年龄_性别识别

Posted 2020-12-10 liuwenhua

参考开源项目：年龄_性别识别

1.识别效果如下图

2.keras模型转pb模型，方便模型的迁移和rknn平台的使用，代码1如下：

from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K
from wide_resnet import WideResNet
import tensorflow as tf
from tensorflow.python.framework import graph_io

print(tf.__version__)
import keras as ks
print(ks.__version__)
import platform

print (platform.python_version())

def freeze_graph(graph, session, output_node_names, model_name):
    with graph.as_default():
        graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
        graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output_node_names)
        graph_io.write_graph(graphdef_frozen, "tmp", os.path.basename(model_name) + ".pb", as_text=False)
        print("done")




def pb_transfer():
    weight_file = "E:\\python_project\\age-gender-estimation-master\\pretrained_models\\weights.28-3.73.hdf5"

    output_fld =‘./‘
    output_graph_name = ‘age-gender.pb‘
    tf.keras.backend.set_learning_phase(0)

    img_size = 64
    model = WideResNet(img_size, depth=16, k=8)()
    model.load_weights(weight_file)
    for out in model.outputs:
        print(out.op.name)

    session = tf.keras.backend.get_session()
    freeze_graph(session.graph, session, [out.op.name for out in model.outputs], weight_file)



if __name__ == ‘__main__‘:
    pb_transfer()

View Code

代码2如下：

# coding=utf-8

from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K
#路径参数
weight_file_path = "E:\\python_project\\age-gender-estimation-master\\pretrained_models\\weights.28-3.73.hdf5"
output_graph_name = ‘ttt.pb‘
#转换函数
def h5_to_pb(h5_model,output_dir,model_name,out_prefix = "output_",log_tensorboard = True):
    if osp.exists(output_dir) == False:
        os.mkdir(output_dir)
    out_nodes = []
    for i in range(len(h5_model.outputs)):
        out_nodes.append(out_prefix + str(i + 1))
        tf.identity(h5_model.output[i],out_prefix + str(i + 1))
    sess = K.get_session()
    from tensorflow.python.framework import graph_util,graph_io
    init_graph = sess.graph.as_graph_def()
    main_graph = graph_util.convert_variables_to_constants(sess,init_graph,out_nodes)
    graph_io.write_graph(main_graph,output_dir,name = model_name,as_text = False)
    if log_tensorboard:
        from tensorflow.python.tools import import_pb_to_tensorboard
        import_pb_to_tensorboard.import_to_tensorboard(osp.join(output_dir,model_name),output_dir)
#输出路径
output_dir = "./"
#加载模型
#h5_model = load_model(weight_file_path)
from keras.models import load_model
import tensorflow as tf
import os
import os.path as osp
from keras import backend as K
from wide_resnet import WideResNet
import tensorflow as tf
weight_file = "E:\\python_project\\age-gender-estimation-master\\pretrained_models\\weights.28-3.73.hdf5"

output_fld =‘./‘
tf.keras.backend.set_learning_phase(0)
img_size = 64
model = WideResNet(img_size, depth=16, k=8)()
model.load_weights(weight_file)
h5_to_pb(model,output_dir = output_dir,model_name = output_graph_name)
print(‘model saved‘)

View Code

3.推理代码如下：

import tensorflow as tf
from tensorflow.python.platform import gfile
import os
import cv2
import numpy as np
import time

from keras.layers import Input, Activation, add, Dense, Flatten, Dropout

#facenet_model_checkpoint ="E:\\python_project\\age-gender-estimation-master\\tmp\\weights.28-3.73.hdf5.pb"
facenet_model_checkpoint ="E:\\python_project\\age-gender-estimation-master\\ttt.pb"


def load_model(model, input_map=None):
    # Check if the model is a model directory (containing a metagraph and a checkpoint file)
    #  or if it is a protobuf file with a frozen graph
    model_exp = os.path.expanduser(model)
    if (os.path.isfile(model_exp)):
        print(‘Model filename: %s‘ % model_exp)
        with gfile.FastGFile(model_exp,‘rb‘) as f:
            graph_def = tf.GraphDef()
            graph_def.ParseFromString(f.read())
            tf.import_graph_def(graph_def, input_map=input_map, name=‘‘)



def main():
    img_size = 64
    with tf.Graph().as_default():
        with tf.Session() as sess:
            print("load model:" + facenet_model_checkpoint)
            load_model(facenet_model_checkpoint)
            print("load over.")


            images_placeholder = tf.get_default_graph().get_tensor_by_name("input_1:0")
            gender = tf.get_default_graph().get_tensor_by_name("pred_gender/Softmax:0")
            age = tf.get_default_graph().get_tensor_by_name("pred_age/Softmax:0")
            while True:
                img = cv2.imread("E:\\python_project\\age-gender-estimation-master\\0036.jpg")
                faces = cv2.resize(img, (img_size, img_size))
                faces = faces[np.newaxis, :, :, :]
                start_time = time.time()
                feed_dict = {images_placeholder: faces}
                results = sess.run([gender,age], feed_dict=feed_dict)
                predicted_genders = results[0]
               # print(predicted_genders)



                ages = np.arange(0, 101).reshape(101, 1)
                predicted_ages = results[1].dot(ages).flatten()
                print("spend_time is", time.time() - start_time)
                print(int(predicted_ages[0]))
                if predicted_genders[0][0] < 0.5:
                    print("m")
                else:
                    print("f")

if __name__ == ‘__main__‘:
    main()

View Code

4.推理时间在tx2上为：70ms