yolov3 kmeans

yolov3在做boundingbox預測的時候,用到了anchor boxes.
.cfg檔案內的配置如下:

[yolo]
mask = 3,4,5
anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319

在用我們自己的資料做訓練的時候,要先修改anchors,匹配我們自己的資料.anchors大小通過聚類得到.

通俗地說,聚類就是把捱得近的資料點劃分到一起.
kmeans演算法的思想很簡單

• 隨便指定k個cluster
• 把點劃分到與之最近的一個cluster
• 上面得到的cluster肯定是不好的,因為一開始的cluster是亂選的嘛
• 更新每個cluster為當前cluster的點的均值.(這時候cluster肯定變準了,為什麼呢?比如當前這個cluster裡有3個點,2個點靠的很近,還有1個點離得稍微遠點,那取均值的話,那相當於靠的很近的2個點有更多投票權,新算出來的cluster的中心會更加靠近這兩個點.你要是非要擡槓:那萬一一開始我隨機指定的cluster中心點就特別準呢,重新取均值反而把中心點弄的不準了?事實上這是kmeans的一個缺陷:比較依賴初始的k個cluster的位置.選擇不恰當的k值可能會導致糟糕的聚類結果。這也是為什麼要進行特徵檢查來決定資料集的聚類數目了。)
• 重新執行上述過程
  • 把點劃分到與之最近的一個cluster
  • 更新每個cluster為當前cluster的點的均值
• 不斷重複上述過程,直至cluster中心變化很小

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)

    for i in range(anchors.shape[0]):
        anchors[i][0]*=width_in_cfg_file/32.
        anchors[i][1]*=height_in_cfg_file/32.
         

    widths = anchors[:,0]
    sorted_indices = np.argsort(widths)

    print('Anchors = ', anchors[sorted_indices])
        
    for i in sorted_indices[:-1]:
        f.write('%0.2f,%0.2f, '%(anchors[i,0],anchors[i,1]))

    #there should not be comma after last anchor, that's why
    f.write('%0.2f,%0.2f\n'%(anchors[sorted_indices[-1:],0],anchors[sorted_indices[-1:],1]))
    
    f.write('%f\n'%(avg_IOU(X,centroids)))
    print()

def kmeans(X,centroids,eps,anchor_file):
    
    N = X.shape[0]
    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)
        
        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):
            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 = '\\path\\to\\voc\\filelist\\train.txt', 
                        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 = 0, 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 line in lines:
                    
        #line = line.replace('images','labels')
        #line = line.replace('img1','labels')
        line = line.replace('JPEGImages','labels')        
        

        line = line.replace('.jpg','.txt')
        line = 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)
 

用法:python3 gen_anchors.py -filelist ./park_train.txt park_train.txt描述了訓練圖片路