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114 lines
4.3 KiB
Python
114 lines
4.3 KiB
Python
# Copyright (C) 2019 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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from tempfile import TemporaryDirectory
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import datumaro.components.extractor as datumaro
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from datumaro.components.dataset import Dataset
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from pyunpack import Archive
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from cvat.apps.dataset_manager.bindings import GetCVATDataExtractor
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from cvat.apps.dataset_manager.util import make_zip_archive
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from .registry import dm_env, exporter, importer
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def _import_task(dataset, task_data):
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tracks = {}
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label_cat = dataset.categories()[datumaro.AnnotationType.label]
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for item in dataset:
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frame_number = int(item.id) - 1 # NOTE: MOT frames start from 1
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frame_number = task_data.abs_frame_id(frame_number)
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for ann in item.annotations:
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if ann.type != datumaro.AnnotationType.bbox:
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continue
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track_id = ann.attributes.get('track_id')
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if track_id is None:
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# Extension. Import regular boxes:
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task_data.add_shape(task_data.LabeledShape(
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type='rectangle',
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label=label_cat.items[ann.label].name,
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points=ann.points,
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occluded=ann.attributes.get('occluded') is True,
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z_order=ann.z_order,
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group=0,
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frame=frame_number,
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attributes=[],
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source='manual',
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))
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continue
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shape = task_data.TrackedShape(
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type='rectangle',
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points=ann.points,
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occluded=ann.attributes.get('occluded') is True,
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outside=False,
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keyframe=True,
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z_order=ann.z_order,
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frame=frame_number,
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attributes=[],
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source='manual',
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)
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# build trajectories as lists of shapes in track dict
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if track_id not in tracks:
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tracks[track_id] = task_data.Track(
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label_cat.items[ann.label].name, 0, 'manual', [])
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tracks[track_id].shapes.append(shape)
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for track in tracks.values():
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# MOT annotations do not require frames to be ordered
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track.shapes.sort(key=lambda t: t.frame)
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# insert outside=True in skips between the frames track is visible
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prev_shape_idx = 0
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prev_shape = track.shapes[0]
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for shape in track.shapes[1:]:
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has_skip = task_data.frame_step < shape.frame - prev_shape.frame
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if has_skip and not prev_shape.outside:
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prev_shape = prev_shape._replace(outside=True,
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frame=prev_shape.frame + task_data.frame_step)
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prev_shape_idx += 1
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track.shapes.insert(prev_shape_idx, prev_shape)
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prev_shape = shape
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prev_shape_idx += 1
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# Append a shape with outside=True to finish the track
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last_shape = track.shapes[-1]
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if last_shape.frame + task_data.frame_step <= \
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int(task_data.meta['task']['stop_frame']):
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track.shapes.append(last_shape._replace(outside=True,
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frame=last_shape.frame + task_data.frame_step)
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)
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task_data.add_track(track)
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@exporter(name='MOT', ext='ZIP', version='1.1')
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def _export(dst_file, instance_data, save_images=False):
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dataset = Dataset.from_extractors(GetCVATDataExtractor(
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instance_data, include_images=save_images), env=dm_env)
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with TemporaryDirectory() as temp_dir:
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dataset.export(temp_dir, 'mot_seq_gt', save_images=save_images)
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make_zip_archive(temp_dir, dst_file)
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@importer(name='MOT', ext='ZIP', version='1.1')
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def _import(src_file, instance_data, load_data_callback=None):
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with TemporaryDirectory() as tmp_dir:
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Archive(src_file.name).extractall(tmp_dir)
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dataset = Dataset.import_from(tmp_dir, 'mot_seq', env=dm_env)
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if load_data_callback is not None:
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load_data_callback(dataset, instance_data)
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# Dirty way to determine instance type to avoid circular dependency
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if hasattr(instance_data, '_db_project'):
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for sub_dataset, task_data in instance_data.split_dataset(dataset):
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_import_task(sub_dataset, task_data)
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else:
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_import_task(dataset, instance_data)
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