video-detect-face-verification/tmp/mtcnn_test.py

# MIT License
# 
# Copyright (c) 2016 David Sandberg
# 
# 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
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# 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
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
import numpy as np
import align.detect_face

g1 = tf.Graph()
with g1.as_default():
    data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
    pnet = align.detect_face.PNet({'data':data})
    sess1 = tf.Session(graph=g1)
    pnet.load('../../data/det1.npy', sess1)
    pnet_fun = lambda img : sess1.run(('conv4-2/BiasAdd:0', 'prob1:0'), feed_dict={'input:0':img})
np.random.seed(666)
img = np.random.rand(1,3,150,150)
img = np.transpose(img, (0,2,3,1))
      
np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
       
# prob1=sess1.run('prob1:0', feed_dict={data:img})
# print(prob1[0,0,0,:])
# conv42=sess1.run('conv4-2/BiasAdd:0', feed_dict={data:img})
# print(conv42[0,0,0,:])
      
# conv42, prob1 = pnet_fun(img)
# print(prob1[0,0,0,:])
# print(conv42[0,0,0,:])


# [ 0.9929  0.0071] prob1, caffe
# [ 0.9929  0.0071] prob1, tensorflow
  
# [ 0.1207 -0.0116 -0.1231 -0.0463] conv4-2, caffe
# [ 0.1207 -0.0116 -0.1231 -0.0463] conv4-2, tensorflow
      

g2 = tf.Graph()
with g2.as_default():
    data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
    rnet = align.detect_face.RNet({'data':data})
    sess2 = tf.Session(graph=g2)
    rnet.load('../../data/det2.npy', sess2)
    rnet_fun = lambda img : sess2.run(('conv5-2/conv5-2:0', 'prob1:0'), feed_dict={'input:0':img})
np.random.seed(666)
img = np.random.rand(73,3,24,24)
img = np.transpose(img, (0,2,3,1))
 
# np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
#  
# prob1=sess2.run('prob1:0', feed_dict={data:img})
# print(prob1[0,:])
# 
# conv52=sess2.run('conv5-2/conv5-2:0', feed_dict={data:img})
# print(conv52[0,:])
  
# [ 0.9945  0.0055] prob1, caffe
# [ 0.1108 -0.0038 -0.1631 -0.0890] conv5-2, caffe
 
# [ 0.9945  0.0055] prob1, tensorflow
# [ 0.1108 -0.0038 -0.1631 -0.0890] conv5-2, tensorflow

    
g3 = tf.Graph()
with g3.as_default():
    data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
    onet = align.detect_face.ONet({'data':data})
    sess3 = tf.Session(graph=g3)
    onet.load('../../data/det3.npy', sess3)
    onet_fun = lambda img : sess3.run(('conv6-2/conv6-2:0', 'conv6-3/conv6-3:0', 'prob1:0'), feed_dict={'input:0':img})
np.random.seed(666)
img = np.random.rand(11,3,48,48)
img = np.transpose(img, (0,2,3,1))
 
# np.set_printoptions(formatter={'float': '{: 0.4f}'.format})
#  
# prob1=sess3.run('prob1:0', feed_dict={data:img})
# print(prob1[0,:])
# print('prob1, tensorflow')
# 
# conv62=sess3.run('conv6-2/conv6-2:0', feed_dict={data:img})
# print(conv62[0,:])
# print('conv6-2, tensorflow')
#  
# conv63=sess3.run('conv6-3/conv6-3:0', feed_dict={data:img})
# print(conv63[0,:])
# print('conv6-3, tensorflow')

# [ 0.9988  0.0012] prob1, caffe
# [ 0.0446 -0.0968 -0.1091 -0.0212] conv6-2, caffe
# [ 0.2429  0.6104  0.4074  0.3104  0.5939  0.2729  0.2132  0.5462  0.7863  0.7568] conv6-3, caffe
  
# [ 0.9988  0.0012] prob1, tensorflow
# [ 0.0446 -0.0968 -0.1091 -0.0212] conv6-2, tensorflow
# [ 0.2429  0.6104  0.4074  0.3104  0.5939  0.2729  0.2132  0.5462  0.7863  0.7568] conv6-3, tensorflow

#pnet_fun = lambda img : sess1.run(('conv4-2/BiasAdd:0', 'prob1:0'), feed_dict={'input:0':img})