Exemple #1
0
    # plt.axis('off')
    # plt.show()

with tf.Graph().as_default() as g:
    # gray_images = []
    # for image in face_images:
    #     grayed_image = tf.image.rgb_to_grayscale(face_image)
    #     gray_images.append(grayed_image)

    images = np.reshape(face_images, [len(faces), 42, 42, 1])
    # images = tf.pack(face_images)
    # images = tf.cast(tf.reshape(images, [len(faces), 42, 42, 1]), tf.float32)
    # print(images)

    with tf.Session() as sess:
        cnn = CNN.CNN_net(False)

        sess.run(tf.global_variables_initializer())
        saver = tf.train.Saver(max_to_keep=1)

        model_file = tf.train.latest_checkpoint('ckpt1/')
        saver.restore(sess, model_file)

        feed_dict_test = {cnn.images: images}
        labels = sess.run(cnn.logits, feed_dict=feed_dict_test)
        # print(labels)

im = Image.open(image_path)
for i in range(len(face_images)):
    emotion = emotions[np.where(labels[i] == np.max(labels[i]))[0][0]]
Exemple #2
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import tensorflow as tf
import datetime
from timer import Timer
from fer2013 import Fer2013
import numpy as np
import CNN

# img1 = utils.load_image("./test_data/tiger.jpeg")
# img1_true_result = [1 if i == 292 else 0 for i in range(7)]  # 1-hot result for tiger

# batch1 = img1.reshape((1, 224, 224, 3))

with tf.Session() as sess:
    cnn = CNN.CNN_net()

    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver(max_to_keep=1)

    train_timer = Timer()
    load_timer = Timer()

    fer2013 = Fer2013('train')
    fer2013_test = Fer2013('val')

    max_iter = 100000
    summary_iter = 10
    epoch = 0

    for step in range(1, max_iter + 1):

        load_timer.tic()