yolov5里的autoanchor.py怎么改为K-means++

在YOLOv5中使用K-means++替代autoanchor.py的步骤如下：

1. 安装依赖：sklearn

pip install sklearn

2. 在train.py中导入sklearn.cluster.KMeans：

from sklearn.cluster import KMeans

3. 将autoanchor.py中的代码替换为以下代码：

def kmean_anchors(dataset, num_anchors, img_size, thr=4.0):
    from tqdm.auto import tqdm
    from itertools import product as product

    shapes = []
    for i in tqdm(range(len(dataset)), desc='Collecting shapes'):
        _, _, height, width = dataset[i]
        shapes.append([width, height])
    shapes = np.array(shapes)

    # K-means聚类
    kmeans = KMeans(n_clusters=num_anchors, n_init=20, max_iter=1000)
    kmeans.fit(shapes)

    # 打印聚类结果
    print('Anchor Sizes:')
    anchors = kmeans.cluster_centers_
    for i, anchor in enumerate(anchors):
        print(f'{i+1}: {anchor[0]:.0f}x{anchor[1]:.0f}')

    # 根据最大边长进行排序
    sorted_indices = np.argsort(anchors.max(axis=1))[::-1]
    sorted_anchors = anchors[sorted_indices]

    # 进行尺寸调整以适应图像大小
    w, h = img_size
    scaled_anchors = np.multiply(sorted_anchors, np.array([w, h])) / thr
    return scaled_anchors

4. 在train.py中调用kmean_anchors函数：

anchors = kmean_anchors(dataset, num_anchors, img_size)

这样就可以使用K-means++替代autoanchor.py了。