Koala_Smart_City/utils.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Created : 2021/04/21
# @Author  : Koala
# @FileName: utils.py

import torch
import numpy as np

def xywh2xyxy(x):
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y

def box_iou(box1, box2):
    def box_area(box):
        return (box[2] - box[0]) * (box[3] - box[1])

    area1 = box_area(box1.T)
    area2 = box_area(box2.T)

    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)


def IoU(box1: np.ndarray, box2: np.ndarray, in_area=True) -> np.ndarray:
    assert box1.ndim == 2 and box2.ndim == 2
    box1 = box1[:, np.newaxis, :]
    box2 = box2[np.newaxis, :, :]
    area1 = (box1[..., 2] - box1[..., 0]) * (box1[..., 3] - box1[..., 1])
    area2 = (box2[..., 2] - box2[..., 0]) * (box2[..., 3] - box2[..., 1])
    max_xy = np.maximum(box1[..., :2], box2[..., :2])
    min_xy = np.minimum(box1[..., 2:4], box2[..., 2:4])
    inter = np.clip(min_xy[..., 0] - max_xy[..., 0], 0, None) * np.clip(min_xy[..., 1] - max_xy[..., 1], 0, None)
    if in_area:
        return inter / area1
    else:
        return inter / (area1 + area2 - inter)