前言
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本文优先于CSDN发布，为保证阅览内容体验主张前往C站：>>>here！！！戳这儿>>
注：本文往后是本人在社区最终一次更新，往后一切内容均在CSDN更新，原因见文末（抱拳）
2023-3-18 天小雨，午觉舒适程度5颗星。使命完结指数2颗星。续接上文：《MidiaPipe +stgcn（时空图卷积网络）实现人体姿态判别（单方针）》，咱们这边需求实现的是一个多方针的检测，而且咱们期望可以适用在家庭这儿领域，因此，在前者算法改善的基础上，咱们还需求加入这个人脸辨认模块。调某度这些现成的开发的API当然可以，可是自己搭建价更高，能装13还不用小钱钱，数据牢牢在手，主打的便是一个安全。
相同的，咱们仍是依照曾经的习惯，一个模块一个模块来进行阐明，写完之后的话，再进行完整版别的开源，可是每一个独立的模块都是开源直接运用的，这个可以定心，也便是说依照本文给到项目结构和代码，是可以直接复现运转的。可是这儿声明一下，这个项目是一个归纳项目，有算法，有前端，有后端，技能系统掩盖vue , python（fastapi），java(SpringBoot)开发这儿开源的模块是算法模块。其他的模块不开源，由于这个也是咱们技能团队做的一个大创项目，一起，这个项目也不算是个小项目，里边掩盖的技能不少，需求一起具备Python和Java开发能力以及以Python为体现的人工智能应用开发能力，所以就算开源全部模块，关于读者的要求是比较高的。
之后我要阐明的一点是，本篇文章中的适当一部分代码，其实是经过NewBing和ChatGPT生成的，运用的第三方库是dlib，我的工作是担任将这些代码进行整合，修正，测验调整。
原理
首要这个人脸辨认的原理的话，这个说实话相关的博文现已烂大街了，所以这儿就简简单单的过一下。这儿的话就首要提到咱们这个自己用到的这个办法，由于相关的办法其实挺多了。那么原理的话首要是这个样子的：
首要这个是一张图片：
之后呢，咱们进行比照的时分呢，先辨认出一个人A，依照相同的流程拿到，A的128的特征向量，然后呢，咱们把这个向量和咱们现已存起来了的向量给进行一个比照，也便是核算一下欧氏间隔，然后呢，得到一个组间隔，然后，咱们得到间隔最小的那个对应的名字就好了。
流程呢大约便是这样的：
项目结构
都说了是个大工厂哈，所以项目结构是这样的：
其他的自己依照结构去创立就好了。
编码
okey, 现在流程搞清楚了，那么就开端组装代码了。
装备
首要是咱们的这个装备项。
import dlib
import os
"""
人脸辨认装备
"""
class FACE_FILE(object):
shape_predictor_path='alg/faceRec/data/data_dlib/shape_predictor_68_face_landmarks.dat'
recognition_model_path='alg/faceRec/data/data_dlib/dlib_face_recognition_resnet_model_v1.dat'
csv_base_path='alg/faceRec/data/csv/features.csv'
faceData_path='alg/faceRec/data/faceData/'
points_faceData_path='alg/faceRec/data/faceData_points/'
faceName_path='alg/faceRec/data/faceName.txt'
imgs_folder_path=os.listdir("alg/faceRec/data/faceData/")
FACE_CONFIG={
"max_collection_image": 50,
"get_points_faceData_flag": True,
"import_all_features_flag":True,
"face_needTo_update":[x for x in range(1, 2)], #选择更新脸部的编号，从0开端
"num_of_person_in_lib":len(FACE_FILE.imgs_folder_path),
"recognition_threshold":0.43,
"predictor": dlib.shape_predictor(FACE_FILE.shape_predictor_path),
"recognition_model": dlib.face_recognition_model_v1(FACE_FILE.recognition_model_path),
"detector":dlib.get_frontal_face_detector(),
}
"""
方针检测装备
"""
DECTION_CONFIG={}
主控函数
之后的，咱们先来看到咱们的这个主控函数是怎样操作的。 咱们这边的任何操作，都只能在主控文件同级目录下进行调用，原因无他，这个途径的问题，必需求进行一个统一。
import cv2 as cv
from client.server.alg.faceRec.buildFace import BuildFace
from client.server.alg.faceRec.collection import Collection
from client.server.alg.faceRec.detectFace import DetectFace
def collection_face():
cam = cv.VideoCapture(0)
Collection().collection_cramer(cam)
cam.release()
cv.destroyAllWindows()
print("收集结束,程序退出!!")
def build_face():
build = BuildFace()
build.building_all()
def detect_face():
cam = cv.VideoCapture(0)
process = DetectFace()
process.detect_from_cam(cam)
if __name__ == '__main__':
# collection_face()
# build_face()
detect_face()
调用的话其实非常简单，所以非常容易就可以做整合，用在具体的功能模块傍边。
人脸收集模块
接下来便是咱们的这个收集模块，效果很简单，便是读取到视频流，然后呢，去辨认到头像，然后裁剪，然后保存在目录下：
"""
担任收集人脸要害点位
"""
import cv2 as cv
import time
import os
from client.server.config import FACE_FILE,FACE_CONFIG
class Collection(object):
"""
供给两种解析办法：
1. 经过opencv的VideoCapture 进行读取，然后得到图画
2. 经过图画进行解析，读取，一起得到框出头像之后的图画
"""
def __init__(self):
self.start_time = 0
self.fps = 0
self.image = None
self.face_img = None
self.face_num = 0
self.last_face_num = 0
self.face_num_change_flag = False
self.quit_flag = False
self.buildNewFolder = False # 按下"n"新建文件夹标
self.save_flag = False # 按下“s”保存人脸数据
self.face_flag = False
self.img_num = 0
self.collect_face_data = True
def get_fps(self):
now = time.time()
time_period = now - self.start_time
self.fps = 1.0 / time_period
self.start_time = now
color = (0,255,0)
if self.fps < 15:
color = (0,0,255)
cv.putText(self.image, str(self.fps.__round__(2)), (20, 50), cv.FONT_HERSHEY_DUPLEX, 1, color)
def save_face_image(self,build_path):
buildFile = build_path
if(not build_path):
buildFile = FACE_FILE.faceData_path + 'person_{}'.format(FACE_CONFIG.get("num_of_person_in_lib"))
if(os.path.exists(buildFile)):
self.buildNewFolder = True
else:
os.makedirs(buildFile)
FACE_CONFIG["num_of_person_in_lib"] = FACE_CONFIG.get("num_of_person_in_lib") + 1
print("存放人脸数据的文件夹创立成功！！！")
self.buildNewFolder = True
if (self.collect_face_data == True and self.buildNewFolder == True):
if (self.face_img.size > 0):
cv.imwrite(
FACE_FILE.faceData_path + 'person_{}/{}.png'.format(FACE_CONFIG.get("num_of_person_in_lib") - 1, self.img_num),
self.face_img)
self.img_num += 1
def key_scan(self, key):
if self.collect_face_data == True:
if self.save_flag == True and self.buildNewFolder == True:
if self.face_img.size > 0:
cv.imwrite(
FACE_FILE.faceData_path + 'person_{}/{}.png'.format(FACE_CONFIG.get("num_of_person_in_lib") - 1, self.img_num),
self.face_img)
self.img_num += 1
if key == ord('s'):
self.save_flag = not self.save_flag
if key == ord('n'):
os.makedirs(FACE_FILE.faceData_path + 'person_{}'.format(FACE_CONFIG.get("num_of_person_in_lib")))
FACE_CONFIG["num_of_person_in_lib"] = FACE_CONFIG.get("num_of_person_in_lib")+1
print("新文件夹树立成功!!")
self.buildNewFolder = True
if key == ord('q'): self.quit_flag = True
def face_detecting(self):
face_location = []
all_face_location = []
faces = FACE_CONFIG.get("detector")(self.image, 0)
self.face_num = len(faces)
if self.face_num != self.last_face_num:
self.face_num_change_flag = True
# print("脸数改变，由{}张变为{}张".format(self.last_face_num, self.face_num))
self.check_times = 0
self.last_face_num = self.face_num
else:
self.face_num_change_flag = False
if len(faces) != 0:
self.face_flag = True
for i, face in enumerate(faces):
face_location.append(face)
w, h = (face.right() - face.left()), (face.bottom() - face.top())
left, right, top, bottom = face.left() - w//4, face.right() + w//4, face.top() - h//2, face.bottom() + h//4
all_face_location.append([left, right, top, bottom])
return face_location, all_face_location
else:
self.face_flag = False
return None
def collection_cramer(self, camera,show=True):
"""
:param camera: 摄像头视频/读取视频
:param show: 是否要展示框选出头像
:return:
当处理结束之后，将保持到好辨认出来的头像
"""
while camera.isOpened() and not self.quit_flag:
val, self.image = camera.read()
if val == False: continue
key = cv.waitKey(1)
res = self.face_detecting()
if res is not None:
_, all_face_location = res
for i in range(self.face_num):
[left, right, top, bottom] = all_face_location[i]
self.face_img = self.image[top:bottom, left:right]
cv.rectangle(self.image, (left, top), (right, bottom), (0, 0, 255))
if self.collect_face_data == True:
cv.putText(self.image, "Face", (int((left + right) / 2) - 50, bottom + 20), cv.FONT_HERSHEY_COMPLEX, 1,
(255, 255, 255))
self.key_scan(key)
self.get_fps()
cv.namedWindow('camera', 0)
if(show):
cv.imshow('camera', self.image)
if(self.img_num>=FACE_CONFIG.get("max_collection_image")):
print("收集结束！！！")
break
camera.release()
cv.destroyAllWindows()
def collection_images(self,images,save_path=None):
"""
:param images: 图片，类型是图片数组，而且目标是opencv读取的图画目标
:param save_path: 图片保存的途径
:return:
假如，传入的图画途径为None的话，那么这儿就履行默认的策略，也便是增量修正人物模型
假如传入的图画有途径的话，那么就直接保存到那里边去
"""
for image in images:
self.image = image
res = self.face_detecting()
if res is not None:
_, all_face_location = res
for i in range(self.face_num):
[left, right, top, bottom] = all_face_location[i]
self.face_img = self.image[top:bottom, left:right]
cv.rectangle(self.image, (left, top), (right, bottom), (0, 0, 255))
if self.collect_face_data == True:
cv.putText(self.image, "Face", (int((left + right) / 2) - 50, bottom + 20), cv.FONT_HERSHEY_COMPLEX, 1,
(255, 255, 255))
self.save_face_image(save_path)
特征提取
这个特征向量的获取的话，其实是分两个部分的，当然最终咱们暴露出来就仅仅一个办法算了。这个特征提取便是树立衔接的进程，也便是存储这个特征。
"""
担任读取收集到的人脸图画，然后去构建人脸对应的信息
"""
import cv2 as cv
import os
import numpy as np
import csv
from tqdm import tqdm
import shutil
from client.server.config import FACE_FILE,FACE_CONFIG
class BuildFace():
def write2csv(self,data, mode):
"""
更新csv文件傍边的数据（这儿面存储的是咱们人脸的特征）
:param data:
:param mode:
:return:
"""
with open(FACE_FILE.csv_base_path, mode, newline='') as wf:
csv_writer = csv.writer(wf)
csv_writer.writerow(data)
def get_features_from_csv(self):
features_in_csv = []
with open(FACE_FILE.csv_base_path, 'r') as rf:
csv_reader = csv.reader(rf)
for row in csv_reader:
for i in range(0, 128):
row[i] = float(row[i])
features_in_csv.append(row)
return features_in_csv
def save_select_in_csv(self,data):
"""
选择性更新人脸数据
:param data:
:return:
"""
features_in_csv = self.get_features_from_csv()
with open(FACE_FILE.csv_base_path, 'w', newline='') as wf:
csv_writer = csv.writer(wf)
for index, i in enumerate(FACE_CONFIG.get("face_needTo_update")):
features_in_csv[i] = data[index]
csv_writer.writerow(features_in_csv[0])
with open(FACE_FILE.csv_base_path, 'a+', newline='') as af:
csv_writer = csv.writer(af)
for j in range(1, len(features_in_csv)):
csv_writer.writerow(features_in_csv[j])
print("csv文件更新完结!!")
def get_128_features(self,person_index):
"""
:param person_index: person_index代表第几个人脸数据文件夹
:return:
"""
num = 0
features = []
imgs_folder = FACE_FILE.imgs_folder_path[person_index]
points_faceImage_path = FACE_FILE.points_faceData_path + imgs_folder
imgs_path = FACE_FILE.faceData_path + imgs_folder + '/'
list_imgs = os.listdir(imgs_path)
imgs_num = len(list_imgs)
if os.path.exists(FACE_FILE.points_faceData_path + imgs_folder):
shutil.rmtree(points_faceImage_path)
os.makedirs(points_faceImage_path)
print("人脸点图文件夹树立成功!!")
with tqdm(total=imgs_num) as pbar:
pbar.set_description(str(imgs_folder))
for j in range(imgs_num):
image = cv.imread(os.path.join(imgs_path, list_imgs[j]))
faces = FACE_CONFIG.get("detector")(image, 1)
if len(faces) != 0:
for z, face in enumerate(faces):
shape = FACE_CONFIG.get("predictor")(image, face)
w, h = (face.right() - face.left()), (face.bottom() - face.top())
left, right, top, bottom = face.left() - w // 4, face.right() + w // 4, face.top() - h // 2, face.bottom() + h // 4
im = image
cv.rectangle(im, (left, top), (right, bottom), (0, 0, 255))
cv.imwrite(points_faceImage_path + '/{}.png'.format(j), im)
if (FACE_CONFIG.get("get_points_faceData_flag") == True):
for p in range(0, 68):
cv.circle(image, (shape.part(p).x, shape.part(p).y), 2, (0,0,255))
cv.imwrite(points_faceImage_path + '/{}.png'.format(j), image)
the_features = list(FACE_CONFIG.get("recognition_model").compute_face_descriptor(image, shape)) # 获取128维特征向量
features.append(the_features)
num += 1
pbar.update(1)
np_f = np.array(features)
res = np.median(np_f, axis=0)
return res
def building_form_config(self):
if (FACE_CONFIG.get("import_all_features_flag") == True):
self.building_all()
else:
peoples = FACE_CONFIG.get("face_needTo_update")
self.building_select(peoples)
def building_all(self):
res = self.get_128_features(person_index=0)
self.write2csv(res, 'w')
for i in range(1, FACE_CONFIG.get("num_of_person_in_lib")):
res = self.get_128_features(person_index=i)
self.write2csv(res, 'a+')
def building_select(self,peoples):
"""
更新某几个人脸，传入对应的下标编号,例如：[0,2,4]
:param peoples:
:return:
"""
select_res = []
for i in peoples:
res = self.get_128_features(person_index=i)
select_res.append(res)
self.save_select_in_csv(select_res)
辨认
辨认的话，这儿也是有两个办法，一个是直接给你一张图片，然后回来到里边的人脸名称，还有一个便是视频直接显现读取，这个看你怎样用，假如你是做QT桌面开发，或者web开发而且需求继续显现视频的话，用这个办法不错，可是算力就上去了，我这边还需求跑别的算法，只有当那个算法履行结束之后，而且被出发了机制我才会进行人脸辨认。
"""
担任
"""
import numpy as np
import csv
import cv2 as cv
from client.server.config import FACE_CONFIG,FACE_FILE
from client.server.alg.faceRec.collection import Collection
class DetectFace(Collection):
def __init__(self):
super(DetectFace, self).__init__()
self.available_max_face_num = 50
self.collect_face_data = False
# 人脸辨认进程不收集数据，固定为False
self.all_features = []
# 存储库中一切特征向量
self.check_features_from_cam = []
# 存储五次检测进程，每次得到的特征向量
self.person_name = []
# 存储的人名映射
self.all_name = []
# 存储预测到的一切人名
self.all_face_location = None
# 存储一帧中一切人脸的坐标
self.middle_point = None
# 存储一张人脸的中心点坐标
self.last_frame_middle_point = []
# 存储上一帧一切人脸的中心点坐标
self.all_e_distance = []
# 存储当前人脸与库中一切人脸特征的欧氏间隔
self.last_now_middlePoint_eDistance = [66666] * (self.available_max_face_num + 10)
# 存储这帧与上一帧每张人脸中心点的欧氏间隔
self.init_process()
for i in range(self.available_max_face_num):
self.all_e_distance.append([])
self.person_name.append([])
self.check_features_from_cam.append([])
self.last_frame_middle_point.append([])
def get_feature_in_csv(self):
# 取得库内一切特征向量
datas = csv.reader(open(FACE_FILE.csv_base_path, 'r'))
for row in datas:
for i in range(128):
row[i] = float(row[i])
self.all_features.append(row)
def get_faceName(self):
# 一切对应的人名
with open(FACE_FILE.faceName_path, 'r', encoding='utf-8') as f:
datas = f.readlines()
for line in datas:
self.all_name.append(line[:-1])
print("现已录入的人名有：{}".format(self.all_name))
def calculate_EuclideanDistance(self, feature1, feature2): # 核算欧氏间隔
np_feature1 = np.array(feature1)
np_feature2 = np.array(feature2)
EuclideanDistance = np.sqrt(np.sum(np.square(np_feature1 - np_feature2)))
return EuclideanDistance
def meadian_filter(self, the_list, num_of_data):
np_list = np.array(the_list)
feature_max = np.max(np_list, axis=0)
feature_min = np.min(np_list, axis=0)
res = (np.sum(np_list, axis=0) - feature_max - feature_min) / (num_of_data - 2)
res.tolist()
return res
def middle_filter(self, the_list):
np_list = np.array(the_list)
return np.median(np_list, axis=0)
def init_process(self):
self.get_feature_in_csv()
self.get_faceName()
def track_link(self):
# 让后续帧的序号与初始帧的序号对应
for index in range(self.face_num):
self.last_now_middlePoint_eDistance[index] = self.calculate_EuclideanDistance(self.middle_point,
self.last_frame_middle_point[
index])
this_face_index = self.last_now_middlePoint_eDistance.index(min(self.last_now_middlePoint_eDistance))
self.last_frame_middle_point[this_face_index] = self.middle_point
return this_face_index
def detect_from_image(self,image):
"""
直接辨认一张图片傍边的人脸，这个开支是最小的，当然这个精确度嘛，没有直接读取视频好一点
由于那个的话确认了好几帧的状况，这个的话仅仅单张图画的。回来的是一个图画的人名列表
可是实际上的话，咱们其实送入的图画其实只会有一个人头像，多方针检测，咱们也是把一张图画
对多个方针进行截取，然后进行辨认，由于需求确认每个人物的序。
:param image:
:param show:
:return:
"""
self.image = image
# self.image = cv.imread('.test_1.jpg')
res = self.face_detecting()
names = []
if res is not None:
face, self.all_face_location = res
max_it = self.face_num if self.face_num < len(res) else len(res)
for i in range(max_it):
[left, right, top, bottom] = self.all_face_location[i]
self.middle_point = [(left + right) / 2, (top + bottom) / 2]
self.face_img = self.image[top:bottom, left:right]
cv.rectangle(self.image, (left, top), (right, bottom), (0, 0, 255))
shape = FACE_CONFIG.get("predictor")(self.image, face[i])
the_features_from_image = list(
FACE_CONFIG.get("recognition_model").compute_face_descriptor(self.image, shape))
e_distance = []
for features in self.all_features:
e_distance.append(self.calculate_EuclideanDistance(the_features_from_image,
features))
if(min(e_distance)<FACE_CONFIG.get("recognition_threshold")):
max_index = int(np.argmin(e_distance))
names.append(self.all_name[max_index])
return names
def detect_from_cam(self,camera):
"""
这儿的话，和咱们收集是相同的，便是传入这个camera目标就好了
:return:
"""
while camera.isOpened() and not self.quit_flag:
val, self.image = camera.read()
if val == False: continue
key = cv.waitKey(1)
res = self.face_detecting() # 0.038s
if res is not None:
face, self.all_face_location = res
for i in range(self.face_num):
[left, right, top, bottom] = self.all_face_location[i]
self.middle_point = [(left + right) / 2, (top + bottom) / 2]
self.face_img = self.image[top:bottom, left:right]
cv.rectangle(self.image, (left, top), (right, bottom), (0, 0, 255))
shape = FACE_CONFIG.get("predictor")(self.image, face[i]) # 0.002s
if self.face_num_change_flag == True or self.check_times <= 5:
if self.face_num_change_flag == True: # 人脸数量有改变，重新进行五次检测
self.check_times = 0
self.last_now_middlePoint_eDistance = [66666 for _ in range(self.available_max_face_num)]
for z in range(self.available_max_face_num):
self.check_features_from_cam[z] = []
if self.check_times < 5:
the_features_from_cam = list(
FACE_CONFIG.get("recognition_model").compute_face_descriptor(self.image, shape))
if self.check_times == 0: # 初始帧
self.check_features_from_cam[i].append(the_features_from_cam)
self.last_frame_middle_point[i] = self.middle_point
else:
this_face_index = self.track_link() # 后续帧需求与初始帧的人脸序号对应
self.check_features_from_cam[this_face_index].append(the_features_from_cam)
elif self.check_times == 5:
features_after_filter = self.middle_filter(self.check_features_from_cam[i])
self.check_features_from_cam[i] = []
for person in range(FACE_CONFIG.get("num_of_person_in_lib")):
e_distance = self.calculate_EuclideanDistance(self.all_features[person],
features_after_filter)
self.all_e_distance[i].append(e_distance)
if min(self.all_e_distance[i]) < FACE_CONFIG.get("recognition_threshold"):
self.person_name[i] = self.all_name[
self.all_e_distance[i].index(min(self.all_e_distance[i]))]
cv.putText(self.image, self.person_name[i],
(int((left + right) / 2) - 50, bottom + 20),
cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
else:
self.person_name[i] = "Unknown"
else:
this_face_index = self.track_link()
cv.putText(self.image, self.person_name[this_face_index],
(int((left + right) / 2) - 50, bottom + 20),
cv.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 255), 2)
self.check_times += 1
for j in range(self.available_max_face_num):
self.all_e_distance[j] = []
"""
在这儿的话，n,s是不会触发的，这儿仅仅用一下这个q罢了，也便是退出
"""
self.key_scan(key)
self.get_fps()
cv.namedWindow('camera', 0)
cv.imshow('camera', self.image)
camera.release()
cv.destroyAllWindows()
测验
最终的话便是这个测验。
暂停更新原因
我是从2021年12月份进入渠道的，一开端的目的其实是为了嫖一下这儿的一些福利，一起其实也是期望可以扩展自己的一些技能影响力，虽无大才，但也不虚。再此之前在CSDN也有点流量，在当时也有3000多个水友的支持。我是一名从高中就开端在这方面play的玩家，触及领域也不算少吧，玩过浸透，拿过国奖，做过课题，从python web 开发到java 开发，再到人工智能都有进入，包含本文提到的也是一个归纳项目。
扯远了，在渠道，从CSDN搬迁加上自己一直在更新的博文到到今日，大大小小也更新了将近240多篇文章。可是从开端到现在，没有得到过官方的任何流量扶持，包含我也参与过签约作者活动，而且保持过适当长时间的更新以及质量保证（CSDN计算优质博文96%以上）。虽不求名利，可是做人都是有傲气的。在文章数量和质量都保持在线的状况下，得不到认可和扶持我是很难了解的，在新年期间也试图和官方沟经过，无果不得终。
我是Huterox，在的昵称是the_way_inf，道阻且长，学习之路渐渐求索。也等待官方给予合理的回复和诚心。我是Huterox,江湖再会儿！
