def run_evaluation(self):
progress = Progress('metric evaluation', self._project_gt.total_items)
for ds_name in self._project_gt.datasets.keys():
ds_gt = self._project_gt.datasets.get(ds_name)
ds_pred = self._project_pred.datasets.get(ds_name)
for sample_name in ds_gt:
try:
ann_gt = Annotation.load_json_file(ds_gt.get_ann_path(sample_name), self._project_gt.meta)
ann_pred = Annotation.load_json_file(ds_pred.get_ann_path(sample_name), self._project_pred.meta)
self._metric.add_pair(ann_gt, ann_pred)
except ValueError as e:
logger.warning('An error has occured ({}). Sample "{}" in dataset "{}" will be skipped'
.format(str(e), sample_name, ds_gt.name))
progress.iter_done_report()
def save_project_as_pascal_voc_detection(save_path, project: Project):
import pascal_voc_writer
# Create root pascal 'datasets' folders
for dataset in project.datasets:
pascal_dataset_path = os.path.join(save_path, dataset.name)
images_dir = os.path.join(pascal_dataset_path, 'JPEGImages')
anns_dir = os.path.join(pascal_dataset_path, 'Annotations')
lists_dir = os.path.join(pascal_dataset_path, 'ImageSets/Layout')
fs_utils.mkdir(pascal_dataset_path)
for subdir in ['ImageSets', # Train list, Val list, etc.
'ImageSets/Layout',
'Annotations',
'JPEGImages']:
fs_utils.mkdir(os.path.join(pascal_dataset_path, subdir))
samples_by_tags = defaultdict(list) # TRAIN: [img_1, img2, ..]
for item_name in dataset:
img_path, ann_path = dataset.get_item_paths(item_name)
no_ext_name = fs_utils.get_file_name(item_name)
pascal_img_path = os.path.join(images_dir, no_ext_name + OUT_IMG_EXT)
pascal_ann_path = os.path.join(anns_dir, no_ext_name + XML_EXT)
if item_name.endswith(OUT_IMG_EXT):
fs_utils.copy_file(img_path, pascal_img_path)
else:
img = image_utils.read(img_path)
image_utils.write(pascal_img_path, img)
ann = Annotation.load_json_file(ann_path, project_meta=project.meta)
# Read tags for images lists generation
for tag in ann.img_tags:
samples_by_tags[tag.name].append((no_ext_name ,len(ann.labels)))
writer = pascal_voc_writer.Writer(path=pascal_img_path,
width=ann.img_size[1],
height=ann.img_size[0])
for label in ann.labels:
obj_class = label.obj_class
rect: Rectangle = label.geometry.to_bbox()
writer.addObject(name=obj_class.name,
xmin = rect.left,
ymin = rect.top,
xmax = rect.right,
ymax = rect.bottom)
writer.save(pascal_ann_path)
save_images_lists(lists_dir, samples_by_tags)
def samples_by_tags(required_tags, project):
"""
Split samples from project by tags
:param required_tags: list of tags names
:param project: supervisely `Project` class object
:return:
"""
img_annotations_groups = defaultdict(list)
for dataset in project:
for item_name in dataset:
item_paths = dataset.get_item_paths(item_name)
ann = Annotation.load_json_file(path=item_paths.ann_path,
project_meta=project.meta)
img_tags = ann.img_tags
for required_tag in required_tags:
if img_tags.has_key(required_tag):
# TODO migrate to ItemPath objects for img_annotations_groups
img_annotations_groups[required_tag].append(
(item_paths.img_path, item_paths.ann_path))
return img_annotations_groups
def ensure_samples_nonempty(samples, tag_name, project_meta):
"""
Args:
samples: list of pairs (image path, annotation path).
tag_name: tag name for messages.
project_meta: input project meta object.
Returns: None
"""
if len(samples) < 1:
raise RuntimeError(
'There are no annotations with tag "{}"'.format(tag_name))
for _, ann_path in samples:
ann = Annotation.load_json_file(ann_path, project_meta)
if len(ann.labels) > 0:
return
raise RuntimeError(
'There are no objects in annotations with tag "{}"'.format(tag_name))
def load_annotation(self, fpath):
# will not resize figures: resize gt instead
return Annotation.load_json_file(fpath, self._project_meta)