def test(args):
#获取所有人脸特征，并存到pkl文件中
model = face_model.FaceModel(args)
pathss=[]
for root, dirs, files in os.walk("/home/huangju/dataset/face_imgs_detected_larger"):
path = [os.path.join(root, name) for name in files]
#print(path)
pathss.extend(path)
#print(pathss)
#print("finish")
count=0 #记录提取特征的人脸的图片数量
d={} #key: path value: feature
for path in tqdm(pathss):
if path.split('/')[-1].split("-")[0]=="train" or path.split('/')[-1].split("-")[0]=="query":
continue
img = cv2.imread(path)
img = model.get_input(img)
if img is not None:
feature = model.get_feature(img)
key="data/msmt17/test/"+path.split('/')[-1].split('-')[1]+"/"+path.split('/')[-1].split('-')[-1]
d[key]=feature
count+=1
#print("count: {}".format(count))
# with open("msmt_face_q_t-own2.pkl","wb") as f:
# pickle.dump(d,f)
with open("face_feature-surv_ms1m_msmt.pkl","wb") as f:
pickle.dump(d,f)
def mkface(imgs_dir, faces_save_dst): args = Args() model = face_model.FaceModel(args) if not os.path.exists(faces_save_dst): os.mkdir(faces_save_dst) folders = os.listdir(imgs_dir) for folder in folders: imgs = os.listdir(os.path.join(imgs_dir, folder)) n = len(imgs) cnt = 0 for img in imgs: start = time.time() img_root_path = os.path.join(imgs_dir, folder, img) try: pic = cv2.imread(img_root_path) pic = model.get_input(pic) # 3 * 112 * 112 if type(pic) == np.ndarray: if not os.path.exists(os.path.join(faces_save_dst, folder)): os.mkdir(os.path.join(faces_save_dst, folder)) cv2.imwrite(os.path.join(faces_save_dst, folder, folder + str(cnt)+'.jpg'), np.transpose(pic, (1,2,0))[:,:,::-1]) cnt += 1 end = time.time() interval = end - start except: continue
def test_Faces(args):
model = face_model.FaceModel(args)
imgs = os.listdir(args.image_path)
flag = False
# 设置阈值，这两个阈值用来判定该人是否是库里的人
a = zip([args.threshold1], [args.threshold2])
for k, v in a:
for img in imgs:
pic = cv2.imread(os.path.join(args.image_path, img))
pic = model.get_input(pic)
if pic is None:
continue
else:
f1 = model.get_feature(pic)
for i in range(faces.shape[0]):
cnt = 0
for j in range(faces.shape[1]):
dist = np.sqrt(np.sum(np.square(f1 - faces[i][j])))
# 如果与库中某人距离小于阈值1
if dist < k:
cnt += 1
# 如果与库中某个人的相似度大于阈值2，则证明是该人
if cnt >= v:
name = labels[i]
testresult.writelines(
os.path.join(args.image_path, img) + ' is ' +
name + '\n')
flag = True
if flag is False:
print("image:" + os.path.join(args.image_path, img) +
'is not in the gallery, refused!' + '\n')
testresult.close()
def main(args):
if sys.version_info < (3, 0):
print("Error: Python2 is slow. Use Python3 for max performance.")
if args.detector and args.encoder:
try:
global g_detector, g_encoder, output_dir, is_save_face, model
output_dir = join(CURRENT_DIR, 'output_face')
is_save_face = int(args.is_save_face)
g_detector = FaceDetectorModels(int(args.detector))
g_encoder = FaceEncoderModels(int(args.encoder))
# init insightface model
model = face_model.FaceModel(args)
print("Parameters: {} {} {}".format(g_detector, g_encoder,
args.data_path))
print("reading images...")
pickle_embs = parallel_extract(args.data_path,
num_worker=1,
min_photo=MIN_PHOTO)
# with open(face_embeddings_path, 'rb') as handle:
# load_check = pickle.load(handle)
# X, Y = seriallize_face_encodings(face_embeddings_path, 10)
print('finished...')
except Exception as ex:
print("Invalid parameter")
print(ex)
return
def load_model(self):
parser = argparse.ArgumentParser(description='face model test')
# general
parser.add_argument('--image-size', default='112,112', help='')
parser.add_argument('--model',
default='../recognition/models/y2-iccv/model, 01',
help='path to load model.')
parser.add_argument('--ga-model',
default='',
help='path to load model.')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument(
'--det',
default=0,
type=int,
help='mtcnn option, 1 means using R+O, 0 means detect from begining'
)
parser.add_argument('--flip',
default=0,
type=int,
help='whether do lr flip aug')
parser.add_argument('--threshold',
default=1.24,
type=float,
help='ver dist threshold')
args = parser.parse_args()
return face_model.FaceModel(args)
def main(args):
# 因为只有一个函数用到了database，但是不是main函数直接调用这个函数，并且这个函数在其他函数里检测的的时候被使用，故而定义为全局变量跨函数调用
global database
model = face_model.FaceModel(args)
print("load model")
if args.npy:
database = load_database_by_npy(args)
print("load npy database")
else:
database = load_database(args.database_path, model)
print("load image database")
#选择工作模式
if args.pattern == 'camera':
detect(args, model, camera=True)
elif args.pattern == 'video':
detect(args, model)
elif args.pattern == 'image':
detect_image(args, model)
else:
print("Error partten!")
del database
exit(0)
del database
def compute_sim():
model = face_model.FaceModel(args)
out_txt = open(
'/media/dhao/系统/05-weiwei/FR/politician_code/result_dist.txt', 'a')
file = open('/media/dhao/系统/05-weiwei/FR/politician_code/same_pairs.txt',
'r')
lines = file.readlines()
for j in range(len(lines)):
name1 = lines[j].strip().split(',')[0]
name2 = lines[j].strip().split(',')[1]
try:
img1 = cv2.imread(name1)
img1 = model.get_input(img1)
f1 = model.get_feature(img1)
img2 = cv2.imread(name2)
img2 = model.get_input(img2)
f2 = model.get_feature(img2)
dist = np.sqrt(np.sum(np.square(f1 - f2)))
out_txt.writelines(name1 + ',' + name2 + ',' + ',' + str(dist) +
'\n')
except:
continue
file.close()
out_txt.close()
def __init__(self, shape, model_path):
args = edict({
"image_size": "%d,%d" % (shape[0], shape[1]),
"model": model_path + ",0",
"gpu": 0, #1,
"threshold": 1.24,
})
self.model = face_model.FaceModel(args)
def __init__(self, args):
self.le = args.le
self.data = pickle.loads(open(args.embeddings, "rb").read())
self.embeddings = np.array(self.data['embeddings'])
self.labelEncode = pickle.loads(open(self.le, "rb").read())
self.labels = self.labelEncode.fit_transform(self.data['names'])
self.detector = MTCNN()
self.embedding_model = face_model.FaceModel(args)
self.classifying_model = load_model(args.model_classify)
def video_test_Faces(args):
start = time.time()
model = face_model.FaceModel(args)
# 设置阈值，这两个阈值用来判定该人是否是库里的人
a = zip([args.threshold1], [args.threshold2])
for k, v in a:
cap = cv2.VideoCapture("/home/lzc274500/nohelmet_face_detect/02.mp4")
while (True):
ret, frame = cap.read()
pic, bboxs = face_detecter_align(frame)
if pic is None: #判断是否有人脸,没有人脸返回
continue
else:
for m in range(1, (pic.shape[0] / 3) + 1):
# print("m", m)
apic = pic[(3 * m - 3):m * 3]
abbox = bboxs[4 * m - 4:(4 * m)]
#apoints = points[10*m-10:10*m]
cv2.rectangle(frame, (int(abbox[0]), int(abbox[1])),
(int(abbox[2]), int(abbox[3])), (0, 255, 0),
2)
# for l in range(0, 5):
# cv2.circle(frame, (int(apoints[l]), int(apoints[l + 5])), 2, (0, 255, 0), 2)
f1 = model.get_feature(apic)
maxl = []
for i in range(faces.shape[0]):
cnt = 0
for j in range(faces.shape[1]):
dist = np.sqrt(np.sum(np.square(f1 - faces[i][j])))
# 如果与库中某人距离小于阈值1
if dist < k:
cnt += 1
z = [i, cnt]
maxl = maxl + z
e = maxl[1::2].index(max(maxl[1::2]))
# 如果与库中某人距离小于阈值2
if max(maxl[1::2]) >= v:
name = labels[e]
else:
name = "unknow"
cv2.putText(frame, name,
(int(abbox[0]), int(abbox[1] - 10)),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
#out.write(frame)
cv2.imshow("Demo", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release() # 关闭相机
#out.release()
cv2.destroyAllWindows()
#cv2.imwrite('chip_' + str(img) + '.png', draw)
testresult.close()
end = time.time()
print('Running time: {} Seconds'.format(end - start))
def __init__(self, arguments, mx_context):
self.args = arguments
self.ctx = mx_context
self.model = face_model.FaceModel(args)
self.detector = MtcnnDetector(model_folder='mtcnn-model/',
ctx=self.ctx,
num_worker=4,
accurate_landmark=False)
self.names = None # Names of the persons in the dataset
self.dataset = None # Collection of features of known names
def val(input_image):
model = face_model.FaceModel(args)
img = model.get_input(input_image)
f2 = model.get_feature(input_image)
features = {}
for i in range(len(f2)):
features['feature' + str(i)] = str(f2[i])
app_json = json.dumps(features)
#print(app_json)
return app_json
def img_to_vec(img):
model = face_model.FaceModel(args)
img = cv2.imread(img)
img = model.get_input(img)
if img is None:
print('------------------No face detected or Multiple faces detected')
return None
else:
print('----------------feature')
f1 = model.get_feature(img)
return f1.tolist()
class Recognition(Singleton):
parser = argparse.ArgumentParser(description='face model test')
parser.add_argument('--image-size', default='112,112', help='')
parser.add_argument('--model', default='./insightface/models/model-r34-amf/model,0', help='path to load model.')
parser.add_argument('--ga-model', default='', help='path to load model.')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument('--det', default=0, type=int, help='mtcnn option, 1 means using R+O, 0 means detect from begining')
parser.add_argument('--flip', default=0, type=int, help='whether do lr flip aug')
parser.add_argument('--threshold', default=1.24, type=float, help='ver dist threshold')
args = parser.parse_args()
extractor = face_model.FaceModel(args)
def __init__(self, arguments, mx_context):
self.args = arguments
self.ctx = mx_context
self.model = face_model.FaceModel(args)
rtpath, epoch = self.args.rt_model.split(',')
self.detector = RetinaFace(rtpath, int(epoch), self.args.gpu, 'net3')
self.dataset = None # Collection of features of known names
self.names = {} # Names of known person
self.persons = [] # List of person detected
self.crop_resolution = int(self.args.image_size.split(',')
[0]) # Resolution to crop person face
self.pv = [float(p) for p in self.args.poses.split(',')]
def test():
model = face_model.FaceModel(args)
for i in range(1):
cnt = 0
out_txt = open(
'/media/dhao/系统/05-weiwei/FR/dataset/test_result/combine_test/1.38/result'
+ str(i + 1) + '.txt', 'a')
file = open(
'/media/dhao/系统/05-weiwei/FR/politician_code/same_pairs.txt', 'r')
lines = file.readlines()
for j in range(len(lines)):
name1 = lines[j].strip().split(',')[0]
name2 = lines[j].strip().split(',')[1]
try:
img1 = cv2.imread(name1)
img1 = model.get_input(img1)
f1 = model.get_feature(img1)
img2 = cv2.imread(name2)
img2 = model.get_input(img2)
f2 = model.get_feature(img2)
dist = np.sqrt(np.sum(np.square(f1 - f2)))
if dist < args.threshold:
# print('they are the same one')
out_txt.writelines(name1 + ',' + name2 + ',' + str(dist) +
',' + str(args.threshold) + '*' * 3 +
str(1) + '\n')
else:
out_txt.writelines(name1 + ',' + name2 + ',' + str(dist) +
',' + str(args.threshold) + '*' * 3 +
str(0) + '\n')
bad_case_folder = '/media/dhao/系统/05-weiwei/FR/dataset/test_result/combine_test/1.38/all' + str(
i) + '/' + str(cnt) + '/'
if not os.path.exists(bad_case_folder):
os.mkdirs(bad_case_folder)
if os.path.isdir(bad_case_folder):
shutil.copy(name1, bad_case_folder)
shutil.copy(name2, bad_case_folder)
cnt += 1
else:
sys.exit(0)
except:
continue
file.close()
out_txt.close()
def main(readerd, readern):
args = parse_arguments()
skip_num = 0
model = face_model.FaceModel(args)
name = []
for row in readern:
name.append(row[0])
embedded = []
for row in readerd:
embedded.append(np.asarray(row, dtype=np.float32))
conn, addr = s.accept()
data = b""
payload_size = struct.calcsize(">L")
while True:
if skip_num % args.skip_frame != 0:
skip_num += 1
continue
while len(data) < payload_size:
data += conn.recv(4096)
packed_msg_size = data[:payload_size]
data = data[payload_size:]
msg_size = struct.unpack(">L", packed_msg_size)[0]
while len(data) < msg_size:
data += conn.recv(4096)
frame_data = data[:msg_size]
data = data[msg_size:]
frame = pickle.loads(frame_data)
frame = cv2.imdecode(frame, cv2.IMREAD_COLOR)
if frame is not None:
ret = face_recognition(frame, model, embedded, name)
if ret == 1:
break
print("RET = ", ret)
data_string = pickle.dumps(ret)
conn.send(data_string)
skip_num += 1
# When everything is done, release the capture
cv2.destroyAllWindows()
def __init__(self):
parser = argparse.ArgumentParser(description='face model test')
# general
parser.add_argument('--image-size', default='112,112', help='')
parser.add_argument('--model', default='insightface/models/model-r100-ii/model,0', help='path to load model.')
parser.add_argument('--ga-model', default='', help='path to load model.')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument('--flip', default=0, type=int, help='whether do lr flip aug')
args = parser.parse_args()
self.embedder = face_model.FaceModel(args)
self.face_db = FaceDatabase()
self.features = []
self.ids = []
def __init__(self,
model_path,
epoch_num='0000',
image_size=(112, 112),
no_face_raise=True):
self.model_path = ','.join([model_path, epoch_num])
self.no_face_raise = no_face_raise
args = argparse.Namespace()
args.model = self.model_path
args.det = 0
args.flip = 0
args.threshold = 1.24
args.ga_model = ''
args.image_size = ",".join([str(i) for i in image_size])
self.model = face_model.FaceModel(args)
def compare_image():
model = face_model.FaceModel(args)
img1 = cv2.imread('./test_image/1.png')
img2 = cv2.imread('./test_image/85.jpg')
time1 = time.time()
img1, _ = model.get_input(img1)
img2, _ = model.get_input(img2)
time2 = time.time()
#f1 = model.get_feature(img)
#print(f1[0:10])
print(img1.shape)
f1 = model.get_feature(img1).flatten()
f2 = model.get_feature(img2).flatten()
sim = sklearn.metrics.pairwise.cosine_similarity([f1, f2])
print(sim)
time3 = time.time()
def age_model():
parser = argparse.ArgumentParser(description='face model test')
# general
parser.add_argument('--image-size', default='112,112', help='')
parser.add_argument('--image', default='Tom_Hanks_54745.png', help='')
parser.add_argument('--model',
default='model/model,0',
help='path to load model.')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument(
'--det',
default=0,
type=int,
help='mtcnn option, 1 means using R+O, 0 means detect from begining')
args = parser.parse_args()
return face_model.FaceModel(args)
def gen_feature(args):
#loading model
model = face_model.FaceModel(args)
no_get_feature = 0
rm_dim_number = 0
#get feature
feature_lebal_id = {"feature": [], "lebal": [], "id": []}
output_dir = args.output_dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
lebal_list = os.listdir(args.data_path)
print("class number", len(lebal_list))
for i_id in range(len(lebal_list)):
lebal_path = os.path.join(args.data_path, lebal_list[i_id])
if os.path.isdir(lebal_path):
print("procesing:{}/{}".format(i_id, len(lebal_list)))
for img in os.listdir(lebal_path):
img_path = os.path.join(lebal_path, img)
# print('img_path',img_path)
try:
# img_RGB = cv2.imread(img_path)
# dim=rm_dim.rm_main(img_RGB,args.dim_threshold)
# if dim==1:
# rm_dim_number=rm_dim_number+1
# continue
# img_input = model.get_input(img_RGB)
img_rgb = misc.imread(img_path)
img_input = np.transpose(img_rgb, (2, 0, 1))
feature = model.get_feature(img_input)
feature_lebal_id['feature'].append(feature)
feature_lebal_id['lebal'].append(lebal_list[i_id])
feature_lebal_id['id'].append(i_id)
except (Exception):
no_get_feature = no_get_feature + 1
print("get feature fail", img_path)
continue
print('dim_images_number', rm_dim_number)
print('fail to get feature', no_get_feature)
print('success to get lebal number', len(feature_lebal_id['lebal']),
len(feature_lebal_id['feature']))
np.save(os.path.join(output_dir, "labels_name.npy"),
feature_lebal_id['lebal'])
np.save(os.path.join(output_dir, "gallery.npy"), feature_lebal_id['id'])
np.save(os.path.join(output_dir, "signatures.npy"),
feature_lebal_id['feature'])
def test_all():
im_path = '/media/dhao/系统/05-weiwei/FR/dataset/测试集'
file = open('/media/dhao/系统/05-weiwei/FR/dataset/bad_case.txt', 'a')
# 人脸检测
folders = os.listdir(im_path)
cnt = 0
name_list = []
for folder in folders:
random.seed(time.time() * 100000 % 10000)
sublist = []
imgs = os.listdir(os.path.join(im_path, folder))
random.shuffle(imgs)
for i in range(0, len(imgs), 2):
try:
model = face_model.FaceModel(args)
pic_path1 = os.path.join(im_path, folder, imgs[i])
img1 = cv2.imread(pic_path1)
img1 = model.get_input(img1)
f1 = model.get_feature(img1)
pic_path2 = os.path.join(im_path, folder, imgs[i + 1])
img2 = cv2.imread(pic_path2)
img2 = model.get_input(img2)
f2 = model.get_feature(img2)
dist = np.sum(np.square(f1 - f2))
if dist < args.threshold:
print('they are the same one')
else:
# pdb.set_trace()
bad_case_folder = '/media/dhao/系统/05-weiwei/FR/dataset/test_result/' + str(
cnt)
os.mkdir(bad_case_folder)
shutil.copy(pic_path1, bad_case_folder)
shutil.copy(pic_path2, bad_case_folder)
cnt += 1
file.writelines(pic_path1 + ',' + pic_path2 + ',' +
str(dist) + ',' + str(args.threshold) +
'\n')
print('they are the different one')
print(dist)
except:
continue
file.close()
def get_emb_array(paths, args):
insight_model = face_model.FaceModel(args)
#num_of_images = len(paths)
emb_array = []
cnt = 1
for path in paths:
if (cnt % 50 == 0):
print(str(cnt) + ' path:' + path)
cnt += 1
img = cv2.imread(path)
img_tem = insight_model.get_input(img)
if img_tem is not None:
img = img_tem
else:
img = np.transpose(img, (2, 0, 1))
emb_array.append(insight_model.get_feature(img))
emb_array = np.array(emb_array)
return emb_array
def main(args):
arcface_model = face_model.FaceModel(args)
gender_model = 'gender/face_model.pkl'
with open(gender_model, 'rb') as f:
clf, labels = pickle.load(f, encoding='latin1')
video_capture = cv2.VideoCapture(0)
classifier_path = "trained_classifier/classifier.pkl"
with open(classifier_path, 'rb') as f:
(model, class_names) = pickle.load(f)
print("Loaded classifier file")
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=.7)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
# Bounding box
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, "align")
# Using two threads, one for recognition, the other for detecton, tracking and showing the results
p1 = ThreadWithReturnValue(target=detect_align_recognize,
args=(
video_capture,
pnet,
rnet,
onet,
model,
class_names,
clf,
labels,
arcface_model,
))
p2 = ThreadWithReturnValue(target=track_and_show,
args=(video_capture, )) #, clf, labels
p1.start()
p2.start()
p1.join()
p2.join()
def load_model(self):
self.db = DBManagement()
# Initialize our lists of extracted facial embeddings and corresponding people names
self.features = []
self.ids = []
#self.genders = []
#self.ages = []
#self.clusters = []
#self.fileNames = []
parser = argparse.ArgumentParser()
parser.add_argument('--image-size', default='112,112', help='')
parser.add_argument(
'--model',
default='insightface/models/model-y1-test2/model,0',
help='path to load model.')
parser.add_argument('--ga-model',
default='insightface/models/gamodel-r50/model,0',
help='path to load model.')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument(
'--det',
default=0,
type=int,
help='mtcnn option, 1 means using R+O, 0 means detect from begining'
)
parser.add_argument('--flip',
default=0,
type=int,
help='whether do lr flip aug')
parser.add_argument('--threshold',
default=1.24,
type=float,
help='ver dist threshold')
args = parser.parse_args()
# Initialize the faces embedder
self.model = face_model.FaceModel(args)
def main(args):
model = face_model.FaceModel(args)
video_capture = cv2.VideoCapture(0)
# init load
img2 = cv2.imread('unknown.jpg')
img2 = model.get_input(img2)
features2 = model.get_feature(img2)
while True:
# Grab a single frame of video
ret, frame = video_capture.read()
# Resize frame of video to 1/4 size for faster face recognition processing
small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
bounding_boxes, landmarks, face_img = model.get_bbox_and_landmarks(
small_frame)
bounding_boxes = bounding_boxes.astype(int)
features1 = model.get_feature(face_img)
# compare_faces
# TODO
for b in bounding_boxes:
# Scale back up face locations since the frame we detected in was scaled to 1/4 size
b *= 4
cv2.rectangle(frame, (b[0], b[1]), (b[2], b[3]), (0, 255, 0), 1)
cv2.imshow('Video', frame)
# Hit 'q' on the keyboard to quit!
if cv2.waitKey(1) & 0xFF == ord('q'):
break
video_capture.release()
cv2.destroyAllWindows()
def main(args):
model = face_model.FaceModel(args)
# 打开文件
dir0 = os.listdir(args.image_path)
count = len(dir0)
for id in dir0:
count -= 1
if count%3000 == 0:
print('last num:', count)
name_path = os.path.join(args.image_path, id)
name_files = get_all_path(name_path)
if len(name_files) < 4:
shutil.rmtree(name_path)
# print(args.image_path + id, 'is < 1 size')
continue
else:
feature = []
img_index = []
for index in range(len(name_files)):
file_path = name_files[index][0][1]["path"]
img = cv2.imread(file_path)
if img.ndim<2:
os.remove(file_path)
continue
# print(file_path)
aligned = model.get_input(img)
if aligned is None:
os.remove(file_path)
continue
else:
f1 = model.get_feature(aligned)
feature.append(f1)
img_index.append(file_path)
review_img(feature, img_index)
name_files = os.listdir(name_path)
if len(name_files) < 2: # 清洗后的数量小于20删除该文件夹
shutil.rmtree(name_path)
def main():
args = parse_arguments()
model = face_model.FaceModel(args)
path = args.data_img
name = [x for x in os.listdir(path) if x != '.DS_Store']
files = [os.path.join(path, f) for f in name]
with open('Data/data.csv', 'w') as csvData:
with open('Data/name.csv', 'w') as csvName:
writerData = csv.writer(csvData)
writerName = csv.writer(csvName)
for i in range(len(files)):
file = files[i]
img_names = os.listdir(file)
img_paths = [os.path.join(file, f) for f in img_names]
for img_path in img_paths:
print(img_path)
img = cv2.imread(img_path)
if img is None:
continue
try:
F1, _ = model.get_input(img)
except:
continue
f1 = model.get_feature(F1[0])
writerData.writerow(f1)
writerName.writerow([name[i]])
csvData.close()
csvName.close()
parser.add_argument(
'--det',
default=0,
type=int,
help='mtcnn option, 1 means using R+O, 0 means detect from begining')
parser.add_argument('--flip',
default=0,
type=int,
help='whether do lr flip aug')
parser.add_argument('--threshold',
default=1.24,
type=float,
help='ver dist threshold')
args = parser.parse_args()
model = face_model.FaceModel(args)
img = cv2.imread('a.png')
img = model.get_input_aligned(img)
# aligned = np.transpose(img, (1, 2, 0))
# aligned = cv2.cvtColor(aligned, cv2.COLOR_RGB2BGR)
# img = cv2.imwrite('a.png', aligned)
# f1 = model.get_ga(img)
# print(f1)
#print(f1[0:10])
gender, age = model.get_ga(img)
print(gender)
print(age)
# sys.exit(0)
# img = cv2.imread('a.png')
# img = model.get_input(img)
# f2 = model.get_gender(img)
