具体参考：https://blog.csdn.net/Arcofcosmos/article/details/120252992（如侵权，速删）
YOLOv5训练自己的数据集，YOLOv5模型中的anchors尺寸适用性已经非常强了，然而依旧可以尝试使用K-means以及K-means++算法找到更适合自己数据集的anchors（也许改进后的AP结果值并没有提高，反而下降不少==
1.K-Means算法
kmeans的具体步骤分为以下几步：
step1：随机设定初始聚类中心
step2：将距离某个聚类中心距离近的样本点归类到该聚类中心，将样本全部归类完毕后得到多个簇
step3：计算每个簇的均值作为新的聚类中心
step4：重复第二步和第三步直至聚类中心不再发生变化
#-------------------------------------------------------------------------------------------------#
# 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，越浅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 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)
def calculate_anchors(dataset_anno_path = r'/home/slave110/code/VOCdevkit/VOC2007/Annotations', anchorsPath = '/home/slave110/code/VOCdevkit/VOC2007/yolo_anchors_kmeans.txt', anchors_num = 9, SIZE = 1000):
# 运行该程序会计算'./VOCdevkit/VOC2007/Annotations'的xml
# 会生成yolo_anchors.txt
# SIZE = 1000
# anchors_num = 9
# # 载入数据集，可以使用VOC的xml
# #path = r'/home/slave110/code/VOCdevkit/VOC2007/Annotations'
# path = r'/home/slave110/code/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(dataset_anno_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(anchorsPath, '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()
if __name__ == '__main__':
#数据集xml注释路径
dataset_anno_path = r'/home/slave110/code/VOCdevkit/VOC2007/Annotations'
#生成的anchors的txt文件保存路径
anchorsPath = '/home/slave110/code/VOCdevkit/VOC2007/yolo_anchors_kmeans.txt'
#生成的anchors数量
anchors_num = 9
#输入的图片尺寸
SIZE = 1000
calculate_anchors(dataset_anno_path, anchorsPath, anchors_num, SIZE)
2.K-Means++算法
K-means++算法是在聚类中心上对K-means算法的改进，K-means++算法选取的聚类中心的距离尽可能大，但聚类的过程不变。
step1：首先从所有样本中随机选定第一个聚类中心
step2：记录所有样本到与其最近的聚类中心的距离
step3：所有非聚类中心样本点被选取作为下一个聚类中心的概率与step2中的距离大小成正比，也就是说距离越远的样本点越有 可能成为下一个聚类中心
step4：重复step2和step3直至选出多个聚类中心
import glob
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 bboxesOverRation(bboxesA,bboxesB):
bboxesA = np.array(bboxesA.astype('float'))
bboxesB = np.array(bboxesB.astype('float'))
M = bboxesA.shape[0]
N = bboxesB.shape[0]
areasA = bboxesA[:,2]*bboxesA[:,3]
areasB = bboxesB[:,2]*bboxesB[:,3]
xA = bboxesA[:,0]+bboxesA[:,2]
yA = bboxesA[:,1]+bboxesA[:,3]
xyA = np.stack([xA,yA]).transpose()
xyxyA = np.concatenate((bboxesA[:,:2],xyA),axis=1)
xB = bboxesB[:,0] +bboxesB[:,2]
yB = bboxesB[:,1]+bboxesB[:,3]
xyB = np.stack([xB,yB]).transpose()
xyxyB = np.concatenate((bboxesB[:,:2],xyB),axis=1)
iouRatio = np.zeros((M,N))
for i in range(M):
for j in range(N):
x1 = max(xyxyA[i,0],xyxyB[j,0]);
x2 = min(xyxyA[i,2],xyxyB[j,2]);
y1 = max(xyxyA[i,1],xyxyB[j,1]);
y2 = min(xyxyA[i,3],xyxyB[j,3]);
Intersection = max(0,(x2-x1))*max(0,(y2-y1));
Union = areasA[i]+areasB[j]-Intersection;
iouRatio[i,j] = Intersection/Union;
return iouRatio
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
x = xmin + 0.5 * (xmax-xmin)
y = ymin + 0.5 * (ymax-ymin)
data.append([x, y, xmax-xmin,ymax-ymin])
return np.array(data)
def estimateAnchorBoxes(trainingData,numAnchors=9,SIZE = 416):
numsObver = trainingData.shape[0]
xyArray = np.zeros((numsObver,2))
trainingData[:,0:2] = xyArray
assert(numsObver>=numAnchors)
# kmeans++
# init
centroids = [] # 初始化中心，kmeans++
centroid_index = np.random.choice(numsObver, 1)
centroids.append(trainingData[centroid_index])
while len(centroids)<numAnchors:
minDistList = []
for box in trainingData:
box = box.reshape((-1,4))
minDist = 1
for centroid in centroids:
centroid = centroid.reshape((-1,4))
ratio = (1-bboxesOverRation(box,centroid)).item()
if ratio<minDist:
minDist = ratio
minDistList.append(minDist)
sumDist = np.sum(minDistList)
prob = minDistList/sumDist
idx = np.random.choice(numsObver,1,replace=True,p=prob)
centroids.append(trainingData[idx])
# kmeans 迭代聚类
maxIterTimes = 100
iter_times = 0
while True:
minDistList = []
minDistList_ind = []
for box in trainingData:
box = box.reshape((-1,4))
minDist = 1
box_belong = 0
for i,centroid in enumerate(centroids):
centroid = centroid.reshape((-1,4))
ratio = (1-bboxesOverRation(box,centroid)).item()
if ratio<minDist:
minDist = ratio
box_belong = i
minDistList.append(minDist)
minDistList_ind.append(box_belong)
centroids_avg = []
for _ in range(numAnchors):
centroids_avg.append([])
for i,anchor_id in enumerate(minDistList_ind):
centroids_avg[anchor_id].append(trainingData[i])
err = 0
for i in range(numAnchors):
if len(centroids_avg[i]):
temp = np.mean(centroids_avg[i],axis=0)
err += np.sqrt(np.sum(np.power(temp-centroids[i],2)))
centroids[i] = np.mean(centroids_avg[i],axis=0)
iter_times+=1
if iter_times>maxIterTimes or err==0:
break
anchorBoxes = np.array([x[2:] for x in centroids])
meanIoU = 1-np.mean(minDistList)
anchorBoxes = anchorBoxes[np.argsort(anchorBoxes[:,0])]
print('acc:{:.2f}%'.format(avg_iou(trainingData[:,2:],anchorBoxes) * 100))
anchorBoxes = anchorBoxes*SIZE
return anchorBoxes,meanIoU
def calculate_anchors(dataset_anno_path = r'/home/slave110/code/VOCdevkit/VOC2007/Annotations', anchorsPath = './yolo_anchors.txt', anchors_num = 9, SIZE = 1000):
data = load_data(dataset_anno_path)
anchors, _ = estimateAnchorBoxes(data, numAnchors = anchors_num, SIZE = SIZE)
print(anchors)
f = open(anchorsPath, 'w')
row = np.shape(anchors)[0]
for i in range(row):
if i == 0:
x_y = "%d,%d" % (anchors[i][0], anchors[i][1])
else:
x_y = ", %d,%d" % (anchors[i][0], anchors[i][1])
f.write(x_y)
f.close()
if __name__ == "__main__":
#数据集xml注释路径
dataset_anno_path = r'/home/slave110/code/VOCdevkit/VOC2007/Annotations'
#生成的anchors的txt文件保存路径
anchorsPath = '/home/slave110/code/VOCdevkit/VOC2007/yolo_anchors.txt'
#生成的anchors数量
anchors_num = 9
#输入的图片尺寸
SIZE = 1000
calculate_anchors(dataset_anno_path, anchorsPath, anchors_num, SIZE)
运行后的结果会保存在txt中。
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