def adjust_annotations(ann_paths, meta, progress):
for ann_path in ann_paths:
temp_json_data = None
with open(ann_path, 'r') as annotation_file:
temp_annotation = Annotation.from_json(json.load(annotation_file),
meta)
# Adjust Image dimension infos for annotation file
new_img_size = tuple(
map(lambda dim: dim + fsoco.FSOCO_IMPORT_BORDER_THICKNESS * 2,
temp_annotation.img_size))
temp_annotation._img_size = new_img_size
# Transform labels according to borders added by watermarking
#translate_label = (lambda label: [label.translate(drow=fsoco.FSOCO_IMPORT_BORDER_THICKNESS, dcol=fsoco.FSOCO_IMPORT_BORDER_THICKNESS)])
#temp_annotation.transform_labels(translate_label)
temp_labels = []
for label in temp_annotation._labels:
# Do stuff to labels
# Add border thickness once to each dimension of the bbox points.
temp_label = label.translate(
fsoco.FSOCO_IMPORT_BORDER_THICKNESS,
fsoco.FSOCO_IMPORT_BORDER_THICKNESS)
temp_labels.append(temp_label)
temp_annotation._labels = temp_labels
# Save transformed annotation
temp_json_data = temp_annotation.to_json()
with open(ann_path, 'w') as annotation_file:
annotation_file.write(json.dumps(temp_json_data))
progress.iter_done_report()
def load_ann(ann_fpath, classes_mapping, project_meta):
ann_packed = load_json_file(ann_fpath)
ann = Annotation.from_json(ann_packed, project_meta)
# ann.normalize_figures() # @TODO: enaaaable!
(h, w) = ann.img_size
gt_boxes, classes_text, classes = [], [], []
for label in ann.labels:
gt = np.zeros((h, w), dtype=np.uint8) # default bkg
gt_idx = classes_mapping.get(label.obj_class.name, None)
if gt_idx is None:
raise RuntimeError(
'Missing class mapping (title to index). Class {}.'.format(
label.obj_class.name))
label.geometry.draw(gt, 1)
if np.sum(gt) > 0:
xmin, ymin, xmax, ymax = get_bbox(gt)
gt_boxes.append([ymin / h, xmin / w, ymax / h, xmax / w])
classes_text.append(label.obj_class.name.encode('utf8'))
# List of string class name of bounding box (1 per box)
classes.append(
gt_idx) # List of integer class id of bounding box (1 per box)
num_boxes = len(gt_boxes)
gt_boxes = np.array(gt_boxes).astype(np.float32)
classes = np.array(classes, dtype=np.int64)
if num_boxes == 0:
gt_boxes = np.reshape(gt_boxes, [0, 4])
return gt_boxes, classes, np.array([num_boxes]).astype(np.int32)[0]
def _do_single_img_inference(self, img, in_msg):
in_project_meta = self._in_project_meta_from_msg(in_msg)
ann_json = in_msg.get('annotation')
if ann_json is not None:
if in_project_meta is None:
raise ValueError('In order to perform inference with annotation you must specify the appropriate'
' project meta.')
ann = Annotation.from_json(ann_json, in_project_meta)
else:
in_project_meta = in_project_meta or ProjectMeta()
ann = Annotation(img.shape[:2])
inference_mode = self._make_inference_mode(in_msg.get(MODE, {}), in_project_meta)
inference_result = inference_mode.infer_annotate(img, ann)
return inference_result.to_json()