yolov4.cfg文件中的先验框尺寸是coco数据集的，通用性可能已经够了，还是想试一下自己设置的能不能提高准确率。 用darknet源码中的gen_anchors.py文件生成自己数据集先验框的尺寸。
代码中IOU的计算部分没看懂，有人能解释一下吗
''' Created on Feb 20, 2017 @author: jumabek ''' 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 width_in_cfg_file = 416. height_in_cfg_file = 416. def IOU(x,centroids): similarities = [] k = len(centroids) for centroid in centroids: c_w,c_h = centroid w,h = x if c_w>=w and c_h>=h: similarity = w*h/(c_w*c_h) elif c_w>=w and c_h<=h: similarity = w*c_h/(w*h + (c_w-w)*c_h) elif c_w<=w and c_h>=h: similarity = c_w*h/(w*h + c_w*(c_h-h)) else: #means both w,h are bigger than c_w and c_h respectively similarity = (c_w*c_h)/(w*h) similarities.append(similarity) # will become (k,) shape return np.array(similarities) def avg_IOU(X,centroids): n,d = X.shape sum = 0. for i in range(X.shape[0]): #note IOU() will return array which contains IoU for each centroid and X[i] // slightly ineffective, but I am too lazy sum+= max(IOU(X[i],centroids)) return sum/n def write_anchors_to_file(centroids,X,anchor_file): f = open(anchor_file,'w') anchors = centroids.copy() print(anchors.shape) print('acc:{:.2f}%'.format(avg_IOU(X, anchors) * 100)) for i in range(anchors.shape[0]): anchors[i][0] = round( anchors[i][0] * width_in_cfg_file) # /32. anchors[i][1] = round( anchors[i][1] * height_in_cfg_file) # /32. widths = anchors[:, 0] sorted_indices = np.argsort(widths) for i in sorted_indices[:-1]: f.write('%d, %d, '%(anchors[i,0],anchors[i,1])) #there should not be comma after last anchor, that's why f.write('%d, %d\n'%(anchors[sorted_indices[-1:],0],anchors[sorted_indices[-1:],1])) out = anchors[sorted_indices] print('Anchors = ', out) # f.write('%f\n'%(avg_IOU(X,centroids))) def kmeans(X,centroids,eps,anchor_file): N = X.shape[0] #锚框个数 iterations = 0 k,dim = centroids.shape prev_assignments = np.ones(N)*(-1) iter = 0 old_D = np.zeros((N,k)) 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) print("iter {}: dists = {}".format(iter,np.sum(np.abs(old_D-D)))) #assign samples to centroids assignments = np.argmin(D,axis=1) # 取出最小点 if (assignments == prev_assignments).all() : print("Centroids = ",centroids) write_anchors_to_file(centroids,X,anchor_file) return #calculate new centroids centroid_sums=np.zeros((k,dim),np.float) for i in range(N):jieguo centroid_sums[assignments[i]]+=X[i] for j in range(k): centroids[j] = centroid_sums[j]/(np.sum(assignments==j)) prev_assignments = assignments.copy() old_D = D.copy() def main(argv): parser = argparse.ArgumentParser() parser.add_argument('-filelist', default = '\\scripts\\VOCdevkit\\VOC2020\\labels', help='path to filelist\n' ) parser.add_argument('-output_dir', default = 'generated_anchors/anchors', type = str, help='Output anchor directory\n' ) parser.add_argument('-num_clusters', default = 9, type = int, help='number of clusters\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 = [] size = np.zeros((1,1,3)) for root, dirs, files in os.walk(r"E:\scripts\VOCdevkit\VOC2020\labels"): for file in files: # 获取文件路径 fileName = os.path.join(root, file) print(fileName) f2 = open(fileName) for line in f2.readlines(): line = line.rstrip('\n') w, h = line.split(' ')[3:] print(w,h) annotation_dims.append(tuple(map(float, (w, h)))) annotation_dims = np.array(annotation_dims) # for line in lines: # # #line = line.replace('images','labels') # #line = line.replace('img1','labels') # #line = line.replace('JPEGImages','labels') # # # #line = line.replace('.jpg','.txt') # l#ine = 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(tuple(map(float,(w,h)))) # annotation_dims = np.array(annotation_dims) 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) 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) print('centroids.shape', centroids.shape) if __name__=="__main__": main(sys.argv)程序运行结果：
