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Az/coco loader format support (#630)

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* added coco dumper
* added license note
* added coco loader
main
Andrey Zhavoronkov 7 years ago committed by Nikita Manovich
parent 8e43dc75ad
commit c2bf8eda01
3 changed files with 167 additions and 68 deletions
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2
CHANGELOG.md
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@ -18,7 +18,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
- Ability to create a custom extractors for unsupported media types
- Added in PDF extractor
- Added in a command line model manager tester
- Ability to dump/load annotations in several formats from UI (CVAT, Pascal VOC, YOLO)
- Ability to dump/load annotations in several formats from UI (CVAT, Pascal VOC, YOLO, MS COCO)
### Changed
- Outside and keyframe buttons in the side panel for all interpolation shapes (they were only for boxes before)

13
README.md
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@ -31,12 +31,13 @@ CVAT is free, online, interactive video and image annotation tool for computer v
Format selection is possible after clicking on the Upload annotation / Dump annotation button.
| Annotation format | Dumper | Loader |
| ------------------------- | ------ | ------ |
| CVAT XML v1.1 for images | X | X |
| CVAT XML v1.1 for a video | X | X |
| Pascal VOC | X | X |
| YOLO | X | X |
| Annotation format | Dumper | Loader |
| ---------------------------------------------------------------------------------- | ------ | ------ |
| [CVAT XML v1.1 for images](cvat/apps/documentation/xml_format.md#annotation) | X | X |
| [CVAT XML v1.1 for a video](cvat/apps/documentation/xml_format.md#interpolation) | X | X |
| [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/) | X | X |
| [YOLO](https://pjreddie.com/darknet/yolo/) | X | X |
| [MS COCO Object Detection](http://cocodataset.org/#format-data) | X | X |
## Links
- [Intel AI blog: New Computer Vision Tool Accelerates Annotation of Digital Images and Video](https://www.intel.ai/introducing-cvat)

220
cvat/apps/annotation/coco.py
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@ -13,48 +13,58 @@ format_spec = {
},
],
"loaders": [
{
"display_name": "{name} {format} {version}",
"format": "JSON",
"version": "1.0",
"handler": "load"
},
],
}
def mask_to_polygon(mask, tolerance=1.0, area_threshold=1):
"""Convert object's mask to polygon [[x1,y1, x2,y2 ...], [...]]
Args:
mask: object's mask presented as 2D array of 0 and 1
tolerance: maximum distance from original points of polygon to approximated
area_threshold: if area of a polygon is less than this value, remove this small object
"""
from skimage import measure
from pycocotools import mask as mask_util
import numpy as np
polygons = []
# pad mask with 0 around borders
padded_mask = np.pad(mask, pad_width=1, mode='constant', constant_values=0)
contours = measure.find_contours(padded_mask, 0.5)
# Fix coordinates after padding
contours = np.subtract(contours, 1)
for contour in contours:
if not np.array_equal(contour[0], contour[-1]):
contour = np.vstack((contour, contour[0]))
contour = measure.approximate_polygon(contour, tolerance)
if len(contour) > 2:
contour = np.flip(contour, axis=1)
reshaped_contour = []
for xy in contour:
reshaped_contour.append(xy[0])
reshaped_contour.append(xy[1])
reshaped_contour = [point if point > 0 else 0 for point in reshaped_contour]
# Check if area of a polygon is enough
rle = mask_util.frPyObjects([reshaped_contour], mask.shape[0], mask.shape[1])
area = mask_util.area(rle)
if sum(area) > area_threshold:
polygons.append(reshaped_contour)
return polygons
def dump(file_object, annotations):
import numpy as np
import json
from skimage import measure
from collections import OrderedDict
from pycocotools import mask as mask_util
from pycocotools import coco as coco_loader
def mask_to_polygon(mask, tolerance=1.0, area_threshold=1):
"""Convert object's mask to polygon [[x1,y1, x2,y2 ...], [...]]
Args:
mask: object's mask presented as 2D array of 0 and 1
tolerance: maximum distance from original points of polygon to approximated
area_threshold: if area of a polygon is less than this value, remove this small object
"""
polygons = []
# pad mask with 0 around borders
padded_mask = np.pad(mask, pad_width=1, mode='constant', constant_values=0)
contours = measure.find_contours(padded_mask, 0.5)
# Fix coordinates after padding
contours = np.subtract(contours, 1)
for contour in contours:
if not np.array_equal(contour[0], contour[-1]):
contour = np.vstack((contour, contour[0]))
contour = measure.approximate_polygon(contour, tolerance)
if len(contour) > 2:
contour = np.flip(contour, axis=1)
reshaped_contour = []
for xy in contour:
reshaped_contour.append(xy[0])
reshaped_contour.append(xy[1])
for rcontour in reshaped_contour:
if rcontour < 0:
rcontour = 0
# Check if area of a polygon is enough
rle = mask_util.frPyObjects([reshaped_contour], mask.shape[0], mask.shape[1])
area = mask_util.area(rle)
if sum(area) > area_threshold:
polygons.append(reshaped_contour)
return polygons
def fix_segments_intersections(polygons, height, width, img_name,
threshold=0.0, ratio_tolerance=0.001, area_threshold=1):
@ -143,11 +153,11 @@ def dump(file_object, annotations):
Args:
result_annotation: output annotation in COCO representation
"""
result_annotation['licenses'].append({
'name': '',
'id': 0,
'url': ''
})
result_annotation['licenses'].append(OrderedDict([
('name', ''),
('id', 0),
('url', ''),
]))
def insert_info_data(annotations, result_annotation):
@ -161,14 +171,14 @@ def dump(file_object, annotations):
date = annotations.meta['dumped']
date = date.split(' ')[0]
year = date.split('-')[0]
result_annotation['info'] = {
'contributor': '',
'date_created': date,
'description': description,
'url': '',
'version': version,
'year': year
}
result_annotation['info'] = OrderedDict([
('contributor', ''),
('date_created', date),
('description', description),
('url', ''),
('version', version),
('year', year),
])
def insert_categories_data(annotations, result_annotation):
@ -188,12 +198,14 @@ def dump(file_object, annotations):
cat_id = 0
for name in names:
category_map[name] = cat_id
categories.append({'id': cat_id, 'name': name, 'supercategory': ''})
categories.append(OrderedDict([
('id', cat_id),
('name', name),
('supercategory', ''),
]))
cat_id += 1
return category_map, categories
categories = []
category_map = {}
label_names = [label[1]["name"] for label in annotations.meta['task']['labels']]
category_map, categories = get_categories(label_names, sort=True)
@ -208,7 +220,7 @@ def dump(file_object, annotations):
image: dictionary with data for image from original annotation
result_annotation: output annotation in COCO representation
"""
new_img = {}
new_img = OrderedDict()
new_img['coco_url'] = ''
new_img['date_captured'] = ''
new_img['flickr_url'] = ''
@ -229,7 +241,7 @@ def dump(file_object, annotations):
obj: includes data for the object [label, polygon]
result_annotation: output annotation in COCO representation
"""
new_anno = {}
new_anno = OrderedDict()
new_anno['category_id'] = category_map[obj['label']]
new_anno['id'] = segm_id
new_anno['image_id'] = image.frame
@ -240,19 +252,18 @@ def dump(file_object, annotations):
new_anno['bbox'] = bbox
result_annotation['annotations'].append(new_anno)
result_annotation = {
'licenses': [],
'info': {},
'categories': [],
'images': [],
'annotations': []
}
result_annotation = OrderedDict([
('licenses', []),
('info', {}),
('categories', []),
('images', []),
('annotations', []),
])
insert_license_data(result_annotation)
insert_info_data(annotations, result_annotation)
category_map = insert_categories_data(annotations, result_annotation)
segm_id = 0
for img in annotations.group_by_frame():
polygons = []
@ -262,6 +273,7 @@ def dump(file_object, annotations):
'label': shape.label,
'points': shape.points,
'z_order': shape.z_order,
'group': shape.group,
}
if shape.type == 'rectangle':
@ -270,6 +282,7 @@ def dump(file_object, annotations):
xbr = polygon['points'][2]
ybr = polygon['points'][3]
polygon['points'] = [xtl, ytl, xbr, ytl, xbr, ybr, xtl, ybr]
polygons.append(polygon)
polygons.sort(key=lambda x: int(x['z_order']))
@ -278,10 +291,27 @@ def dump(file_object, annotations):
insert_image_data(img, result_annotation)
polygons = fix_segments_intersections(polygons, img.height, img.width, img.name)
# combine grouped polygons with the same label
grouped_poligons = OrderedDict()
ungrouped_poligons = []
for polygon in polygons:
group_id = polygon['group']
label = polygon['label']
if group_id != 0:
if group_id not in grouped_poligons:
grouped_poligons[group_id] = OrderedDict()
if label not in grouped_poligons[group_id]:
grouped_poligons[group_id][label] = polygon
else:
grouped_poligons[group_id][label]['points'].extend(polygon['points'])
else:
ungrouped_poligons.append(polygon)
polygons = ungrouped_poligons + [poly for group in grouped_poligons.values() for poly in group.values()]
# Create new annotation for this image
for poly in polygons:
for segm_id, poly in enumerate(polygons):
insert_annotation_data(img, category_map, segm_id, poly, result_annotation)
segm_id += 1
file_object.write(json.dumps(result_annotation, indent=2).encode())
file_object.flush()
@ -291,3 +321,71 @@ def dump(file_object, annotations):
coco_loader.COCO(file_object.name)
except:
raise
def load(file_object, annotations):
from pycocotools import coco as coco_loader
from pycocotools import mask as mask_utils
import numpy as np
def get_filename(path):
import os
return os.path.splitext(os.path.basename(path))[0]
def match_frame(frame_info, filename):
import re
# try to match by filename
yolo_filename = get_filename(filename)
for frame_number, info in frame_info.items():
cvat_filename = get_filename(info["path"])
if cvat_filename == yolo_filename:
return frame_number
# try to extract frame number from filename
numbers = re.findall(r"\d+", filename)
if numbers and len(numbers) == 1:
return int(numbers[0])
raise Exception("Cannot match filename or determinate framenumber for {} filename".format(filename))
coco = coco_loader.COCO(file_object.name)
labels={cat['id']: cat['name'] for cat in coco.loadCats(coco.getCatIds())}
group_idx = 0
for img_id in coco.getImgIds():
anns = coco.loadAnns(coco.getAnnIds(imgIds=img_id))
img = coco.loadImgs(ids=img_id)[0]
frame_number = match_frame(annotations.frame_info, img['file_name'])
for ann in anns:
group = 0
label_name = labels[ann['category_id']]
if 'segmentation' in ann:
polygons = []
# polygon
if ann['iscrowd'] == 0:
polygons = ann['segmentation']
# mask
else:
if isinstance(ann['segmentation']['counts'], list):
rle = mask_utils.frPyObjects([ann['segmentation']], img['height'], img['width'])
else:
rle = [ann['segmentation']]
mask = np.array(mask_utils.decode(rle), dtype=np.uint8)
mask = np.sum(mask, axis=2)
mask = np.array(mask > 0, dtype=np.uint8)
polygons = mask_to_polygon(mask)
if len(polygons) > 1:
group_idx += 1
group = group_idx
for polygon in polygons:
annotations.add_shape(annotations.LabeledShape(
type='polygon',
frame=frame_number,
label=label_name,
points=polygon,
occluded=False,
attributes=[],
group=group,
))

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