Question

from Config import Config

from FaceDetection.MTCNNDetect import MTCNNDetect
import cv2
import tensorflow as tf
import keras
from keras import backend as K
from keras.layers import Input, Lambda, Dense, Dropout, Convolution2D, MaxPooling2D, Flatten, Concatenate, concatenate
from keras.models import Model


face_detect = MTCNNDetect(model_path=Config.MTCNN_MODEL)

from FaceRecognition.TensorflowGraph import FaceRecGraph
from src.FaceAlignment import AlignCustom
from FaceRecognition.FaceFeature import FaceFeature


FRGraph = FaceRecGraph()
aligner = AlignCustom()
extract_feature = FaceFeature(FRGraph)

img1 = cv2.imread('data5/s1/8.jpg')
rects, landmarks = face_detect.detect_face(img1, Config.MINIMUM_FACE_SIZE_FOR_REGISTRATION)
# print("length of rect", len(rects))
if len(rects) == 1:
    for i, rect in enumerate(rects):
        if rect != []:
            aligned_face = aligner.align(160, img1, landmarks[i])
            vector1 = np.squeeze(extract_feature.get_features(np.expand_dims(aligned_face, axis=0)), axis=0)


img2 = cv2.imread('data5/s1/10.jpg')
rects, landmarks = face_detect.detect_face(img2, Config.MINIMUM_FACE_SIZE_FOR_REGISTRATION)
# print("length of rect", len(rects))
if len(rects) == 1:
    for i, rect in enumerate(rects):
        if rect != []:
            aligned_face = aligner.align(160, img2, landmarks[i])
            vector2 = np.squeeze(extract_feature.get_features(np.expand_dims(aligned_face, axis=0)), axis=0)



vec1 = vector1.reshape(1, 128)
vec2 = vector2.reshape(1, 128)

data_tensor1 = tf.convert_to_tensor(vec1)
data_tensor2 = tf.convert_to_tensor(vec2)

input_dim = (224,224,3)
img_a = tf.keras.layers.Input(shape=input_dim)
img_b = tf.keras.layers.Input(shape=input_dim)

L1_layer = Lambda(lambda tensors: tf.abs(tensors[0] - tensors[1]))
L1_distance = L1_layer([data_tensor1, data_tensor2])

prediction = Dense(1, activation='sigmoid')(L1_distance)

siamese_net = Model(inputs=[img_a, img_b], outputs=prediction)
siamese_net.summary()

我正在尝试在现有FaceNet模型的基础上创建一个暹罗网络。我正在使用预先训练的模型创建特征向量。

Vector1和Vector2是我正在馈送到我的暹罗网络的特征向量。取得它们之间的绝对差异并将其馈送到密集层。

定义模型时出现以下错误：

*回溯（最近通话最近）：

中的文件“ C：/ Users / techolution / Desktop / Facenet / Create Vectors / Siamese Network-Test.py”，第104行

siamese_net = Model(inputs=[img_a, img_b], outputs=prediction)

文件“ C：\ Users \ techolution \ Desktop \ Facenet \ Create Vectors \ venv \ lib \ site-packages \ keras \ legacy \ interfaces.py”，包装中的第91行返回func（* args，** kwargs）

第94行中的文件“ C：\ Users \ techolution \ Desktop \ Facenet \ Create Vectors \ venv \ lib \ site-packages \ keras \ engine \ network.py”， init self._init_graph_network（* args，** kwargs）

_init_graph_network中第241行的文件“ C：\ Users \ techolution \ Desktop \ Facenet \ Create Vectors \ venv \ lib \ site-packages \ keras \ engine \ network.py” self.inputs，self.outputs）

_map_graph_network中的第1434行，文件“ C：\ Users \ techolution \ Desktop \ Facenet \ Create Vectors \ venv \ lib \ site-packages \ keras \ engine \ network.py” tensor_index = tensor_index）

build_map中的文件“ C：\ Users \ techolution \ Desktop \ Facenet \ Create Vectors \ venv \ lib \ site-packages \ keras \ engine \ network.py”，第1421行 node_index，张量索引）

在build_map中的文件“ C：\ Users \ techolution \ Desktop \ Facenet \ Create Vectors \ venv \ lib \ site-packages \ keras \ engine \ network.py”，行1393 节点= layer._inbound_nodes [node_index]

AttributeError：“ NoneType”对象没有属性“ _inbound_nodes” *

AttributeError：“ NoneType”对象没有属性“ _inbound_nodes” Keras

0 个答案: