最近实验要用到fisher vector，但是自己写不出来，网上关于python实现的也很少，不过老天不负有心人，怎么说python强大呢，本渣渣找到了一个大佬写的第三方库fishervector，安装完成之后直接调用函数就OK啦！安装方式也很简单，就是直接pip就可以了，具体的可以看这个大佬的github!
# -*- coding: utf-8 -*-
"""
Created on Mon Jan 7 14:05:59 2019
@author: 13260
"""
from fishervector import FisherVectorGMM
import numpy as np
"""
功能：保存特征文件
输入：path 保存路径
feature 待保存的特征文件
输出：无
"""
def save_file(data,path):
np.save(path,data)
print("[INFO] File have been saved !")
"""
功能：加载文件
输入：文件路径
输出：加载的文件
"""
def load_file(f_path,l_path):
features = np.load(f_path)
labels = np.load(l_path)
print("[INFO] Feature and label file have been loaded !")
return features,labels
"""
功能：对图像特征进行FV编码
输入：提取的卷积特征集合
输出：编码之后的fisher vector 形式：(num,2*k,d)
"""
def fisher_vector(feature,path,num,train_rate):
features = np.load(feature)
length = features.shape[0]
max_num = length*train_rate
train_data = features[:max_num]
test_data = features[max_num:]
fv_gmm = FisherVectorGMM(n_kernels=num).fit(train_data)
print("[INFO] GMM training is completed !")
fisher_v = fv_gmm.predict(test_data)
print("[INFO] Fisher vector is completed !")
return fisher_v
"""
功能：将获得的fisher vector进行展开
输入：待展开的向量，形式(num,row,dimen)
输出：展开后的向量,形式(num,row*)
"""
def fv_flatten(feature):
length = feature.shape[0]
flatten_fv = []
for i in range(length):
tmp = feature[i].flatten()
flatten_fv.append(tmp)
flatten_fv = np.array(flatten_fv)
print("[INFO] Feature flatten is completed !")
return flatten_fv
if __name__ == "__main__":
path = "F:/python/features/DenseNet/densenet_fv.npy"
feature = np.load(path)
f = fv_flatten(feature)
s_path = "F:/python/features/DenseNet/densenet_fv_flatten.npy"
save_file(f,s_path)
