yolov4 计算自己数据集先验框的长宽

yolov4.cfg文件中的先验框尺寸是coco数据集的，通用性可能已经够了，还是想试一下自己设置的能不能提高准确率。
用darknet源码中的gen_anchors.py文件生成自己数据集先验框的尺寸。

代码中IOU的计算部分没看懂，有人能解释一下吗

'''
Created on Feb 20, 2017

@author: jumabek
'''
from os import listdir
from os.path import isfile, join
import argparse
#import cv2
import numpy as np
import sys
import os
import shutil
import random 
import math

width_in_cfg_file = 416.
height_in_cfg_file = 416.

def IOU(x,centroids):
    similarities = []
    k = len(centroids)
    for centroid in centroids:
        c_w,c_h = centroid
        w,h = x
        if c_w>=w and c_h>=h:
            similarity = w*h/(c_w*c_h)
        elif c_w>=w and c_h<=h:
            similarity = w*c_h/(w*h + (c_w-w)*c_h)
        elif c_w<=w and c_h>=h:
            similarity = c_w*h/(w*h + c_w*(c_h-h))
        else: #means both w,h are bigger than c_w and c_h respectively
            similarity = (c_w*c_h)/(w*h)
        similarities.append(similarity) # will become (k,) shape
    return np.array(similarities) 

def avg_IOU(X,centroids):
    n,d = X.shape
    sum = 0.
    for i in range(X.shape[0]):
        #note IOU() will return array which contains IoU for each centroid and X[i] // slightly ineffective, but I am too lazy
        sum+= max(IOU(X[i],centroids))
    return sum/n

def write_anchors_to_file(centroids,X,anchor_file):
    f = open(anchor_file,'w')
    
    anchors = centroids.copy()
    print(anchors.shape)




    print('acc:{:.2f}%'.format(avg_IOU(X, anchors) * 100))
    for i in range(anchors.shape[0]):
        anchors[i][0] = round( anchors[i][0] * width_in_cfg_file)  # /32.
        anchors[i][1] = round( anchors[i][1] * height_in_cfg_file)  # /32.

    widths = anchors[:, 0]
    sorted_indices = np.argsort(widths)
    
    for i in sorted_indices[:-1]:
        f.write('%d, %d, '%(anchors[i,0],anchors[i,1]))
    #there should not be comma after last anchor, that's why
    f.write('%d, %d\n'%(anchors[sorted_indices[-1:],0],anchors[sorted_indices[-1:],1]))

    out = anchors[sorted_indices]
    print('Anchors = ', out)
    # f.write('%f\n'%(avg_IOU(X,centroids)))
   

def kmeans(X,centroids,eps,anchor_file):
    
    N = X.shape[0]  #锚框个数
    iterations = 0
    k,dim = centroids.shape
    prev_assignments = np.ones(N)*(-1)    
    iter = 0
    old_D = np.zeros((N,k))

    while True:
        D = [] 
        iter+=1           
        for i in range(N):
            d = 1 - IOU(X[i],centroids)
            D.append(d)
        D = np.array(D) # D.shape = (N,k)
        
        print("iter {}: dists = {}".format(iter,np.sum(np.abs(old_D-D))))
            
        #assign samples to centroids 
        assignments = np.argmin(D,axis=1)   # 取出最小点
        
        if (assignments == prev_assignments).all() :
            print("Centroids = ",centroids)
            write_anchors_to_file(centroids,X,anchor_file)
            return

        #calculate new centroids
        centroid_sums=np.zeros((k,dim),np.float)
        for i in range(N):jieguo
            centroid_sums[assignments[i]]+=X[i]        
        for j in range(k):            
            centroids[j] = centroid_sums[j]/(np.sum(assignments==j))
        
        prev_assignments = assignments.copy()     
        old_D = D.copy()  

def main(argv):
    parser = argparse.ArgumentParser()
    parser.add_argument('-filelist', default = '\\scripts\\VOCdevkit\\VOC2020\\labels',
                        help='path to filelist\n' )
    parser.add_argument('-output_dir', default = 'generated_anchors/anchors', type = str, 
                        help='Output anchor directory\n' )  
    parser.add_argument('-num_clusters', default = 9, type = int,
                        help='number of clusters\n' )  

    args = parser.parse_args()
    
    if not os.path.exists(args.output_dir):
        os.mkdir(args.output_dir)
     # f = open(args.filelist)
    #
    # lines = [line.rstrip('\n') for line in f.readlines()]
    
    annotation_dims = []

    size = np.zeros((1,1,3))

    for root, dirs, files in os.walk(r"E:\scripts\VOCdevkit\VOC2020\labels"):
        for file in files:
            # 获取文件路径
            fileName = os.path.join(root, file)
            print(fileName)
            f2 = open(fileName)
            for line in f2.readlines():
                line = line.rstrip('\n')
                w, h = line.split(' ')[3:]
                print(w,h)
                annotation_dims.append(tuple(map(float, (w, h))))
    annotation_dims = np.array(annotation_dims)

    # for line in lines:
    #
    #     #line = line.replace('images','labels')
    #     #line = line.replace('img1','labels')
    #     #line = line.replace('JPEGImages','labels')
    #
    #
    #     #line = line.replace('.jpg','.txt')
    #     l#ine = line.replace('.png','.txt')
    #     print(line)
    #     f2 = open(line)
    #     for line in f2.readlines():
    #         line = line.rstrip('\n')
    #         w,h = line.split(' ')[3:]
    #         #print(w,h)
    #         annotation_dims.append(tuple(map(float,(w,h))))
    # annotation_dims = np.array(annotation_dims)
  
    eps = 0.005
    
    if args.num_clusters == 0:
        for num_clusters in range(1,11): #we make 1 through 10 clusters 
            anchor_file = join( args.output_dir,'anchors%d.txt'%(num_clusters))

            indices = [ random.randrange(annotation_dims.shape[0]) for i in range(num_clusters)]
            centroids = annotation_dims[indices]
            kmeans(annotation_dims,centroids,eps,anchor_file)
            print('centroids.shape', centroids.shape)
    else:
        anchor_file = join( args.output_dir,'anchors%d.txt'%(args.num_clusters))
        indices = [ random.randrange(annotation_dims.shape[0]) for i in range(args.num_clusters)]
        centroids = annotation_dims[indices]
        kmeans(annotation_dims,centroids,eps,anchor_file)

        print('centroids.shape', centroids.shape)

if __name__=="__main__":
    main(sys.argv)

程序运行结果：