def compare(filename):
imfile = File(filename)
for root, dir, file in os.walk('faceimg/'):
for f in file:
#if('.png' in f):
cmpres = api.compare(image_file1=imfile,
image_file2=File(root + f)) #比较
name = None
try:
if (cmpres['confidence'] > 80):
name = f.split('.')[0]
return (name) #返回文件名
except:
pass
return None
def addFace(self):
user_name = self.main.name.toPlainText()
print(user_name)
if user_name:
try:
res = self.api.detect(image_file=File(self.img_file))
faceList = res.faces
if len(faceList) > 0:
try:
self.api.faceset.addface(
outer_id=self.faceset,
face_tokens=faceList[0].face_token)
try:
self.api.face.setuserid(
face_token=faceList[0].face_token,
user_id=user_name)
self.face_ids.append(user_name)
self.face_tokens.append(faceList[0].face_token)
self.popupMsg.popupmsg('加了: ' + user_name, '增加人脸')
except:
self.popupMsg.popupmsg('设置人脸名字失败', '增加人脸')
except:
self.popupMsg.popupmsg('增加人脸失败', '增加人脸')
else:
self.popupMsg.popupmsg('检测不到人脸', '增加人脸')
except:
self.popupMsg.popupmsg('检测人脸失败', '增加人脸')
else:
self.popupMsg.popupmsg('请输入名字', '增加人脸')
self.main.name.setPlainText("")
def addNewFace(self):
global faceSet_img
faceSet_img = QtWidgets.QFileDialog.getOpenFileName(
self, 'open the dialog', "C:\\Users\Administrator\Desktop",
'JEPG files(*.jpg);;PNG files(*.PNG)')
faceStr = []
#存储face_token
#创建一个faceset
api.faceset.create(outer_id='test2')
#探测图片中的face
res = api.detect(image_file=File(faceSet_img[0]),
return_attributes='emotion')
print_result("detect resule", res)
#将返回值中的数据复制给faceList
faceList = res["faces"]
#创建一个str数组存储所有face的facetoken
for i in range(len(faceList)):
faceStr.append(faceList[i]["face_token"])
#在创建时就获得face_token并将其写入文件
self.data_manager.face_token_set = faceStr
self.data_manager.write_face_token_to_csv(
self.data_manager.face_token_set)
self.loading.show()
self.add_thread.faceList = faceList
self.add_thread.start()
def searchFace(self, face_search_img):
#不显示图片是因为多线程的问题，单一线程会导致图片和搜索同时，搜索完了图片还没加载
print(face_search_img)
QtWidgets.QMessageBox.information(
self, '请稍等', '将在几秒钟内完成签到',
QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No,
QtWidgets.QMessageBox.Yes)
#首先显示要搜索的图片
serach_res = api.search(image_file=File(face_search_img),
outer_id='test2')
detec_res = api.detect(image_file=File(face_search_img),
return_attributes='emotion')
print_result("搜索结果", serach_res)
print_result("探测结果", detec_res)
#serach_list用于将serach_res中的faces部分信息转换为列表
#输出特定的face_token
serach_result = serach_res['results']
#输出对应的置信值
serach_confidence = serach_result[0]['confidence']
face_token = serach_result[0]['face_token']
threshold_up = serach_res['thresholds']['1e-5']
detec_res = detec_res['faces']
down_emotion_detect = (
detec_res[0]['attributes']['emotion']['disgust'] +
detec_res[0]['attributes']['emotion']['anger'] +
detec_res[0]['attributes']['emotion']['fear'] +
detec_res[0]['attributes']['emotion']['sadness']) / 4
up_emotion_detect = (
detec_res[0]['attributes']['emotion']['happiness'] +
detec_res[0]['attributes']['emotion']['neutral']) / 2
if (serach_confidence >= threshold_up):
print(face_token + '到位了')
self.data_manager.add_arrive_data(
face_token,
QtCore.QDateTime.currentDateTime().toString(
'yyyy-MM-dd hh:mm:ss'))
self.data_manager.add_emotion_data(
face_token, (up_emotion_detect / down_emotion_detect))
self.data_manager.write_data_to_csv()
self.data_manager.write_emotion_to_csv()
QtWidgets.QMessageBox.information(
self, '成功', face_token + "您已经签到成功",
QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No,
QtWidgets.QMessageBox.Yes)
def register():
# 获取图片文件 name = register
img = request.files.get('register')
path = basedir + "\static\\"
img_name = 'register.jpg'
file_path = path + img_name
img.save(file_path)
# 人脸检测：https://console.faceplusplus.com.cn/documents/4888373
detect_result = api.detect(image_file=File(img_register),
return_attributes="")
# print_result (print_function_title ("人脸检测"), detect_result)
# 获取face_token
detect_result = json.dumps(eval(str(detect_result)))
detect_result = json.loads(detect_result)
# 如果成功，返回值里有faces字段
if 'faces' in detect_result:
faces = json.dumps(eval(str(detect_result['faces'])))
faces = faces[1:-1]
faces = json.loads(faces)
face_token = faces['face_token']
print('新上传的face_token' + face_token)
# 根据face_token设置user_id
user_id = request.form['user_id']
# Face set_user_id
set_id_result = api.face.setuserid(face_token=str(face_token),
user_id=str(user_id))
print('set_user_id的结果：', set_id_result)
# 将新建的用户加入FaceSet
add_face_result = api.faceset.addface(face_tokens=face_token,
outer_id='check on work')
add_face_result = json.dumps(eval(str(add_face_result)))
add_face_result = json.loads(add_face_result)
print('新增数目：', add_face_result['face_added'])
return '新学生 ' + user_id + ' 注册成功'
# 请求错误（有错误码）
elif 'error_message' in detect_result:
error = detect_result['error_message']
print(error)
return '识别失败，请重试！'
# 其他错误（网络等原因）
else:
return '未知错误'
def savelandmarks_facepp(filename):
api = API()
video = loadvideo(filename)
landmarks = []
print('Number of Frames: %d' % (len(video)))
cnt = 0
for image in video:
cv2.imwrite('temp.jpg', image)
landmarks.append(get_landmarks_facepp(File('./temp.jpg'), api))
cnt = cnt + 1
print('%f%%' % (cnt / len(video) * 100))
# Dump landmarks to file
with open(filename.split('.')[0] + '.facepp', 'wb') as f:
pickle.dump(landmarks, f)
def identify(self):
try:
search_result = self.api.search(image_file=File(self.img_file),
outer_id=self.faceset)
face_confidence = search_result.results[0].confidence
user_name = search_result.results[0].user_id
if face_confidence > 50:
self.main.name.setPlainText(user_name)
self.speaker.speakMsg(user_name + '，你好，欢迎回来')
self.arduino.open_door()
else:
self.speaker.speakMsg("对不起，没有你的记录")
except:
self.popupMsg.popupmsg('人脸检测API调用失败', '人脸检测')
def detect(filename, frame, name=None, savepath=None):
b1 = File(filename)
res = api.detect(
image_file=b1
) #, return_attributes="gender,age,smiling,headpose,facequality,"
# "blur,eyestatus,emotion,ethnicity,beauty,"
# "mouthstatus,skinstatus")
#print_result(printFuctionTitle("人脸检测"), res)
for face in res['faces']:
rect = face['face_rectangle'] #人脸所在的矩形框
w, h, l, t = rect['width'], rect['height'], rect['left'], rect['top']
#cv2.rectangle(frame,(rect['left'],rect['top']),(rect['left']+rect['width'],rect['top']+rect['height']),(0,255,0), 5)
#cv2.imwrite(filename,frame)
if name:
cv2.imwrite(savepath + name + '.png', frame[t:t + h,
l:l + w]) #保存人脸图像
return w, h, l, t
def train(path2):
api.faceset.delete(outer_id='STFace', check_empty=0)
# 1.创建一个faceSet
ret = api.faceset.create(outer_id='STFace')
# path2="/home/marigolci/Downloads/face_recognition-master/examples/FacialSet"
for name in os.listdir(path2):
faceResStr = ""
res = api.detect(image_file=File(os.path.join(path2, str(name))))
faceList = res["faces"]
for index in range(len(faceList)):
# if(index==0):
faceResStr = faceResStr + faceList[index]["face_token"]
# else:
# faceResStr = faceResStr + ","+faceList[index]["face_token"]
api.faceset.addface(outer_id='STFace', face_tokens=faceResStr)
connection[name] = faceResStr
name = os.path.splitext(name)[0]
def build_gallery_probe(gallery_imgs, probe_imgs):
# gallery info
# Key: Face Token
# Value: Person id
gallery = {}
# probe info
# Key: Face Token
# Value: Person id
probe = {}
# init api
api = API()
# delete useless face_set
api.faceset.delete(outer_id='face_recognition', check_empty=0)
# # 1.create faceSet
ret = api.faceset.create(outer_id='face_recognition')
#
# # 2.add pics to faceSet(face_token)
img_path = gb.glob(gallery_imgs)
for img in img_path:
faceResStr = ""
res = api.detect(image_file=File(img))
# get person's name
person_name = img[-12:][0:5]
print(person_name)
print(res)
faceList = res["faces"]
for index in range(len(faceList)):
gallery[faceList[index]["face_token"]] = person_name
if index == 0:
faceResStr = faceResStr + faceList[index]["face_token"]
else:
faceResStr = faceResStr + "," + faceList[index]["face_token"]
api.faceset.addface(outer_id='face_recognition',
face_tokens=faceResStr)
print(gallery)
# build probe set
img_path = gb.glob(probe_imgs)
for img in img_path:
faceResStr = ""
res = api.detect(image_file=File(img))
# get person's name
person_name = img[-12:][0:5]
print(person_name)
print(res)
faceList = res["faces"]
probe[faceList[0]["face_token"]] = person_name
print("probe")
print(probe)
json.dump(gallery, open('gallery.json', 'w'))
json.dump(probe, open('probe.json', 'w'))
''' '''
with open("./gallery.json", 'r') as g:
gallery = json.load(g)
with open("./probe.json", 'r') as p:
probe = json.load(p)
genuine_scores = []
imposter_scores = []
for gallery_key, gallery_value in gallery.items():
for probe_key, probe_value in probe.items():
result = api.compare(face_token1=gallery_key,
face_token2=probe_key)
res = {}
res['gallery'] = gallery_value
res['probe'] = probe_value
res['confidence'] = result['confidence']
if gallery_value == probe_value:
genuine_scores.append(res)
else:
imposter_scores.append(res)
json.dump(genuine_scores, open('genuine_scores.json', 'w'))
json.dump(imposter_scores, open('imposter_scores.json', 'w'))
def transmit():
# 获取图片文件 name = transmit
img = request.files.get('transmit')
# 定义一个图片存放的位置 存放在static下面
path = basedir + "\static\\"
# 图片名称
img_name = 'transmit.jpg'
# 图片path和名称组成图片的保存路径
file_path = path + img_name
# 保存图片
img.save(file_path)
# 开始搜索相似脸人脸信息
search_result = api.search(image_file=File(img_transmit),
outer_id='check on work')
print_result('search', search_result)
# 整个json转化为字符串
search_result = json.dumps(eval(str(search_result)))
# 整个json转化为字典
search_result = json.loads(search_result)
print(search_result)
# 如果成功（返回值中有results字段），返回user_id
if 'results' in search_result:
# results转化为字符串
results = json.dumps(eval(str(search_result['results'])))
# results去除首位的方括号
results = results[1:-1]
# result转化为元组
results = json.loads(results)
user_id = results["user_id"]
# 判断结果可信度
if results['confidence'] > 80:
# todo 签到记录写入数据库
# SQL 插入语句
# mysql 初始化
db = pymysql.connect(host="localhost",
user="******",
password="******",
db="check_on_work")
# 加入数据库作为历史纪录
try:
with db.cursor() as cursor:
sql = "INSERT INTO `register` (`student_id`) VALUES ('%s')" % (
user_id)
cursor.execute(sql)
db.commit()
finally:
db.close()
pass
# 返回user_id
return '签到成功：' + user_id
else:
return '验证失败'
# 失败返回错误信息
elif 'error_message' in search_result:
error = search_result['error_message']
return error
elif not search_result['faces']:
return '未识别到人脸'
else:
return '未知错误'
luke_bowsher = './KnightbookScraping/Grade-12/Male/Luke Bowsher_male_Grade-12.png'
matt_phua = './KnightbookScraping/Grade-12/Male/Matthew Phua_male_Grade-12.png'
abby_doll = './KnightbookScraping/Grade-12/Female/Abigail Doll_female_Grade-12.png'
neha_tarkad = './KnightbookScraping/Grade-12/Female/Neha Tarakad Juneja_female_Grade-12.png'
max_dostart = './KnightbookScraping/Grade-12/Male/Max Dostart-Meers Dostart Meers_male_Grade-12.png'
paras_arora = './KnightbookScraping/Grade-12/Male/Paras Arora_male_Grade-12.png'
joseph_ma = './KnightbookScraping/Grade-12/Male/Joseph Ma Chou_male_Grade-12.png'
#此方法专用来打印api返回的信息
def print_result(hit, result):
print(hit)
print('\n'.join(" " + i for i in pformat(result, width=75).split('\n')))
def printFuctionTitle(title):
return "\n" + "-" * 60 + title + "-" * 60
# 初始化对象，进行api的调用工作
api = API()
#人脸融合：https://console.faceplusplus.com.cn/documents/20813963
#template_rectangle参数中的数据要通过人脸检测api来获取
mergeFace_res = api.mergeface(template_file=File(matt_phua),
merge_file=File(santa))
print_result("mergeFace", mergeFace_res)
# Start Fusion
PythonSDK.ImagePro.ImageProCls.getMergeImg(mergeFace_res["result"])
# 2,向faceSet中添加人脸信息(face_token)
# 3，开始搜索
# 初始化对象，进行api的调用工作
api = API()
# 删除无用的人脸库，这里删除了，如果在项目中请注意是否要删除
api.faceset.delete(outer_id='testset', check_empty=0)
# 1.创建一个faceSet
ret = api.faceset.create(outer_id='testset',
display_name='测试用set',
tags='useless')
# 2.向faceSet中添加人脸信息(face_token)
faceResStr = ""
res = api.detect(image_file=File(faceSet_img))
faceList = res["faces"]
for index in range(len(faceList)):
if index == 0:
faceResStr = faceResStr + faceList[index]["face_token"]
else:
faceResStr = faceResStr + "," + faceList[index]["face_token"]
api.faceset.addface(outer_id='testset', face_tokens=faceResStr, user_id='bbb')
# 3.开始搜索相似脸人脸信息
search_result = api.search(image_file=File(face_search_img),
outer_id='testset')
print_result('search', search_result)
#res = api.detect(image_url=detech_img_url, return_attributes="gender,age,smiling,headpose,facequality,"
#"blur,eyestatus,emotion,ethnicity,beauty,"
#"mouthstatus,skinstatus")
#print_result(printFuctionTitle("人脸检测"), res)
img = cv2.imread('./imgResource/img.jpg')
cv2.namedWindow('raw pic')
cv2.imshow('raw pic', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
faceStr = []
face_string = ''
face_string_2 = ''
api.faceset.delete(outer_id='test2', check_empty=0)
ret = api.faceset.create(outer_id='test2')
res = api.detect(image_file=File(faceSet_img), return_attributes='emotion')
print_result("detect resule", res)
faceList = res["faces"]
print(len(faceList))
for i in range(len(faceList)):
api.faceset.addface(outer_id='test2',
face_tokens=faceList[i]["face_token"])
print(faceList[i]["face_token"])
for i in range(len(faceList)):
faceStr.append(faceList[i]["face_token"])
r = api.faceset.getdetail(outer_id='test2')
print_result('faceset结果', r)
# # 开始抠像
# PythonSDK.ImagePro.ImageProCls.getSegmentImg("./imgResource/demo-segment.b64")
# -----------------------------------------------------------证件识别部分-------------------------------------------
# 身份证识别:https://console.faceplusplus.com.cn/documents/5671702
# ocrIDCard_res = api.ocridcard(image_url="https://gss0.bdstatic.com/94o3dSag_xI4khGkpoWK1HF6hhy/baike/"
# "c0%3Dbaike80%2C5%2C5%2C80%2C26/sign=7a16a1be19178a82da3177f2976a18e8"
# "/902397dda144ad34a1b2dcf5d7a20cf431ad85b7.jpg")
# print_result('ocrIDCard', ocrIDCard_res)
# 银行卡识别:https://console.faceplusplus.com.cn/documents/10069553
# ocrBankCard_res = api.ocrbankcard(image_url="http://pic.5tu.cn/uploads/allimg/1107/191634534200.jpg")
# print_result('ocrBankCard', ocrBankCard_res)
# -----------------------------------------------------------图像识别部分-------------------------------------------
# 人脸融合：https://console.faceplusplus.com.cn/documents/20813963
# template_rectangle参数中的数据要通过人脸检测api来获取
# mergeFace_res = api.mergeface(template_file=File(segment_img), merge_file=File(merge_img),
# template_rectangle="130,180,172,172")
# print_result("mergeFace", mergeFace_res)
#
# # 开始融合
# PythonSDK.ImagePro.ImageProCls.getMergeImg(mergeFace_res["result"])
# 人脸关键点检测： https://api-cn.faceplusplus.com/facepp/v1/face/thousandlandmark
landmarks = api.thousandlandmark(image_file=File(face_search_img),
return_landmark="all")
print_result(printFuctionTitle("人脸关键点检测"), landmarks)