37,718
社区成员
发帖
与我相关
我的任务
分享
# -*- coding: utf-8 -*-
import numpy as np
import random
import argparse
import os
#参数名称
parser = argparse.ArgumentParser(description='使用该脚本生成YOLO-V3的anchor boxes\n')
parser.add_argument('--input_annotation_txt_dir',required=True,type=str,help='文件')
parser.add_argument('--output_anchors_txt',required=True,type=str,help='文件')
parser.add_argument('--input_num_anchors',required=True,default=6,type=int,help='anchor个数')
parser.add_argument('--input_cfg_width',required=True,type=int,help="宽")
parser.add_argument('--input_cfg_height',required=True,type=int,help="高")
args = parser.parse_args()
'''
centroids 聚类点 尺寸是 numx2,类型是ndarray
annotation_array 其中之一的标注框
'''
def IOU(annotation_array,centroids):
#
similarities = []
#其中一个标注框
w,h = annotation_array
for centroid in centroids:
c_w,c_h = centroid
if c_w >=w and c_h >= h:#第1中情况
similarity = w*h/(c_w*c_h)
elif c_w >= w and c_h <= h:#第2中情况
similarity = w*c_h/(w*h + (c_w - w)*c_h)
elif c_w <= w and c_h >= h:#第3种情况
similarity = c_w*h/(w*h +(c_h - h)*c_w)
else:#第3种情况
similarity = (c_w*c_h)/(w*h)
similarities.append(similarity)
#将列表转换为ndarray
return np.array(similarities,np.float32) #返回的是一维数组,尺寸为(num,)
'''
k_means:k均值聚类
annotations_array 所有的标注框的宽高,N个标注框,尺寸是Nx2,类型是ndarray
centroids 聚类点 尺寸是 numx2,类型是ndarray
'''
def k_means(annotations_array,centroids,eps=0.00005,iterations=2):
#
N = annotations_array.shape[0]#C=2
num = centroids.shape[0]
#损失函数
distance_sum_pre = -1
assignments_pre = -1*np.ones(N,dtype=np.int64)
#
iteration = 0
#循环处理
while(True):
#
iteration += 1
#
distances = []
#循环计算每一个标注框与所有的聚类点的距离(IOU)
for i in range(N):
distance = 1 - IOU(annotations_array[i],centroids)
distances.append(distance)
#列表转换成ndarray
distances_array = np.array(distances,np.float32)#该ndarray的尺寸为 Nxnum
#找出每一个标注框到当前聚类点最近的点
assignments = np.argmin(distances_array,axis=1)#计算每一行的最小值的位置索引
#计算距离的总和,相当于k均值聚类的损失函数
distances_sum = np.sum(distances_array)
#计算新的聚类点
centroid_sums = np.zeros(centroids.shape,np.float32)
for i in range(N):
centroid_sums[assignments[i]] += annotations_array[i]#计算属于每一聚类类别的和
for j in range(num):
centroids[j] = centroid_sums[j]/(np.sum(assignments==j))
#前后两次的距离变化
diff = abs(distances_sum-distance_sum_pre)
#打印结果
print("iteration: {},distance: {}, diff: {}, avg_IOU: {}\n".format(iteration,distances_sum,diff,np.sum(1-distances_array)/(N*num)))
#三种情况跳出while循环:1:循环20000次,2:eps计算平均的距离很小 3:以上的情况
if (assignments==assignments_pre).all():
print("按照前后两次的得到的聚类结果是否相同结束循环\n")
break
if diff < eps:
print("按照eps结束循环\n")
break
if iteration > iterations:
print("按照迭代次数结束循环\n")
break
#记录上一次迭代
distance_sum_pre = distances_sum
assignments_pre = assignments.copy()
if __name__=='__main__':
#聚类点的个数,anchor boxes的个数
num_clusters = args.input_num_anchors
#索引出文件夹中的每一个标注文件的名字(.txt)
names = os.listdir(args.input_annotation_txt_dir)
#标注的框的宽和高
annotations_w_h = []
for name in names:
txt_path = os.path.join(args.input_annotation_txt_dir,name)
#读取txt文件中的每一行
f = open(txt_path,'r')
for line in f.readlines():
line = line.rstrip('\n')
w,h = line.split(' ')[3:]#这时读到的w,h是字符串类型
#eval()函数用来将字符串转换为数值型
annotations_w_h.append((eval(w),eval(h)))
f.close()
#将列表annotations_w_h转换为numpy中的array,尺寸是(N,2),N代表多少框
annotations_array = np.array(annotations_w_h,dtype=np.float32)
N = annotations_array.shape[0]
#对于k-means聚类,随机初始化聚类点
random_indices = [random.randrange(N) for i in range(num_clusters)]#产生随机数
centroids = annotations_array[random_indices]
#k-means聚类
k_means(annotations_array,centroids,0.00005,2)
#对centroids按照宽排序,并写入文件
widths = centroids[:,0]
sorted_indices = np.argsort(widths)
anchors = centroids[sorted_indices]
#将anchor写入文件并保存
f_anchors = open(args.output_anchors_txt,'w')
#
for anchor in anchors:
f_anchors.write('%d,%d'%(int(anchor[0]*args.input_cfg_width),int(anchor[1]*args.input_cfg_height)))
f_anchors.write('\n')