models.py

import os
from keras.models import *
from keras.layers import *
from keras.applications import *
from keras import backend as K
import tensorflow as tf
import mtcnn_detect_face


def emotion_recognition_model(weight_path):
    base_model = MobileNet(weights=None, include_top=False, input_shape=(128, 128, 3))
    x = base_model.output
    x = GlobalAveragePooling2D()(x)
    x = Reshape((1, 1, 1024), name='reshape_1')(x)
    x = Dropout(1e-3, name='dropout')(x)
    x = Conv2D(7, (1, 1), padding='same', name='conv_preds')(x)
    x = Activation('softmax', name='act_softmax')(x)
    predictions = Reshape((7,), name='reshape_2')(x)
    model =  Model(inputs=base_model.input, outputs=predictions)
    model.load_weights(weight_path)
    return model

def create_mtcnn(sess, model_path):
    with tf.variable_scope('pnet2'):
        data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
        pnet = mtcnn_detect_face.PNet({'data':data})
        pnet.load(os.path.join(model_path, 'det1.npy'), sess)
    with tf.variable_scope('rnet2'):
        data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
        rnet = mtcnn_detect_face.RNet({'data':data})
        rnet.load(os.path.join(model_path, 'det2.npy'), sess)
    with tf.variable_scope('onet2'):
        data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
        onet = mtcnn_detect_face.ONet({'data':data})
        onet.load(os.path.join(model_path, 'det3.npy'), sess)
    return pnet, rnet, onet
    
def get_src_landmarks(x0, x1, y0, y1, pnts):
    """
    x0, x1, y0, y1: (smoothed) bbox coord.
    pnts: landmarks predicted by MTCNN
    """    
    src_landmarks = [(int(pnts[i+5][0]-x0), 
                      int(pnts[i][0]-y0)) for i in range(5)]
    return src_landmarks

def process_mtcnn_bbox(bboxes, im_shape):
    """
    output bbox coordinate of MTCNN is (y0, x0, y1, x1)
    Here we process the bbox coord. to a square bbox with ordering (x0, y1, x1, y0)
    """
    for i, bbox in enumerate(bboxes):
        y0, x0, y1, x1 = bboxes[i,0:4]
        w, h = int(y1 - y0), int(x1 - x0)
        length = (w + h)/2
        center = (int((x1+x0)/2),int((y1+y0)/2))
        new_x0 = np.max([0, (center[0]-length//2)])#.astype(np.int32)
        new_x1 = np.min([im_shape[0], (center[0]+length//2)])#.astype(np.int32)
        new_y0 = np.max([0, (center[1]-length//2)])#.astype(np.int32)
        new_y1 = np.min([im_shape[1], (center[1]+length//2)])#.astype(np.int32)
        bboxes[i,0:4] = new_x0, new_y1, new_x1, new_y0
    return bboxes