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# Author:LiPu
import numpy as np
import pandas as pd
import os
class YOLO_Kmeans:
def __init__(self, cluster_number, data_file, anchors_file):
self.cluster_number = cluster_number # 6 or 9
self.data_file = data_file
self.anchors_file = anchors_file
def iou(self, boxes, clusters): # 1 box -> k clusters, boxes代表实际的每个box的宽和高， clusters为聚类中心
n = boxes.shape[0]
k = self.cluster_number
box_area = boxes[:, 0] * boxes[:, 1] # box的面积长x宽
box_area = box_area.repeat(k) # 将每个box的面积重复6次，构成一个1行6列的数组
box_area = np.reshape(box_area, (n, k))
cluster_area = clusters[:, 0] * clusters[:, 1] # 计算每个聚类中心的面积
cluster_area = np.tile(cluster_area, [1, n])
cluster_area = np.reshape(cluster_area, (n, k)) # 将聚类中心构成与输入boxes具有同样维度的矩阵，可以避免循环运算
box_w_matrix = np.reshape(boxes[:, 0].repeat(k), (n, k))
cluster_w_matrix = np.reshape(np.tile(clusters[:, 0], (1, n)), (n, k))
min_w_matrix = np.minimum(cluster_w_matrix, box_w_matrix)
box_h_matrix = np.reshape(boxes[:, 1].repeat(k), (n, k))
cluster_h_matrix = np.reshape(np.tile(clusters[:, 1], (1, n)), (n, k))
min_h_matrix = np.minimum(cluster_h_matrix, box_h_matrix)
inter_area = np.multiply(min_w_matrix, min_h_matrix)
result = inter_area / (box_area + cluster_area - inter_area)
return result
def avg_iou(self, boxes, clusters):
accuracy = np.mean([np.max(self.iou(boxes, clusters), axis=1)])
return accuracy
def kmeans(self, boxes, k, dist=np.median):
box_number = boxes.shape[0]
distances = np.empty((box_number, k))
last_nearest = np.zeros((box_number,))
np.random.seed()
clusters = boxes[np.random.choice(
box_number, k, replace=False)] # init k clusters从原来的众多box中随机选取6个box
while True:
distances = 1 - self.iou(boxes, clusters)
current_nearest = np.argmin(distances, axis=1)
if (last_nearest == current_nearest).all():
break # clusters won't change
for cluster in range(k):
clusters[cluster] = dist( # update clusters
boxes[current_nearest == cluster], axis=0)
last_nearest = current_nearest
return clusters
def result2txt(self, data): # 将data保存进txt文档中，覆盖方式
f = open(self.anchors_file, 'w')
row = np.shape(data)[0]
for i in range(row):
if i == 0:
x_y = "%d,%d" % (data[i][0], data[i][1]) # 若data只有1行，直接两个数据用逗号隔开保存
else:
x_y = ", %d,%d" % (data[i][0], data[i][1]) # 若data有多行，就将每行数据以空格隔开
f.write(x_y)
f.close()
def txt2boxes(self):
test = os.listdir(self.data_file + 'test/')
train = os.listdir(self.data_file + 'train/')
dataSet = []
for i in train:
f = open(self.data_file + 'train/' + i, 'r')
for line in f:
infos = line.split(" ")
width = int(float(infos[3]) * 416)
height = int(float(infos[4]) * 416)
dataSet.append([width, height])
f.close()
for i in test:
f = open(self.data_file + 'test/' + i, 'r')
for line in f:
infos = line.split(" ")
width = int(float(infos[3]) * 416)
height = int(float(infos[4]) * 416)
dataSet.append([width, height])
f.close()
result = np.array(dataSet)
return result
def csv2boxes(self):
dataSet = []
train_data = pd.read_csv(self.data_file, header=None)
for value_i in train_data.values:
end_num = len(value_i)
for i in range(1, end_num, 5):
width = value_i[i + 2] - value_i[i]
height = value_i[i + 3] - value_i[i + 1]
dataSet.append([width, height]) ##获取所有box的宽和高
result = np.array(dataSet)
return result
def txt2clusters(self):
# all_boxes = self.csv2boxes() # 获取所有box的宽和高
all_boxes = self.txt2boxes()
result = self.kmeans(all_boxes, k=self.cluster_number)
result = result[np.lexsort(result.T[0, None])] # 对于聚类结果，按照第一维度进行从小到大排序
self.result2txt(result)
print("K anchors:\n {}".format(result))
print("Accuracy: {:.2f}%".format(
self.avg_iou(all_boxes, result) * 100))
if __name__ == "__main__":
cluster_number = 6 # tiny-yolo--6, yolo--9
data_file = "labels/"
anchors_file = "anchors_6.txt"
kmeans = YOLO_Kmeans(cluster_number=cluster_number, data_file=data_file, anchors_file=anchors_file)
kmeans.txt2clusters()
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