yolov7k-means先验框聚类代码

以下是使用yolov7进行k-means先验框聚类的Python代码：

import numpy as np
import random

# 读取训练集中所有图片的宽和高
def load_dataset(dataset_path):
    with open(dataset_path, 'r') as f:
        lines = f.readlines()
        lines = [line.strip() for line in lines]
        dataset = []
        for line in lines:
            image_path = line
            with Image.open(image_path) as img:
                width, height = img.size
                dataset.append([width, height])
    return np.array(dataset)

# 计算两个先验框之间的IOU
def iou(box1, box2):
    x1, y1, w1, h1 = box1
    x2, y2, w2, h2 = box2

    intersection_x1 = max(x1-w1/2, x2-w2/2)
    intersection_y1 = max(y1-h1/2, y2-h2/2)
    intersection_x2 = min(x1+w1/2, x2+w2/2)
    intersection_y2 = min(y1+h1/2, y2+h2/2)

    intersection_area = max(intersection_x2-intersection_x1, 0) * max(intersection_y2-intersection_y1, 0)
    box1_area = w1 * h1
    box2_area = w2 * h2

    return intersection_area / (box1_area + box2_area - intersection_area)

# 进行k-means聚类
def kmeans(data, k, iters=100):
    centroids = random.sample(list(data), k)
    for i in range(iters):
        clusters = [[] for _ in range(k)]
        for d in data:
            distances = [iou(d, c) for c in centroids]
            cluster_id = np.argmax(distances)
            clusters[cluster_id].append(d)

        new_centroids = []
        for i, cluster in enumerate(clusters):
            if len(cluster) == 0:
                new_centroids.append(centroids[i])
            else:
                cluster_mean = np.mean(cluster, axis=0)
                new_centroids.append(cluster_mean)

        centroids = new_centroids

    return centroids

if __name__ == '__main__':
    dataset_path = 'train.txt'
    dataset = load_dataset(dataset_path)

    k = 9 # 聚类的数量
    centroids = kmeans(dataset, k)

    print(centroids)

这个代码会从一个包含训练集所有图片路径的文件中读取训练集图片的宽和高，然后进行k-means聚类，得到k个先验框。最后，程序会输出这k个先验框的宽和高。