聚类kmeans算法在yolov3中的应用 (2)

计算每一个cluster中的样本点总和,取平均,更新cluster质心.

for i in range(N): centroid_sums[assignments[i]]+=X[i] #将每一个样本划分到对应质心 for j in range(k): centroids[j] = centroid_sums[j]/(np.sum(assignments==j)) #更新质心

不断重复上述过程,直到质心不再变化 聚类完成.

保存聚类得到的anchor box大小 def write_anchors_to_file(centroids,X,anchor_file): f = open(anchor_file,'w') anchors = centroids.copy() print(anchors.shape) for i in range(anchors.shape[0]): anchors[i][0]*=width_in_cfg_file/32. anchors[i][1]*=height_in_cfg_file/32. 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()

由于yolo要求的label文件中,填写的是相对于width,height的比例.所以得到的anchor box的大小要乘以模型输入图片的尺寸.
上述代码里

anchors[i][0]*=width_in_cfg_file/32. anchors[i][1]*=height_in_cfg_file/32.

这里除以32是yolov2的算法要求. yolov3实际上不需要.参见以下链接https://github.com/pjreddie/darknet/issues/911

for Yolo v2: width=704 height=576 in cfg-file
./darknet detector calc_anchors data/hand.data -num_of_clusters 5 -width 22 -height 18 -show
for Yolo v3: width=704 height=576 in cfg-file
./darknet detector calc_anchors data/hand.data -num_of_clusters 9 -width 704 -height 576 -show
And you can use any images with any sizes.

完整代码见https://github.com/AlexeyAB/darknet/tree/master/scripts
用法:python3 gen_anchors.py -filelist ../build/darknet/x64/data/park_train.txt