frame_id = match_dm_item(DatasetItem(id=frame),
task_data,
root_hint=root_hint)
frame_info = task_data.frame_info[frame_id]
except Exception:
pass
if frame_info is not None:
image_info[frame] = (frame_info['height'], frame_info['width'])
dataset = dm_env.make_importer('yolo')(tmp_dir, image_info=image_info) \
.make_dataset()
import_dm_annotations(dataset, task_data)
def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'vgg_face2', env=dm_env)
dataset.transform('rename', r"|([^/]+/)?(.+)|\2|")
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
# put label map from the task if not present
labelmap_file = osp.join(tmp_dir, 'labelmap.txt')
if not osp.isfile(labelmap_file):
labels_meta = instance_data.meta['project']['labels'] \
if isinstance(instance_data, ProjectData) else instance_data.meta['task']['labels']
labels = (label['name'] + ':::' for _, label in labels_meta)
with open(labelmap_file, 'w') as f:
f.write('\n'.join(labels))
# support flat archive layout
anno_dir = osp.join(tmp_dir, 'Annotations')
if not osp.isdir(anno_dir):
anno_files = glob(osp.join(tmp_dir, '**', '*.xml'), recursive=True)
subsets_dir = osp.join(tmp_dir, 'ImageSets', 'Main')
os.makedirs(subsets_dir, exist_ok=True)
with open(osp.join(subsets_dir, 'train.txt'), 'w') as subset_file:
for f in anno_files:
subset_file.write(osp.splitext(osp.basename(f))[0] + '\n')
os.makedirs(anno_dir, exist_ok=True)
for f in anno_files:
shutil.move(f, anno_dir)
dataset = Dataset.import_from(tmp_dir, 'voc', env=dm_env)
dataset.transform('masks_to_polygons')
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'icdar_text_segmentation', env=dm_env)
dataset.transform(AddLabelToAnns, 'icdar')
dataset.transform('masks_to_polygons')
import_dm_annotations(dataset, task_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
dataset = dm_env.make_importer('tf_detection_api')(
tmp_dir).make_dataset()
import_dm_annotations(dataset, task_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
image_info = {}
frames = [YoloExtractor.name_from_path(osp.relpath(p, tmp_dir))
for p in glob(osp.join(tmp_dir, '**', '*.txt'), recursive=True)]
root_hint = find_dataset_root(
[DatasetItem(id=frame) for frame in frames], instance_data)
for frame in frames:
frame_info = None
try:
frame_id = match_dm_item(DatasetItem(id=frame), instance_data,
root_hint=root_hint)
frame_info = instance_data.frame_info[frame_id]
except Exception: # nosec
pass
if frame_info is not None:
image_info[frame] = (frame_info['height'], frame_info['width'])
dataset = Dataset.import_from(tmp_dir, 'yolo',
env=dm_env, image_info=image_info)
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
# put label map from the task if not present
labelmap_file = osp.join(tmp_dir, 'labelmap.txt')
if not osp.isfile(labelmap_file):
labels = (label['name'] + ':::'
for _, label in task_data.meta['task']['labels'])
with open(labelmap_file, 'w') as f:
f.write('\n'.join(labels))
# support flat archive layout
anno_dir = osp.join(tmp_dir, 'Annotations')
if not osp.isdir(anno_dir):
anno_files = glob(osp.join(tmp_dir, '**', '*.xml'), recursive=True)
subsets_dir = osp.join(tmp_dir, 'ImageSets', 'Main')
os.makedirs(subsets_dir, exist_ok=True)
with open(osp.join(subsets_dir, 'train.txt'), 'w') as subset_file:
for f in anno_files:
subset_file.write(osp.splitext(osp.basename(f))[0] + '\n')
os.makedirs(anno_dir, exist_ok=True)
for f in anno_files:
shutil.move(f, anno_dir)
dataset = dm_env.make_importer('voc')(tmp_dir).make_dataset()
masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
dataset = dataset.transform(masks_to_polygons)
import_dm_annotations(dataset, task_data)
def load(file_object, annotations):
from pyunpack import Archive
import os.path as osp
from tempfile import TemporaryDirectory
from glob import glob
from datumaro.plugins.yolo_format.importer import YoloImporter
from cvat.apps.dataset_manager.bindings import import_dm_annotations
archive_file = file_object if isinstance(file_object, str) else getattr(
file_object, "name")
with TemporaryDirectory() as tmp_dir:
Archive(archive_file).extractall(tmp_dir)
image_info = {}
anno_files = glob(osp.join(tmp_dir, '**', '*.txt'), recursive=True)
for filename in anno_files:
filename = osp.basename(filename)
frame_info = None
try:
frame_info = annotations.frame_info[int(
osp.splitext(filename)[0])]
except Exception:
pass
try:
frame_info = annotations.match_frame(filename)
frame_info = annotations.frame_info[frame_info]
except Exception:
pass
if frame_info is not None:
image_info[osp.splitext(filename)[0]] = \
(frame_info['height'], frame_info['width'])
dm_project = YoloImporter()(tmp_dir, image_info=image_info)
dm_dataset = dm_project.make_dataset()
import_dm_annotations(dm_dataset, annotations)
def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'market1501', env=dm_env)
dataset.transform(AttrToLabelAttr, 'market-1501')
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir,
'icdar_word_recognition',
env=dm_env)
dataset.transform(CaptionToLabel, 'icdar')
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'wider_face', env=dm_env)
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
dataset = dm_env.make_importer('voc')(tmp_dir).make_dataset()
masks_to_polygons = dm_env.transforms.get('masks_to_polygons')
dataset = dataset.transform(masks_to_polygons)
import_dm_annotations(dataset, task_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
if glob(osp.join(tmp_dir, '*.txt')):
dataset = Dataset.import_from(tmp_dir, 'imagenet_txt', env=dm_env)
else:
dataset = Dataset.import_from(tmp_dir, 'imagenet', env=dm_env)
import_dm_annotations(dataset, task_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'lfw')
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, task_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
if glob(osp.join(tmp_dir, '*.txt')):
dataset = dm_env.make_importer('imagenet_txt')(
tmp_dir).make_dataset()
else:
dataset = dm_env.make_importer('imagenet')(tmp_dir).make_dataset()
import_dm_annotations(dataset, task_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'icdar_text_segmentation', env=dm_env)
dataset.transform(AddLabelToAnns, 'icdar')
dataset.transform('masks_to_polygons')
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'voc', env=dm_env)
dataset.transform('masks_to_polygons')
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir,
'icdar_text_localization',
env=dm_env)
dataset.transform(AddLabelToAnns, 'icdar')
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
if glob(osp.join(tmp_dir, '*.txt')):
dataset = Dataset.import_from(tmp_dir, 'imagenet_txt', env=dm_env)
else:
dataset = Dataset.import_from(tmp_dir, 'imagenet', env=dm_env)
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, task_data):
if zipfile.is_zipfile(src_file):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = dm_env.make_importer('coco')(tmp_dir).make_dataset()
import_dm_annotations(dataset, task_data)
else:
dataset = dm_env.make_extractor('coco_instances', src_file.name)
import_dm_annotations(dataset, task_data)
def _import(src_file, task_data):
if zipfile.is_zipfile(src_file):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'kitti_raw', env=dm_env)
import_dm_annotations(dataset, task_data)
else:
dataset = Dataset.import_from(src_file.name, 'kitti_raw', env=dm_env)
import_dm_annotations(dataset, task_data)
def _import(src_file, instance_data):
if zipfile.is_zipfile(src_file):
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'coco', env=dm_env)
import_dm_annotations(dataset, instance_data)
else:
dataset = Dataset.import_from(src_file.name,
'coco_instances', env=dm_env)
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
if zipfile.is_zipfile(src_file):
zipfile.ZipFile(src_file).extractall(tmp_dir)
dataset = Dataset.import_from(tmp_dir, 'sly_pointcloud', env=dm_env)
else:
dataset = Dataset.import_from(src_file.name,
'sly_pointcloud', env=dm_env)
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def load(file_object, annotations):
from pyunpack import Archive
from tempfile import TemporaryDirectory
from datumaro.plugins.tf_detection_api_format.importer import TfDetectionApiImporter
from cvat.apps.dataset_manager.bindings import import_dm_annotations
archive_file = file_object if isinstance(file_object, str) else getattr(
file_object, "name")
with TemporaryDirectory() as tmp_dir:
Archive(archive_file).extractall(tmp_dir)
dm_project = TfDetectionApiImporter()(tmp_dir)
dm_dataset = dm_project.make_dataset()
import_dm_annotations(dm_dataset, annotations)
def load(file_object, annotations):
from pyunpack import Archive
from tempfile import TemporaryDirectory
from datumaro.plugins.labelme_format import LabelMeImporter
from datumaro.components.project import Environment
from cvat.apps.dataset_manager.bindings import import_dm_annotations
archive_file = file_object if isinstance(file_object, str) else getattr(
file_object, "name")
with TemporaryDirectory() as tmp_dir:
Archive(archive_file).extractall(tmp_dir)
dm_dataset = LabelMeImporter()(tmp_dir).make_dataset()
masks_to_polygons = Environment().transforms.get('masks_to_polygons')
dm_dataset = dm_dataset.transform(masks_to_polygons)
import_dm_annotations(dm_dataset, annotations)
def _import(src_file, instance_data, load_data_callback=None):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
labelmap_file = osp.join(tmp_dir, 'label_colors.txt')
if not osp.isfile(labelmap_file):
colormap = {
label: info[0]
for label, info in make_colormap(instance_data).items()
}
write_label_map(labelmap_file, colormap)
dataset = Dataset.import_from(tmp_dir, 'cityscapes', env=dm_env)
dataset.transform('masks_to_polygons')
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data):
with TemporaryDirectory() as tmp_dir:
Archive(src_file.name).extractall(tmp_dir)
color_map = {k: v[0] for k, v in make_colormap(instance_data).items()}
color_map_path = osp.join(tmp_dir, KittiPath.LABELMAP_FILE)
if not osp.isfile(color_map_path):
write_label_map(color_map_path, color_map)
dataset = Dataset.import_from(tmp_dir, format='kitti', env=dm_env)
labels_meta = instance_data.meta['project']['labels'] \
if isinstance(instance_data, ProjectData) else instance_data.meta['task']['labels']
if 'background' not in [label['name'] for _, label in labels_meta]:
dataset.filter('/item/annotation[label != "background"]',
filter_annotations=True)
dataset.transform('masks_to_polygons')
import_dm_annotations(dataset, instance_data)
def _import(src_file, instance_data, load_data_callback=None):
is_zip = zipfile.is_zipfile(src_file)
src_file.seek(0)
if is_zip:
with TemporaryDirectory() as tmp_dir:
zipfile.ZipFile(src_file).extractall(tmp_dir)
if isinstance(instance_data, ProjectData):
dataset = Dataset.import_from(tmp_dir, 'cvat', env=dm_env)
if load_data_callback is not None:
load_data_callback(dataset, instance_data)
import_dm_annotations(dataset, instance_data)
else:
anno_paths = glob(osp.join(tmp_dir, '**', '*.xml'),
recursive=True)
for p in anno_paths:
load_anno(p, instance_data)
else:
load_anno(src_file, instance_data)