def infer_on_image(image,
graph,
model,
idx_to_class_title,
project_meta,
confidence_tag_meta,
min_confidence=0):
with graph.as_default():
[results] = model.detect([image], verbose=0)
res_labels = []
for mask_idx, class_id in enumerate(results['class_ids']):
confidence = results['scores'][mask_idx]
if confidence < min_confidence:
continue
bool_mask = results['masks'][:, :, mask_idx] != 0
class_geometry = Bitmap(data=bool_mask)
cls_title = idx_to_class_title[class_id]
label = Label(geometry=class_geometry,
obj_class=project_meta.get_obj_class(cls_title))
confidence_tag = Tag(confidence_tag_meta,
value=round(float(confidence), 4))
label = label.add_tag(confidence_tag)
res_labels.append(label)
return res_labels
def detection_preds_to_sly_rects(
idx_to_class, network_prediction: DetectionNetworkPrediction,
img_shape, min_score_threshold, score_tag_meta) -> list:
"""
Converts network detection results to Supervisely Labels with Rectangle geometry.
Args:
idx_to_class: Dict matching predicted boxes with appropriate ObjClass.
network_prediction: Network predictions packed into DetectionNetworkPrediction instance.
img_shape: Size(height, width) of image that was used for inference.
min_score_threshold: All detections with less scores will be dropped.
score_tag_meta: TagMeta instance for score tags.
Returns:
A list containing labels with detection rectangles.
"""
labels = []
thr_mask = np.squeeze(network_prediction.scores) > min_score_threshold
for box, class_id, score in zip(
np.squeeze(network_prediction.boxes)[thr_mask],
np.squeeze(network_prediction.classes)[thr_mask],
np.squeeze(network_prediction.scores)[thr_mask]):
xmin = round(float(box[1] * img_shape[1]))
ymin = round(float(box[0] * img_shape[0]))
xmax = round(float(box[3] * img_shape[1]))
ymax = round(float(box[2] * img_shape[0]))
rect = Rectangle(top=ymin, left=xmin, bottom=ymax, right=xmax)
class_obj = idx_to_class[int(class_id)]
label = Label(geometry=rect, obj_class=class_obj)
score_tag = Tag(score_tag_meta, value=round(float(score), 4))
label = label.add_tag(score_tag)
labels.append(label)
return labels
