137 lines
4.7 KiB
Python
137 lines
4.7 KiB
Python
# 从人脸图像文件中提取人脸特征存入 CSV
|
|
# Get features from images and save into features_all.csv
|
|
|
|
# Author: coneypo
|
|
# Blog: http://www.cnblogs.com/AdaminXie
|
|
# GitHub: https://github.com/coneypo/Dlib_face_recognition_from_camera
|
|
# Mail: coneypo@foxmail.com
|
|
|
|
# Created at 2018-05-11
|
|
# Updated at 2019-02-25
|
|
|
|
# 增加录入多张人脸到 CSV 的功能
|
|
|
|
# return_128d_features() 获取某张图像的 128D 特征
|
|
# write_into_csv() 获取某个路径下所有图像的特征，并写入 CSV
|
|
# compute_the_mean() 从 CSV 中读取 128D 特征，并计算特征均值
|
|
|
|
import cv2
|
|
import os
|
|
import dlib
|
|
from skimage import io
|
|
import csv
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
# 要读取人脸图像文件的路径
|
|
path_photos_from_camera = "data/data_faces_from_camera/"
|
|
# 储存人脸特征 csv 的路径
|
|
path_csv_from_photos = "data/data_csvs_from_camera/"
|
|
|
|
# Dlib 正向人脸检测器
|
|
detector = dlib.get_frontal_face_detector()
|
|
|
|
# Dlib 人脸预测器
|
|
predictor = dlib.shape_predictor("data/data_dlib/shape_predictor_5_face_landmarks.dat")
|
|
|
|
# Dlib 人脸识别模型
|
|
# Face recognition model, the object maps human faces into 128D vectors
|
|
facerec = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
|
|
|
|
|
|
# 返回单张图像的 128D 特征
|
|
def return_128d_features(path_img):
|
|
img = io.imread(path_img)
|
|
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
faces = detector(img_gray, 1)
|
|
|
|
print("检测到人脸的图像：", path_img, "\n")
|
|
|
|
# 因为有可能截下来的人脸再去检测，检测不出来人脸了
|
|
# 所以要确保是 检测到人脸的人脸图像 拿去算特征
|
|
if len(faces) != 0:
|
|
shape = predictor(img_gray, faces[0])
|
|
face_descriptor = facerec.compute_face_descriptor(img_gray, shape)
|
|
else:
|
|
face_descriptor = 0
|
|
print("no face")
|
|
|
|
# print(face_descriptor)
|
|
return face_descriptor
|
|
|
|
|
|
# 将文件夹中照片特征提取出来, 写入 CSV
|
|
# path_faces_personX: 图像文件夹的路径
|
|
# path_csv_from_photos: 要生成的 CSV 路径
|
|
|
|
def write_into_csv(path_faces_personX, path_csv_from_photos):
|
|
photos_list = os.listdir(path_faces_personX)
|
|
with open(path_csv_from_photos, "w", newline="") as csvfile:
|
|
writer = csv.writer(csvfile)
|
|
if photos_list:
|
|
for i in range(len(photos_list)):
|
|
# 调用return_128d_features()得到128d特征
|
|
print("正在读的人脸图像：", path_faces_personX + "/" + photos_list[i])
|
|
features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
|
|
# print(features_128d)
|
|
# 遇到没有检测出人脸的图片跳过
|
|
if features_128d == 0:
|
|
i += 1
|
|
else:
|
|
writer.writerow(features_128d)
|
|
else:
|
|
print("Warning: Empty photos in "+path_faces_personX+'/')
|
|
writer.writerow("")
|
|
|
|
|
|
# 读取某人所有的人脸图像的数据，写入 person_X.csv
|
|
faces = os.listdir(path_photos_from_camera)
|
|
faces.sort()
|
|
for person in faces:
|
|
print("##### " + person + " #####")
|
|
print(path_csv_from_photos + person + ".csv")
|
|
write_into_csv(path_photos_from_camera + person, path_csv_from_photos + person + ".csv")
|
|
print('\n')
|
|
|
|
|
|
# 从 CSV 中读取数据，计算 128D 特征的均值
|
|
def compute_the_mean(path_csv_from_photos):
|
|
column_names = []
|
|
|
|
# 128D 特征
|
|
for feature_num in range(128):
|
|
column_names.append("features_" + str(feature_num + 1))
|
|
|
|
# 利用 pandas 读取 csv
|
|
rd = pd.read_csv(path_csv_from_photos, names=column_names)
|
|
|
|
if rd.size != 0:
|
|
# 存放 128D 特征的均值
|
|
feature_mean_list = []
|
|
|
|
for feature_num in range(128):
|
|
tmp_arr = rd["features_" + str(feature_num + 1)]
|
|
tmp_arr = np.array(tmp_arr)
|
|
# 计算某一个特征的均值
|
|
tmp_mean = np.mean(tmp_arr)
|
|
feature_mean_list.append(tmp_mean)
|
|
else:
|
|
feature_mean_list = []
|
|
return feature_mean_list
|
|
|
|
|
|
# 存放所有特征均值的 CSV 的路径
|
|
path_csv_from_photos_feature_all = "data/features_all.csv"
|
|
|
|
# 存放人脸特征的 CSV 的路径
|
|
path_csv_from_photos = "data/data_csvs_from_camera/"
|
|
|
|
with open(path_csv_from_photos_feature_all, "w", newline="") as csvfile:
|
|
writer = csv.writer(csvfile)
|
|
csv_rd = os.listdir(path_csv_from_photos)
|
|
csv_rd.sort()
|
|
print("##### 得到的特征均值 / The generated average values of features stored in: #####")
|
|
for i in range(len(csv_rd)):
|
|
feature_mean_list = compute_the_mean(path_csv_from_photos + csv_rd[i])
|
|
print(path_csv_from_photos + csv_rd[i])
|
|
writer.writerow(feature_mean_list) |