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kmeans_for_anchors.py
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kmeans_for_anchors.py
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#-------------------------------------------------------------------------------------------------------#
# kmeans虽然会对数据集中的框进行聚类，但是很多数据集由于框的大小相近，聚类出来的9个框相差不大，
# 这样的框反而不利于模型的训练。因为不同的特征层适合不同大小的先验框，shape越小的特征层适合越大的先验框
# 原始网络的先验框已经按大中小比例分配好了，不进行聚类也会有非常好的效果。
#-------------------------------------------------------------------------------------------------------#
import glob
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
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)]
iter = 0
while True:
#-------------------------------------------------------------#
# 计算当前框和先验框的宽高比例
#-------------------------------------------------------------#
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
if iter % 5 == 0:
print('iter: {:d}. avg_iou:{:.2f}'.format(iter, avg_iou(box, cluster)))
iter += 1
return cluster, near
def load_data(path):
data = []
#-------------------------------------------------------------#
# 对于每一个xml都寻找box
#-------------------------------------------------------------#
for xml_file in tqdm(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__':
np.random.seed(0)
#-------------------------------------------------------------#
# 运行该程序会计算'./VOCdevkit/VOC2007/Annotations'的xml
# 会生成yolo_anchors.txt
#-------------------------------------------------------------#
input_shape = [416, 416]
anchors_num = 9
#-------------------------------------------------------------#
# 载入数据集，可以使用VOC的xml
#-------------------------------------------------------------#
path = 'VOCdevkit/VOC2007/Annotations'
#-------------------------------------------------------------#
# 载入所有的xml
# 存储格式为转化为比例后的width,height
#-------------------------------------------------------------#
print('Load xmls.')
data = load_data(path)
print('Load xmls done.')
#-------------------------------------------------------------#
# 使用k聚类算法
#-------------------------------------------------------------#
print('K-means boxes.')
cluster, near = kmeans(data, anchors_num)
print('K-means boxes done.')
data = data * np.array([input_shape[1], input_shape[0]])
cluster = cluster * np.array([input_shape[1], input_shape[0]])
#-------------------------------------------------------------#
# 绘图
#-------------------------------------------------------------#
for j in range(anchors_num):
plt.scatter(data[near == j][:,0], data[near == j][:,1])
plt.scatter(cluster[j][0], cluster[j][1], marker='x', c='black')
plt.savefig("kmeans_for_anchors.jpg")
plt.show()
print('Save kmeans_for_anchors.jpg in root dir.')
cluster = cluster[np.argsort(cluster[:, 0] * cluster[:, 1])]
print('avg_ratio:{:.2f}'.format(avg_iou(data, cluster)))
print(cluster)
f = open("yolo_anchors.txt", 'w')
row = np.shape(cluster)[0]
for i in range(row):
if i == 0:
x_y = "%d,%d" % (cluster[i][0], cluster[i][1])
else:
x_y = ", %d,%d" % (cluster[i][0], cluster[i][1])
f.write(x_y)
f.close()