MTCNNWrapper.py

# MIT License
#
# Copyright (c) 2017 Baoming Wang
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
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# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# SOFTWARE.

"""
  This class works as a wrapper class for MTCNN model class.
  It reads the images, loads the MTCNN model into memory and
  performs the facial detection along with landmark detection
"""

import cv2
import sys
import time
import argparse
import numpy as np
import tensorflow as tf

from mtcnn_src.mtcnn import PNet, RNet, ONet
from mtcnn_src.tools import detect_face, get_model_filenames

class MTCNNWrapper:
    def __init__(self):
        # directory for trained model
        self.model_dir = './mtcnn_model/all_in_one'

        # three thresholds for pnet, rnet, onet respectively
        self.threshold = [0.8, 0.8, 0.8]

        # the minimum size of face to detect
        self.minsize = 20
        
        # the scale stride of original image
        self.factor = 0.7

    def get_result_dict(self, image=None):
        file_paths = get_model_filenames(self.model_dir)
        with tf.device('/gpu:0'):
            with tf.Graph().as_default():
                config = tf.ConfigProto(allow_soft_placement=True)
                with tf.Session(config=config) as sess:
                    if len(file_paths) == 3:
                        image_pnet = tf.placeholder(
                                tf.float32, [None, None, None, 3])
                        pnet = PNet({'data':image_pnet}, mode='test')
                        out_tensor_pnet = pnet.get_all_output()

                        image_rnet = tf.placeholder(
                                tf.float32, [None, 24, 24, 3])
                        rnet = RNet({'data':image_rnet}, mode='test')
                        out_tensor_rnet = rnet.get_all_output()

                        saver_pnet = tf.train.Saver(
                                [v for v in tf.global_variables()
                                    if v.name[0:5] == 'pnet/'])
                        saver_rnet = tf.train.Saver(
                                [v for v in tf.global_variables()
                                    if v.name[0:5] == "rnet/"])
                        saver_onet = tf.train.Saver(
                                [v for v in tf.global_variables()
                                    if v.name[0:5] == "onet/"])

                        saver_pnet.restore(sess, file_paths[0])
                        def pnet_fun(img): return sess.run(
                            out_tensor_pnet, feed_dict={image_pnet: img})
    
                        saver_rnet.restore(sess, file_paths[1])
    
                        def rnet_fun(img): return sess.run(
                            out_tensor_rnet, feed_dict={image_rnet: img})
    
                        saver_onet.restore(sess, file_paths[2])
    
                        def onet_fun(img): return sess.run(
                            out_tensor_onet, feed_dict={image_onet: img})
                    else:
                        saver = tf.train.import_meta_graph(file_paths[0])
                        saver.restore(sess, file_paths[1])
    
                        def pnet_fun(img): return sess.run(
                            ('softmax/Reshape_1:0',
                             'pnet/conv4-2/BiasAdd:0'),
                            feed_dict={
                                'Placeholder:0': img})
    
                        def rnet_fun(img): return sess.run(
                            ('softmax_1/softmax:0',
                             'rnet/conv5-2/rnet/conv5-2:0'),
                            feed_dict={
                                'Placeholder_1:0': img})
    
                        def onet_fun(img): return sess.run(
                            ('softmax_2/softmax:0',
                             'onet/conv6-2/onet/conv6-2:0',
                             'onet/conv6-3/onet/conv6-3:0'),
                            feed_dict={
                                'Placeholder_2:0': img})
    
                    start_time = time.time()
                    rectangles, points = detect_face(image, self.minsize,
                                                     pnet_fun, rnet_fun, onet_fun,
                                                     self.threshold, self.factor)
                    duration = time.time() - start_time
    
                    print(duration)
                    #print(type(rectangles))
                    #points = np.transpose(points)
                    return rectangles, points


if __name__ == "__main__":
    obj = MTCNNWrapper()
    img = cv2.imread('./images/IMG_2706.JPG')
    rect, pts = obj.get_result_dict(img)
    print ('rect->', rect)
    print ('pts->', pts)