w, h = img.shape

ret = np.empty((w, h, 3), dtype=np.uint8)

ret[:, :, 0] = ret[:, :, 1] = ret[:, :, 2] = img

img=cv2.imread(pjoin(person_dir, f))

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 判断数组维度

if gray.ndim == 2:

img = to_rgb(gray)

data = {}

for guy in os.listdir(data_dir):

person_dir = pjoin(data_dir, guy)

curr_pics = [read_img(person_dir, f) for f in os.listdir(person_dir)]

# 存储每一类人的文件夹内所有图片

data[guy] = curr_pics

# from align_dataset_mtcnn.py

minsize = 20 # minimum size of face

threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold

factor = 0.709 # scale factor

#mtcnn

print('Creating networks and loading parameters')

with tf.Graph().as_default():

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=1.0)

sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))

with sess.as_default():

pnet, rnet, onet = detect_face.create_mtcnn(sess, './data')

# 读取图片

img = image

# 获取图片的shape

img_size = np.asarray(img.shape)[0:2]

# 返回边界框数组 （参数分别是输入图片 脸部最小尺寸 三个网络 阈值 factor不清楚）

bounding_boxes, _ = detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)

if len(bounding_boxes) < 1:

return 0,0,0

else:

crop=[]

det=bounding_boxes

det[:,0]=np.maximum(det[:,0], 0)

det[:,1]=np.maximum(det[:,1], 0)

det[:,2]=np.minimum(det[:,2], img_size[1])

det[:,3]=np.minimum(det[:,3], img_size[0])

# det[:,0]=np.maximum(det[:,0]-margin/2, 0)

# det[:,1]=np.maximum(det[:,1]-margin/2, 0)

# det[:,2]=np.minimum(det[:,2]+margin/2, img_size[1])

# det[:,3]=np.minimum(det[:,3]+margin/2, img_size[0])

det=det.astype(int)

for i in range(len(bounding_boxes)):

temp_crop=img[det[i,1]:det[i,3],det[i,0]:det[i,2],:]

aligned=misc.imresize(temp_crop, (image_size, image_size), interp='bilinear')

prewhitened = facenet.prewhiten(aligned)

crop.append(prewhitened)

crop_image=np.stack(crop)

model_dir = args.model_dir

with tf.Graph().as_default():

with tf.Session() as sess:

meta_file, ckpt_file = facenet.get_model_filenames(args.model_dir)

facenet.load_model(model_dir,meta_file,ckpt_file)

print('建立facenet embedding模型')

# 返回给定名称的tensor

images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")

embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")

phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")

model = joblib.load('./models/knn_classifier.model')

#开启ip摄像头

video="http://admin:[email protected]:8081/" #此处@后的ipv4 地址需要修改为自己的地址

# 参数为0表示打开内置摄像头，参数是视频文件路径则打开视频

capture =cv2.VideoCapture(video)

cv2.namedWindow("camera",1)

c=0

num = 0

frame_interval=3 # frame intervals

while True:

ret, frame = capture.read()

timeF = frame_interval

# print(shape(frame))

detect_face=[]

if(c%timeF == 0):

find_results=[]

# cv2.imshow("camera",frame)

gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

if gray.ndim == 2:

img = to_rgb(gray)

det,crop_image,j= load_and_align_data(img, 160, 44, 1.0)

if j:

feed_dict = {images_placeholder: crop_image, phase_train_placeholder:False }

emb = sess.run(embeddings, feed_dict=feed_dict)

for xx in range(len(emb)):

print(type(emb[xx,:]),emb[xx,:].shape)

detect_face.append(emb[xx,:])

detect_face=np.array(detect_face)

detect_face=detect_face.reshape(-1,128)

print('facenet embedding模型建立完毕')

predict = model.predict(detect_face)

print(predict)

result=[]

for i in range(len(predict)):

if predict[i]==0:

result.append('Chen')

elif predict[i]==1:

result.append('Reenie')

# 绘制矩形框并标注文字

for rec_position in range(len(det)):

cv2.rectangle(frame,(det[rec_position,0],det[rec_position,1]),(det[rec_position,2],det[rec_position,3]),(0, 255, 0), 2, 8, 0)

cv2.putText(

frame,

result[rec_position],

(det[rec_position,0],det[rec_position,1]),

cv2.FONT_HERSHEY_COMPLEX_SMALL,

0.8,

(0, 0 ,255),

thickness = 2,

lineType = 2)

cv2.imshow('camera',frame)

c+=1

key = cv2.waitKey(3)

if key == 27:

#esc键退出

print("esc break...")

break

if key == ord(' '):

# 保存一张图像

num = num+1

filename = "frames_%s.jpg" % num

cv2.imwrite(filename,frame)

# When everything is done, release the capture

parser = argparse.ArgumentParser()

parser.add_argument('--model_dir', type=str,

help='Directory containing the meta_file and ckpt_file', default='./20170512-110547')

#parser.add_argument('--dlib_face_predictor', type=str,

#help='File containing the dlib face predictor.', default='../data/shape_predictor_68_face_landmarks.dat')

#parser.add_argument('--image_size', type=int,

# help='Image size (height, width) in pixels.', default=160)

#parser.add_argument('image_files', type=str, nargs='+', help='Images to compare')

#parser.add_argument('--margin', type=int,

# help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)

#parser.add_argument('--gpu_memory_fraction', type=float,

# help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)