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get_features_into_csv_tensorflow.py
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get_features_into_csv_tensorflow.py
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from scipy import misc
import tensorflow as tf
import numpy as np
import pandas as pd
import dlib
import os
import csv
import cv2
import shutil
import facenet
import align.detect_face
model_path="20180402-114759/20180402-114759.pb" #模型保存的路径
image_paths_uncalculated='data_faces_from_camera/Uncalculated/' #Uncalculated文件夹下为未经过计算转化的人脸文件
image_paths_calculated='data_faces_from_camera/calculated/' #calculated文件夹下为经过计算转化的人脸文件
path_csv_feature="data_csvs_from_camera/" #存放每个人的人脸的csv
path_csv_feature_all="features_all_tensorflow.csv" #存放全部人的人脸特征
detector = dlib.get_frontal_face_detector()
#找到人脸以及给出人脸框数组
def load_and_align_data(image_path,image_size,margin): #margin为要剪裁的余量
minisize=20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
img = misc.imread(os.path.expanduser(image_path)) # 读取图片
img_size = np.asarray(img.shape)[0:2] # img[0]为宽度，img[1]为高度
bounding_boxes, _ = align.detect_face.detect_face(img, minisize, pnet, rnet, onet, threshold,
factor) # 读取并对齐人脸，bounding_boxes为人脸框数组，形状为[n,5]，n代表边框数，这里一般只有1张人脸，5对应x1，y1，x2，y2，score，分别是左上角横坐标，左上角纵坐标，右下角横坐标，右下角纵坐标，人脸置信度
det=np.squeeze(bounding_boxes[0,0:4]) #假设图片里的人脸数为1，所以这里要去除边框数那一维
bb=np.zeros(4,dtype=np.int32) #bb为要剪裁的人脸区域
bb[0]=np.maximum(det[0]-margin/2,0) #左上角x1
bb[1]=np.maximum(det[1]-margin/2,0) #左上角y1
bb[2]=np.minimum(det[2]+margin/2,img_size[1]) #右下角x2
bb[3]=np.minimum(det[3]+margin/2,img_size[0]) #右下角y2
cropped=img[bb[1]:bb[3],bb[0]:bb[2],:] #根据bb来裁剪原图片中的人脸
aligned=misc.imresize(cropped,(image_size,image_size),interp='bilinear') #将图片缩放为卷积神经网络模型输入的大小
prewhitened=facenet.prewhiten(aligned) #对裁剪出的人脸进行图片标准化处理
return prewhitened
#计算并返回人脸特征
def return_512D_features(image_path,images_placeholder,embeddings,phase_train_placeholder):
images=load_and_align_data(image_path,160,44) #使用MTCNN算法找到图片中的人脸并给出人脸框数组
images=images.reshape(1,160,160,3)
feed_dict={images_placeholder:images,phase_train_placeholder:False}
emb=sess.run(embeddings,feed_dict=feed_dict) #计算人脸特征，得到人脸特征数组
return emb
#将uncalculated文件夹下面的人脸计算转化为512维的特征数组并写进个人csv文件
def compute_feaure_and_write_into_csv(image_paths_uncalculated,images_placeholder,embeddings,phase_train_placeholder):
person_list=os.listdir(image_paths_uncalculated)
if len(person_list)>0: #uncalculated文件夹下有人脸文件才计算，否则输出没有人脸
for person in person_list: #person为uncalculated中每个文件夹的名字，这里建议命名为人的名字
print(path_csv_feature+person+".csv")
path_csv=path_csv_feature+person+".csv" #path_csv为以某人的名字命名的csv文件的路径
path_faces_personX=image_paths_uncalculated+person #path_faces_personX为每个人的人脸文件夹
images=os.listdir(path_faces_personX) #列出每个人脸文件夹下面的图片
with open(path_csv,"w",newline="") as csvfile:
writer=csv.writer(csvfile) #创建一个writer对象
for i in range(len(images)):
if os.path.exists(path_faces_personX + "/" + images[i]):
print("正在读的人脸图像：", path_faces_personX + "/" + images[i])
feature_512D=return_512D_features(path_faces_personX+"/"+images[i],images_placeholder,embeddings,phase_train_placeholder) #计算每张人脸图片，返回第i张图片的特征数组
feature_512D=np.squeeze(feature_512D) #将feature_512D转化为1维数组
feature_512D=list(feature_512D)
if len(feature_512D)<1: #feature_512D长度小于1可能是没检测到人脸，所以跳过
i+=1
else:
writer.writerow(feature_512D) #将feature_512D写进csv文件
shutil.move(image_paths_uncalculated+person,image_paths_calculated) #当一个人的所有图片被计算完后，就把这个人的人脸文件夹移到calculated文件夹下
else:
print("没有人脸可计算")
def compute_the_mean_and_write_into_all_csv(path_csv_feature):
with open(path_csv_feature_all,"w",newline="")as csvfile:
writer=csv.writer(csvfile)
csv_rd=os.listdir(path_csv_feature) #csv_rd为path_csv_feature下所有人的csv文件
for i in range(len(csv_rd)):
path_csv_rd=path_csv_feature+csv_rd[i] #path_csv_rd为每个人的csv文件的路径
column_names=[] #给下面要读取的每个人的csv文件起列名
for feature_num in range(512):
column_names.append("features_" + str(feature_num + 1))
f=open(path_csv_rd) #打开某人的csv文件
rd=pd.read_csv(f,names=column_names) #读取某人的csv文件，制定列名为column_names
feature_mean=[] #feature_mean为将要写入features_all_tensorflow.csv的列表，一共513列，第1列为名字，剩余512列为特征值
name=path_csv_rd.split('/')[1].split('.')[0] #因为csv文件是以人的名字命名的，所以直接从文件名中获取人的名字
feature_mean.append(name) #第一列存入人的名字
for feature_num in range(512):
tmp_arr=rd["features_" + str(feature_num + 1)] #从某人的csv文件中读取第feature_num列，对应的列名为feature_num + 1
tmp_arr=np.array(tmp_arr) #转化为array
tmp_mean=np.mean(tmp_arr) #计算那一列的特征均值
feature_mean.append(tmp_mean) #将那一列加入到feature_mean中
writer.writerow(feature_mean) #读取完512列后就写入到features_all_tensorflow.csv
if __name__=="__main__":
with tf.Graph().as_default():
config=tf.ConfigProto()
config.gpu_options.allocator_type="BFC"
sess=tf.Session(config=config)
with sess.as_default():
pnet,rnet,onet=align.detect_face.create_mtcnn(sess,None) #加载MTCNN的3层网络，用来检测人脸
# Load the model
facenet.load_model(model_path) #加载人脸识别模型，用来识别人脸
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
compute_feaure_and_write_into_csv(image_paths_uncalculated,images_placeholder,embeddings,phase_train_placeholder) #计算uncalculated文件夹下面所有人的人脸特征，将每个人的人脸特征存放到path_csv_feature中
compute_the_mean_and_write_into_all_csv(path_csv_feature)