def mk_final_pred_item():
CHECKPOINT_SAVE_PATH = os.path.join(os.path.dirname(__file__), os.pardir, 'model_checkpoints')
tf.reset_default_graph()
# used to map the output from the prediction to the emotion class
X = tf.placeholder(
tf.float32, shape=[None, 48, 48, 3]
)
keep_prob = tf.placeholder(tf.float32)
y_conv = model(X, keep_prob)
with tf.Session(config=config) as sess:
saver = tf.train.Saver()
saver.restore(sess, os.path.join(CHECKPOINT_SAVE_PATH, 'model.ckpt'))
name_item = from_cam(sess,X,y_conv,keep_prob)
print(name_item)
return name_item
files = glob.glob(emoji_png_files_path)
logger.info('loading the emoji png files in memory ...')
import platform
if platform.system() == 'Windows':
split_string = '\\'
else:
split_string = '/'
for file in tqdm.tqdm(files):
logger.debug('file path: {}'.format(file))
emoji_to_pic[file.split(split_string)[-1].split('.')[0]] = cv2.imread(
file, -1)
X = tf.placeholder(tf.float32, shape=[None, 48, 48, 1])
keep_prob = tf.placeholder(tf.float32)
y_conv = model(X, keep_prob)
saver = tf.train.Saver()
with tf.Session(config=config) as sess:
saver.restore(sess, os.path.join(CHECKPOINT_SAVE_PATH, 'model.ckpt'))
logger.info('Opening the camera for getting the video feed ...')
logger.info('PRESS "q" AT ANY TIME TO EXIT!')
from_cam(sess)