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kmeans_for_anchors.py
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kmeans_for_anchors.py
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import glob
import random
import xml.etree.ElementTree as ET
import numpy as np
def cas_iou(box,cluster):
x = np.minimum(cluster[:,0],box[0])
y = np.minimum(cluster[:,1],box[1])
intersection = x * y
area1 = box[0] * box[1]
area2 = cluster[:,0] * cluster[:,1]
iou = intersection / (area1 + area2 -intersection)
return iou
def avg_iou(box,cluster):
return np.mean([np.max(cas_iou(box[i],cluster)) for i in range(box.shape[0])])
def kmeans(box,k):
# 取出一共有多少框
row = box.shape[0]
# 每个框各个点的位置
distance = np.empty((row,k))
# 最后的聚类位置
last_clu = np.zeros((row,))
np.random.seed()
# 随机选5个当聚类中心
cluster = box[np.random.choice(row,k,replace = False)]
# cluster = random.sample(row, k)
while True:
# 计算每一行距离五个点的iou情况。
for i in range(row):
distance[i] = 1 - cas_iou(box[i],cluster)
# 取出最小点
near = np.argmin(distance,axis=1)
if (last_clu == near).all():
break
# 求每一个类的中位点
for j in range(k):
cluster[j] = np.median(
box[near == j],axis=0)
last_clu = near
return cluster
def load_data(path):
data = []
# 对于每一个xml都寻找box
for xml_file in glob.glob('{}/*xml'.format(path)):
tree = ET.parse(xml_file)
height = int(tree.findtext('./size/height'))
width = int(tree.findtext('./size/width'))
if height<=0 or width<=0:
continue
# 对于每一个目标都获得它的宽高
for obj in tree.iter('object'):
xmin = int(float(obj.findtext('bndbox/xmin'))) / width
ymin = int(float(obj.findtext('bndbox/ymin'))) / height
xmax = int(float(obj.findtext('bndbox/xmax'))) / width
ymax = int(float(obj.findtext('bndbox/ymax'))) / height
xmin = np.float64(xmin)
ymin = np.float64(ymin)
xmax = np.float64(xmax)
ymax = np.float64(ymax)
# 得到宽高
data.append([xmax-xmin,ymax-ymin])
return np.array(data)
if __name__ == '__main__':
# 运行该程序会计算'./VOCdevkit/VOC2007/Annotations'的xml
# 会生成yolo_anchors.txt
SIZE = 416
anchors_num = 9
# 载入数据集,可以使用VOC的xml
path = r'./VOCdevkit/VOC2007/Annotations'
# 载入所有的xml
# 存储格式为转化为比例后的width,height
data = load_data(path)
# 使用k聚类算法
out = kmeans(data,anchors_num)
out = out[np.argsort(out[:,0])]
print('acc:{:.2f}%'.format(avg_iou(data,out) * 100))
print(out*SIZE)
data = out*SIZE
f = open("yolo_anchors.txt", 'w')
row = np.shape(data)[0]
for i in range(row):
if i == 0:
x_y = "%d,%d" % (data[i][0], data[i][1])
else:
x_y = ", %d,%d" % (data[i][0], data[i][1])
f.write(x_y)
f.close()