yolox/utils/boxes.py

#!/usr/bin/env python3
# Copyright (c) Megvii Inc. All rights reserved.

import numpy as np

import torch
import torchvision

__all__ = [
    "filter_box",
    "postprocess",
    "bboxes_iou",
    "matrix_iou",
    "adjust_box_anns",
    "xyxy2xywh",
    "xyxy2cxcywh",
    "cxcywh2xyxy",
]


def filter_box(output, scale_range):
    """
    output: (N, 5+class) shape
    """
    min_scale, max_scale = scale_range
    w = output[:, 2] - output[:, 0]
    h = output[:, 3] - output[:, 1]
    keep = (w * h > min_scale * min_scale) & (w * h < max_scale * max_scale)
    return output[keep]


def postprocess(prediction, num_classes, conf_thre=0.7, nms_thre=0.45):
    # TODO: 矢印のみの推論を弾くような処理が必要
    box_corner = prediction.new(prediction.shape)
    # xの中心座標から左上の座標、右下の座標に変換
    # 左上
    box_corner[:, :, 0] = prediction[:, :, 0] - prediction[:, :, 2] / 2
    box_corner[:, :, 1] = prediction[:, :, 1] - prediction[:, :, 3] / 2
    # 右下
    box_corner[:, :, 2] = prediction[:, :, 0] + prediction[:, :, 2] / 2
    box_corner[:, :, 3] = prediction[:, :, 1] + prediction[:, :, 3] / 2
    # 予測結果の座標を変換して、box_cornerに格納
    prediction[:, :, :4] = box_corner[:, :, :4]

    output = [None for _ in range(len(prediction))]
    for i, image_pred in enumerate(prediction):

        # If none are remaining => process next image
        if not image_pred.size(0):
            continue
        # Get score and class with highest confidence
        # 1. 閾値を超える各クラスの確信度に対するマスクを取得
        conf_mask_multi = (image_pred[:, 5:5 + num_classes] * image_pred[:, 4].unsqueeze(-1)) >= conf_thre

        # 2. マスクを使用して、対応するクラスの確信度とクラスインデックスを取得
        class_conf_multi = image_pred[:, 5:5 + num_classes][conf_mask_multi]
        class_idx_multi = (conf_mask_multi.nonzero(as_tuple=True)[1]).float().unsqueeze(-1)

        # 3. detections_multiテンソルを作成
        detections_multi = torch.cat((
            image_pred[:, :5].repeat_interleave(torch.sum(conf_mask_multi, dim=1), dim=0),
            class_conf_multi.unsqueeze(-1),
            class_idx_multi
        ), 1)
        # 4. NMSを実行 ここではクラスごとに実行する(red と arrowが混在している場合に対応するため)
        multi_nm_out_index = torchvision.ops.batched_nms(
            detections_multi[:, :4],
            detections_multi[:, 4] * detections_multi[:, 5],
            detections_multi[:, 6],
            nms_thre,
        )
        detections_multi = detections_multi[multi_nm_out_index]

        if output[i] is None:
            output[i] = detections_multi
        else:
            output[i] = torch.cat((output[i], detections_multi))
    return output


def bboxes_iou(bboxes_a, bboxes_b, xyxy=True):
    if bboxes_a.shape[1] != 4 or bboxes_b.shape[1] != 4:
        raise IndexError

    if xyxy:
        tl = torch.max(bboxes_a[:, None, :2], bboxes_b[:, :2])
        br = torch.min(bboxes_a[:, None, 2:], bboxes_b[:, 2:])
        area_a = torch.prod(bboxes_a[:, 2:] - bboxes_a[:, :2], 1)
        area_b = torch.prod(bboxes_b[:, 2:] - bboxes_b[:, :2], 1)
    else:
        tl = torch.max(
            (bboxes_a[:, None, :2] - bboxes_a[:, None, 2:] / 2),
            (bboxes_b[:, :2] - bboxes_b[:, 2:] / 2),
        )
        br = torch.min(
            (bboxes_a[:, None, :2] + bboxes_a[:, None, 2:] / 2),
            (bboxes_b[:, :2] + bboxes_b[:, 2:] / 2),
        )

        area_a = torch.prod(bboxes_a[:, 2:], 1)
        area_b = torch.prod(bboxes_b[:, 2:], 1)
    en = (tl < br).type(tl.type()).prod(dim=2)
    area_i = torch.prod(br - tl, 2) * en  # * ((tl < br).all())
    return area_i / (area_a[:, None] + area_b - area_i)


def matrix_iou(a, b):
    """
    return iou of a and b, numpy version for data augenmentation
    """
    lt = np.maximum(a[:, np.newaxis, :2], b[:, :2])
    rb = np.minimum(a[:, np.newaxis, 2:], b[:, 2:])

    area_i = np.prod(rb - lt, axis=2) * (lt < rb).all(axis=2)
    area_a = np.prod(a[:, 2:] - a[:, :2], axis=1)
    area_b = np.prod(b[:, 2:] - b[:, :2], axis=1)
    return area_i / (area_a[:, np.newaxis] + area_b - area_i + 1e-12)


def adjust_box_anns(bbox, scale_ratio, padw, padh, w_max, h_max):
    bbox[:, 0::2] = np.clip(bbox[:, 0::2] * scale_ratio + padw, 0, w_max)
    bbox[:, 1::2] = np.clip(bbox[:, 1::2] * scale_ratio + padh, 0, h_max)
    return bbox


def xyxy2xywh(bboxes):
    bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]
    bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]
    return bboxes


def xyxy2cxcywh(bboxes):
    bboxes[:, 2] = bboxes[:, 2] - bboxes[:, 0]
    bboxes[:, 3] = bboxes[:, 3] - bboxes[:, 1]
    bboxes[:, 0] = bboxes[:, 0] + bboxes[:, 2] * 0.5
    bboxes[:, 1] = bboxes[:, 1] + bboxes[:, 3] * 0.5
    return bboxes


def cxcywh2xyxy(bboxes):
    bboxes[:, 0] = bboxes[:, 0] - bboxes[:, 2] * 0.5
    bboxes[:, 1] = bboxes[:, 1] - bboxes[:, 3] * 0.5
    bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]
    bboxes[:, 3] = bboxes[:, 1] + bboxes[:, 3]
    return bboxes