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