def test_get_anno_vec(self):
img_anno = model.ImageAnno()
anno = model.TwoDAnno()
anno.bbox = REF_BBOX
img_anno.twod_annos.append(anno)
anno = model.TwoDAnno()
anno.point = REF_POINT
img_anno.twod_annos.append(anno)
anno = model.TwoDAnno()
anno.line = REF_LINE
img_anno.twod_annos.append(anno)
anno = model.TwoDAnno()
anno.polygon = REF_POLYGON
img_anno.twod_annos.append(anno)
print('Check BBox')
bb_list = img_anno.get_anno_vec(anno_type='bbox')
assert check_bbox(REF_BBOX, bb_list[0])
print('Check Point')
point_list = img_anno.get_anno_vec(anno_type='point')
assert check_point(REF_POINT, point_list[0])
print('Check Line')
line_list = img_anno.get_anno_vec(anno_type='line')
line = line_list[0]
check_line(REF_LINE, line)
print('Check Polygon')
polygon_list = img_anno.get_anno_vec(anno_type='polygon')
check_polygon(REF_POLYGON, polygon_list[0])
def empty_img_anno():
dbm = DBMan(config.LOSTConfig())
img_anno = model.ImageAnno(img_path='path/to/img1.jpg')
fs = add_local_fs(dbm, 'local_fs_for_empty_img_anno')
img_anno.fs_id = fs.idx
dbm.add(img_anno)
dbm.commit()
yield img_anno
dbm.delete(img_anno)
dbm.commit()
delete_local_fs(dbm, fs)
def empty_img_anno():
dbm = DBMan(config.LOSTConfig())
test_user = testils.get_user(dbm)
tree = testils.get_voc_label_tree(dbm)
label_leaf_id = tree.get_child_vec(tree.root.idx)[0]
img_anno = model.ImageAnno(img_path='path/to/img1.jpg')
dbm.add(img_anno)
dbm.commit()
yield img_anno
dbm.delete(img_anno)
dbm.commit()
def full_img_anno():
dbm = DBMan(config.LOSTConfig())
test_user = testils.get_user(dbm)
tree = testils.get_voc_label_tree(dbm)
label_vec = tree.get_child_vec(tree.root.idx)
twod_anno = model.TwoDAnno(data=json.dumps({
'x': REF_BBOX[0],
'y': REF_BBOX[1],
'w': REF_BBOX[2],
'h': REF_BBOX[3]
}),
dtype=dtype.TwoDAnno.BBOX)
lbl = model.Label(label_leaf_id=label_vec[0])
dbm.add(lbl)
twod_anno.labels.append(lbl)
twod_anno.annotator = test_user
twod_anno2 = model.TwoDAnno(data=json.dumps({
'x': REF_POINT[0],
'y': REF_POINT[1]
}),
dtype=dtype.TwoDAnno.POINT)
lbl = model.Label(label_leaf_id=label_vec[1])
dbm.add(lbl)
twod_anno2.labels.append(lbl)
twod_anno2.annotator = test_user
line = model.TwoDAnno()
lbl = model.Label(label_leaf_id=label_vec[4])
line.labels.append(lbl)
dbm.add(lbl)
line.annotator = test_user
line.line = REF_LINE
img_anno = model.ImageAnno(img_path='path/to/img1.jpg')
fs = add_local_fs(dbm, 'local_fs_for_full_img_anno')
img_anno.fs_id = fs.idx
lbl = model.Label(label_leaf_id=label_vec[3])
dbm.add(lbl)
img_anno.labels.append(lbl)
dbm.commit()
img_anno.twod_annos.append(twod_anno)
img_anno.twod_annos.append(twod_anno2)
img_anno.twod_annos.append(line)
# dbm.add(img_anno.labels)
dbm.add(img_anno)
# dbm.add(twod_anno)
dbm.commit()
yield img_anno
# dbm.delete(twod_anno.labels)
dbm.delete(twod_anno)
# dbm.delete(twod_anno2.labels)
dbm.delete(twod_anno2)
# dbm.delete(img_anno.labels)
dbm.delete(img_anno)
dbm.commit()
delete_local_fs(dbm, fs)
def _add_annos(self,
pe,
img_path,
img_labels=None,
img_sim_class=None,
annos=[],
anno_types=[],
anno_labels=[],
anno_sim_classes=[],
frame_n=None,
video_path=None,
anno_task_id=None,
fs=None,
img_meta=None,
anno_meta=None):
'''Add annos in list style to an image.
Args:
pe (PipeElement): The connected PipeElement where annotation should be provided for.
img_path (str): Path to the image where annotations are added for.
img_labels (list of int or str): Labels that will be assigned to the image. The label should
represented by a label_leaf_id or label_name.
img_sim_class (int): A culster id that will be used to cluster this image
in the MIA annotation tool.
annos (list of list): A list of
POINTs: [x,y]
BBOXes: [x,y,w,h]
LINEs or POLYGONs: [[x,y], [x,y], ...]
anno_types (list of str): Can be 'point', 'bbox', 'line', 'polygon'
anno_labels (list of int): Labels for the twod annos.
Each label in the list is represented by a label_leaf_id.
(see also :class:`model.LabelLeaf`).
anno_sim_classes (list of ints): List of arbitrary cluster ids
that are used to cluster annotations in the MIA annotation tool.
frame_n (int): If *img_path* belongs to a video *frame_n* indicates
the framenumber.
video_path (str): If *img_path* belongs to a video this is the path to
this video.
anno_task_id (int): Id of the assigned annotation task.
fs (obj): The filesystem where image is located.
User lost standard filesystem if no filesystem was given.
You can get this Filesystem object from a DataSource-Element by calling
get_fs method.
img_meta (dict): Dictionary with meta information that should be added to the
image annotation. Each meta key will be added as column during annotation export.
the dict-value will be row content.
anno_meta (list of dict): List of dictionaries with meta information that
should be added to a specific annotation. Each meta key will be
added as column during annotation export. The dict-value will be row content.
'''
if img_sim_class is None:
img_sim_class = 1
if video_path is not None:
video_path = self._script.get_rel_path(video_path)
if fs is None:
fs_db = self._script._dbm.get_fs(name='lost_data')
fs = DummyFileMan(fs_db)
fm = file_man.FileMan(fs_db=fs.lost_fs)
rel_img_path = fm.make_path_relative(img_path)
img_anno = model.ImageAnno(
anno_task_id=anno_task_id,
img_path=rel_img_path,
abs_path=os.path.join(fm.root_path, rel_img_path),
state=state.Anno.UNLOCKED,
result_id=self._result_map[pe.idx],
iteration=self._script._pipe_element.iteration,
frame_n=frame_n,
video_path=video_path,
sim_class=img_sim_class,
fs_id=fs.lost_fs.idx)
if img_meta is not None:
img_anno.meta = json.dumps(img_meta, default=_json_default)
self._script._dbm.add(img_anno)
if img_labels is not None:
self._update_labels(img_labels, img_anno)
if len(annos) != len(anno_types):
raise ValueError(
'*anno_types* and *annos* need to be of same size!')
for i, vec in enumerate(annos):
anno = model.TwoDAnno(
iteration=self._script._pipe_element.iteration,
anno_task_id=anno_task_id,
state=state.Anno.UNLOCKED)
if anno_meta is not None:
if len(annos) != len(anno_meta):
raise ValueError(
'*anno_meta* and *annos* need to be of same size!')
anno.meta = json.dumps(anno_meta[i], default=_json_default)
if anno_types[i] == 'point':
anno.point = vec
elif anno_types[i] == 'bbox':
anno.bbox = vec
elif anno_types[i] == 'line':
anno.line = vec
elif anno_types[i] == 'polygon':
anno.polygon = vec
if anno_labels:
if len(anno_labels) != len(annos):
raise ValueError(
'*anno_labels* and *annos* need to be of same size!')
label_leaf_ids = anno_labels[i]
self._update_labels(label_leaf_ids, anno)
if anno_sim_classes:
if len(anno_sim_classes) != len(annos):
raise ValueError(
'*anno_sim_classes* and *annos* need to have same size!'
)
anno.sim_class = anno_sim_classes[i]
else:
anno.sim_class = 1
img_anno.twod_annos.append(anno)
def _request_lds(self,
pe,
lds,
fm=None,
anno_meta_keys=[],
img_meta_keys=[],
img_path_key='img_path'):
'''Request annos from LOSTDataset.
Args:
lds (LOSTDataset): A lost dataset object. Request all annotation in this
dataset again.
pe (PipelineElement): PipelineElement of the annotations task where
annotations should be requested for.
fm (FileMan): A file_man object.
img_meta_keys (list): Keys that should be used for img_anno meta information
anno_meta_keys (list or *all*): Keys that should be used for two_d_anno meta information.
If all, all keys of lds will be added as meta information.
img_path_key (str): Column that should be used as img_path
'''
if 'anno_format' in lds.df:
if len(lds.df[lds.df['anno_format'] != 'rel']) > 0:
raise Exception(
'All anno in LOSTDataset need to be in rel format!')
else:
self._script.logger.warning('anno_format column is missing in lds')
if 'anno_style' in lds.df:
bb_df = lds.df[lds.df['anno_dtype'] == 'bbox']
if len(bb_df[bb_df['anno_style'] != 'xcycwh']) > 0:
raise Exception(
'All anno in bboxes need to be in xcycwh anno_style!')
else:
self._script.logger.warning('anno_style column is missing in lds')
fm_cache = dict()
# db_anno_task = self._script._dbm.get_anno_task(anno_task_id=anno_task_id)
anno_task = pipe_elements.AnnoTask(pe, self._script._dbm)
lbl_map = anno_task.lbl_map
for img_path, df in lds.df.groupby(img_path_key):
fm = self._get_lds_fm(df, fm_cache, fm)
if 'img_sim_class' in df:
if df['img_sim_class'].values[0]:
img_sim_class = df['img_sim_class'].values[0]
else:
img_sim_class = 1
else:
img_sim_class = 1
rel_img_path = fm.make_path_relative(img_path)
anno_task_id = pe.anno_task.idx
img_anno = model.ImageAnno(
anno_task_id=anno_task_id,
img_path=rel_img_path,
abs_path=os.path.join(fm.root_path, rel_img_path),
state=state.Anno.UNLOCKED,
result_id=self._result_map[pe.idx],
iteration=self._script._pipe_element.iteration,
# frame_n=df['img_frame_n'].values[0],
sim_class=img_sim_class,
fs_id=fm.fs.lost_fs.idx)
if len(img_meta_keys) > 0:
# anno.meta = json.dumps(row[img_meta_keys].to_dict())
img_anno.meta = json.dumps(df.iloc[0][img_meta_keys].to_dict(),
default=_json_default)
self._script._dbm.add(img_anno)
# if img_labels is not None:
if 'img_lbl' in df:
img_lbls = df['img_lbl'].values[0]
if img_lbls:
if len(img_lbls) > 0:
self._update_labels(img_lbls, img_anno, lbl_map)
if 'anno_data' in df:
anno_df = df[~df['anno_data'].isnull()]
if len(anno_df) > 0:
# for i, vec in enumerate(annos):
for idx, row in anno_df.iterrows():
anno = model.TwoDAnno(
iteration=self._script._pipe_element.iteration,
anno_task_id=anno_task_id,
state=state.Anno.UNLOCKED)
if len(anno_meta_keys) > 0:
if anno_meta_keys == 'all':
anno.meta = json.dumps(row.to_dict(),
default=_json_default)
else:
anno.meta = json.dumps(
row[anno_meta_keys].to_dict(),
default=_json_default)
if row['anno_dtype'] == 'point':
anno.point = row['anno_data']
elif row['anno_dtype'] == 'bbox':
anno.bbox = row['anno_data']
elif row['anno_dtype'] == 'line':
anno.line = row['anno_data']
elif row['anno_dtype'] == 'polygon':
anno.polygon = row['anno_data']
if 'anno_lbl' in row:
if len(row['anno_lbl']) > 0:
# if len(anno_labels) != len(annos):
# raise ValueError('*anno_labels* and *annos* need to be of same size!')
# label_leaf_ids = anno_labels[i]
self._update_labels(row['anno_lbl'], anno,
lbl_map)
if 'anno_sim_class' in row:
if row['anno_sim_class']:
# if len(anno_sim_classes) != len(annos):
# raise ValueError('*anno_sim_classes* and *annos* need to have same size!')
anno.sim_class = row['anno_sim_class']
else:
anno.sim_class = 1
img_anno.twod_annos.append(anno)
def _add_annos(self, pe, img_path, img_label=None, img_sim_class=None,
annos=[], anno_types=[], anno_labels=[], anno_sim_classes=[], frame_n=None,
video_path=None, anno_task_id=None):
'''Add annos in list style to an image.
Args:
pe (PipeElement): The connected PipeElement where annotation should be provided for.
img_path (str): Path to the image where annotations are added for.
img_label (int): Labels that will be assigned to the image. The label should
represented by a label_leaf_id.
img_sim_class (int): A culster id that will be used to cluster this image
in the MIA annotation tool.
annos (list of list): A list of
POINTs: [x,y]
BBOXes: [x,y,w,h]
LINEs or POLYGONs: [[x,y], [x,y], ...]
anno_types (list of str): Can be 'point', 'bbox', 'line', 'polygon'
anno_labels (list of int): Labels for the twod annos.
Each label in the list is represented by a label_leaf_id.
(see also :class:`model.LabelLeaf`).
anno_sim_classes (list of ints): List of arbitrary cluster ids
that are used to cluster annotations in the MIA annotation tool.
frame_n (int): If *img_path* belongs to a video *frame_n* indicates
the framenumber.
video_path (str): If *img_path* belongs to a video this is the path to
this video.
anno_task_id (int): Id of the assigned annotation task.
'''
if img_sim_class is None:
img_sim_class = 1
if video_path is not None:
video_path = self._script.get_rel_path(video_path)
rel_img_path = self._script.file_man.make_path_relative(img_path)
img_anno = model.ImageAnno(anno_task_id=anno_task_id,
img_path=rel_img_path,
state=state.Anno.UNLOCKED,
result_id=self._result_map[pe.idx],
iteration=self._script._pipe_element.iteration,
frame_n=frame_n,
video_path=video_path,
sim_class=img_sim_class)
self._script._dbm.add(img_anno)
if img_label is not None:
img_anno.label = model.Label(label_leaf_id=img_label)
if len(annos) != len(anno_types):
raise ValueError('*anno_types* and *annos* need to be of same size!')
for i, vec in enumerate(annos):
anno = model.TwoDAnno(iteration=self._script._pipe_element.iteration,
anno_task_id=anno_task_id, state=state.Anno.UNLOCKED)
if anno_types[i] == 'point':
anno.point = vec
elif anno_types[i] == 'bbox':
anno.bbox = vec
elif anno_types[i] == 'line':
anno.line = vec
elif anno_types[i] == 'polygon':
anno.polygon = vec
if anno_labels:
if len(anno_labels) != len(annos):
raise ValueError('*anno_labels* and *annos* need to be of same size!')
label_leaf_id = anno_labels[i]
if label_leaf_id is not None:
anno.label = model.Label(label_leaf_id=label_leaf_id)
if anno_sim_classes:
if len(anno_sim_classes) != len(annos):
raise ValueError('*anno_sim_classes* and *annos* need to have same size!')
anno.sim_class = anno_sim_classes[i]
else:
anno.sim_class = 1
img_anno.twod_annos.append(anno)
