| from retinaface.anchor import decode_tf, prior_box_tf |
| import tensorflow as tf |
|
|
|
|
| def extract_detections(bbox_regressions, landm_regressions, classifications, image_sizes, iou_th=0.4, score_th=0.02): |
| min_sizes = [[16, 32], [64, 128], [256, 512]] |
| steps = [8, 16, 32] |
| variances = [0.1, 0.2] |
| preds = tf.concat( |
| [bbox_regressions, |
| landm_regressions, |
| tf.ones_like(classifications[:, 0][..., tf.newaxis]), |
| classifications[:, 1][..., tf.newaxis]], 1) |
| priors = prior_box_tf(image_sizes, min_sizes, steps, False) |
| decode_preds = decode_tf(preds, priors, variances) |
|
|
| selected_indices = tf.image.non_max_suppression( |
| boxes=decode_preds[:, :4], |
| scores=decode_preds[:, -1], |
| max_output_size=tf.shape(decode_preds)[0], |
| iou_threshold=iou_th, |
| score_threshold=score_th) |
|
|
| out = tf.gather(decode_preds, selected_indices) |
|
|
| return out |
|
|
