def __out_pipe_process(self, result):
result, inference = result
if inference.get_meta('expand_dims'):
num_detections = int(result['num_detections'][0])
detection_classes = result['detection_classes'][
0][:num_detections].astype(np.uint8)
detection_boxes = result['detection_boxes'][0][:num_detections]
detection_scores = result['detection_scores'][0][:num_detections]
if 'detection_masks' in result:
detection_masks = result['detection_masks'][0][:num_detections]
else:
detection_masks = None
results = InferedDetections(inference.get_input(),
num_detections,
detection_boxes,
detection_classes,
detection_scores,
masks=detection_masks,
is_normalized=True,
get_category_fnc=self.get_category,
annotator=self.annotate)
else:
results = []
for i in range(len(result['num_detections'])):
num_detections = int(result['num_detections'][i])
detection_classes = result['detection_classes'][
i][:num_detections].astype(np.uint8)
detection_boxes = result['detection_boxes'][i][:num_detections]
detection_scores = result['detection_scores'][
i][:num_detections]
if 'detection_masks' in result:
detection_masks = result['detection_masks'][
i][:num_detections]
else:
detection_masks = None
results.append(
InferedDetections(inference.get_input()[i],
num_detections,
detection_boxes,
detection_classes,
detection_scores,
masks=detection_masks,
is_normalized=True,
get_category_fnc=self.get_category,
annotator=self.annotate))
inference.set_result(results)
if inference.get_return_pipe():
return '\0'
return inference
def __out_pipe_process(self, result):
(out_boxes, out_classes, out_scores), inference = result
out_boxes = np.array([[0 if y < 0 else y for y in x]
for x in out_boxes])
result = InferedDetections(inference.get_input(),
len(out_boxes),
out_boxes,
out_classes,
out_scores,
masks=None,
is_normalized=False,
get_category_fnc=self.get_category,
annotator=self.annotate)
inference.set_result(result)
return inference
def __out_pipe_process(self, result):
result, inference = result
num_detections = int(result['num_detections'][0])
detection_classes = result['detection_classes'][0][:num_detections].astype(np.uint8)
detection_boxes = result['detection_boxes'][0][:num_detections]
detection_scores = result['detection_scores'][0][:num_detections]
if 'detection_masks' in result:
detection_masks = result['detection_masks'][0][:num_detections]
else:
detection_masks = None
result = InferedDetections(inference.get_input(), num_detections, detection_boxes, detection_classes,
detection_scores,
masks=detection_masks, is_normalized=True, get_category_fnc=self.get_category,
annotator=self.annotate)
inference.set_result(result)
return inference