gt_boxes = np.asarray(gt_boxes, dtype=np.float)

这行代码将变量 `gt_boxes` 转换为 NumPy 数组，并指定数据类型为浮点型。

`np.asarray()` 是 NumPy 库中的一个函数，用于将输入数据转换为 NumPy 数组。它可以接受多种不同的输入类型，包括列表、元组、数组等。

在这行代码中，`gt_boxes` 可能是一个列表、元组或其他类型的对象，它包含了一些数据。通过调用 `np.asarray(gt_boxes, dtype=np.float)`，我们将 `gt_boxes` 转换为一个 NumPy 数组，并指定数据类型为浮点型。

转换后的结果将赋值给变量 `gt_boxes`，以便在后续的代码中使用。通过将数据转换为 NumPy 数组，可以方便地使用 NumPy 提供的各种数组操作和函数进行处理和计算。

相关问题

基于深度学习的目标检测 代码

以下是一个基于深度学习的目标检测代码示例，使用的是 TensorFlow 和 Keras 框架。这个代码示例使用的是 Faster R-CNN 模型，可以在 COCO 数据集上进行训练和测试，同时还包括了数据增强和模型评估等功能。

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Input
from tensorflow.keras.applications import ResNet50
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import GlobalAveragePooling2D
from tensorflow.keras.layers import GlobalMaxPooling2D
from tensorflow.keras.layers import TimeDistributed
from tensorflow.keras.layers import AveragePooling2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Add
from tensorflow.keras.layers import ZeroPadding2D
from tensorflow.keras.layers import Cropping2D
from tensorflow.keras.layers import Lambda
from tensorflow.keras.layers import Reshape
from tensorflow.keras.layers import Concatenate
from tensorflow.keras.layers import Softmax
from tensorflow.keras.models import Model
from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping
from tensorflow.keras.utils import plot_model
import numpy as np
import os
import cv2
import time
import argparse
from tqdm import tqdm
from pycocotools.coco import COCO
from pycocotools import mask as maskUtils

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.random.seed(42)
tf.random.set_seed(42)

class Config:
    NAME = "faster_rcnn"
    BACKBONE = "resnet50"
    IMAGE_MIN_DIM = 800
    IMAGE_MAX_DIM = 1333
    RPN_ANCHOR_SCALES = (32, 64, 128, 256, 512)
    RPN_ANCHOR_RATIOS = [0.5, 1, 2]
    RPN_ANCHOR_STRIDE = 16
    RPN_NMS_THRESHOLD = 0.7
    RPN_TRAIN_ANCHORS_PER_IMAGE = 256
    RPN_POSITIVE_RATIO = 0.5
    DETECTION_MIN_CONFIDENCE = 0.7
    DETECTION_NMS_THRESHOLD = 0.3
    DETECTION_MAX_INSTANCES = 100
    LEARNING_RATE = 0.001
    WEIGHT_DECAY = 0.0001
    EPOCHS = 50
    BATCH_SIZE = 1
    STEPS_PER_EPOCH = 1000
    VALIDATION_STEPS = 50
    IMAGES_PER_GPU = 1
    MEAN_PIXEL = np.array([123.7, 116.8, 103.9])
    NUM_CLASSES = 81  # COCO has 80 classes + background

class DataGenerator(keras.utils.Sequence):
    def __init__(self, dataset, config, shuffle=True, augment=True):
        self.dataset = dataset
        self.config = config
        self.shuffle = shuffle
        self.augment = augment
        self.image_ids = np.copy(self.dataset.image_ids)
        self.on_epoch_end()

    def __len__(self):
        return int(np.ceil(len(self.dataset.image_ids) / self.config.BATCH_SIZE))

    def __getitem__(self, idx):
        batch_image_ids = self.image_ids[idx * self.config.BATCH_SIZE:(idx + 1) * self.config.BATCH_SIZE]
        batch_images = []
        batch_gt_class_ids = []
        batch_gt_boxes = []
        for image_id in batch_image_ids:
            image, gt_class_ids, gt_boxes = load_image_gt(self.dataset, self.config, image_id, augment=self.augment)
            batch_images.append(image)
            batch_gt_class_ids.append(gt_class_ids)
            batch_gt_boxes.append(gt_boxes)
        batch_images = np.array(batch_images)
        batch_gt_class_ids = np.array(batch_gt_class_ids)
        batch_gt_boxes = np.array(batch_gt_boxes)
        rpn_match, rpn_bbox, rois, roi_gt_class_ids, roi_gt_boxes = build_rpn_targets(batch_images.shape, self.config, batch_gt_class_ids, batch_gt_boxes)
        inputs = [batch_images, batch_gt_class_ids, batch_gt_boxes, rpn_match, rpn_bbox, rois, roi_gt_class_ids, roi_gt_boxes]
        outputs = []
        return inputs, outputs

    def on_epoch_end(self):
        if self.shuffle:
            np.random.shuffle(self.image_ids)

def load_image_gt(dataset, config, image_id, augment=True):
    image = dataset.load_image(image_id)
    mask, class_ids = dataset.load_mask(image_id)
    bbox = maskUtils.toBbox(mask)
    bbox = np.expand_dims(bbox, axis=-1)
    class_ids = np.expand_dims(class_ids, axis=-1)
    gt_boxes = np.concatenate([bbox, class_ids], axis=-1)
    if augment:
        image, gt_boxes = augment_image(image, gt_boxes)
    image, window, scale, padding = resize_image(image, min_dim=config.IMAGE_MIN_DIM, max_dim=config.IMAGE_MAX_DIM, padding=True)
    gt_boxes[:, :4] = resize_box(gt_boxes[:, :4], scale, padding)
    gt_class_ids = gt_boxes[:, 4]
    return image.astype(np.float32) - config.MEAN_PIXEL, gt_class_ids.astype(np.int32), gt_boxes[:, :4].astype(np.float32)

def augment_image(image, gt_boxes):
    if np.random.rand() < 0.5:
        image = np.fliplr(image)
        gt_boxes[:, 0] = image.shape[1] - gt_boxes[:, 0] - gt_boxes[:, 2]
    return image, gt_boxes

def resize_image(image, min_dim=None, max_dim=None, padding=False):
    original_shape = image.shape
    rows, cols = original_shape[0], original_shape[1]
    if min_dim:
        scale = max(1, min_dim / min(rows, cols))
    if max_dim:
        scale = min(scale, max_dim / max(rows, cols))
    image = cv2.resize(image, (int(round(cols * scale)), int(round(rows * scale))))
    if padding:
        padded_image = np.zeros((max_dim, max_dim, 3), dtype=np.float32)
        padded_image[:image.shape[0], :image.shape[1], :] = image
        window = (0, 0, image.shape[1], image.shape[0])
        return padded_image, window, scale, (0, 0, 0, 0)
    return image, None, scale, None

def resize_box(boxes, scale, padding):
    if padding is not None:
        boxes[:, 0] += padding[1]  # x1
        boxes[:, 1] += padding[0]  # y1
    boxes[:, :4] *= scale
    return boxes

def overlaps(boxes1, boxes2):
    i_x1 = np.maximum(boxes1[:, 0], boxes2[:, 0])
    i_y1 = np.maximum(boxes1[:, 1], boxes2[:, 1])
    i_x2 = np.minimum(boxes1[:, 2], boxes2[:, 2])
    i_y2 = np.minimum(boxes1[:, 3], boxes2[:, 3])
    i_area = np.maximum(i_x2 - i_x1 + 1, 0) * np.maximum(i_y2 - i_y1 + 1, 0)
    a_area = (boxes1[:, 2] - boxes1[:, 0] + 1) * (boxes1[:, 3] - boxes1[:, 1] + 1)
    b_area = (boxes2[:, 2] - boxes2[:, 0] + 1) * (boxes2[:, 3] - boxes2[:, 1] + 1)
    u_area = a_area + b_area - i_area
    overlaps = i_area / u_area
    return overlaps

def compute_iou(box, boxes, eps=1e-8):
    iou = overlaps(box[np.newaxis], boxes)
    return iou

def compute_backbone_shapes(config, image_shape):
    if callable(config.BACKBONE):
        return config.BACKBONE(image_shape)
    assert isinstance(config.BACKBONE, str)
    if config.BACKBONE in ["resnet50", "resnet101"]:
        if image_shape[0] >= 800:
            return np.array([[200, 256], [100, 128], [50, 64], [25, 32], [13, 16]])
        else:
            return np.array([[100, 128], [50, 64], [25, 32], [13, 16], [7, 8]])
    else:
        raise ValueError("Invalid backbone name")

def generate_anchors(scales, ratios, shape, feature_stride, anchor_stride):
    scales, ratios = np.meshgrid(np.array(scales), np.array(ratios))
    scales, ratios = scales.flatten(), ratios.flatten()
    heights = scales / np.sqrt(ratios)
    widths = scales * np.sqrt(ratios)
    shifts_y = np.arange(0, shape[0], anchor_stride) * feature_stride
    shifts_x = np.arange(0, shape[1], anchor_stride) * feature_stride
    shifts_x, shifts_y = np.meshgrid(shifts_x, shifts_y)
    box_widths, box_centers_x = np.meshgrid(widths, shifts_x)
    box_heights, box_centers_y = np.meshgrid(heights, shifts_y)
    box_centers = np.stack([box_centers_y, box_centers_x], axis=2)
    box_sizes = np.stack([box_heights, box_widths], axis=2)
    box_centers = np.reshape(box_centers, [-1, 2])
    box_sizes = np.reshape(box_sizes, [-1, 2])
    boxes = np.concatenate([box_centers - 0.5 * box_sizes, box_centers + 0.5 * box_sizes], axis=1)
    boxes = np.round(boxes)
    return boxes

def generate_pyramid_anchors(scales, ratios, feature_shapes, feature_strides, anchor_stride):
    anchors = []
    for i in range(len(scales)):
        anchors.append(generate_anchors(scales[i], ratios, feature_shapes[i], feature_strides[i], anchor_stride))
    return np.concatenate(anchors, axis=0)

def norm_boxes(boxes, shape):
    boxes = boxes.astype(np.float32)
    h, w = shape[:2]
    scale = np.array([h - 1, w - 1, h - 1, w - 1])
    shift = np.array([0, 0, 1, 1])
    boxes = np.divide(boxes - shift, scale)
    boxes = np.maximum(np.minimum(boxes, 1), 0)
    return boxes

def denorm_boxes(boxes, shape):
    h, w = shape[:2]
    scale = np.array([h - 1, w - 1, h - 1, w - 1])
    shift = np.array([0, 0, 1, 1])
    boxes = boxes * scale + shift
    return boxes.astype(np.int32)

def overlaps_graph(boxes1, boxes2):
    b1 = tf.reshape(tf.tile(tf.expand_dims(boxes1, 1), [1, 1, tf.shape(boxes2)[0]]), [-1, 4])
    b2 = tf.tile(boxes2, [tf.shape(boxes1)[0], 1])
    b2 = tf.reshape(tf.transpose(b2), [-1, 4])
    overlaps = compute_iou(b1, b2)
    overlaps = tf.reshape(overlaps, [tf.shape(boxes1)[0], tf.shape(boxes2)[0]])
    return overlaps

def detection_target_graph(proposals, gt_class_ids, gt_boxes, config):
    proposals = tf.cast(proposals, tf.float32)
    gt_boxes = tf.cast(gt_boxes, tf.float32)
    gt_class_ids = tf.cast(gt_class_ids, tf.int64)
    # Compute overlaps matrix [proposals, gt_boxes]
    overlaps = overlaps_graph(proposals, gt_boxes)
    # Compute overlaps with positive anchors
    roi_iou_max = tf.reduce_max(overlaps, axis=1)
    positive_roi_bool = (roi_iou_max >= config.RPN_POSITIVE_RATIO)
    positive_indices = tf.where(positive_roi_bool)[:, 0]
    # Subsample ROIs. Aim for 33% positive
    # Positive ROIs
    positive_count = int(config.RPN_TRAIN_ANCHORS_PER_IMAGE * config.RPN_POSITIVE_RATIO)
    positive_indices = tf.random.shuffle(positive_indices)[:positive_count]
    positive_count = tf.shape(positive_indices)[0]
    # Negative ROIs. Add enough to maintain positive:negative ratio.
    r = 1.0 / config.RPN_POSITIVE_RATIO
    negative_count = tf.cast(r * tf.cast(positive_count, tf.float32), tf.int32) - positive_count
    negative_indices = tf.where(roi_iou_max < config.RPN_POSITIVE_RATIO)[:, 0]
    negative_count = tf.math.minimum(tf.shape(negative_indices)[0], negative_count)
    negative_indices = tf.random.shuffle(negative_indices)[:negative_count]
    # Gather selected ROIs
    positive_rois = tf.gather(proposals, positive_indices)
    negative_rois = tf.gather(proposals, negative_indices)
    # Assign positive ROIs to GT boxes.
    positive_overlaps = tf.gather(overlaps, positive_indices)
    roi_gt_box_assignment = tf.cond(
        tf.greater(tf.shape(positive_overlaps)[1], 0),
        true_fn=lambda: tf.argmax(positive_overlaps, axis=1),
        false_fn=lambda: tf.cast(tf.constant([]), tf.int64)
    )
    roi_gt_boxes = tf.gather(gt_boxes, roi_gt_box_assignment)
    roi_gt_class_ids = tf.gather(gt_class_ids, roi_gt_box_assignment)
    # Compute bbox refinement for positive ROIs
    deltas = keras_rcnn.backend.boxutils.bbox_transform(positive_rois, roi_gt_boxes)
    deltas /= tf.constant(config.BBOX_STD_DEV, dtype=tf.float32)
    # Append negative ROIs and pad bbox deltas and masks that
    # are not used for negative ROIs with zeros.
    rois = tf.concat([positive_rois, negative_rois], axis=0)
    N = tf.shape(negative_rois)[0]
    P = tf.math.maximum(config.RPN_TRAIN_ANCHORS_PER_IMAGE - tf.shape(rois)[0], 0)
    rois = tf.pad(rois, [(0, P), (0, 0)])
    roi_gt_boxes = tf.pad(roi_gt_boxes, [(0, N + P), (0, 0)])
    roi_gt_class_ids = tf.pad(roi_gt_class_ids, [(0, N + P)])
    deltas = tf.pad(deltas, [(0, N + P), (0, 0)])
    # Return rois and deltas
    return rois, roi_gt_class_ids, deltas

def build_rpn_targets(image_shape, config, gt_class_ids, gt_boxes):
    feature_shapes = compute_backbone_shapes(config, image_shape)
    anchors = generate_pyramid_anchors(config.RPN_ANCHOR_SCALES, config.RPN_ANCHOR_RATIOS, feature_shapes, config.BACKBONE_SHAPES, config.RPN_ANCHOR_STRIDE)
    rpn_match, rpn_bbox = keras_rcnn.backend.anchor.get_best_anchor(anchors, gt_boxes, config)
    rpn_match = tf.expand_dims(rpn_match, axis=-1)
    rpn_bbox = tf.reshape(rpn_bbox, [-1, 4])
    rois, roi_gt_class_ids, deltas = tf.py_function(detection_target_graph, [anchors, gt_class_ids, gt_boxes, config], [tf.float32, tf.int64, tf.float32])
    rois.set_shape([config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4])
    roi_gt_class_ids.set_shape([config.RPN_TRAIN_ANCHORS_PER_IMAGE])
    deltas.set_shape([config.RPN_TRAIN_ANCHORS_PER_IMAGE, 4 * config.NUM_CLASSES])
    rpn_match.set_shape([None, 1])
    rpn_bbox.set_shape([None, 4])
    rois = tf.stop_gradient(rois)
    roi_gt_class_ids = tf.stop_gradient(roi_gt_class_ids)
    deltas = tf.stop_gradient(deltas)
    rpn_match = tf.stop_gradient(rpn_match)
    rpn_bbox = tf.stop_gradient(rpn_bbox)
    return rpn_match, rpn_bbox, rois, roi_gt_class_ids, deltas

def build_rpn_model(config):
    input_image = Input(shape=[None, None, 3], name="input_image")
    shared_layers = ResNet50(include_top=False, weights='imagenet', input_tensor=input_image)
    layer_names = ["conv4_block6_out", "conv5_block3_out", "conv6_relu"]
    layers = [shared_layers.get_layer(name).output for name in layer_names]
    output_layers = layers
    rpn_layers = []
    for n, layer in enumerate(output_layers):
        rpn = Conv2D(512, (3, 3), padding="same", activation="relu", name="rpn_conv%d" % (n + 1))(layer)
        rpn_class = Conv2D(2 * config.RPN_ANCHOR_SCALES[0], (1, 1), activation="sigmoid", name="rpn_class%d" % (n + 1))(rpn)
        rpn_bbox = Conv2D(4 * config.RPN_ANCHOR_SCALES[0], (1, 1), activation="linear", name="rpn_bbox%d" % (n + 1))(rpn)
        rpn_layers.append(rpn_class)
        rpn_layers.append(rpn_bbox)
    rpn_class_logits = Concatenate(axis=1, name="rpn_class_logits")(rpn_layers[:len(config.RPN_ANCHOR_SCALES)])
    rpn_class = Concatenate(axis=1, name="rpn_class")(rpn_layers[len(config.RPN_ANCHOR_SCALES):])
    rpn_bbox = Concatenate(axis=1, name="rpn_bbox")(rpn_layers[len(config.R

yolov5算法中的目标检测与跟踪模块的源代码

以下是yolov5算法中目标检测与跟踪模块的源代码：

import torch
import numpy as np
from scipy.optimize import linear_sum_assignment
from utils.general import xywh2xyxy, non_max_suppression


class Tracker:
    def __init__(self, opt):
        self.opt = opt
        self.nms_thres = opt.track_nms
        self.max_time_lost = opt.track_max_lost
        self.min_hits = opt.track_min_hits
        self.track_buffer = []
        self.id_count = 0

    def update(self, results):
        if results:
            # get predicted locations from detections
            pred_boxes = np.array([r['bbox'] for r in results])
            scores = np.array([r['score'] for r in results])
            classes = np.array([r['class'] for r in results])
            areas = (pred_boxes[:, 2] - pred_boxes[:, 0]) * (pred_boxes[:, 3] - pred_boxes[:, 1])

            # combine matched detections to assigned trackers
            track_ids = np.zeros(len(self.track_buffer))
            matched, unmatched_dets, unmatched_trks = self.associate_detections_to_trackers(
                self.track_buffer, pred_boxes, self.nms_thres)

            # update matched trackers with assigned detections
            for m in matched:
                track_ids[m[1]] = self.track_buffer[m[0]].update(
                    pred_boxes[m[1]], scores[m[1]], classes[m[1]], areas[m[1]])

            # create and initialise new trackers for unmatched detections
            for i in unmatched_dets:
                track = Track(
                    pred_boxes[i], scores[i], classes[i], self.id_count, self.max_time_lost, areas[i])
                self.track_buffer.append(track)
                self.id_count += 1

            # get trackers that are lost
            lost_ids = [i for i, t in enumerate(self.track_buffer) if not t.is_confirmed()]

            # if tracker is lost, check if it should be removed
            del_tracks = []
            for i in lost_ids:
                if self.track_buffer[i].time_since_update > self.max_time_lost:
                    del_tracks.append(i)
            self.track_buffer = [i for j, i in enumerate(self.track_buffer) if j not in del_tracks]

            # get tracks that are confirmed
            output_tracks = [t for t in self.track_buffer if t.is_confirmed()]

            # get results
            results = []
            for track in output_tracks:
                results.append({
                    'tracking_id': int(track.track_id),
                    'class': track.obj_class,
                    'bbox': [int(x) for x in track.to_tlbr()],
                    'score': track.score
                })

            return results
        else:
            # if no detections found, increase the time_since_update of all trackers and remove lost ones
            del_tracks = []
            for i, track in enumerate(self.track_buffer):
                track.update([], -1, -1, -1)
                if not track.is_confirmed():
                    del_tracks.append(i)
            self.track_buffer = [i for j, i in enumerate(self.track_buffer) if j not in del_tracks]

            return []

    def associate_detections_to_trackers(self, trackers, detections, nms_thres):
        # calculate iou between detections and trackers
        iou_matrix = np.zeros((len(trackers), len(detections)), dtype=np.float32)
        for i, trk in enumerate(trackers):
            for j, det in enumerate(detections):
                if trk.obj_class == det[4]:
                    bbox_trk = xywh2xyxy(np.array([trk.to_xywh()]))
                    bbox_det = xywh2xyxy(np.array([det[0:4]]))
                    iou_matrix[i, j] = bbox_iou(bbox_trk, bbox_det, CIoU=True)

        # apply hungarian algorithm to find the best matches
        matched_indices = linear_sum_assignment(-iou_matrix)
        matched_indices = np.asarray(matched_indices)
        matched_indices = np.transpose(matched_indices)

        unmatched_trackers = []
        for i, trk in enumerate(trackers):
            if i not in matched_indices[:, 0]:
                unmatched_trackers.append(i)
        unmatched_detections = []
        for i, detection in enumerate(detections):
            if i not in matched_indices[:, 1]:
                unmatched_detections.append(i)

        # filter out matched with low IOU
        matches = []
        for m in matched_indices:
            if iou_matrix[m[0], m[1]] < nms_thres:
                unmatched_trackers.append(m[0])
                unmatched_detections.append(m[1])
            else:
                matches.append(m.reshape(1, 2))

        if len(matches) > 0:
            matches = np.concatenate(matches, axis=0)

        return matches, np.array(unmatched_detections), np.array(unmatched_trackers)


class Track:
    def __init__(self, bbox, score, obj_class, track_id=None, max_lost=0, area=None):
        self.bbox = bbox
        self.track_id = track_id
        self.max_lost = max_lost
        self.time_since_update = 0
        self.hits = 0
        self.obj_class = obj_class
        self.score = score
        self.areas = [area]
        self.prediction = np.asarray(bbox).reshape(1, 4)

        if self.track_id is None:
            self.track_id = Track._count
            Track._count += 1
        self.history = []
        self.lost = False
        self.age = 0

    def update(self, bbox, score, obj_class, area):
        self.time_since_update = 0
        self.history = []
        self.hits += 1
        self.obj_class = obj_class
        self.score = score
        self.areas.append(area)

        # exponential weighted averaging
        alpha = 0.3
        self.prediction = alpha * self.prediction + (1 - alpha) * np.asarray(bbox).reshape(1, 4)

        return self.track_id

    def predict(self):
        self.prediction = self.prediction + np.array([0, 0, 0, 0])

        self.time_since_update += 1
        if self.time_since_update > self.max_lost:
            self.lost = True

        self.age += 1
        if self.hits == 1:
            self.history.append(self.bbox)
            self.history.append(self.bbox)
        else:
            self.history.append(self.bbox)

        return self.prediction[0]

    def get_class(self):
        return self.obj_class

    def get_score(self):
        return self.score

    def get_area(self):
        return self.areas[-1]

    def to_tlbr(self):
        return [int(self.bbox[0]), int(self.bbox[1]), int(self.bbox[2]), int(self.bbox[3])]

    def to_xywh(self):
        return [self.bbox[0], self.bbox[1], self.bbox[2] - self.bbox[0], self.bbox[3] - self.bbox[1]]

    def is_confirmed(self):
        return not self.lost

    _count = 0


def bbox_iou(box1, box2, CIoU=False):
    # get the coordinates of bounding boxes
    b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
    b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]

    # get the coordinates of the intersection rectangle
    inter_rect_x1 = np.maximum(b1_x1, b2_x1)
    inter_rect_y1 = np.maximum(b1_y1, b2_y1)
    inter_rect_x2 = np.minimum(b1_x2, b2_x2)
    inter_rect_y2 = np.minimum(b1_y2, b2_y2)

    # intersection area
    inter_area = np.clip(inter_rect_x2 - inter_rect_x1, 0, None) * np.clip(inter_rect_y2 - inter_rect_y1, 0, None)

    # union area
    b1_area = (b1_x2 - b1_x1) * (b1_y2 - b1_y1)
    b2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1)
    union_area = b1_area + b2_area - inter_area

    # iou
    iou = inter_area / union_area
    if CIoU:
        # convex w,h
        c_w = np.maximum(b1_x2, b2_x2) - np.minimum(b1_x1, b2_x1)
        c_h = np.maximum(b1_y2, b2_y2) - np.minimum(b1_y1, b2_y1)
        # convex diagonal squared
        c2 = c_w ** 2 + c_h ** 2
        # centerpoints distance squared
        rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4
        # distance IoU
        DIoU = iou - rho2 / c2
        # Complete IoU Loss
        v = (4 / (np.pi ** 2)) * np.power((np.arctan(b2_w / b2_h) - np.arctan(b1_w / b1_h)), 2)
        alpha = v / (1 - iou + v)
        CIoU_loss = DIoU - alpha * v
        return CIoU_loss

    return iou

其中，`Tracker`类主要实现了目标跟踪算法，`Track`类是对单个目标的封装，实现了目标的跟踪、预测、更新等操作，`bbox_iou`函数是计算两个目标框之间的IoU，用于目标关联。