YOLOv3中K-Means聚类出新数据集的Anchor尺寸
参考博客：
聚类kmeans算法在yolov3中的应用 https://www.cnblogs.com/sdu20112013/p/10937717.html
这篇博客写得非常详细，也贴出了github代码：https://github.com/AlexeyAB/darknet/blob/master/scripts/gen_anchors.py
整体代码如下：
1 ''' 2 Created on Feb 20, 2017 3 @author: jumabek 4 ''' 5 from os import listdir 6 from os.path import isfile, join 7 import argparse 8 # import cv2 9 import numpy as np 10 import sys 11 import os 12 import shutil 13 import random 14 import math 15 width_in_cfg_file = 416. 16 height_in_cfg_file = 416. 17 18 19 def IOU(x, centroids): 20 ''' 21 :param x: 当前gt的w和h 22 :param centroids: 质心 23 :return:当前gt与每个质心的相似度，np.array形式 24 ''' 25 similarities = [] 26 k = len(centroids) 27 for centroid in centroids: 28 c_w, c_h = centroid 29 w, h = x 30 if c_w >= w and c_h >= h: 31 similarity = w * h / (c_w * c_h) 32 elif c_w >= w and c_h <= h: 33 similarity = w * c_h / (w * h + (c_w - w) * c_h) # 交叉面积/总面积 34 elif c_w <= w and c_h >= h: 35 similarity = c_w * h / (w * h + c_w * (c_h - h)) 36 else: # means both w,h are bigger than c_w and c_h respectively 37 similarity = (c_w * c_h) / (w * h) 38 similarities.append(similarity) # will become (k,) shape 39 return np.array(similarities) 40 41 42 def avg_IOU(X, centroids): 43 n, d = X.shape 44 sum = 0. 45 for i in range(X.shape[0]): 46 # note IOU() will return array which contains IoU for each centroid and X[i] // slightly ineffective, but I am too lazy 47 sum += max(IOU(X[i], centroids)) 48 return sum / n 49 50 51 def write_anchors_to_file(centroids, X, anchor_file): 52 f = open(anchor_file, 'w') 53 anchors = centroids.copy() 54 print(anchors.shape) 55 for i in range(anchors.shape[0]): 56 anchors[i][0] *= width_in_cfg_file # / 32. YOLOv3不用除以32 # 归一化后的宽高乘以预设的图片宽高 57 anchors[i][1] *= height_in_cfg_file # / 32. 58 widths = anchors[:, 0] 59 sorted_indices = np.argsort(widths) 60 print('Anchors = ', anchors[sorted_indices]) 61 for i in sorted_indices[:-1]: 62 # 将前n-1个anchor写入txt 63 f.write('%0.2f,%0.2f, ' % (anchors[i, 0], anchors[i, 1])) 64 # there should not be comma after last anchor, that's why 65 # 最后一个anchor写完以后需要换行，所以单独填写 66 f.write('%0.2f,%0.2f\n' % (anchors[sorted_indices[-1:], 0], anchors[sorted_indices[-1:], 1])) 67 f.write('%f\n' % (avg_IOU(X, centroids))) 68 print() 69 70 71 def kmeans(X, centroids, eps, anchor_file): 72 ''' 73 74 :param X: annotation_dims,所有的标注信息中的宽和高 75 :param centroids: 随机生成的质心 76 :param eps: 77 :param anchor_file: 保存结果的文件 78 :return: 79 ''' 80 N = X.shape[0] 81 iterations = 0 82 k, dim = centroids.shape 83 prev_assignments = np.ones(N) * (-1) 84 iter = 0 85 old_D = np.zeros((N, k)) 86 while True: 87 D = [] 88 iter += 1 89 for i in range(N): 90 # 计算gt框与质心之间的距离，相似度越大，说明当前gt越接近于质心，此距离就应该越小 91 d = 1 - IOU(X[i], centroids) 92 D.append(d) 93 D = np.array(D) # D.shape = (N,k) 94 print("iter {}: dists = {}".format(iter, np.sum(np.abs(old_D - D)))) 95 # assign samples to centroids 96 assignments = np.argmin(D, axis=1) # 返回每一行的最小值的下标.即当前样本应该归为k个质心中的哪一个质心. 97 if (assignments == prev_assignments).all(): # 质心已经不再变化 98 print("Centroids = ", centroids) 99 write_anchors_to_file(centroids, X, anchor_file) 100 return 101 # calculate new centroids，更新质心 102 centroid_sums = np.zeros((k, dim), np.float) 103 for i in range(N): 104 centroid_sums[assignments[i]] += X[i] 105 for j in range(k): 106 centroids[j] = centroid_sums[j] / (np.sum(assignments == j)) 107 prev_assignments = assignments.copy() 108 old_D = D.copy() 109 110 111 def main(argv): 112 parser = argparse.ArgumentParser() 113 parser.add_argument('-filelist', default='F://BaiduNetdiskDownload//trainall_name.txt', 114 help='path to filelist\n') 115 parser.add_argument('-output_dir', default='F://BaiduNetdiskDownload//generated_anchors//anchors//', type=str, 116 help='Output anchor directory\n') 117 parser.add_argument('-num_clusters', default=6, type=int, 118 help='number of clusters\n') 119 args = parser.parse_args() 120 if not os.path.exists(args.output_dir): 121 os.mkdir(args.output_dir) 122 f = open(args.filelist) 123 lines = [line.rstrip('\n') for line in f.readlines()] 124 annotation_dims = [] 125 size = np.zeros((1, 1, 3)) 126 for line in lines: 127 # 注意路径问题，通过替换图片路径中的Images为labels来找到标签信息 128 line = line.replace('Images','labels') 129 # line = line.replace('img1','labels') 130 # line = line.replace('JPEGImages', 'labels') 131 line = line.replace('.jpg', '.txt') 132 line = line.replace('.png', '.txt') 133 print(line) 134 135 f2 = open(line) 136 for line in f2.readlines(): 137 line = line.rstrip('\n') 138 w, h = line.split(' ')[3:] # 得到标注文件的宽和高[0 0.83984 0.40700 0.17188 0.47218] 139 # print(w,h) 140 annotation_dims.append(tuple(map(float, (w, h)))) 141 annotation_dims = np.array(annotation_dims) 142 eps = 0.005 143 if args.num_clusters == 0: 144 for num_clusters in range(1, 11): # we make 1 through 10 clusters 145 anchor_file = join(args.output_dir, 'anchors%d.txt' % (num_clusters)) 146 indices = [random.randrange(annotation_dims.shape[0]) for i in range(num_clusters)] 147 centroids = annotation_dims[indices] 148 kmeans(annotation_dims, centroids, eps, anchor_file) 149 print('centroids.shape', centroids.shape) 150 else: 151 anchor_file = join(args.output_dir, 'anchors%d.txt' % (args.num_clusters)) # 保存结果的文件 152 # 在所有labels数量范围内随机生成质心的索引数，生成num_clusters个 153 indices = [random.randrange(annotation_dims.shape[0]) for i in range(args.num_clusters)] 154 # 生成质心 155 centroids = annotation_dims[indices] 156 # 调用kmeans 157 kmeans(annotation_dims, centroids, eps, anchor_file) 158 print('centroids.shape', centroids.shape) 159 160 161 if __name__ == "__main__": 162 main(sys.argv)
使用生成YOLOv3 anchor时需要注意
anchors[i][0] *= width_in_cfg_file # / 32. YOLOv3不用除以32 # 归一化后的宽高乘以预设的图片宽高
最后生成的结果，6个anchors：
7.90,21.48, 18.72,61.61, 34.67,138.55, 65.49,251.30, 104.70,64.11, 144.33,434.60
0.582349
可以看出宽高比都为1：3左右，结合我使用的是行人检测的数据集，这个比例还算正常。但第5组数据（104.70，64.11）不符合这个宽高比
