def verify(self, thresh): # 识别比对
global VIDEO_IMAGE
global isPass
global first
new_person = 0 # newperson = 1时则说明有人来
knownflag = 0
unknownflag = 0
currentunknownum = 0
currentknownum = 0
idname = [] # 可显示id照片的编号列表
currentDate = datetime.now().strftime('%Y_%m_%d')
currentTime = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
currentTime_img = datetime.now().strftime('%Y-%m-%d %H-%M-%S')
# if self.pic_time != None and (datetime.datetime.now() - self.pic_time).seconds > pic_time:
# self.image_clear()
image1 = None
for i in range(VIDEO_IMAGE.qsize()):
image1 = VIDEO_IMAGE.get()
#image1 = cv2.imread('./user_pic/13800000001.bmp') # 加载图片测试
if image1 is not None:
print('get image')
image1 = cv2.flip(image1, 1)
width = int(image1.shape[1] / 2)
height = int(image1.shape[0] / 2)
image = cv2.resize(image1.copy(), (width, height))
try:
# total_start = time.time()
bboxs = self.naivedlib.getALLFaces(image)
# bbox_end = time.time()
print('bbox detect took ')
bboxs = [dlib.rectangle(int(bbox.left() * downsample_ratio), int(bbox.top() * downsample_ratio),
int(bbox.right() * downsample_ratio), int(bbox.bottom() * downsample_ratio))
for bbox in bboxs]
facenum = len(bboxs)
if bboxs is not None:
new_person = 1
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
for bbox in bboxs:
# 对每个人脸进行操作
print('bbox is detected')
query = []
left_eye, right_eye = self.naivedlib.get_eyes(image1, bbox)
start = time.time()
save_croped, crop_image = crop_rotate_part(image1[:, :, ::-1], bbox, left_eye, right_eye,
bbox.width() * 0.895)
save_croped = cv2.cvtColor(save_croped, cv2.COLOR_BGR2RGB)
# bbox_temp = [bbox.left(),bbox.top(),bbox.right(),bbox.bottom()]
# crop_image = crop_only(image1,bbox_temp)
# cv2.imwrite('image.jpg',crop_image)
end = time.time()
print('\tpreprocess took {}'.format(end - start))
flip_image = cv2.flip(crop_image.copy(), 1)
crop_image = proc_img(crop_image, is_whiten=False)
flip_image = proc_img(flip_image, is_whiten=False)
query.append(crop_image[0])
query.append(flip_image[0])
prob_image = np.asarray(query)
start = time.time()
feature = self.extractor.extract_feature(prob_image)
end = time.time()
print('\textract feature took {}'.format(end - start))
feature = feature / np.linalg.norm(feature)
idx_list = self.search_eign.search(feature) # 获取搜索后的排列
idx_list = [idx_list[0][i] for i in range(idx_list.shape[1])]
score_list = []
name_list = []
for idx in idx_list:
score = 1 - spatial.distance.cosine(feature, self.feature_data[int(idx)])
name = {value: key for key, value in self.index_dict.items()}[int(idx)]
score_list.append(score)
name_list.append(name)
#print(name_list)
#print(score_list)
score = 1 - spatial.distance.cosine(feature, self.feature_data[int(idx_list[0])])
thresh = float(thresh)
# print(thresh)
if score > thresh:
knownflag = 1
name = name_list[0]
if self.knownum == 0:
self.knownum = 1
first = 1
self.summary["comtime"] = currentTime
self.knownlist.append(name)
if not os.path.exists('./image_saved/{}'.format(currentDate)):
os.mkdir('./image_saved/{}'.format(currentDate))
os.mkdir('./image_saved/{}/unknown'.format(currentDate))
os.mkdir('./image_saved/{}/known'.format(currentDate))
cv2.imwrite('./image_saved/{}/known/{}_{}.jpg'.format(currentDate, currentTime_img,
name),
save_croped)
idname.append(name) # 当前帧中的已知访客name
print("欢迎" + name)
print("推送")
push_info = self.push_summary_known(name)
s = json.dumps(push_info)
r = requests.post(faceverify_post_url, data=s)
print(r)
#face.insert_summary()
# print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
else:
if name not in self.knownlist:
self.knownlist.append(name)
self.knownum = self.knownum + 1
cv2.imwrite('./image_saved/{}/known/{}_{}.jpg'.format(currentDate, currentTime_img,
name),
save_croped)
idname.append(name) # 当前帧中的已知访客name
print("欢迎" + name)
print("推送")
push_info = self.push_summary_known(name)
s = json.dumps(push_info)
r = requests.post(faceverify_post_url, data=s)
print(r)
#face.insert_summary()
# print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
else:
unknownflag = 1
if self.unknownum == 0:
print("第一个未知人员")
first = 1
currentunknownum = currentunknownum + 1
self.unknownum = 1
if self.summary == []:
self.summary["comtime"] = currentTime
if not os.path.exists('./image_saved/{}'.format(currentDate)):
os.mkdir('./image_saved/{}'.format(currentDate))
os.mkdir('./image_saved/{}/unknown'.format(currentDate))
os.mkdir('./image_saved/{}/known'.format(currentDate))
cv2.imwrite('./image_saved/{}/unknown/{}_{}.jpg'.format(currentDate, currentTime_img,
self.unknownum), save_croped)
self.unknownfeature = feature[np.newaxis, :]
print("推送")
push_info = self.push_summary_unknown()
s = json.dumps(push_info)
r = requests.post(faceverify_post_url, data=s)
print(r)
# face.insert_summary()
else:
unthresh = 0.5
self.search_eign_unknown = Search_engin(self.unknownfeature) # 未知访客搜索
self.search_eign_unknown.train()
unknown_idx = self.search_eign_unknown.search(feature)
unknown_idx = [unknown_idx[0][i] for i in range(unknown_idx.shape[1])]
print(unknown_idx)
uscore = 1 - spatial.distance.cosine(feature, self.unknownfeature[int(unknown_idx[0])])
print(uscore)
if uscore < unthresh: # 第一次识别出该未知人员
self.unknownum = self.unknownum + 1
currentunknownum = currentunknownum + 1
# self.unknownlist[self.unknownum] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
cv2.imwrite(
'./image_saved/{}/unknown/{}_{}.jpg'.format(currentDate, currentTime_img,
self.unknownum), save_croped)
self.unknownfeature = np.vstack((self.unknownfeature, feature))
print("推送")
push_info = self.push_summary_unknown()
s = json.dumps(push_info)
r = requests.post(faceverify_post_url, data=s)
print(r)
#face.insert_summary()
else:
new_person = 0
except:
pass
else:
print("图片为空！")
return (new_person)
def verify_img(img):
image1 = img
p_id_list = []
api_unknown = 0
if image1 is not None:
with open('./data/index_enter.pkl', 'rb') as f:
index_dict = pkl.load(f)
with open('./data/database_enter.npy', 'rb') as f:
feature_data = np.load('./data/database_enter.npy')
search_eign = Search_engin(feature_data)
search_eign.train()
#print('get image')
image1 = cv2.flip(image1, 1)
width = int(image1.shape[1] / 2)
height = int(image1.shape[0] / 2)
image = cv2.resize(image1.copy(), (width, height))
try:
bboxs = naivedlib.getALLFaces(image)
print('bbox detect took ')
bboxs = [dlib.rectangle(int(bbox.left() * downsample_ratio), int(bbox.top() * downsample_ratio),
int(bbox.right() * downsample_ratio), int(bbox.bottom() * downsample_ratio))
for bbox in bboxs]
facenum = len(bboxs)
if bboxs is not None:
for bbox in bboxs:
# 对每个人脸进行操作
#print('bbox is detected')
query = []
left_eye, right_eye = naivedlib.get_eyes(image1, bbox)
start = time.time()
save_croped, crop_image = crop_rotate_part(image1[:, :, ::-1], bbox, left_eye, right_eye,
bbox.width() * 0.895)
save_croped = cv2.cvtColor(save_croped, cv2.COLOR_BGR2RGB)
end = time.time()
print('\tpreprocess took {}'.format(end - start))
flip_image = cv2.flip(crop_image.copy(), 1)
crop_image = proc_img(crop_image, is_whiten=False)
flip_image = proc_img(flip_image, is_whiten=False)
query.append(crop_image[0])
query.append(flip_image[0])
prob_image = np.asarray(query)
start = time.time()
feature = extractor.extract_feature(prob_image)
end = time.time()
print('\textract feature took {}'.format(end - start))
feature = feature / np.linalg.norm(feature)
idx_list = search_eign.search(feature) # 获取搜索后的排列
idx_list = [idx_list[0][i] for i in range(idx_list.shape[1])]
score_list = []
name_list = []
for idx in idx_list:
score = 1 - spatial.distance.cosine(feature, feature_data[int(idx)])
name = {value: key for key, value in index_dict.items()}[int(idx)]
score_list.append(score)
name_list.append(name)
#print(name_list)
#print(score_list)
score = 1 - spatial.distance.cosine(feature, feature_data[int(idx_list[0])])
#print(thresh)
if score > thresh:
p_id_list.append(name_list[0])
else:
api_unknown = api_unknown + 1
except:
pass
#print(p_id_list)
return (p_id_list, api_unknown)
def verify(self, thresh):
global VIDEO_IMAGE
global isPass
knownflag = 0
unknownflag = 0
currentunknownum = 0
currentknownum = 0
idname = [] # 可显示id照片的编号列表
currentDate = datetime.now().strftime('%Y_%m_%d')
currentTime = datetime.now().strftime('%Y-%m-%d %H-%M-%S')
if currentDate != self.datetime: #新日期触发
self.today_clean(self.datetime)
self.datetime = currentDate
web = Web_Browser()
web.setModal(False)
web.setWindowTitle(_translate("Face_Verification", "比对结果", None))
# if self.pic_time != None and (datetime.datetime.now() - self.pic_time).seconds > pic_time:
# self.image_clear()
for i in range(probe_image.qsize()):
image1 = probe_image.get()
#image1 = cv2.imread('./card/2.jpg') # 加载图片测试
if image1 is not None:
print('get image')
image1 = cv2.flip(image1, 1)
width = int(image1.shape[1] / 2)
height = int(image1.shape[0] / 2)
image = cv2.resize(image1.copy(), (width, height))
try:
#total_start = time.time()
bboxs = self.naivedlib.getALLFaces(image)
#bbox_end = time.time()
print('bbox detect took ')
bboxs = [dlib.rectangle(int(bbox.left() * downsample_ratio), int(bbox.top() * downsample_ratio),
int(bbox.right() * downsample_ratio), int(bbox.bottom() * downsample_ratio))
for bbox in bboxs]
facenum = len(bboxs)
#print(facenum)#人脸数
# index_list = []
#idname = [] # 可显示id照片的编号列表
if bboxs is not None:
print(time.strftime('%H:%M:%S', time.localtime(time.time())))
for bbox in bboxs:
# 对每个人脸进行操作
print('bbox is detected')
query = []
left_eye, right_eye = self.naivedlib.get_eyes(image1, bbox)
start = time.time()
save_croped, crop_image = crop_rotate_part(image1[:, :, ::-1], bbox, left_eye, right_eye,
bbox.width() * 0.895)
save_croped = cv2.cvtColor(save_croped, cv2.COLOR_BGR2RGB)
# bbox_temp = [bbox.left(),bbox.top(),bbox.right(),bbox.bottom()]
# crop_image = crop_only(image1,bbox_temp)
# cv2.imwrite('image.jpg',crop_image)
end = time.time()
print('\tpreprocess took {}'.format(end - start))
flip_image = cv2.flip(crop_image.copy(), 1)
crop_image = proc_img(crop_image, is_whiten=False)
flip_image = proc_img(flip_image, is_whiten=False)
query.append(crop_image[0])
query.append(flip_image[0])
prob_image = np.asarray(query)
start = time.time()
feature = self.extractor.extract_feature(prob_image)
end = time.time()
print('\textract feature took {}'.format(end - start))
feature = feature / np.linalg.norm(feature)
idx_list = self.search_eign.search(feature) # 获取搜索后的排列
idx_list = [idx_list[0][i] for i in range(idx_list.shape[1])]
score_list = []
name_list = []
for idx in idx_list:
score = 1 - spatial.distance.cosine(feature, self.feature_data[int(idx)])
name = {value: key for key, value in self.index_dict.items()}[int(idx)]
score_list.append(score)
name_list.append(name)
print(name_list)
print(score_list)
#person_list = [name.split('_')[0] for name in name_list]
#person_set = list(set(person_list))
#person_num = len(person_set)
#print(person_num)
score = 1 - spatial.distance.cosine(feature, self.feature_data[int(idx_list[0])])
thresh = float(thresh)
print(thresh)
if score > thresh:
knownflag = 1
name = name_list[0]
if self.knownum == 0:
self.knownum = 1
if not os.path.exists('./image_saved/{}'.format(currentDate)):
os.mkdir('./image_saved/{}'.format(currentDate))
os.mkdir('./image_saved/{}/unknown'.format(currentDate))
os.mkdir('./image_saved/{}/known'.format(currentDate))
cv2.imwrite('./image_saved/{}/known/{}_{}.jpg'.format(currentDate, currentTime,
name),
save_croped)
idname.append(name) #当前帧中的已知访客name
print("欢迎" + name)
print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
else:
img_list = os.listdir(self.known_pic_path) # 返回指定的文件夹包含的文件或文件夹的名字的列表
knownlist = [item.split('.')[0] for item in img_list]
knownlist = [item.split('_')[1] for item in knownlist]
if name not in knownlist:
cv2.imwrite('./image_saved/{}/known/{}_{}.jpg'.format(currentDate, currentTime,
name),
save_croped)
idname.append(name) #当前帧中的已知访客name
self.knownum = self.knownum + 1
print("欢迎" + name)
print(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())))
else:
unknownflag = 1
if self.unknownum == 0:
print("第一个未知人员")
currentunknownum = currentunknownum + 1
self.unknownum = 1
#self.unknownlist[self.unknownum] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if not os.path.exists('./image_saved/{}'.format(currentDate)):
os.mkdir('./image_saved/{}'.format(currentDate))
os.mkdir('./image_saved/{}/unknown'.format(currentDate))
os.mkdir('./image_saved/{}/known'.format(currentDate))
cv2.imwrite('./image_saved/{}/unknown/{}_{}.jpg'.format(currentDate, currentTime,
self.unknownum),save_croped)
self.unknownfeature = feature[np.newaxis, :]
else:
unthresh = 0.6
self.search_eign_unknown = Search_engin(self.unknownfeature) # 未知访客搜索
self.search_eign_unknown.train()
unknown_idx = self.search_eign_unknown.search(feature)
unknown_idx = [unknown_idx[0][i] for i in range(unknown_idx.shape[1])]
print(unknown_idx)
uscore = 1 - spatial.distance.cosine(feature, self.unknownfeature[int(unknown_idx[0])])
print(uscore)
if uscore < unthresh: # 当天第一次识别出该未知人员
self.unknownum = self.unknownum + 1
currentunknownum = currentunknownum + 1
#self.unknownlist[self.unknownum] = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
cv2.imwrite('./image_saved/{}/unknown/{}_{}.jpg'.format(currentDate, currentTime,
self.unknownum), save_croped)
self.unknownfeature = np.vstack((self.unknownfeature, feature))
#print("第二个")
#print(time.strftime('%H:%M:%S', time.localtime(time.time())))
if knownflag == 1:
#print(idname)
currentknownum = len(idname) # 当前帧已知访客数量
#self.knownum = self.knownum + currentknownum
for i in range(currentknownum):
self.id_image = cv2.imread('./user_pic/{}.bmp'.format(idname[i]))
self.display_id_image(self.knownum)
if unknownflag == 1:
print("未知人总数:{}".format(self.unknownum))
# print("当前帧未知人数:{}".format(currentunknownum))
with open(self.unknownfeature_path, 'wb') as f:
self.unknownfeature = np.asarray(self.unknownfeature)
np.save(f, self.unknownfeature)
for i in range(currentunknownum):
self.id_image2 = cv2.imread('./image_saved/{}/unknown/{}_{}.jpg'.format(currentDate, currentTime, self.unknownum))
self.display_id_image2(self.unknownum)
#print(time.strftime('%H:%M:%S', time.localtime(time.time())))
else:
web.write_result("未检测到人脸", 'red')
web.exec_()
except:
pass
else:
pass