正文
(
'Being processed picture %s'
% index)
# 从摄像头读取照片
success, img = camera.read()
# 转为灰度图片
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用detector进行人脸检测
dets = detector(gray_img, 1)
for
i, d
in
enumerate(dets):
x1 = d.top()
if
d.top() > 0
else
0
y1 = d.bottom()
if
d.bottom() > 0
else
0
x2 = d.left()
if
d.left() > 0
else
0
y2 = d.right()
if
d.right() > 0
else
0
face = img[x1:y1,x2:y2]
# 调整图片的对比度与亮度， 对比度与亮度值都取随机数，这样能增加样本的多样性
face = relight(face, random.uniform(0.5, 1.5), random.randint(-50, 50))
face = cv2.resize(face, (size,size))
cv2.imshow(
'image'
, face)
cv2.imwrite(output_dir+
'/'
+str(index)+
'.jpg'
, face)
index += 1
key = cv2.waitKey(30) & 0xff
if
key == 27:
break
else
:
print
(
'Finished!'
)
# 释放摄像头 release camera
camera.release()
# 删除建立的窗口 delete all the windows
cv2.destroyAllWindows()
break
运行效果：
2.2 分析每张人脸的特征值并存入csv文件
根据抓取的图片和人脸识别模型->训练得到的20个的68个特征数据集以及1个平均特征值存入csv文件
“每张图片的68个特征数据集可以不用存取，他们只是中间量，计算平均值以后就可以抛弃了，这里把他们输出出来只是为了方便学习。
代码：
# 从人脸图像文件中提取人脸特征存入 CSV
# Features extraction from images and save into features_all.csv
# return_128d_features() 获取某张图像的128D特征
# compute_the_mean() 计算128D特征均值
from cv2 import cv2 as cv2
import os
import dlib
from skimage import io
import csv
import numpy as np
# 要读取人脸图像文件的路径
path_images_from_camera = "D:/No1WorkSpace/JupyterNotebook/Facetrainset/"
# Dlib 正向人脸检测器
detector = dlib.get_frontal_face_detector()
# Dlib 人脸预测器
predictor = dlib.shape_predictor("D:/No1WorkSpace/JupyterNotebook/model/shape_predictor_68_face_landmarks.dat")
# Dlib 人脸识别模型
# Face recognition model, the object maps human faces into 128D vectors
face_rec = dlib.face_recognition_model_v1("D:/No1WorkSpace/JupyterNotebook/model/dlib_face_recognition_resnet_model_v1.dat")
# 返回单张图像的 128D 特征
def return_128d_features(path_img):
img_rd = io.imread(path_img)
img_gray = cv2.cvtColor(img_rd, cv2.COLOR_BGR2RGB)
faces = detector(img_gray, 1)
print("%-40s %-20s" % ("检测到人脸的图像 / image with faces detected:", 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 = face_rec.compute_face_descriptor(img_gray, shape)
else:
face_descriptor = 0
print("no face")
return face_descriptor
# 将文件夹中照片特征提取出来, 写入 CSV
def return_features_mean_personX(path_faces_personX):
features_list_personX = []
photos_list = os.listdir(path_faces_personX)
if photos_list:
for i in range(len(photos_list)):
with open("D:/No1WorkSpace/JupyterNotebook/feature/featuresGiao"+str(i)+".csv", "w", newline="") as csvfile:
writer = csv.writer(csvfile)
# 调用return_128d_features()得到128d特征
print("%-40s %-20s" % ("正在读的人脸图像 / image to read:", path_faces_personX + "/" + photos_list[i]))
features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])
print(features_128d)
writer.writerow(features_128d)
# 遇到没有检测出人脸的图片跳过
if features_128d == 0:
i += 1
else:
features_list_personX.append(features_128d)
else:
print("文件夹内图像文件为空 / Warning: No images in " + path_faces_personX + '/', '\n')
# 计算 128D 特征的均值
# N x 128D -> 1 x 128D
if
