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 run_inference(self):
inference_mode = InferenceModeFactory.create(
self._inference_mode_config, self._in_project.meta,
self._single_image_inference)
out_project = Project(
os.path.join(TaskPaths.RESULTS_DIR, self._in_project.name),
OpenMode.CREATE)
out_project.set_meta(inference_mode.out_meta)
progress_bar = Progress('Model applying: ',
self._in_project.total_items)
for in_dataset in self._in_project:
out_dataset = out_project.create_dataset(in_dataset.name)
for in_item_name in in_dataset:
# Use output project meta so that we get an annotation that is already in the context of the output
# project (with added object classes etc).
in_item_paths = in_dataset.get_item_paths(in_item_name)
in_img = sly_image.read(in_item_paths.img_path)
in_ann = Annotation.load_json_file(in_item_paths.ann_path,
inference_mode.out_meta)
logger.trace('Will process image',
extra={
'dataset_name': in_dataset.name,
'image_name': in_item_name
})
inference_annotation = inference_mode.infer_annotate(
in_img, in_ann)
out_dataset.add_item_file(in_item_name,
in_item_paths.img_path,
ann=inference_annotation)
progress_bar.iter_done_report()
report_inference_finished()
def single_inference_process_fn(inference_initializer, inference_mode_config,
in_project_meta_json, request_queue,
response_queue):
"""Loads a separate model, processes requests from request_queue, results go to result_queue.
None request signals the process to finish.
"""
single_image_inference = inference_initializer()
inference_mode = InferenceModeFactory.create(
inference_mode_config, ProjectMeta.from_json(in_project_meta_json),
single_image_inference)
out_meta_json = inference_mode.out_meta.to_json()
req = ''
while req is not None:
req = request_queue.get()
if req is not None:
in_img = sly_image.read(req.item_paths.img_path)
in_ann = Annotation.load_json_file(req.item_paths.ann_path,
inference_mode.out_meta)
ann = inference_mode.infer_annotate(in_img, in_ann)
resp = InferenceResponse(ds_name=req.ds_name,
item_name=req.item_name,
item_paths=req.item_paths,
ann_json=ann.to_json(),
meta_json=out_meta_json)
response_queue.put(resp)
request_queue.task_done()
def save_project_as_pascal_voc_detection(save_path, project: Project):
# Create root pascal 'datasets' folders
for dataset in project.datasets:
pascal_dataset_path = os.path.join(save_path, dataset.name)
pascal_dataset_relative_path = os.path.relpath(pascal_dataset_path,
save_path)
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_dataset_relative_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 single_inference_process_fn(inference_initializer, inference_mode_config,
in_project_meta_json, request_queue,
result_meta_queue, progress_queue, project):
"""Loads a separate model, processes requests from request_queue, results go to result_queue.
None request signals the process to finish.
"""
single_image_inference = inference_initializer()
inference_mode = InferenceModeFactory.create(
inference_mode_config, ProjectMeta.from_json(in_project_meta_json),
single_image_inference)
project_meta_sent = False
req = ''
while req is not None:
req = request_queue.get()
if req is not None:
# Send the resulting project meta to the parent project to make sure we only write the meta JSON once.
if not project_meta_sent:
try:
result_meta_queue.put(inference_mode.out_meta.to_json(),
block=False)
except queue.Full:
pass
project_meta_sent = True
in_ann = Annotation.load_json_file(req.item_paths.ann_path,
inference_mode.out_meta)
ann = inference_mode.infer_annotate_image_file(
req.item_paths.img_path, in_ann)
out_dataset = project.datasets.get(req.ds_name)
out_dataset.add_item_file(req.item_name,
req.item_paths.img_path,
ann=ann,
_validate_img=False,
_use_hardlink=True)
progress_queue.put(1)
def load_annotation(self, fpath):
# will not resize figures: resize gt instead
return Annotation.load_json_file(fpath, self._project_meta)