用python实现yolov5的k-means聚类

首先，需要安装PyTorch和NumPy库。

接下来，我们将实现一个函数，该函数将输入的bbox列表聚类为k个簇。我们将使用k-means算法实现聚类，并使用IoU（交并比）作为距离度量。

import numpy as np
import torch

def kmeans(bboxes, k, max_iter=100):
    '''
    Args:
        bboxes: a list of bboxes, each bbox is a tuple (x1, y1, x2, y2)
        k: number of clusters
        max_iter: maximum number of iterations for k-means algorithm
    Returns:
        a numpy array of k clusters, each cluster contains the following fields:
            - cx: x-coordinate of cluster center
            - cy: y-coordinate of cluster center
            - w: width of bbox
            - h: height of bbox
    '''
    # convert bboxes to numpy array
    bboxes = np.array(bboxes)

    # randomly select k initial centers
    centers = bboxes[np.random.choice(len(bboxes), k, replace=False)]

    for i in range(max_iter):
        # compute IoU between each bbox and each center
        ious = []
        for bbox in bboxes:
            iou = []
            for center in centers:
                iou.append(compute_iou(bbox, center))
            ious.append(iou)
        ious = np.array(ious)

        # assign each bbox to the cluster with the highest IoU
        labels = ious.argmax(axis=1)

        # update centers
        for j in range(k):
            cluster = bboxes[labels == j]
            if len(cluster) == 0:
                continue
            centers[j] = np.mean(cluster, axis=0)

    # convert centers to numpy array
    centers = np.array([(cx, cy, w, h) for (cx, cy, w, h) in centers])

    return centers

该函数首先将输入的bbox列表转换为numpy数组，并随机选择k个初始中心。然后，它使用IoU作为距离度量，将每个bbox分配给具有最高IoU的簇。最后，它使用每个簇中bbox的平均值更新簇中心，并重复这个过程直到收敛或达到最大迭代次数。

我们还需要实现一个函数来计算两个bbox之间的IoU。这是一个常见的计算机视觉度量，用于衡量两个bbox之间的重叠程度。

def compute_iou(bbox1, bbox2):
    '''
    Args:
        bbox1: a tuple (x1, y1, x2, y2), representing a bbox
        bbox2: a tuple (x1, y1, x2, y2), representing a bbox
    Returns:
        IoU (intersection over union) between bbox1 and bbox2
    '''
    x1 = max(bbox1[0], bbox2[0])
    y1 = max(bbox1[1], bbox2[1])
    x2 = min(bbox1[2], bbox2[2])
    y2 = min(bbox1[3], bbox2[3])
    if x1 >= x2 or y1 >= y2:
        return 0
    intersection = (x2 - x1) * (y2 - y1)
    area1 = (bbox1[2] - bbox1[0]) * (bbox1[3] - bbox1[1])
    area2 = (bbox2[2] - bbox2[0]) * (bbox2[3] - bbox2[1])
    union = area1 + area2 - intersection
    return intersection / union

现在，我们可以使用上述函数来聚类yolov5的anchor boxes。我们的输入数据将是yolov5训练数据集中的bbox列表，该数据集可以在https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.yaml中找到。

首先，我们需要加载数据集并提取所有的bbox。我们将使用PyTorch框架来加载数据集。

import yaml
import os

def load_data():
    # load data config
    with open('yolov5s.yaml') as f:
        data_config = yaml.load(f, Loader=yaml.FullLoader)['train']

    # load dataset
    dataset = torch.utils.data.ConcatDataset([
        torch.utils.data.LoadImages(os.path.join(data_config['data'], path), batch_size=1, img_size=data_config['img_size'])
        for path in data_config['path']
    ])

    # extract bboxes
    bboxes = []
    for _, targets, _ in dataset:
        for target in targets:
            bboxes.extend(target[:, 1:5].tolist())

    return bboxes

我们加载yolov5s.yaml配置文件并使用PyTorch的LoadImages函数加载数据集。然后，我们提取每个目标的bbox，并将其添加到bboxes列表中。

接下来，我们可以使用上述函数来聚类anchor boxes。

bboxes = load_data()
anchors = kmeans(bboxes, k=9)
print(anchors)

这将输出9个anchor boxes的中心坐标和尺寸。这些anchor boxes的尺寸应该越来越大，以对应于不同的物体大小。