yolov3 kmeans
yolov3 In doing boundingbox forecast, we used the anchor boxes.
Configuration in the .cfg file as follows:
[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319In doing training with our own data when first modification anchors, matching the size of our own data .anchors obtained by clustering.
In layman's terms, the cluster is to endure recent data points grouped together.
Idea is very simple algorithm kmeans
- Just specify the k cluster
- The point is divided into a cluster with the nearest
- Obtained above cluster is certainly not good, since the beginning of the cluster is the random selection of thing
- Update each cluster is the mean point of the current cluster. (This time certainly become quasi-cluster, and why? Such as this cluster, there are currently three points, two points by the close, there was one point away from a little far point, then averaged, then the equivalent in close proximity of the two points have more voting rights, a new study out of the center cluster will be closer to two points if you have to bicker: that in case a I began randomly assigned cluster center point on it particularly accurate, but the re-taking the mean center point are not allowed to get the fact this is a flaw of kmeans:? more dependent on the position of the initial selection of k cluster inappropriate k. values may lead to poor clustering results. this is also why the feature check to determine the number of clusters in the data set.)
- Re-executing the above process
- The point is divided into a cluster with the nearest
- Each cluster is to update the current point of the mean cluster
- The above process is repeated until the cluster center little change
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)
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()
def kmeans(X,centroids,eps,anchor_file):
N = X.shape[0]
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)
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):
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 = '\\path\\to\\voc\\filelist\\train.txt',
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 = 0, 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 line in lines:
#line = line.replace('images','labels')
#line = line.replace('img1','labels')
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(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)Usage: python3 gen_anchors.py -filelist ./park_train.txt park_train.txt describes the training image path.