# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np


def output_postprocess(outputs, img_metas=None):
    detection_boxes = []
    detection_scores = []
    detection_classes = []
    img_metas_list = []

    for i in range(len(outputs)):
        if img_metas:
            img_metas_list.append(img_metas[i])
        if outputs[i] is not None:
            bboxes = outputs[i][:, 0:4] if outputs[i] is not None else None
            if img_metas:
                bboxes /= img_metas[i]['scale_factor'][0]
            detection_boxes.append(bboxes.cpu().numpy())
            detection_scores.append(
                (outputs[i][:, 4] * outputs[i][:, 5]).cpu().numpy())
            detection_classes.append(outputs[i][:, 6].cpu().numpy().astype(
                np.int32))
        else:
            detection_boxes.append(None)
            detection_scores.append(None)
            detection_classes.append(None)

    test_outputs = {
        'detection_boxes': detection_boxes,
        'detection_scores': detection_scores,
        'detection_classes': detection_classes,
        'img_metas': img_metas_list
    }

    return test_outputs