马春杰杰k-means需要有数据，中心点个数是需要人为指定的，位置可以随机初始化，但是还需要度量到聚类中心的距离。这里怎么度量这个距离是很关键的。
距离度量如果使用标准的欧氏距离，大盒子会比小盒子产生更多的错误。例
因此这里使用其他的距离度量公式。聚类的目的是anchor boxes和临近的ground truth有更大的IOU值，这和anchor box的尺寸没有直接关系。自定义的距离度量公式：
到聚类中心的距离越小越好，但IOU值是越大越好，所以使用 1 – IOU，这样就保证距离越小，IOU值越大。
![[DL]YOLO中anchor box是如何通过聚类确定的](https://ypyssl.machunjie.com/machunjie/20190910233805.png_machunjie.png)
代码实现主要是AlexeyAB/darknet中scripts/gen_anchors.py，这里根据yolov2，yolov3的版本不同进行部分修改。yolov2的配置文件yolov2.cfg需要的anchors是相对特征图的，值很小基本都小于13；yolov3的配置文件yolov3.cfg需要的3个anchors是相对于原图来说的，相对都比较大。还有输入图片的大小（32的倍数）对于输出也是有影响的。
例：
yolov2.cfg中[region] anchors = 0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
yolov3.cfg中[region] anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
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from os import listdir from os.path import isfile, join import argparse #import cv2 import numpy as np import sys import os import shutil import random import math def IOU(x,centroids): ''' :param x: 某一个ground truth的w,h :param centroids: anchor的w,h的集合[(w,h),(),...]，共k个 :return: 单个ground truth box与所有k个anchor box的IoU值集合 ''' IoUs = [] w, h = x # ground truth的w,h for centroid in centroids: c_w,c_h = centroid #anchor的w,h if c_w>=w and c_h>=h: #anchor包围ground truth iou = w*h/(c_w*c_h) elif c_w>=w and c_h<=h: #anchor宽矮 iou = w*c_h/(w*h + (c_w-w)*c_h) elif c_w<=w and c_h>=h: #anchor瘦长 iou = c_w*h/(w*h + c_w*(c_h-h)) else: #ground truth包围anchor means both w,h are bigger than c_w and c_h respectively iou = (c_w*c_h)/(w*h) IoUs.append(iou) # will become (k,) shape return np.array(IoUs) def avg_IOU(X,centroids): ''' :param X: ground truth的w,h的集合[(w,h),(),...] :param centroids: anchor的w,h的集合[(w,h),(),...]，共k个 ''' n,d = X.shape sum = 0. for i in range(X.shape[0]): sum+= max(IOU(X[i],centroids)) #返回一个ground truth与所有anchor的IoU中的最大值 return sum/n #对所有ground truth求平均 def write_anchors_to_file(centroids,X,anchor_file,input_shape,yolo_version): ''' :param centroids: anchor的w,h的集合[(w,h),(),...]，共k个 :param X: ground truth的w,h的集合[(w,h),(),...] :param anchor_file: anchor和平均IoU的输出路径 ''' f = open(anchor_file,'w') anchors = centroids.copy() print(anchors.shape) if yolo_version=='yolov2': for i in range(anchors.shape[0]): #yolo中对图片的缩放倍数为32倍，所以这里除以32， # 如果网络架构有改变，根据实际的缩放倍数来 #求出anchor相对于缩放32倍以后的特征图的实际大小（yolov2） anchors[i][0]*=input_shape/32. anchors[i][1]*=input_shape/32. elif yolo_version=='yolov3': for i in range(anchors.shape[0]): #求出yolov3相对于原图的实际大小 anchors[i][0]*=input_shape anchors[i][1]*=input_shape else: print("the yolo version is not right!") exit(-1) widths = anchors[:,0] sorted_indices = np.argsort(widths) print('Anchors = ', anchors[sorted_indices]) for i in sorted_indices[:-1]: f.write('%0.2f,%0.2f, '%(anchors[i,0],anchors[i,1])) #there should not be comma after last anchor, that's why f.write('%0.2f,%0.2f\n'%(anchors[sorted_indices[-1:],0],anchors[sorted_indices[-1:],1])) f.write('%f\n'%(avg_IOU(X,centroids))) print() def kmeans(X,centroids,eps,anchor_file,input_shape,yolo_version): N = X.shape[0] #ground truth的个数 iterations = 0 print("centroids.shape",centroids) k,dim = centroids.shape #anchor的个数k以及w,h两维，dim默认等于2 prev_assignments = np.ones(N)*(-1) #对每个ground truth分配初始标签 iter = 0 old_D = np.zeros((N,k)) #初始化每个ground truth对每个anchor的IoU while True: D = [] iter+=1 for i in range(N): d = 1 - IOU(X[i],centroids) D.append(d) D = np.array(D) # D.shape = (N,k) 得到每个ground truth对每个anchor的IoU print("iter {}: dists = {}".format(iter,np.sum(np.abs(old_D-D)))) #计算每次迭代和前一次IoU的变化值 #assign samples to centroids assignments = np.argmin(D,axis=1) #将每个ground truth分配给距离d最小的anchor序号 if (assignments == prev_assignments).all() : #如果前一次分配的结果和这次的结果相同，就输出anchor以及平均IoU print("Centroids = ",centroids) write_anchors_to_file(centroids,X,anchor_file,input_shape,yolo_version) return #calculate new centroids centroid_sums=np.zeros((k,dim),np.float) #初始化以便对每个簇的w,h求和 for i in range(N): centroid_sums[assignments[i]]+=X[i] #将每个簇中的ground truth的w和h分别累加 for j in range(k): #对簇中的w,h求平均 centroids[j] = centroid_sums[j]/(np.sum(assignments==j)+1) prev_assignments = assignments.copy() old_D = D.copy() def main(argv): parser = argparse.ArgumentParser() parser.add_argument('-filelist', default = r'E:\BaiduNetdiskDownload\darknetHG8245\scripts\train.txt', help='path to filelist\n' ) parser.add_argument('-output_dir', default = r'E:\BaiduNetdiskDownload\darknetHG8245', type = str, help='Output anchor directory\n' ) parser.add_argument('-num_clusters', default = 0, type = int, help='number of clusters\n' ) ''' 需要注意的是yolov2输出的值比较小是相对特征图来说的， yolov3输出值较大是相对原图来说的， 所以yolov2和yolov3的输出是有区别的 ''' parser.add_argument('-yolo_version', default='yolov2', type=str, help='yolov2 or yolov3\n') parser.add_argument('-yolo_input_shape', default=416, type=int, help='input images shape，multiples of 32. etc. 416*416\n') args = parser.parse_args() if not os.path.exists(args.output_dir): os.mkdir(args.output_dir) f = open(args.filelist) lines = [line.rstrip('\n') for line in f.readlines()] annotation_dims = [] for line in lines: line = line.replace('JPEGImages','labels') line = line.replace('.jpg','.txt') line = line.replace('.png','.txt') print(line) f2 = open(line) for line in f2.readlines(): line = line.rstrip('\n') w,h = line.split(' ')[3:] #print(w,h) annotation_dims.append((float(w),float(h))) annotation_dims = np.array(annotation_dims) #保存所有ground truth框的(w,h) eps = 0.005 if args.num_clusters == 0: for num_clusters in range(1,11): #we make 1 through 10 clusters anchor_file = join( args.output_dir,'anchors%d.txt'%(num_clusters)) indices = [ random.randrange(annotation_dims.shape[0]) for i in range(num_clusters)] centroids = annotation_dims[indices] kmeans(annotation_dims,centroids,eps,anchor_file,args.yolo_input_shape,args.yolo_version) print('centroids.shape', centroids.shape) else: anchor_file = join( args.output_dir,'anchors%d.txt'%(args.num_clusters)) indices = [ random.randrange(annotation_dims.shape[0]) for i in range(args.num_clusters)] centroids = annotation_dims[indices] kmeans(annotation_dims,centroids,eps,anchor_file,args.yolo_input_shape,args.yolo_version) print('centroids.shape', centroids.shape) if __name__=="__main__": main(sys.argv) |
这是其中的yolov3的结果
![[DL]YOLO中anchor box是如何通过聚类确定的](https://ypyssl.machunjie.com/machunjie/20190910233856.png_machunjie.png)
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