Move annotation formats to dataset manager (#1256)
* Move formats to dataset manager * Unify datataset export and anno export implementations * Add track_id to TrackedShape, export tracked shapes * Replace MOT format * Replace LabelMe format * Add new formats to dm * Add dm tests * Extend TrackedShape * Enable dm test in CI * Fix tests * Add import * Fix tests * Fix mot track ids * Fix mot format * Update attribute logic in labelme tests * Use common code in yolo * Put datumaro in path in settings * Expect labels file in MOT next to annotations file * Add MOT format description * Add import * Add labelme format description * Linter fix * Linter fix2 * Compare attributes ordered * Update docs * Update testsmain
zhiltsov-max
6 years ago
committed by
GitHub
parent
e87ec38476
commit
887c6f0432
@ -1,307 +0,0 @@
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# Copyright (C) 2019 Intel Corporation
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#
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# SPDX-License-Identifier: MIT
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format_spec = {
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"name": "LabelMe",
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"dumpers": [
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{
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"display_name": "{name} {format} {version} for images",
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"format": "ZIP",
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"version": "3.0",
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"handler": "dump_as_labelme_annotation"
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}
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],
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"loaders": [
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{
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"display_name": "{name} {format} {version}",
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"format": "ZIP",
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"version": "3.0",
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"handler": "load",
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}
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],
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}
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_DEFAULT_USERNAME = 'cvat'
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_MASKS_DIR = 'Masks'
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def dump_frame_anno(frame_annotation):
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from collections import defaultdict
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from lxml import etree as ET
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root_elem = ET.Element('annotation')
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ET.SubElement(root_elem, 'filename').text = frame_annotation.name
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ET.SubElement(root_elem, 'folder').text = ''
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source_elem = ET.SubElement(root_elem, 'source')
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ET.SubElement(source_elem, 'sourceImage').text = ''
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ET.SubElement(source_elem, 'sourceAnnotation').text = 'CVAT'
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image_elem = ET.SubElement(root_elem, 'imagesize')
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ET.SubElement(image_elem, 'nrows').text = str(frame_annotation.height)
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ET.SubElement(image_elem, 'ncols').text = str(frame_annotation.width)
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groups = defaultdict(list)
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for obj_id, shape in enumerate(frame_annotation.labeled_shapes):
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obj_elem = ET.SubElement(root_elem, 'object')
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ET.SubElement(obj_elem, 'name').text = str(shape.label)
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ET.SubElement(obj_elem, 'deleted').text = '0'
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ET.SubElement(obj_elem, 'verified').text = '0'
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ET.SubElement(obj_elem, 'occluded').text = \
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'yes' if shape.occluded else 'no'
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ET.SubElement(obj_elem, 'date').text = ''
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ET.SubElement(obj_elem, 'id').text = str(obj_id)
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parts_elem = ET.SubElement(obj_elem, 'parts')
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if shape.group:
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groups[shape.group].append((obj_id, parts_elem))
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else:
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ET.SubElement(parts_elem, 'hasparts').text = ''
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ET.SubElement(parts_elem, 'ispartof').text = ''
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if shape.type == 'rectangle':
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ET.SubElement(obj_elem, 'type').text = 'bounding_box'
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poly_elem = ET.SubElement(obj_elem, 'polygon')
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x0, y0, x1, y1 = shape.points
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points = [ (x0, y0), (x1, y0), (x1, y1), (x0, y1) ]
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for x, y in points:
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point_elem = ET.SubElement(poly_elem, 'pt')
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ET.SubElement(point_elem, 'x').text = '%.2f' % x
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ET.SubElement(point_elem, 'y').text = '%.2f' % y
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ET.SubElement(poly_elem, 'username').text = _DEFAULT_USERNAME
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elif shape.type == 'polygon':
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poly_elem = ET.SubElement(obj_elem, 'polygon')
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for x, y in zip(shape.points[::2], shape.points[1::2]):
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point_elem = ET.SubElement(poly_elem, 'pt')
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ET.SubElement(point_elem, 'x').text = '%.2f' % x
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ET.SubElement(point_elem, 'y').text = '%.2f' % y
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ET.SubElement(poly_elem, 'username').text = _DEFAULT_USERNAME
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elif shape.type == 'polyline':
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pass
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elif shape.type == 'points':
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pass
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else:
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raise NotImplementedError("Unknown shape type '%s'" % shape.type)
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attrs = ['%s=%s' % (a.name, a.value) for a in shape.attributes]
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ET.SubElement(obj_elem, 'attributes').text = ', '.join(attrs)
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for _, group in groups.items():
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leader_id, leader_parts_elem = group[0]
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leader_parts = [str(o_id) for o_id, _ in group[1:]]
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ET.SubElement(leader_parts_elem, 'hasparts').text = \
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','.join(leader_parts)
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ET.SubElement(leader_parts_elem, 'ispartof').text = ''
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for obj_id, parts_elem in group[1:]:
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ET.SubElement(parts_elem, 'hasparts').text = ''
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ET.SubElement(parts_elem, 'ispartof').text = str(leader_id)
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return ET.tostring(root_elem, encoding='unicode', pretty_print=True)
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def dump_as_labelme_annotation(file_object, annotations):
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import os.path as osp
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from zipfile import ZipFile, ZIP_DEFLATED
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with ZipFile(file_object, 'w', compression=ZIP_DEFLATED) as output_zip:
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for frame_annotation in annotations.group_by_frame():
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xml_data = dump_frame_anno(frame_annotation)
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filename = osp.splitext(frame_annotation.name)[0] + '.xml'
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output_zip.writestr(filename, xml_data)
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def parse_xml_annotations(xml_data, annotations, input_zip):
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from datumaro.util.mask_tools import mask_to_polygons
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from io import BytesIO
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from lxml import etree as ET
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import numpy as np
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import os.path as osp
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from PIL import Image
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def parse_attributes(attributes_string):
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parsed = []
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if not attributes_string:
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return parsed
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read = attributes_string.split(',')
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read = [a.strip() for a in read if a.strip()]
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for attr in read:
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if '=' in attr:
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name, value = attr.split('=', maxsplit=1)
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parsed.append(annotations.Attribute(name, value))
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else:
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parsed.append(annotations.Attribute(attr, '1'))
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return parsed
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root_elem = ET.fromstring(xml_data)
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frame_number = annotations.match_frame(root_elem.find('filename').text)
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parsed_annotations = dict()
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group_assignments = dict()
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root_annotations = set()
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for obj_elem in root_elem.iter('object'):
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obj_id = int(obj_elem.find('id').text)
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ann_items = []
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attributes = []
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attributes_elem = obj_elem.find('attributes')
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if attributes_elem is not None and attributes_elem.text:
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attributes = parse_attributes(attributes_elem.text)
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occluded = False
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occluded_elem = obj_elem.find('occluded')
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if occluded_elem is not None and occluded_elem.text:
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occluded = (occluded_elem.text == 'yes')
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deleted = False
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deleted_elem = obj_elem.find('deleted')
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if deleted_elem is not None and deleted_elem.text:
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deleted = bool(int(deleted_elem.text))
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poly_elem = obj_elem.find('polygon')
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segm_elem = obj_elem.find('segm')
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type_elem = obj_elem.find('type') # the only value is 'bounding_box'
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if poly_elem is not None:
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points = []
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for point_elem in poly_elem.iter('pt'):
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x = float(point_elem.find('x').text)
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y = float(point_elem.find('y').text)
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points.append(x)
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points.append(y)
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label = obj_elem.find('name').text
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if label and attributes:
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label_id = annotations._get_label_id(label)
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if label_id:
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attributes = [a for a in attributes
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if annotations._get_attribute_id(label_id, a.name)
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]
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else:
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attributes = []
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else:
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attributes = []
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if type_elem is not None and type_elem.text == 'bounding_box':
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xmin = min(points[::2])
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xmax = max(points[::2])
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ymin = min(points[1::2])
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ymax = max(points[1::2])
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ann_items.append(annotations.LabeledShape(
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type='rectangle',
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frame=frame_number,
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label=label,
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points=[xmin, ymin, xmax, ymax],
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occluded=occluded,
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attributes=attributes,
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))
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else:
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ann_items.append(annotations.LabeledShape(
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type='polygon',
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frame=frame_number,
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label=label,
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points=points,
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occluded=occluded,
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attributes=attributes,
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))
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elif segm_elem is not None:
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label = obj_elem.find('name').text
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if label and attributes:
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label_id = annotations._get_label_id(label)
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if label_id:
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attributes = [a for a in attributes
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if annotations._get_attribute_id(label_id, a.name)
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]
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else:
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attributes = []
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else:
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attributes = []
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mask_file = segm_elem.find('mask').text
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mask = input_zip.read(osp.join(_MASKS_DIR, mask_file))
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mask = np.asarray(Image.open(BytesIO(mask)).convert('L'))
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mask = (mask != 0)
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polygons = mask_to_polygons(mask)
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for polygon in polygons:
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ann_items.append(annotations.LabeledShape(
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type='polygon',
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frame=frame_number,
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label=label,
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points=polygon,
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occluded=occluded,
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attributes=attributes,
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))
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if not deleted:
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parsed_annotations[obj_id] = ann_items
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parts_elem = obj_elem.find('parts')
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if parts_elem is not None:
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children_ids = []
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hasparts_elem = parts_elem.find('hasparts')
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if hasparts_elem is not None and hasparts_elem.text:
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children_ids = [int(c) for c in hasparts_elem.text.split(',')]
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parent_ids = []
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ispartof_elem = parts_elem.find('ispartof')
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if ispartof_elem is not None and ispartof_elem.text:
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parent_ids = [int(c) for c in ispartof_elem.text.split(',')]
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if children_ids and not parent_ids and hasparts_elem.text:
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root_annotations.add(obj_id)
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group_assignments[obj_id] = [None, children_ids]
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# assign a single group to the whole subtree
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current_group_id = 0
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annotations_to_visit = list(root_annotations)
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while annotations_to_visit:
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ann_id = annotations_to_visit.pop()
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ann_assignment = group_assignments[ann_id]
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group_id, children_ids = ann_assignment
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if group_id:
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continue
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if ann_id in root_annotations:
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current_group_id += 1 # start a new group
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group_id = current_group_id
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ann_assignment[0] = group_id
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# continue with children
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annotations_to_visit.extend(children_ids)
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assert current_group_id == len(root_annotations)
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for ann_id, ann_items in parsed_annotations.items():
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group_id = 0
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if ann_id in group_assignments:
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ann_assignment = group_assignments[ann_id]
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group_id = ann_assignment[0]
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for ann_item in ann_items:
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if group_id:
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ann_item = ann_item._replace(group=group_id)
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if isinstance(ann_item, annotations.LabeledShape):
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annotations.add_shape(ann_item)
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else:
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raise NotImplementedError()
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def load(file_object, annotations):
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from zipfile import ZipFile
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with ZipFile(file_object, 'r') as input_zip:
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for filename in input_zip.namelist():
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if not filename.endswith('.xml'):
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continue
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xml_data = input_zip.read(filename)
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parse_xml_annotations(xml_data, annotations, input_zip)
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@ -1,109 +0,0 @@
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# SPDX-License-Identifier: MIT
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format_spec = {
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"name": "MOT",
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"dumpers": [
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{
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"display_name": "{name} {format} {version}",
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"format": "CSV",
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"version": "1.0",
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"handler": "dump"
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},
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],
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"loaders": [
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{
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"display_name": "{name} {format} {version}",
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"format": "CSV",
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"version": "1.0",
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"handler": "load",
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}
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],
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}
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MOT = [
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"frame_id",
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"track_id",
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"xtl",
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"ytl",
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"width",
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"height",
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"confidence",
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"class_id",
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"visibility"
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]
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def dump(file_object, annotations):
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""" Export track shapes in MOT CSV format. Due to limitations of the MOT
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format, this process only supports rectangular interpolation mode
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annotations.
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"""
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import csv
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import io
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# csv requires a text buffer
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with io.TextIOWrapper(file_object, encoding="utf-8") as csv_file:
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writer = csv.DictWriter(csv_file, fieldnames=MOT)
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for i, track in enumerate(annotations.tracks):
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for shape in track.shapes:
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# MOT doesn't support polygons or 'outside' property
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if shape.type != 'rectangle':
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continue
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writer.writerow({
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"frame_id": shape.frame,
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"track_id": i,
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"xtl": shape.points[0],
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"ytl": shape.points[1],
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"width": shape.points[2] - shape.points[0],
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"height": shape.points[3] - shape.points[1],
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"confidence": 1,
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"class_id": track.label,
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"visibility": 1 - int(shape.occluded)
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})
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def load(file_object, annotations):
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""" Read MOT CSV format and convert objects to annotated tracks.
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"""
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import csv
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import io
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tracks = {}
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# csv requires a text buffer
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|
||||||
with io.TextIOWrapper(file_object, encoding="utf-8") as csv_file:
|
|
||||||
reader = csv.DictReader(csv_file, fieldnames=MOT)
|
|
||||||
for row in reader:
|
|
||||||
# create one shape per row
|
|
||||||
xtl = float(row["xtl"])
|
|
||||||
ytl = float(row["ytl"])
|
|
||||||
xbr = xtl + float(row["width"])
|
|
||||||
ybr = ytl + float(row["height"])
|
|
||||||
shape = annotations.TrackedShape(
|
|
||||||
type="rectangle",
|
|
||||||
points=[xtl, ytl, xbr, ybr],
|
|
||||||
occluded=float(row["visibility"]) == 0,
|
|
||||||
outside=False,
|
|
||||||
keyframe=False,
|
|
||||||
z_order=0,
|
|
||||||
frame=int(row["frame_id"]),
|
|
||||||
attributes=[],
|
|
||||||
)
|
|
||||||
# build trajectories as lists of shapes in track dict
|
|
||||||
track_id = int(row["track_id"])
|
|
||||||
if track_id not in tracks:
|
|
||||||
tracks[track_id] = annotations.Track(row["class_id"], track_id, [])
|
|
||||||
tracks[track_id].shapes.append(shape)
|
|
||||||
for track in tracks.values():
|
|
||||||
# Set outside=True for the last shape since MOT has no support
|
|
||||||
# for this flag
|
|
||||||
last = annotations.TrackedShape(
|
|
||||||
type=track.shapes[-1].type,
|
|
||||||
points=track.shapes[-1].points,
|
|
||||||
occluded=track.shapes[-1].occluded,
|
|
||||||
outside=True,
|
|
||||||
keyframe=track.shapes[-1].keyframe,
|
|
||||||
z_order=track.shapes[-1].z_order,
|
|
||||||
frame=track.shapes[-1].frame,
|
|
||||||
attributes=track.shapes[-1].attributes,
|
|
||||||
)
|
|
||||||
track.shapes[-1] = last
|
|
||||||
annotations.add_track(track)
|
|
||||||
@ -0,0 +1,309 @@
|
|||||||
|
|
||||||
|
# Copyright (C) 2020 Intel Corporation
|
||||||
|
#
|
||||||
|
# SPDX-License-Identifier: MIT
|
||||||
|
|
||||||
|
class _GitImportFix:
|
||||||
|
import sys
|
||||||
|
former_path = sys.path[:]
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def apply(cls):
|
||||||
|
# HACK: fix application and module name clash
|
||||||
|
# 'git' app is found earlier than a library in the path.
|
||||||
|
# The clash is introduced by unittest discover
|
||||||
|
import sys
|
||||||
|
print('apply')
|
||||||
|
|
||||||
|
apps_dir = __file__[:__file__.rfind('/dataset_manager/')]
|
||||||
|
assert 'apps' in apps_dir
|
||||||
|
try:
|
||||||
|
sys.path.remove(apps_dir)
|
||||||
|
except ValueError:
|
||||||
|
pass
|
||||||
|
|
||||||
|
for name in list(sys.modules):
|
||||||
|
if name.startswith('git.') or name == 'git':
|
||||||
|
m = sys.modules.pop(name, None)
|
||||||
|
del m
|
||||||
|
|
||||||
|
import git
|
||||||
|
assert apps_dir not in git.__file__
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def restore(cls):
|
||||||
|
import sys
|
||||||
|
print('restore')
|
||||||
|
|
||||||
|
for name in list(sys.modules):
|
||||||
|
if name.startswith('git.') or name == 'git':
|
||||||
|
m = sys.modules.pop(name)
|
||||||
|
del m
|
||||||
|
|
||||||
|
sys.path.insert(0, __file__[:__file__.rfind('/dataset_manager/')])
|
||||||
|
|
||||||
|
import importlib
|
||||||
|
importlib.invalidate_caches()
|
||||||
|
|
||||||
|
def _setUpModule():
|
||||||
|
_GitImportFix.apply()
|
||||||
|
import cvat.apps.dataset_manager.task as dm
|
||||||
|
from cvat.apps.engine.models import Task
|
||||||
|
globals()['dm'] = dm
|
||||||
|
globals()['Task'] = Task
|
||||||
|
|
||||||
|
import sys
|
||||||
|
sys.path.insert(0, __file__[:__file__.rfind('/dataset_manager/')])
|
||||||
|
|
||||||
|
def tearDownModule():
|
||||||
|
_GitImportFix.restore()
|
||||||
|
|
||||||
|
from io import BytesIO
|
||||||
|
import os
|
||||||
|
import random
|
||||||
|
import tempfile
|
||||||
|
|
||||||
|
from PIL import Image
|
||||||
|
from django.contrib.auth.models import User, Group
|
||||||
|
from rest_framework.test import APITestCase, APIClient
|
||||||
|
from rest_framework import status
|
||||||
|
|
||||||
|
_setUpModule()
|
||||||
|
|
||||||
|
|
||||||
|
def generate_image_file(filename):
|
||||||
|
f = BytesIO()
|
||||||
|
width = random.randint(10, 200)
|
||||||
|
height = random.randint(10, 200)
|
||||||
|
image = Image.new('RGB', size=(width, height))
|
||||||
|
image.save(f, 'jpeg')
|
||||||
|
f.name = filename
|
||||||
|
f.seek(0)
|
||||||
|
|
||||||
|
return f
|
||||||
|
|
||||||
|
def create_db_users(cls):
|
||||||
|
group_user, _ = Group.objects.get_or_create(name="user")
|
||||||
|
|
||||||
|
user_dummy = User.objects.create_superuser(username="test", password="test", email="")
|
||||||
|
user_dummy.groups.add(group_user)
|
||||||
|
|
||||||
|
cls.user = user_dummy
|
||||||
|
|
||||||
|
class ForceLogin:
|
||||||
|
def __init__(self, user, client):
|
||||||
|
self.user = user
|
||||||
|
self.client = client
|
||||||
|
|
||||||
|
def __enter__(self):
|
||||||
|
if self.user:
|
||||||
|
self.client.force_login(self.user,
|
||||||
|
backend='django.contrib.auth.backends.ModelBackend')
|
||||||
|
|
||||||
|
return self
|
||||||
|
|
||||||
|
def __exit__(self, exception_type, exception_value, traceback):
|
||||||
|
if self.user:
|
||||||
|
self.client.logout()
|
||||||
|
|
||||||
|
class TaskExportTest(APITestCase):
|
||||||
|
def setUp(self):
|
||||||
|
self.client = APIClient()
|
||||||
|
|
||||||
|
@classmethod
|
||||||
|
def setUpTestData(cls):
|
||||||
|
create_db_users(cls)
|
||||||
|
|
||||||
|
def _generate_task(self):
|
||||||
|
task = {
|
||||||
|
"name": "my task #1",
|
||||||
|
"owner": '',
|
||||||
|
"assignee": '',
|
||||||
|
"overlap": 0,
|
||||||
|
"segment_size": 100,
|
||||||
|
"z_order": False,
|
||||||
|
"labels": [
|
||||||
|
{
|
||||||
|
"name": "car",
|
||||||
|
"attributes": [
|
||||||
|
{
|
||||||
|
"name": "model",
|
||||||
|
"mutable": False,
|
||||||
|
"input_type": "select",
|
||||||
|
"default_value": "mazda",
|
||||||
|
"values": ["bmw", "mazda", "renault"]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"name": "parked",
|
||||||
|
"mutable": True,
|
||||||
|
"input_type": "checkbox",
|
||||||
|
"default_value": False
|
||||||
|
},
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{"name": "person"},
|
||||||
|
]
|
||||||
|
}
|
||||||
|
task = self._create_task(task, 3)
|
||||||
|
|
||||||
|
annotations = {
|
||||||
|
"version": 0,
|
||||||
|
"tags": [
|
||||||
|
{
|
||||||
|
"frame": 0,
|
||||||
|
"label_id": task["labels"][0]["id"],
|
||||||
|
"group": None,
|
||||||
|
"attributes": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"shapes": [
|
||||||
|
{
|
||||||
|
"frame": 0,
|
||||||
|
"label_id": task["labels"][0]["id"],
|
||||||
|
"group": None,
|
||||||
|
"attributes": [
|
||||||
|
{
|
||||||
|
"spec_id": task["labels"][0]["attributes"][0]["id"],
|
||||||
|
"value": task["labels"][0]["attributes"][0]["values"][0]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"spec_id": task["labels"][0]["attributes"][1]["id"],
|
||||||
|
"value": task["labels"][0]["attributes"][0]["default_value"]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"points": [1.0, 2.1, 100, 300.222],
|
||||||
|
"type": "rectangle",
|
||||||
|
"occluded": False
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"frame": 1,
|
||||||
|
"label_id": task["labels"][1]["id"],
|
||||||
|
"group": None,
|
||||||
|
"attributes": [],
|
||||||
|
"points": [2.0, 2.1, 100, 300.222, 400, 500, 1, 3],
|
||||||
|
"type": "polygon",
|
||||||
|
"occluded": False
|
||||||
|
},
|
||||||
|
],
|
||||||
|
"tracks": [
|
||||||
|
{
|
||||||
|
"frame": 0,
|
||||||
|
"label_id": task["labels"][0]["id"],
|
||||||
|
"group": None,
|
||||||
|
"attributes": [
|
||||||
|
{
|
||||||
|
"spec_id": task["labels"][0]["attributes"][0]["id"],
|
||||||
|
"value": task["labels"][0]["attributes"][0]["values"][0]
|
||||||
|
},
|
||||||
|
],
|
||||||
|
"shapes": [
|
||||||
|
{
|
||||||
|
"frame": 0,
|
||||||
|
"points": [1.0, 2.1, 100, 300.222],
|
||||||
|
"type": "rectangle",
|
||||||
|
"occluded": False,
|
||||||
|
"outside": False,
|
||||||
|
"attributes": [
|
||||||
|
{
|
||||||
|
"spec_id": task["labels"][0]["attributes"][1]["id"],
|
||||||
|
"value": task["labels"][0]["attributes"][1]["default_value"]
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"frame": 1,
|
||||||
|
"attributes": [],
|
||||||
|
"points": [2.0, 2.1, 100, 300.222],
|
||||||
|
"type": "rectangle",
|
||||||
|
"occluded": True,
|
||||||
|
"outside": True
|
||||||
|
},
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"frame": 1,
|
||||||
|
"label_id": task["labels"][1]["id"],
|
||||||
|
"group": None,
|
||||||
|
"attributes": [],
|
||||||
|
"shapes": [
|
||||||
|
{
|
||||||
|
"frame": 1,
|
||||||
|
"attributes": [],
|
||||||
|
"points": [1.0, 2.1, 100, 300.222],
|
||||||
|
"type": "rectangle",
|
||||||
|
"occluded": False,
|
||||||
|
"outside": False
|
||||||
|
}
|
||||||
|
]
|
||||||
|
},
|
||||||
|
]
|
||||||
|
}
|
||||||
|
self._put_api_v1_task_id_annotations(task["id"], annotations)
|
||||||
|
|
||||||
|
return task, annotations
|
||||||
|
|
||||||
|
def _create_task(self, data, size):
|
||||||
|
with ForceLogin(self.user, self.client):
|
||||||
|
response = self.client.post('/api/v1/tasks', data=data, format="json")
|
||||||
|
assert response.status_code == status.HTTP_201_CREATED, response.status_code
|
||||||
|
tid = response.data["id"]
|
||||||
|
|
||||||
|
images = {
|
||||||
|
"client_files[%d]" % i: generate_image_file("image_%d.jpg" % i)
|
||||||
|
for i in range(size)
|
||||||
|
}
|
||||||
|
images["image_quality"] = 75
|
||||||
|
response = self.client.post("/api/v1/tasks/{}/data".format(tid), data=images)
|
||||||
|
assert response.status_code == status.HTTP_202_ACCEPTED, response.status_code
|
||||||
|
|
||||||
|
response = self.client.get("/api/v1/tasks/{}".format(tid))
|
||||||
|
task = response.data
|
||||||
|
|
||||||
|
return task
|
||||||
|
|
||||||
|
def _put_api_v1_task_id_annotations(self, tid, data):
|
||||||
|
with ForceLogin(self.user, self.client):
|
||||||
|
response = self.client.put("/api/v1/tasks/{}/annotations".format(tid),
|
||||||
|
data=data, format="json")
|
||||||
|
|
||||||
|
return response
|
||||||
|
|
||||||
|
def _test_export(self, format_name, save_images=False):
|
||||||
|
self.assertTrue(format_name in [f['tag'] for f in dm.EXPORT_FORMATS])
|
||||||
|
|
||||||
|
task, _ = self._generate_task()
|
||||||
|
project = dm.TaskProject.from_task(
|
||||||
|
Task.objects.get(pk=task["id"]), self.user.username)
|
||||||
|
|
||||||
|
with tempfile.TemporaryDirectory() as test_dir:
|
||||||
|
project.export(format_name, test_dir, save_images=save_images)
|
||||||
|
|
||||||
|
self.assertTrue(os.listdir(test_dir))
|
||||||
|
|
||||||
|
def test_datumaro(self):
|
||||||
|
self._test_export(dm.EXPORT_FORMAT_DATUMARO_PROJECT, save_images=False)
|
||||||
|
|
||||||
|
def test_coco(self):
|
||||||
|
self._test_export('cvat_coco', save_images=True)
|
||||||
|
|
||||||
|
def test_voc(self):
|
||||||
|
self._test_export('cvat_voc', save_images=True)
|
||||||
|
|
||||||
|
def test_tf_detection_api(self):
|
||||||
|
self._test_export('cvat_tfrecord', save_images=True)
|
||||||
|
|
||||||
|
def test_yolo(self):
|
||||||
|
self._test_export('cvat_yolo', save_images=True)
|
||||||
|
|
||||||
|
def test_mot(self):
|
||||||
|
self._test_export('cvat_mot', save_images=True)
|
||||||
|
|
||||||
|
def test_labelme(self):
|
||||||
|
self._test_export('cvat_label_me', save_images=True)
|
||||||
|
|
||||||
|
def test_formats_query(self):
|
||||||
|
formats = dm.get_export_formats()
|
||||||
|
|
||||||
|
expected = set(f['tag'] for f in dm.EXPORT_FORMATS)
|
||||||
|
actual = set(f['tag'] for f in formats)
|
||||||
|
self.assertSetEqual(expected, actual)
|
||||||
@ -0,0 +1,68 @@
|
|||||||
|
# Copyright (C) 2019 Intel Corporation
|
||||||
|
#
|
||||||
|
# SPDX-License-Identifier: MIT
|
||||||
|
|
||||||
|
format_spec = {
|
||||||
|
"name": "LabelMe",
|
||||||
|
"dumpers": [
|
||||||
|
{
|
||||||
|
"display_name": "{name} {format} {version}",
|
||||||
|
"format": "ZIP",
|
||||||
|
"version": "3.0",
|
||||||
|
"handler": "dump"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"loaders": [
|
||||||
|
{
|
||||||
|
"display_name": "{name} {format} {version}",
|
||||||
|
"format": "ZIP",
|
||||||
|
"version": "3.0",
|
||||||
|
"handler": "load",
|
||||||
|
}
|
||||||
|
],
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
from datumaro.components.converter import Converter
|
||||||
|
class CvatLabelMeConverter(Converter):
|
||||||
|
def __init__(self, save_images=False):
|
||||||
|
self._save_images = save_images
|
||||||
|
|
||||||
|
def __call__(self, extractor, save_dir):
|
||||||
|
from datumaro.components.project import Environment, Dataset
|
||||||
|
|
||||||
|
env = Environment()
|
||||||
|
id_from_image = env.transforms.get('id_from_image_name')
|
||||||
|
|
||||||
|
extractor = extractor.transform(id_from_image)
|
||||||
|
extractor = Dataset.from_extractors(extractor) # apply lazy transforms
|
||||||
|
|
||||||
|
converter = env.make_converter('label_me', save_images=self._save_images)
|
||||||
|
converter(extractor, save_dir=save_dir)
|
||||||
|
|
||||||
|
def dump(file_object, annotations):
|
||||||
|
from cvat.apps.dataset_manager.bindings import CvatAnnotationsExtractor
|
||||||
|
from cvat.apps.dataset_manager.util import make_zip_archive
|
||||||
|
from tempfile import TemporaryDirectory
|
||||||
|
|
||||||
|
extractor = CvatAnnotationsExtractor('', annotations)
|
||||||
|
converter = CvatLabelMeConverter()
|
||||||
|
with TemporaryDirectory() as temp_dir:
|
||||||
|
converter(extractor, save_dir=temp_dir)
|
||||||
|
make_zip_archive(temp_dir, file_object)
|
||||||
|
|
||||||
|
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)
|
||||||
@ -0,0 +1,89 @@
|
|||||||
|
# SPDX-License-Identifier: MIT
|
||||||
|
format_spec = {
|
||||||
|
"name": "MOT",
|
||||||
|
"dumpers": [
|
||||||
|
{
|
||||||
|
"display_name": "{name} {format} {version}",
|
||||||
|
"format": "ZIP",
|
||||||
|
"version": "1.1",
|
||||||
|
"handler": "dump"
|
||||||
|
},
|
||||||
|
],
|
||||||
|
"loaders": [
|
||||||
|
{
|
||||||
|
"display_name": "{name} {format} {version}",
|
||||||
|
"format": "ZIP",
|
||||||
|
"version": "1.1",
|
||||||
|
"handler": "load",
|
||||||
|
}
|
||||||
|
],
|
||||||
|
}
|
||||||
|
|
||||||
|
|
||||||
|
from datumaro.plugins.mot_format import \
|
||||||
|
MotSeqGtConverter as _MotConverter
|
||||||
|
class CvatMotConverter(_MotConverter):
|
||||||
|
NAME = 'cvat_mot'
|
||||||
|
|
||||||
|
def dump(file_object, annotations):
|
||||||
|
from cvat.apps.dataset_manager.bindings import CvatAnnotationsExtractor
|
||||||
|
from cvat.apps.dataset_manager.util import make_zip_archive
|
||||||
|
from tempfile import TemporaryDirectory
|
||||||
|
|
||||||
|
extractor = CvatAnnotationsExtractor('', annotations)
|
||||||
|
converter = CvatMotConverter()
|
||||||
|
with TemporaryDirectory() as temp_dir:
|
||||||
|
converter(extractor, save_dir=temp_dir)
|
||||||
|
make_zip_archive(temp_dir, file_object)
|
||||||
|
|
||||||
|
|
||||||
|
def load(file_object, annotations):
|
||||||
|
from pyunpack import Archive
|
||||||
|
from tempfile import TemporaryDirectory
|
||||||
|
from datumaro.plugins.mot_format import MotSeqImporter
|
||||||
|
import datumaro.components.extractor as datumaro
|
||||||
|
from cvat.apps.dataset_manager.bindings import match_frame
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
tracks = {}
|
||||||
|
|
||||||
|
dm_dataset = MotSeqImporter()(tmp_dir).make_dataset()
|
||||||
|
label_cat = dm_dataset.categories()[datumaro.AnnotationType.label]
|
||||||
|
|
||||||
|
for item in dm_dataset:
|
||||||
|
frame_id = match_frame(item, annotations)
|
||||||
|
|
||||||
|
for ann in item.annotations:
|
||||||
|
if ann.type != datumaro.AnnotationType.bbox:
|
||||||
|
continue
|
||||||
|
|
||||||
|
track_id = ann.attributes.get('track_id')
|
||||||
|
if track_id is None:
|
||||||
|
continue
|
||||||
|
|
||||||
|
shape = annotations.TrackedShape(
|
||||||
|
type='rectangle',
|
||||||
|
points=ann.points,
|
||||||
|
occluded=ann.attributes.get('occluded') == True,
|
||||||
|
outside=False,
|
||||||
|
keyframe=False,
|
||||||
|
z_order=ann.z_order,
|
||||||
|
frame=frame_id,
|
||||||
|
attributes=[],
|
||||||
|
)
|
||||||
|
|
||||||
|
# build trajectories as lists of shapes in track dict
|
||||||
|
if track_id not in tracks:
|
||||||
|
tracks[track_id] = annotations.Track(
|
||||||
|
label_cat.items[ann.label].name, 0, [])
|
||||||
|
tracks[track_id].shapes.append(shape)
|
||||||
|
|
||||||
|
for track in tracks.values():
|
||||||
|
# MOT annotations do not require frames to be ordered
|
||||||
|
track.shapes.sort(key=lambda t: t.frame)
|
||||||
|
# Set outside=True for the last shape in a track to finish the track
|
||||||
|
track.shapes[-1] = track.shapes[-1]._replace(outside=True)
|
||||||
|
annotations.add_track(track)
|
||||||
Loading…
Reference in New Issue