def postprocess(output):
# extract_predictions
total_boxes = np.zeros((0, 9), np.float)
for idx, outputs in enumerate(output):
scales = input_meta['scales'][idx]
mapping = outputs[self._output_layers['pnet']
['probabilities']][0, 1]
regions = outputs[self._output_layers['pnet']['regions']][0]
boxes = utils.generate_bounding_box(mapping, regions, scales,
0.6)
if len(boxes) != 0:
pick = utils.nms(boxes, 0.5)
if np.size(pick) > 0:
boxes = boxes[pick]
if len(boxes) != 0:
total_boxes = np.concatenate((total_boxes, boxes), axis=0)
if np.size(total_boxes) == 0:
return np.zeros((0, 5))
pick = utils.nms(total_boxes, 0.7)
total_boxes = total_boxes[pick]
return utils.bbreg(total_boxes[:, :5],
total_boxes[:, 5:],
include_bound=False)
def postprocess(output):
score = output[self._output_layers['onet']['probabilities']][:, 1]
regions = output[self._output_layers['onet']['regions']]
bboxes = utils.calibrate_bboxes(prev_stage_output, score, regions)
pick = utils.nms(bboxes, 0.7, 'min')
bboxes_to_remove = np.setdiff1d(np.arange(len(bboxes)), pick)
return np.delete(bboxes, bboxes_to_remove, axis=0)
