yolov3-kmeans算法计算先验anchor的python实现

yolov3中需读取自己数据的xml文件,用kmeans算法，来生成合理的9个具有长宽的anchor先验框，以便更好地训练自己的数据。

若您没有自己的xml数据集：请点击此链接:https://github.com/caichunbing/kmeans/blob/master/xml.zip下载。

#================================================================
#   Copyright (C) 2019 * Ltd. All rights reserved.
#
#   Editor      : pycharm
#   File name   : kmeans.py
#   Author      : caichunbing
#   Created date: 2019-10-18
#   Description :yolov3-kmeans聚类算法及可视化
#
#================================================================


import numpy as np
import matplotlib.pyplot as plt
import glob
import xml.etree.ElementTree as ET



def loadDataSet(xml_filepath):  # general function to parse tab -delimited floats
    dataMat = []  # assume last column is target value
    for xml_file in glob.glob(xml_filepath + '/*.xml'):
        tree = ET.parse(xml_file)
        root = tree.getroot()
        for obj in root.findall('object'):
            bbox = obj.find('bndbox')
            xmin = bbox.find('xmin').text.strip()
            xmax = bbox.find('xmax').text.strip()
            ymin = bbox.find('ymin').text.strip()
            ymax = bbox.find('ymax').text.strip()
            w=float(xmax)-float(xmin)
            h=float(ymax)-float(ymin)
            dataMat.append([w,h])
    return np.mat(dataMat)


def distEclud(vecA, vecB):
    dist=np.sqrt(np.sum(np.power(vecA - vecB, 2)))
    return dist  # la.norm(vecA-vecB)

def randCent(dataSet, k):
    n = np.shape(dataSet)[1]
    centroids = np.mat(np.zeros((k, n)))  # create centroid mat
    for j in range(n):  # create random cluster centers, within bounds of each dimension
        minJ = min(dataSet[:, j])
        rangeJ = float(max(dataSet[:, j]) - minJ)
        centroids[:, j] = np.mat(minJ + rangeJ * np.random.rand(k, 1))
    return centroids


def kMeans(dataSet, k, distMeas=distEclud, createCent=randCent):
    m = np.shape(dataSet)[0]
    clusterAssment = np.mat(np.zeros((m, 2)))  # create mat to assign data points
    # to a centroid, also holds SE of each point
    centroids = createCent(dataSet, k)
    clusterChanged = True
    while clusterChanged:
        clusterChanged = False
        for i in range(m):  # for each data point assign it to the closest centroid
            minDist = np.inf
            minIndex = -1
            for j in range(k):
                distJI = distMeas(centroids[j, :], dataSet[i, :])

                if distJI < minDist:
                    minDist = distJI
                    minIndex = j
            if clusterAssment[i, 0] != minIndex: clusterChanged = True
            clusterAssment[i, :] = minIndex, minDist ** 2
        for cent in range(k):  # recalculate centroids
            ptsInClust = dataSet[np.nonzero(clusterAssment[:, 0].A == cent)[0]]  # get all the point in this cluster
            centroids[cent, :] = np.mean(ptsInClust, axis=0)  # assign centroid to mean
    return centroids, clusterAssment


def show(w,h,centroid_w,centroid_h):
    fig = plt.figure()
    fig.suptitle("kmeans")

    ax1 = fig.add_subplot(1, 1, 1)
    ax1.scatter(w, h, s=10, color='b')
    ax1.scatter(centroid_w,centroid_h,s=10,color='r')

    plt.show()



def write_anchors(centroid,anchor_path):
    f=open(anchor_path,"w")

    #获得按面积从小到大排序的索引，根据面积从小到大写入到anchors文件
    dict={}
    for i in range(len(centroid)):
        area=centroid[i][0]*centroid[i][1]
        dict[i]=area
    list = sorted(dict.items(), key=lambda x: x[1])

    str_line=""
    for i in range(len(centroid)):
        for j in range(len(centroid[0])):
            print(centroid[list[i][0]][j])
            str_line+=str(round(centroid[list[i][0]][j],1))+','
    f.write(str_line[0:-1])




xml_filepath="./after_image_xml/xml/train"
anchor_path="./anchors/anchors.txt"

if __name__ == '__main__':
    dataSet=loadDataSet(xml_filepath)
    print("dataSet.shape:",dataSet.shape)

    centroid,cluster=kMeans(dataSet, 9,distEclud,randCent)

    count=1
    #此处循环是为了让centroid不生成含有nan的值为止
    while np.all(np.isnan(centroid) == False) !=True:
        print("count:",count)
        centroid, cluster = kMeans(dataSet, 9, distEclud, randCent)
        count+=1

    write_anchors(centroid.tolist(), anchor_path)
    w = dataSet[:, 0].tolist()
    h = dataSet[:, 1].tolist()
    centoid_w=centroid[:,0].tolist()
    centoid_h=centroid[:,1].tolist()
    show(w,h,centoid_w,centoid_h)

anchors.txt中数据如下：
9个anchor框，按面积从小到大排列

25.1,19.2,19.9,32.7,26.5,52.9,32.4,69.8,37.5,104.1,45.3,87.2,39.8,141.9,49.3,121.8,53.4,170.1

可视化如下图：

最后

以上就是陶醉荔枝为你收集整理的yolov3-kmeans算法计算先验anchor的python实现的全部内容，希望文章能够帮你解决yolov3-kmeans算法计算先验anchor的python实现所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。