def load_all_data(self):
# 将以前标记的数据全部读入(直接读入的是特征), 用LSH Forest保存,方便计算距离
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
try:
all_pic_feature, all_label = self.load_train_data(
self.all_pic_feature_data_folder)
train_label = np.asarray(all_label)
if len(all_pic_feature) == len(
train_label) and len(train_label) > 0:
start = time.time()
self.lshf.fit(all_pic_feature, train_label)
self.all_pic_feature = list(all_pic_feature)
self.all_labels = list(train_label)
end = time.time()
self.load_time = end
self.user_count = Counter(self.all_labels)
log_file.write('\t'.join(
map(str, [
self.user_count, 'fit all data time :', (end - start)
])) + '\n')
log_file.close()
except:
traceback.print_exc()
log_file.close()
return
def extract_feature(face_array):
'''
先计算角度, 满足条件后再进行对齐和识别
:param face_array: RGB读入的数据(dtype=uint8) [0-255]
:return:
'''
start = time.time()
current_day = get_current_day()
current_time = get_current_time()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
blur_sign, blur_var = is_blur(face_array)
if blur_sign:
print 'blur_filter', blur_var
feature_vector = np.reshape(np.asarray([0] * feature_dim), (1, feature_dim))
log_file.write('\t'.join(map(str, [current_time, 'blur', 'not_process', 'recognition_time :',
(time.time() - start)])) + '\n')
return feature_vector
need_process = check_face_img(face_img=face_array)
if not need_process:
feature_vector = np.reshape(np.asarray([0] * feature_dim), (1, feature_dim))
log_file.write('\t'.join(map(str, [current_time, 'pose', 'not_process', 'recognition_time :',
(time.time() - start)])) + '\n')
return feature_vector
# 自己检测对齐
face_align_array = align_face_rgb_array(face_array, bb=None)
face_align_array = normalize_rgb_array(cv2.resize(face_align_array, (size, size)))
feature_vector = get_Conv_FeatureMap([face_align_array, 0])[0].copy()
log_file.write('\t'.join(map(str, [current_time, 'process', 'recognition_time :', (time.time() - start)])) + '\n')
log_file.close()
return feature_vector
def add_one_new_pic(self, pic_path, label):
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
try:
# 读入数据时已经转换成需要的尺寸
result = self.extract_pic_feature(pic_path)
if result == None:
return False
face_pic, pic_feature = result
self.add_one_pic(pic_feature, label)
pic_name = os.path.split(pic_path)[1]
this_person_pic_folder = os.path.join(self.all_pic_data_folder,
label)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder, label)
if not os.path.exists(this_person_pic_folder):
os.makedirs(this_person_pic_folder)
if not os.path.exists(this_person_feature_folder):
os.makedirs(this_person_feature_folder)
# 直接存储图片对应的特征, 同时保存图片文件
this_pic_feature_name = os.path.join(this_person_feature_folder,
pic_name + '.p')
msgpack_numpy.dump(pic_feature, open(this_pic_feature_name, 'wb'))
this_pic_face_name = os.path.join(this_person_pic_folder,
pic_name + '.jpg')
cv2.imwrite(this_pic_face_name, face_pic)
log_file.write(
'\t'.join(map(str, [pic_path, this_pic_face_name])) + '\n')
return True
except:
traceback.print_exc()
return False
def cal_nearest_sim(self, current_feature):
nearest_sim_list = []
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
try:
length = len(self.nearest)
for k in range(length):
try:
person_name, pre_feature = self.nearest[k]
# 不在考虑时间, 只考虑图片的相似度
this_sim = pw.cosine_similarity(
np.reshape(np.asarray(pre_feature),
(1, len(pre_feature))),
np.reshape(np.asarray(current_feature),
(1, len(current_feature))))
nearest_sim_list.append(
(this_sim, verification_model.predict(this_sim),
person_name))
except:
log_file.write('cal_nearest_sim error' + '\n')
traceback.print_exc()
continue
return nearest_sim_list
except:
traceback.print_exc()
return nearest_sim_list
def add_one_new_pic(self, pic_path, label):
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
try:
# 读入数据时已经转换成需要的尺寸
result = self.extract_pic_feature(pic_path)
if result == None:
return False
face_pic, pic_feature = result
self.add_one_pic(pic_feature, label)
pic_name = os.path.split(pic_path)[1]
this_person_pic_folder = os.path.join(self.all_pic_data_folder, label)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, label)
if not os.path.exists(this_person_pic_folder):
os.makedirs(this_person_pic_folder)
if not os.path.exists(this_person_feature_folder):
os.makedirs(this_person_feature_folder)
# 直接存储图片对应的特征, 同时保存图片文件
this_pic_feature_name = os.path.join(this_person_feature_folder, pic_name + '.p')
msgpack_numpy.dump(pic_feature, open(this_pic_feature_name, 'wb'))
this_pic_face_name = os.path.join(this_person_pic_folder, pic_name + '.jpg')
cv2.imwrite(this_pic_face_name, face_pic)
log_file.write('\t'.join(map(str, [pic_path, this_pic_face_name]))+'\n')
return True
except:
traceback.print_exc()
return False
def add_all_new_pic(self):
'''
将从上次加载数据到当前新增的文件都加载到LSH Forest(有可能是新增加一个人,还有可能是对已有的人增加新图片)
遍历文件夹(self.all_pic_data_folder),根据文件的时间判断是否需要加入该图片
用户新加入的图片先进行人脸检测, 如果能够检测到人脸，使用检测结果， 否则使用用户的原始图片
'''
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
start = time.time()
person_list = os.listdir(self.all_pic_data_folder)
add_num = 0
for person in person_list:
if self.must_same_str in person or self.maybe_same_str in person or self.new_person_str in person:
continue
person_path = os.path.join(self.all_pic_data_folder, person)
if not os.path.isdir(person_path):
continue
pic_list = os.listdir(person_path)
for pic in pic_list:
pic_path = os.path.join(person_path, pic)
last_modify_time = os.stat(pic_path).st_atime
if last_modify_time > self.load_time:
# 请求本地服务
request = {
"label": person,
"request_type": 'add',
"one_pic_feature": pic_path
}
url = "http://127.0.0.1:%d/" % port
result = image_request(request, url)
try:
add_flag = json.loads(result)["add"]
if not add_flag: # 加载失败
log_file.write('\t'.join(
map(str, ['no add file :', pic_path])) + '\n')
else:
add_num += 1
except:
log_file.write(
'\t'.join(map(str, ['no add file :', pic_path])) +
'\n')
traceback.print_exc()
continue
add_num += 1
end = time.time()
if add_num > 0:
self.load_time = end
log_file.write(
'\t'.join(map(str, ['self.load_time', self.load_time])) + '\n')
log_file.write('\t'.join(
map(str, [
'add pic num :', add_num, 'Dynamic increase time :',
(end - start)
])) + '\n')
log_file.close()
else:
log_file.close()
def check_face_img(face_img):
# pose_predict(姿势): [[pitch, yaw, roll]](Pitch: 俯仰; Yaw: 摇摆; Roll: 倾斜)
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": str(time.time())
}
result = requests.post(angle_url, data=request)
try:
if result.status_code == 200:
pose_predict = json.loads(result.content)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write('\t'.join(map(str, ['pose filter request'])) +
'\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(
base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write(
'\t'.join(map(str, ['pose filter detect'])) + '\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < pitch_threshold and math.fabs(
yaw) < yaw_threshold and math.fabs(
roll) < roll_threshold:
log_file.write('\t'.join(
map(str, ['pose not filter',
str(pose_predict[0])])) + '\n')
log_file.close()
return True
else:
log_file.write('\t'.join(
map(str,
['pose filter threshold',
str(pose_predict[0])])) + '\n')
log_file.close()
return False
else:
return False
except:
traceback.print_exc()
log_file.close()
return False
def check_face_img(self, face_img, image_id):
# 计算角度
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
# 姿势检测
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": image_id,
}
url = "http://%s:%d/" % (check_ip, check_port)
result = image_request(request, url)
try:
pose_predict = json.loads(result)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write(
'\t'.join(map(str, [image_id, 'pose filter request'])) +
'\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(
base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write(
'\t'.join(map(str, [image_id, 'pose filter detect'])) +
'\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < self.pitch_threshold and \
math.fabs(yaw) < self.yaw_threshold and \
math.fabs(roll) < self.roll_threshold:
log_file.close()
return True
else:
log_file.write('\t'.join(
map(str, [image_id, 'pose filter threshold'])) + '\n')
log_file.close()
return False
except:
traceback.print_exc()
log_file.close()
return False
def add_all_new_pic(self):
'''
将从上次加载数据到当前新增的文件都加载到LSH Forest(有可能是新增加一个人,还有可能是对已有的人增加新图片)
遍历文件夹(self.all_pic_data_folder),根据文件的时间判断是否需要加入该图片
用户新加入的图片先进行人脸检测, 如果能够检测到人脸，使用检测结果， 否则使用用户的原始图片
'''
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
start = time.time()
person_list = os.listdir(self.all_pic_data_folder)
add_num = 0
for person in person_list:
if self.must_same_str in person or self.maybe_same_str in person or self.new_person_str in person:
continue
person_path = os.path.join(self.all_pic_data_folder, person)
if not os.path.isdir(person_path):
continue
pic_list = os.listdir(person_path)
for pic in pic_list:
pic_path = os.path.join(person_path, pic)
last_modify_time = os.stat(pic_path).st_atime
if last_modify_time > self.load_time:
# 请求本地服务
request = {
"label": person,
"request_type": 'add',
"one_pic_feature": pic_path
}
url = "http://127.0.0.1:%d/"%port
result = image_request(request, url)
try:
add_flag = json.loads(result)["add"]
if not add_flag:# 加载失败
log_file.write('\t'.join(map(str, ['no add file :', pic_path]))+'\n')
else:
add_num += 1
except:
log_file.write('\t'.join(map(str, ['no add file :', pic_path]))+'\n')
traceback.print_exc()
continue
add_num += 1
end = time.time()
if add_num > 0:
self.load_time = end
log_file.write('\t'.join(map(str, ['self.load_time', self.load_time]))+'\n')
log_file.write('\t'.join(map(str, ['add pic num :', add_num,
'Dynamic increase time :', (end - start)]))+'\n')
log_file.close()
else:
log_file.close()
def extract_feature(face_array):
'''
:param face_array: RGB读入的数据(dtype=uint8) [0-255]
:return:
'''
start = time.time()
current_day = get_current_day()
current_time = get_current_time()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_align_array = align_face_rgb_array(face_array, bb=dlib.rectangle(0, 0, face_array.shape[1], face_array.shape[0]))
face_align_array = normalize_rgb_array(cv2.resize(face_align_array, (size, size)))
feature_vector = get_Conv_FeatureMap([face_align_array, 0])[0].copy()
log_file.write('\t'.join(map(str, [current_time, 'recognition_time :', (time.time() - start)])) + '\n')
log_file.close()
print (time.time() - start)
return feature_vector
def post(self):
start = time.time()
current_day = get_current_day()
current_time = get_current_time()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
pic_binary_data = self.request.body
open('/tmp/face_recog_tmp/'+str(time.time())+'.jpg', 'wb').write(pic_binary_data)
img_buffer = StringIO(pic_binary_data)
img_array = np.array(Image.open(img_buffer))
img_array_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
print 'img_array_bgr.shape :', img_array_bgr.shape
result = detect_recognize_face(img_array_bgr)
self.write(result)
end = time.time()
log_file.write('\t'.join(map(str, [current_time, 'get_pic', 'all_time :', (end - start)])) + '\n')
log_file.close()
def check_face_img(self, face_img, image_id):
# 计算角度
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
# 姿势检测
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {
"request_type": 'check_pose',
"face_img_str": face_img_str,
"image_id": image_id,
}
url = "http://%s:%d/" % (check_ip, check_port)
result = image_request(request, url)
try:
pose_predict = json.loads(result)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write('\t'.join(map(str, [image_id, 'pose filter request'])) + '\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write('\t'.join(map(str, [image_id, 'pose filter detect'])) + '\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < self.pitch_threshold and \
math.fabs(yaw) < self.yaw_threshold and \
math.fabs(roll) < self.roll_threshold:
log_file.close()
return True
else:
log_file.write('\t'.join(map(str, [image_id, 'pose filter threshold'])) + '\n')
log_file.close()
return False
except:
traceback.print_exc()
log_file.close()
return False
def extract_feature(face_array):
'''
先计算角度, 满足条件后再进行对齐和识别
:param face_array: RGB读入的数据(dtype=uint8) [0-255]
:return:
'''
start = time.time()
current_day = get_current_day()
current_time = get_current_time()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
blur_sign, blur_var = is_blur(face_array)
if blur_sign:
print 'blur_filter', blur_var
feature_vector = np.reshape(np.asarray([0] * feature_dim),
(1, feature_dim))
log_file.write('\t'.join(
map(str, [
current_time, 'blur', 'not_process', 'recognition_time :',
(time.time() - start)
])) + '\n')
return feature_vector
need_process = check_face_img(face_img=face_array)
if not need_process:
feature_vector = np.reshape(np.asarray([0] * feature_dim),
(1, feature_dim))
log_file.write('\t'.join(
map(str, [
current_time, 'pose', 'not_process', 'recognition_time :',
(time.time() - start)
])) + '\n')
return feature_vector
# 自己检测对齐
face_align_array = align_face_rgb_array(face_array, bb=None)
face_align_array = normalize_rgb_array(
cv2.resize(face_align_array, (size, size)))
feature_vector = get_Conv_FeatureMap([face_align_array, 0])[0].copy()
log_file.write('\t'.join(
map(str, [
current_time, 'process', 'recognition_time :', (time.time() -
start)
])) + '\n')
log_file.close()
return feature_vector
def check_face_img(face_img):
# pose_predict(姿势): [[pitch, yaw, roll]](Pitch: 俯仰; Yaw: 摇摆; Roll: 倾斜)
'''
:param face_img: 人脸对应的矩阵
:param image_id: 图片id
:return: 是否进行识别(False:不进行识别)
'''
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
face_img_str = base64.b64encode(msgpack_numpy.dumps(face_img))
request = {"request_type": 'check_pose', "face_img_str": face_img_str, "image_id": str(time.time())}
result = requests.post(angle_url, data=request)
try:
if result.status_code == 200:
pose_predict = json.loads(result.content)["pose_predict"]
if not pose_predict: # 加载失败
log_file.write('\t'.join(map(str, ['pose filter request'])) + '\n')
log_file.close()
return False
else:
pose_predict = msgpack_numpy.loads(base64.b64decode(pose_predict))
if pose_predict == None:
log_file.write('\t'.join(map(str, ['pose filter detect'])) + '\n')
log_file.close()
return False
pitch, yaw, roll = pose_predict[0]
if math.fabs(pitch) < pitch_threshold and math.fabs(yaw) < yaw_threshold and math.fabs(roll) < roll_threshold:
log_file.write('\t'.join(map(str, ['pose not filter', str(pose_predict[0])])) + '\n')
log_file.close()
return True
else:
log_file.write('\t'.join(map(str, ['pose filter threshold', str(pose_predict[0])])) + '\n')
log_file.close()
return False
else:
return False
except:
traceback.print_exc()
log_file.close()
return False
def find_current_new_person_id(self):
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
old_person_id = []
person_list = os.listdir(self.all_pic_data_folder)
for person in person_list:
if person.startswith(self.new_person_str):
tmp = person[len(self.new_person_str):].split('_')
if len(tmp) > 0:
this_id = int(tmp[0])
old_person_id.append(this_id)
if len(old_person_id) == 0:
current_new_person_id = 0
else:
current_new_person_id = max(old_person_id) + 1
log_file.write('\t'.join(map(str, ['current_new_person_id :', current_new_person_id]))+'\n')
log_file.close()
return current_new_person_id
def load_all_data(self):
# 将以前标记的数据全部读入,用LSH Forest保存,方便计算距离
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
train_data, train_label = load_train_data(self.all_pic_data_folder)
if len(train_label) == 0:
return
pic_feature = self.extract_pic_feature(train_data)
start = time.time()
self.lshf.fit(pic_feature, train_label)
self.all_pic_feature = list(pic_feature)
self.all_labels = list(train_label)
end = time.time()
self.load_time = end
self.user_count = Counter(self.all_labels)
log_file.write('\t'.join(map(str, [self.user_count,
'fit all data time :', (end - start)]))+'\n')
log_file.close()
def post(self):
start = time.time()
current_day = get_current_day()
current_time = get_current_time()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
pic_binary_data = self.request.body
open('/tmp/face_recog_tmp/' + str(time.time()) + '.jpg',
'wb').write(pic_binary_data)
img_buffer = StringIO(pic_binary_data)
img_array = np.array(Image.open(img_buffer))
img_array_bgr = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
print 'img_array_bgr.shape :', img_array_bgr.shape
result = detect_recognize_face(img_array_bgr)
self.write(result)
end = time.time()
log_file.write('\t'.join(
map(str, [current_time, 'get_pic', 'all_time :', (end - start)])) +
'\n')
log_file.close()
def load_all_data(self):
# 将以前标记的数据全部读入,用LSH Forest保存,方便计算距离
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
train_data, train_label = load_train_data(self.all_pic_data_folder)
if len(train_label) == 0:
return
pic_feature = self.extract_pic_feature(train_data)
start = time.time()
self.lshf.fit(pic_feature, train_label)
self.all_pic_feature = list(pic_feature)
self.all_labels = list(train_label)
end = time.time()
self.load_time = end
self.user_count = Counter(self.all_labels)
log_file.write('\t'.join(
map(str, [self.user_count, 'fit all data time :',
(end - start)])) + '\n')
log_file.close()
def find_current_new_person_id(self):
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
old_person_id = []
person_list = os.listdir(self.all_pic_data_folder)
for person in person_list:
if person.startswith(self.new_person_str):
tmp = person[len(self.new_person_str):].split('_')
if len(tmp) > 0:
this_id = int(tmp[0])
old_person_id.append(this_id)
if len(old_person_id) == 0:
current_new_person_id = 0
else:
current_new_person_id = max(old_person_id) + 1
log_file.write('\t'.join(
map(str, ['current_new_person_id :', current_new_person_id])) +
'\n')
log_file.close()
return current_new_person_id
def load_all_data(self):
# 将以前标记的数据全部读入(直接读入的是特征), 用LSH Forest保存,方便计算距离
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
try:
all_pic_feature, all_label = self.load_train_data(self.all_pic_feature_data_folder)
train_label = np.asarray(all_label)
if len(all_pic_feature) == len(train_label) and len(train_label) > 0:
start = time.time()
self.lshf.fit(all_pic_feature, train_label)
self.all_pic_feature = list(all_pic_feature)
self.all_labels = list(train_label)
end = time.time()
self.load_time = end
self.user_count = Counter(self.all_labels)
log_file.write('\t'.join(map(str, [self.user_count,
'fit all data time :', (end - start)]))+'\n')
log_file.close()
except:
traceback.print_exc()
log_file.close()
return
def cal_nearest_sim(self, current_feature):
nearest_sim_list = []
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
try:
length = len(self.nearest)
for k in range(length):
try:
person_name, pre_feature = self.nearest[k]
# 不在考虑时间, 只考虑图片的相似度
this_sim = pw.cosine_similarity(np.reshape(np.asarray(pre_feature), (1, len(pre_feature))),
np.reshape(np.asarray(current_feature), (1, len(current_feature))))
nearest_sim_list.append((this_sim, verification_model.predict(this_sim), person_name))
except:
log_file.write('cal_nearest_sim error'+'\n')
traceback.print_exc()
continue
return nearest_sim_list
except:
traceback.print_exc()
return nearest_sim_list
def recognize_online_cluster(self, image, image_id):
'''
:param image: 将得到的图片进行识别,加入的LSH Forest,根据距离计算proba(不同的距离对应不同的准确率,根据已有的dist计算阈值);
和已经设定的阈值判断是不是一个新出现的人,确定是原来已有的人,还是不确定是原来已有的人
:return:
'''
start = time.time()
need_add = False
need_save = False
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
log_file.write('\t'.join(map(str, ["receive image", image_id, time.time()])) + '\n')
feature_str = ''
try:
image = base64.decodestring(image)
image = zlib.decompress(image)
im = cv2.imdecode(np.fromstring(image, dtype=np.uint8), 1)
log_file.write('\t'.join(map(str, ['shape :', im.shape[0], im.shape[1]])) + '\n')
# 图片尺寸过滤
if im.shape[0] < size_threshold or im.shape[1] < size_threshold:
log_file.write('\t'.join(map(str, ['stat recognize_time :', (time.time() - start), 'small_size'])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 清晰度过滤
blur_sign, blur_var = is_blur(cv2.resize(im, (96, 96)))
if blur_sign:
log_file.write('\t'.join(map(str, ['stat recognize_time :', (time.time() - start), 'blur_filter', blur_var])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 保存传过来的图片
# img_file = '/tmp/research_face/%s.jpg' %image_id
time_slot = get_time_slot(image_id)
if time_slot == None:
time_slot = 'error'
time_slot_dir = os.path.join(tmp_face_dir, time_slot)
if not os.path.exists(time_slot_dir):
os.makedirs(time_slot_dir)
img_file = os.path.join(time_slot_dir, image_id+'.jpg')
cv2.imwrite(img_file, im)
except:
traceback.print_exc()
log_file.close()
return self.unknown, 1.0, feature_str, need_save
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
result = self.extract_pic_feature(img_file)
if result == None:
log_file.write('\t'.join(map(str, ['stat not_find_face', 'time :', (time.time() - start)]))+'\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
face_pic, im_feature = result
try:
# nearest_sim_list的格式和dist_label_list的格式一样,这样可以将两个list合并,一起计算(这样不用考虑时间的因素)
# 在识别出人名后将人名和feature放入到self.nearest
nearest_sim_list = self.cal_nearest_sim(current_feature=im_feature)
except:
traceback.print_exc()
nearest_sim_list = []
log_file.write('\t'.join(map(str, ['nearest_sim_list :', map(str, nearest_sim_list)])) + '\n')
feature_str = base64.b64encode(msgpack_numpy.dumps(im_feature))
log_file.write('\t'.join(map(str, ['extract_feature_time :', (time.time() - start)]))+'\n')
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
tmp_list = self.find_k_neighbors_with_lsh(im_feature)
nearest_sim_list.sort(key=lambda x: x[0], reverse=True)
nearest_sim_list.extend(tmp_list)
dist_label_list = nearest_sim_list[:]
# 计算
log_file.write('\t'.join(map(str, ['dist_label_list :', map(str, dist_label_list)])) + '\n')
if dist_label_list == None:
this_id = self.must_be_not_same_id
this_label = self.new_person_str + str(self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 不管概率, 都要将最新的一张图片加入到self.nearest
self.nearest.append((this_label, im_feature))
log_file.write('\t'.join(map(str, ['self.nearest :', map(str, self.nearest)])) + '\n')
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
if this_id == self.same_pic_id:
need_add = False
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
this_person_pic_folder = os.path.join(self.all_pic_data_folder, this_label+self.must_same_str)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, this_label+self.must_same_str)
elif this_id == self.must_be_not_same_id:
this_label = self.new_person_str + str(self.current_new_person_id)
self.current_new_person_id += 1
this_person_pic_folder = os.path.join(self.all_pic_data_folder, this_label)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, this_label)
need_add = True
need_save = True
elif this_id == self.maybe_same_id:
this_person_pic_folder = os.path.join(self.all_pic_data_folder, this_label + self.maybe_same_str)
this_person_feature_folder = os.path.join(self.all_pic_feature_data_folder, this_label + self.maybe_same_str)
need_add = False # prob在灰度区域的不如入,其余情况加入
need_save = True
else:
log_file.write('\t'.join(map(str, ['error para :', this_id]))+'\n')
if need_save:
try:
if not os.path.exists(this_person_pic_folder):
os.makedirs(this_person_pic_folder)
if not os.path.exists(this_person_feature_folder):
os.makedirs(this_person_feature_folder)
# 直接存储图片对应的特征, 同时保存图片文件
this_pic_feature_name = os.path.join(this_person_feature_folder, image_id+'.p')
msgpack_numpy.dump(im_feature, open(this_pic_feature_name, 'wb'))
this_pic_face_name = os.path.join(this_person_pic_folder, image_id+'.jpg')
cv2.imwrite(this_pic_face_name, face_pic)
except:
traceback.print_exc()
return self.unknown, 1.0, feature_str, False
# 加入LSH Forest --- partial_fit
if need_add:
self.add_one_pic(im_feature, this_label)
# 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
end = time.time()
log_file.write('\t'.join(map(str, ['stat recognize_time :',(end - start), 'this_id :', self.trans_dic.get(this_id)]))+'\n')
log_file.close()
need_save = True
return this_label.replace(self.must_same_str, ''), str(dist_label_list[0][0]), str(feature_str), str(need_save)
else:
# 灰度区域,不显示人名
end = time.time()
log_file.write('\t'.join(map(str, ['stat gray_area :',(end - start)]))+'\n')
log_file.close()
return self.unknown, str(dist_label_list[0][0]), str(feature_str), str(False)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(100.0), str(feature_str), str(False)
def recognize_online_cluster(self, image, image_id):
'''
:param image: 将得到的图片进行识别,加入的LSH Forest,根据距离计算proba(不同的距离对应不同的准确率,根据已有的dist计算阈值);
和已经设定的阈值判断是不是一个新出现的人,确定是原来已有的人,还是不确定是原来已有的人
:return:
'''
start = time.time()
need_add = False
need_save = False
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
log_file.write(
'\t'.join(map(str, ["receive image", image_id,
time.time()])) + '\n')
feature_str = ''
try:
image = base64.decodestring(image)
image = zlib.decompress(image)
im = cv2.imdecode(np.fromstring(image, dtype=np.uint8), 1)
log_file.write(
'\t'.join(map(str, ['shape :', im.shape[0], im.shape[1]])) +
'\n')
# 图片尺寸过滤
if im.shape[0] < size_threshold or im.shape[1] < size_threshold:
log_file.write('\t'.join(
map(str, [
'stat recognize_time :', (time.time() -
start), 'small_size'
])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 清晰度过滤
blur_sign, blur_var = is_blur(cv2.resize(im, (96, 96)))
if blur_sign:
log_file.write('\t'.join(
map(str, [
'stat recognize_time :',
(time.time() - start), 'blur_filter', blur_var
])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
# 保存传过来的图片
# img_file = '/tmp/research_face/%s.jpg' %image_id
time_slot = get_time_slot(image_id)
if time_slot == None:
time_slot = 'error'
time_slot_dir = os.path.join(tmp_face_dir, time_slot)
if not os.path.exists(time_slot_dir):
os.makedirs(time_slot_dir)
img_file = os.path.join(time_slot_dir, image_id + '.jpg')
cv2.imwrite(img_file, im)
except:
traceback.print_exc()
log_file.close()
return self.unknown, 1.0, feature_str, need_save
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
result = self.extract_pic_feature(img_file)
if result == None:
log_file.write('\t'.join(
map(str, [
'stat not_find_face', 'time :', (time.time() - start)
])) + '\n')
log_file.close()
return self.unknown, 1.0, feature_str, need_save
face_pic, im_feature = result
try:
# nearest_sim_list的格式和dist_label_list的格式一样,这样可以将两个list合并,一起计算(这样不用考虑时间的因素)
# 在识别出人名后将人名和feature放入到self.nearest
nearest_sim_list = self.cal_nearest_sim(
current_feature=im_feature)
except:
traceback.print_exc()
nearest_sim_list = []
log_file.write('\t'.join(
map(str, ['nearest_sim_list :',
map(str, nearest_sim_list)])) + '\n')
feature_str = base64.b64encode(msgpack_numpy.dumps(im_feature))
log_file.write('\t'.join(
map(str, ['extract_feature_time :', (time.time() - start)])) +
'\n')
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
tmp_list = self.find_k_neighbors_with_lsh(im_feature)
nearest_sim_list.sort(key=lambda x: x[0], reverse=True)
nearest_sim_list.extend(tmp_list)
dist_label_list = nearest_sim_list[:]
# 计算
log_file.write('\t'.join(
map(str, ['dist_label_list :',
map(str, dist_label_list)])) + '\n')
if dist_label_list == None:
this_id = self.must_be_not_same_id
this_label = self.new_person_str + str(
self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 不管概率, 都要将最新的一张图片加入到self.nearest
self.nearest.append((this_label, im_feature))
log_file.write(
'\t'.join(map(str, ['self.nearest :',
map(str, self.nearest)])) + '\n')
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
if this_id == self.same_pic_id:
need_add = False
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
this_person_pic_folder = os.path.join(
self.all_pic_data_folder, this_label + self.must_same_str)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder,
this_label + self.must_same_str)
elif this_id == self.must_be_not_same_id:
this_label = self.new_person_str + str(
self.current_new_person_id)
self.current_new_person_id += 1
this_person_pic_folder = os.path.join(self.all_pic_data_folder,
this_label)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder, this_label)
need_add = True
need_save = True
elif this_id == self.maybe_same_id:
this_person_pic_folder = os.path.join(
self.all_pic_data_folder, this_label + self.maybe_same_str)
this_person_feature_folder = os.path.join(
self.all_pic_feature_data_folder,
this_label + self.maybe_same_str)
need_add = False # prob在灰度区域的不如入,其余情况加入
need_save = True
else:
log_file.write('\t'.join(map(str, ['error para :', this_id])) +
'\n')
if need_save:
try:
if not os.path.exists(this_person_pic_folder):
os.makedirs(this_person_pic_folder)
if not os.path.exists(this_person_feature_folder):
os.makedirs(this_person_feature_folder)
# 直接存储图片对应的特征, 同时保存图片文件
this_pic_feature_name = os.path.join(
this_person_feature_folder, image_id + '.p')
msgpack_numpy.dump(im_feature,
open(this_pic_feature_name, 'wb'))
this_pic_face_name = os.path.join(this_person_pic_folder,
image_id + '.jpg')
cv2.imwrite(this_pic_face_name, face_pic)
except:
traceback.print_exc()
return self.unknown, 1.0, feature_str, False
# 加入LSH Forest --- partial_fit
if need_add:
self.add_one_pic(im_feature, this_label)
# 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
end = time.time()
log_file.write('\t'.join(
map(str, [
'stat recognize_time :', (end - start), 'this_id :',
self.trans_dic.get(this_id)
])) + '\n')
log_file.close()
need_save = True
return this_label.replace(self.must_same_str, ''), str(
dist_label_list[0][0]), str(feature_str), str(need_save)
else:
# 灰度区域,不显示人名
end = time.time()
log_file.write(
'\t'.join(map(str, ['stat gray_area :', (end - start)])) +
'\n')
log_file.close()
return self.unknown, str(
dist_label_list[0][0]), str(feature_str), str(False)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(100.0), str(feature_str), str(False)
def recognize_online_cluster(self, image, image_id):
'''
:param image: 将得到的图片进行识别,加入的LSH Forest,根据距离计算proba(不同的距离对应不同的准确率,根据已有的dist计算阈值);
和已经设定的阈值判断是不是一个新出现的人,确定是原来已有的人,还是不确定是原来已有的人
# 增加统计的功能, 方便以后计算过滤原因和比例, 以及识别比例(same, not_same, maybe_same)
:return:
'''
start = time.time()
need_add = False
has_save_num = 0
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day + '.txt'), 'a')
log_file.write(
'\t'.join(map(str, ["receive image", image_id,
time.time()])) + '\n')
try:
image = base64.decodestring(image)
image = zlib.decompress(image)
im = cv2.imdecode(np.fromstring(image, dtype=np.uint8), 1)
time_slot = get_time_slot(image_id)
if time_slot == None:
time_slot = 'error'
time_slot_dir = os.path.join(tmp_face_dir, time_slot)
if not os.path.exists(time_slot_dir):
os.makedirs(time_slot_dir)
tmp_pic_path = os.path.join(time_slot_dir, image_id + '.jpg')
cv2.imwrite(tmp_pic_path, im)
blur_result = is_blur(im)
blur_sign, blur_var = blur_result
if blur_sign:
log_file.write('\t'.join(
map(str, ['stat', 'blur_filter', blur_var, image_id])) +
'\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
align_face_img = align_face(tmp_pic_path)
if align_face_img == None:
log_file.write('\t'.join(
map(str, ['stat', 'detect_filter', blur_var, image_id])) +
'\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
else:
# 使用重新检测并对对齐的人脸进行识别
im = align_face_img
# 对检测到的人脸重新进行模糊检测
blur_result = is_blur(im)
blur_sign, blur_var = blur_result
if blur_sign:
log_file.write('\t'.join(
map(str, ['stat', 'blur_filter', blur_var, image_id])) +
'\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
need_process = self.check_face_img(im, image_id)
if not need_process:
log_file.write('\t'.join(
map(str, ['stat', 'pose_filter', blur_var, image_id])) +
'\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
im = cv2.resize(im, (PIC_SHAPE[1], PIC_SHAPE[2]),
interpolation=cv2.INTER_LINEAR)
im = im[:, :, ::-1] * 1.0
im = im - avg
im = im.transpose((2, 0, 1))
im = im[None, :]
except:
traceback.print_exc()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
im_feature = extract_feature_from_numpy(im)
try:
# nearest_sim_list的格式和dist_label_list的格式一样,这样可以将两个list合并,一起计算(这样不用考虑时间的因素)
# 在识别出人名后将人名和feature放入到self.nearest
nearest_sim_list = self.cal_nearest_sim(
current_feature=im_feature)
except:
traceback.print_exc()
nearest_sim_list = []
log_file.write('\t'.join(
map(str, ['nearest_sim_list :',
map(str, nearest_sim_list)])) + '\n')
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
dist_label_list = self.find_k_neighbors_with_lsh(im_feature)
dist_label_list.extend(nearest_sim_list)
dist_label_list = self.filter_result(dist_label_list)
dist_label_list.sort(key=lambda x: x[0], reverse=True)
# 计算
if dist_label_list == None:
this_id = self.must_be_not_same_id
this_label = self.new_person_str + str(
self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
log_file.write('\t'.join(
map(str, ['stat', 'recognize_id', blur_var, this_id])) + '\n')
if dist_label_list != None and len(dist_label_list) > 0:
log_file.write('\t'.join(
map(str, ['dist_label_list :',
map(str, dist_label_list)])) + '\n')
need_save = False
if this_id == self.same_pic_id:
need_add = False
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
this_person_folder = os.path.join(
self.all_pic_data_folder, this_label + self.must_same_str)
elif this_id == self.must_be_not_same_id:
this_label = self.new_person_str + str(
self.current_new_person_id)
self.current_new_person_id += 1
this_person_folder = os.path.join(self.all_pic_data_folder,
this_label)
need_add = True
need_save = True
elif this_id == self.maybe_same_id:
this_person_folder = os.path.join(
self.all_pic_data_folder, this_label + self.maybe_same_str)
need_add = False # prob在灰度区域的不如入,其余情况加入
need_save = True
else:
log_file.write('\t'.join(map(str, ['error para :', this_id])) +
'\n')
if need_save:
try:
if not os.path.exists(this_person_folder):
os.makedirs(this_person_folder)
os.chmod(this_person_folder,
stat.S_IRWXG + stat.S_IRWXO + stat.S_IRWXU)
this_pic_name = os.path.join(this_person_folder,
image_id + '.png')
imsave(this_pic_name, np.transpose(im[0], (1, 2, 0)))
except:
traceback.print_exc()
return self.unknown, 1.0, has_save_num, False
# 加入LSH Forest --- partial_fit
if need_add:
self.add_one_pic(im_feature, this_label)
has_save_num += 1
# 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
end = time.time()
log_file.write('\t'.join(
map(str, [
'stat recognize_time :', (end - start), 'this_id :',
self.trans_dic.get(this_id)
])) + '\n')
log_file.close()
return this_label.replace(self.must_same_str, ''), \
str(dist_label_list[0][0]), str(has_save_num), str(need_add)
else:
# 灰度区域,不显示人名
end = time.time()
log_file.write('\t'.join(
map(str, ['gray area recog time :', (end - start)])) +
'\n')
log_file.close()
# return this_label.replace(self.maybe_same_str, ''), \
# str(dist_label_list[0][0]), str(has_save_num), str(need_add)
return self.unknown, str(
dist_label_list[0][0]), str(has_save_num), str(need_add)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(100.0), str(has_save_num), str(False)
def recognize_online_cluster(self, image, image_id):
'''
:param image: 将得到的图片进行识别,加入的LSH Forest,根据距离计算proba(不同的距离对应不同的准确率,根据已有的dist计算阈值);
和已经设定的阈值判断是不是一个新出现的人,确定是原来已有的人,还是不确定是原来已有的人
# 增加统计的功能, 方便以后计算过滤原因和比例, 以及识别比例(same, not_same, maybe_same)
:return:
'''
start = time.time()
need_add = False
has_save_num = 0
current_day = get_current_day()
log_file = open(os.path.join(log_dir, current_day+'.txt'), 'a')
log_file.write('\t'.join(map(str, ["receive image", image_id, time.time()])) + '\n')
try:
image = base64.decodestring(image)
image = zlib.decompress(image)
im = cv2.imdecode(np.fromstring(image, dtype=np.uint8), 1)
time_slot = get_time_slot(image_id)
if time_slot == None:
time_slot = 'error'
time_slot_dir = os.path.join(tmp_face_dir, time_slot)
if not os.path.exists(time_slot_dir):
os.makedirs(time_slot_dir)
tmp_pic_path = os.path.join(time_slot_dir, image_id+'.jpg')
cv2.imwrite(tmp_pic_path, im)
blur_result = is_blur(im)
blur_sign, blur_var = blur_result
if blur_sign:
log_file.write('\t'.join(map(str, ['stat', 'blur_filter', blur_var, image_id]))+'\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
align_face_img = align_face(tmp_pic_path)
if align_face_img == None:
log_file.write('\t'.join(map(str, ['stat', 'detect_filter', blur_var, image_id])) + '\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
else:
# 使用重新检测并对对齐的人脸进行识别
im = align_face_img
# 对检测到的人脸重新进行模糊检测
blur_result = is_blur(im)
blur_sign, blur_var = blur_result
if blur_sign:
log_file.write('\t'.join(map(str, ['stat', 'blur_filter', blur_var, image_id]))+'\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
need_process = self.check_face_img(im, image_id)
if not need_process:
log_file.write('\t'.join(map(str, ['stat', 'pose_filter', blur_var, image_id])) + '\n')
log_file.close()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
im = cv2.resize(im, (PIC_SHAPE[1], PIC_SHAPE[2]), interpolation=cv2.INTER_LINEAR)
im = im[:, :, ::-1]*1.0
im = im - avg
im = im.transpose((2, 0, 1))
im = im[None, :]
except:
traceback.print_exc()
return self.unknown, 1.0, self.has_save_pic_feature, need_add
try:
# 流程 : 找距离最近的图片 ; 计算prob ; 在线聚类 ; 加入LSH Forest
im_feature = extract_feature_from_numpy(im)
try:
# nearest_sim_list的格式和dist_label_list的格式一样,这样可以将两个list合并,一起计算(这样不用考虑时间的因素)
# 在识别出人名后将人名和feature放入到self.nearest
nearest_sim_list = self.cal_nearest_sim(current_feature=im_feature)
except:
traceback.print_exc()
nearest_sim_list = []
log_file.write('\t'.join(map(str, ['nearest_sim_list :', map(str, nearest_sim_list)])) + '\n')
# 找距离最近的图片 --- 用LSH Forest 找出最近的10张图片,然后分别计算距离
dist_label_list = self.find_k_neighbors_with_lsh(im_feature)
dist_label_list.extend(nearest_sim_list)
dist_label_list = self.filter_result(dist_label_list)
dist_label_list.sort(key=lambda x: x[0], reverse=True)
# 计算
if dist_label_list == None:
this_id = self.must_be_not_same_id
this_label = self.new_person_str + str(self.current_new_person_id)
else:
# 计算prob --- 根据距离计算prob
this_id, this_label = self.evaluate_result(dist_label_list)
# 在线聚类 --- 根据dist确定是重新增加一个人还是加入到已有的人中
log_file.write('\t'.join(map(str, ['stat', 'recognize_id', blur_var, this_id])) + '\n')
if dist_label_list != None and len(dist_label_list) > 0:
log_file.write('\t'.join(map(str, ['dist_label_list :', map(str, dist_label_list)])) + '\n')
need_save = False
if this_id == self.same_pic_id:
need_add = False
elif this_id == self.must_be_same_id:
need_add = False
need_save = True
this_person_folder = os.path.join(self.all_pic_data_folder, this_label+self.must_same_str)
elif this_id == self.must_be_not_same_id:
this_label = self.new_person_str + str(self.current_new_person_id)
self.current_new_person_id += 1
this_person_folder = os.path.join(self.all_pic_data_folder, this_label)
need_add = True
need_save = True
elif this_id == self.maybe_same_id:
this_person_folder = os.path.join(self.all_pic_data_folder, this_label+self.maybe_same_str)
need_add = False # prob在灰度区域的不如入,其余情况加入
need_save = True
else:
log_file.write('\t'.join(map(str, ['error para :', this_id])) + '\n')
if need_save:
try:
if not os.path.exists(this_person_folder):
os.makedirs(this_person_folder)
os.chmod(this_person_folder, stat.S_IRWXG + stat.S_IRWXO + stat.S_IRWXU)
this_pic_name = os.path.join(this_person_folder, image_id+'.png')
imsave(this_pic_name, np.transpose(im[0], (1, 2, 0)))
except:
traceback.print_exc()
return self.unknown, 1.0, has_save_num, False
# 加入LSH Forest --- partial_fit
if need_add:
self.add_one_pic(im_feature, this_label)
has_save_num += 1
# 根据label和image_id可以存生成文件名,确定是否要存储文件[可以选择在服务器和本地同时存储]
if this_id == self.same_pic_id or this_id == self.must_be_not_same_id or this_id == self.must_be_same_id:
end = time.time()
log_file.write('\t'.join(map(str, ['stat recognize_time :', (end - start), 'this_id :', self.trans_dic.get(this_id)])) + '\n')
log_file.close()
return this_label.replace(self.must_same_str, ''), \
str(dist_label_list[0][0]), str(has_save_num), str(need_add)
else:
# 灰度区域,不显示人名
end = time.time()
log_file.write('\t'.join(map(str, ['gray area recog time :',(end - start)])) + '\n')
log_file.close()
# return this_label.replace(self.maybe_same_str, ''), \
# str(dist_label_list[0][0]), str(has_save_num), str(need_add)
return self.unknown, str(dist_label_list[0][0]), str(has_save_num), str(need_add)
except:
traceback.print_exc()
log_file.close()
return self.unknown, str(100.0), str(has_save_num), str(False)
