text: ''' Tensorflow implementation of the face detection / alignment algorithm found at
  https://github.com/kpzhang93/MTCNN_face_detection_alignment
  '''
  # 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, division, print_function
  from six import string_types, iteritems
  import tensorflow as tf
  from scipy import misc
  import numpy as np
  import os
  
  
  def layer(op):
    '''Decorator for composable network layers.'''
  
    def layer_decorated(self, *args, **kwargs):
      # Automatically set a name if not provided.
      name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
      # Figure out the layer inputs.
      if len(self.terminals) == 0:
        raise RuntimeError('No input variables found for layer %s.' % name)
      elif len(self.terminals) == 1:
        layer_input = self.terminals[0]
      else:
        layer_input = list(self.terminals)
      # Perform the operation and get the output.
      layer_output = op(self, layer_input, *args, **kwargs)
      # Add to layer LUT.
      self.layers[name] = layer_output
      # This output is now the input for the next layer.
      self.feed(layer_output)
      # Return self for chained calls.
      return self
  
    return layer_decorated
  
  class Network(object):
  
    def __init__(self, inputs, trainable=True):
      # The input nodes for this network
      self.inputs = inputs
      # The current list of terminal nodes
      self.terminals = []
      # Mapping from layer names to layers
      self.layers = dict(inputs)
      # If true, the resulting variables are set as trainable
      self.trainable = trainable
  
      self.setup()
  
    def setup(self):
      '''Construct the network. '''
      raise NotImplementedError('Must be implemented by the subclass.')
  
    def load(self, data_path, session, ignore_missing=False):
      '''Load network weights.
      data_path: The path to the numpy-serialized network weights
      session: The current TensorFlow session
      ignore_missing: If true, serialized weights for missing layers are ignored.
      '''
      data_dict = np.load(data_path, encoding='latin1', allow_pickle=True).item() #pylint: disable=no-member
  
      for op_name in data_dict:
        with tf.variable_scope(op_name, reuse=True):
          for param_name, data in iteritems(data_dict[op_name]):
            try:
              var = tf.get_variable(param_name)
              session.run(var.assign(data))
            except ValueError:
              if not ignore_missing:
                raise
  
    def feed(self, *args):
      '''Set the input(s) for the next operation by replacing the terminal nodes.
      The arguments can be either layer names or the actual layers.
      '''
      assert len(args) != 0
      self.terminals = []
      for fed_layer in args:
        if isinstance(fed_layer, string_types):
          try:
            fed_layer = self.layers[fed_layer]
          except KeyError:
            raise KeyError('Unknown layer name fed: %s' % fed_layer)
        self.terminals.append(fed_layer)
      return self
  
    def get_output(self):
      '''Returns the current network output.'''
      return self.terminals[-1]
  
    def get_unique_name(self, prefix):
      '''Returns an index-suffixed unique name for the given prefix.
      This is used for auto-generating layer names based on the type-prefix.
      '''
      ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
      return '%s_%d' % (prefix, ident)
  
    def make_var(self, name, shape):
      '''Creates a new TensorFlow variable.'''
      return tf.get_variable(name, shape, trainable=self.trainable)
  
    def validate_padding(self, padding):
      '''Verifies that the padding is one of the supported ones.'''
      assert padding in ('SAME', 'VALID')
  
    @layer
    def conv(self,
         inp,
         k_h,
         k_w,
         c_o,
         s_h,
         s_w,
         name,
         relu=True,
         padding='SAME',
         group=1,
         biased=True):
      # Verify that the padding is acceptable
      self.validate_padding(padding)
      # Get the number of channels in the input
      c_i = int(inp.get_shape()[-1])
      # Verify that the grouping parameter is valid
      assert c_i % group == 0
      assert c_o % group == 0
      # Convolution for a given input and kernel
      convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
      with tf.variable_scope(name) as scope:
        kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
        # This is the common-case. Convolve the input without any further complications.
        output = convolve(inp, kernel)
        # Add the biases
        if biased:
          biases = self.make_var('biases', [c_o])
          output = tf.nn.bias_add(output, biases)
        if relu:
          # ReLU non-linearity
          output = tf.nn.relu(output, name=scope.name)
        return output
  
    @layer
    def prelu(self, inp, name):
      with tf.variable_scope(name):
        i = int(inp.get_shape()[-1])
        alpha = self.make_var('alpha', shape=(i,))
        output = tf.nn.relu(inp) + tf.multiply(alpha, -tf.nn.relu(-inp))
      return output
  
    @layer
    def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
      self.validate_padding(padding)
      return tf.nn.max_pool(inp,
                  ksize=[1, k_h, k_w, 1],
                  strides=[1, s_h, s_w, 1],
                  padding=padding,
                  name=name)
  
    @layer
    def fc(self, inp, num_out, name, relu=True):
      with tf.variable_scope(name):
        input_shape = inp.get_shape()
        if input_shape.ndims == 4:
          # The input is spatial. Vectorize it first.
          dim = 1
          for d in input_shape[1:].as_list():
            dim *= int(d)
          feed_in = tf.reshape(inp, [-1, dim])
        else:
          feed_in, dim = (inp, input_shape[-1].value)
        weights = self.make_var('weights', shape=[dim, num_out])
        biases = self.make_var('biases', [num_out])
        op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
        fc = op(feed_in, weights, biases, name=name)
        return fc
  
  
    '''
    Multi dimensional softmax,
    refer to https://github.com/tensorflow/tensorflow/issues/210
    compute softmax along the dimension of target
    the native softmax only supports batch_size x dimension
    '''
    @layer
    def softmax(self, target, axis, name=None):
      max_axis = tf.reduce_max(target, axis, keepdims=True)
      target_exp = tf.exp(target-max_axis)
      normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
      softmax = tf.div(target_exp, normalize, name)
      return softmax
  
  class PNet(Network):
    def setup(self):
      (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
         .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
         .prelu(name='PReLU1')
         .max_pool(2, 2, 2, 2, name='pool1')
         .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
         .prelu(name='PReLU2')
         .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
         .prelu(name='PReLU3')
         .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
         .softmax(3,name='prob1'))
  
      (self.feed('PReLU3') #pylint: disable=no-value-for-parameter
         .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))
  
  class RNet(Network):
    def setup(self):
      (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
         .conv(3, 3, 28, 1, 1, padding='VALID', relu=False, name='conv1')
         .prelu(name='prelu1')
         .max_pool(3, 3, 2, 2, name='pool1')
         .conv(3, 3, 48, 1, 1, padding='VALID', relu=False, name='conv2')
         .prelu(name='prelu2')
         .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
         .conv(2, 2, 64, 1, 1, padding='VALID', relu=False, name='conv3')
         .prelu(name='prelu3')
         .fc(128, relu=False, name='conv4')
         .prelu(name='prelu4')
         .fc(2, relu=False, name='conv5-1')
         .softmax(1,name='prob1'))
  
      (self.feed('prelu4') #pylint: disable=no-value-for-parameter
         .fc(4, relu=False, name='conv5-2'))
  
  class ONet(Network):
    def setup(self):
      (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
         .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv1')
         .prelu(name='prelu1')
         .max_pool(3, 3, 2, 2, name='pool1')
         .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv2')
         .prelu(name='prelu2')
         .max_pool(3, 3, 2, 2, padding='VALID', name='pool2')
         .conv(3, 3, 64, 1, 1, padding='VALID', relu=False, name='conv3')
         .prelu(name='prelu3')
         .max_pool(2, 2, 2, 2, name='pool3')
         .conv(2, 2, 128, 1, 1, padding='VALID', relu=False, name='conv4')
         .prelu(name='prelu4')
         .fc(256, relu=False, name='conv5')
         .prelu(name='prelu5')
         .fc(2, relu=False, name='conv6-1')
         .softmax(1, name='prob1'))
  
      (self.feed('prelu5') #pylint: disable=no-value-for-parameter
         .fc(4, relu=False, name='conv6-2'))
  
      (self.feed('prelu5') #pylint: disable=no-value-for-parameter
         .fc(10, relu=False, name='conv6-3'))
  
  def create_mtcnn(sess, model_path):
    if not model_path:
      model_path,_ = os.path.split(os.path.realpath(__file__))
  
    with tf.variable_scope('pnet'):
      data = tf.placeholder(tf.float32, (None,None,None,3), 'input')
      pnet = PNet({'data':data})
      pnet.load(os.path.join(model_path, 'model', 'det1.npy'), sess)
    with tf.variable_scope('rnet'):
      data = tf.placeholder(tf.float32, (None,24,24,3), 'input')
      rnet = RNet({'data':data})
      rnet.load(os.path.join(model_path, 'model', 'det2.npy'), sess)
    with tf.variable_scope('onet'):
      data = tf.placeholder(tf.float32, (None,48,48,3), 'input')
      onet = ONet({'data':data})
      onet.load(os.path.join(model_path, 'model', 'det3.npy'), sess)
  
    pnet_fun = lambda img : sess.run(('pnet/conv4-2/BiasAdd:0', 'pnet/prob1:0'), feed_dict={'pnet/input:0':img})
    rnet_fun = lambda img : sess.run(('rnet/conv5-2/conv5-2:0', 'rnet/prob1:0'), feed_dict={'rnet/input:0':img})
    onet_fun = lambda img : sess.run(('onet/conv6-2/conv6-2:0', 'onet/conv6-3/conv6-3:0', 'onet/prob1:0'), feed_dict={'onet/input:0':img})
    return pnet_fun, rnet_fun, onet_fun
  
  def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
    '''Detects faces in an image, and returns bounding boxes and points for them.
    img: input image
    minsize: minimum faces' size
    pnet, rnet, onet: caffemodel
    threshold: threshold=[th1, th2, th3], th1-3 are three steps's threshold
    factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
    '''
    factor_count=0
    total_boxes=np.empty((0,9))
    points=np.empty(0)
    h=img.shape[0]
    w=img.shape[1]
    minl=np.amin([h, w])
    m=12.0/minsize
    minl=minl*m
    # create scale pyramid
    scales=[]
    while minl>=12:
      scales += [m*np.power(factor, factor_count)]
      minl = minl*factor
      factor_count += 1
  
    # first stage
    for scale in scales:
      hs=int(np.ceil(h*scale))
      ws=int(np.ceil(w*scale))
      im_data = imresample(img, (hs, ws))
      im_data = (im_data-127.5)*0.0078125
      img_x = np.expand_dims(im_data, 0)
      img_y = np.transpose(img_x, (0,2,1,3))
      out = pnet(img_y)
      out0 = np.transpose(out[0], (0,2,1,3))
      out1 = np.transpose(out[1], (0,2,1,3))
  
      boxes, _ = generateBoundingBox(out1[0,:,:,1].copy(), out0[0,:,:,:].copy(), scale, threshold[0])
  
      # inter-scale nms
      pick = nms(boxes.copy(), 0.5, 'Union')
      if boxes.size>0 and pick.size>0:
        boxes = boxes[pick,:]
        total_boxes = np.append(total_boxes, boxes, axis=0)
  
    numbox = total_boxes.shape[0]
    if numbox>0:
      pick = nms(total_boxes.copy(), 0.7, 'Union')
      total_boxes = total_boxes[pick,:]
      regw = total_boxes[:,2]-total_boxes[:,0]
      regh = total_boxes[:,3]-total_boxes[:,1]
      qq1 = total_boxes[:,0]+total_boxes[:,5]*regw
      qq2 = total_boxes[:,1]+total_boxes[:,6]*regh
      qq3 = total_boxes[:,2]+total_boxes[:,7]*regw
      qq4 = total_boxes[:,3]+total_boxes[:,8]*regh
      total_boxes = np.transpose(np.vstack([qq1, qq2, qq3, qq4, total_boxes[:,4]]))
      total_boxes = rerec(total_boxes.copy())
      total_boxes[:,0:4] = np.fix(total_boxes[:,0:4]).astype(np.int32)
      dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
  
    numbox = total_boxes.shape[0]
    if numbox>0:
      # second stage
      tempimg = np.zeros((24,24,3,numbox))
      for k in range(0,numbox):
        tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
        tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
        if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
          tempimg[:,:,:,k] = imresample(tmp, (24, 24))
        else:
          return np.empty()
      tempimg = (tempimg-127.5)*0.0078125
      tempimg1 = np.transpose(tempimg, (3,1,0,2))
      out = rnet(tempimg1)
      out0 = np.transpose(out[0])
      out1 = np.transpose(out[1])
      score = out1[1,:]
      ipass = np.where(score>threshold[1])
      total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
      mv = out0[:,ipass[0]]
      if total_boxes.shape[0]>0:
        pick = nms(total_boxes, 0.7, 'Union')
        total_boxes = total_boxes[pick,:]
        total_boxes = bbreg(total_boxes.copy(), np.transpose(mv[:,pick]))
        total_boxes = rerec(total_boxes.copy())
  
    numbox = total_boxes.shape[0]
    if numbox>0:
      # third stage
      total_boxes = np.fix(total_boxes).astype(np.int32)
      dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(total_boxes.copy(), w, h)
      tempimg = np.zeros((48,48,3,numbox))
      for k in range(0,numbox):
        tmp = np.zeros((int(tmph[k]),int(tmpw[k]),3))
        tmp[dy[k]-1:edy[k],dx[k]-1:edx[k],:] = img[y[k]-1:ey[k],x[k]-1:ex[k],:]
        if tmp.shape[0]>0 and tmp.shape[1]>0 or tmp.shape[0]==0 and tmp.shape[1]==0:
          tempimg[:,:,:,k] = imresample(tmp, (48, 48))
        else:
          return np.empty()
      tempimg = (tempimg-127.5)*0.0078125
      tempimg1 = np.transpose(tempimg, (3,1,0,2))
      out = onet(tempimg1)
      out0 = np.transpose(out[0])
      out1 = np.transpose(out[1])
      out2 = np.transpose(out[2])
      score = out2[1,:]
      points = out1
      ipass = np.where(score>threshold[2])
      points = points[:,ipass[0]]
      total_boxes = np.hstack([total_boxes[ipass[0],0:4].copy(), np.expand_dims(score[ipass].copy(),1)])
      mv = out0[:,ipass[0]]
  
      w = total_boxes[:,2]-total_boxes[:,0]+1
      h = total_boxes[:,3]-total_boxes[:,1]+1
      points[0:5,:] = np.tile(w,(5, 1))*points[0:5,:] + np.tile(total_boxes[:,0],(5, 1))-1
      points[5:10,:] = np.tile(h,(5, 1))*points[5:10,:] + np.tile(total_boxes[:,1],(5, 1))-1
      if total_boxes.shape[0]>0:
        total_boxes = bbreg(total_boxes.copy(), np.transpose(mv))
        pick = nms(total_boxes.copy(), 0.7, 'Min')
        total_boxes = total_boxes[pick,:]
        points = points[:,pick]
  
    return total_boxes, points
  
  
  def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, onet, threshold, factor):
    '''Detects faces in a list of images
    images: list containing input images
    detection_window_size_ratio: ratio of minimum face size to smallest image dimension
    pnet, rnet, onet: caffemodel
    threshold: threshold=[th1 th2 th3], th1-3 are three steps's threshold [0-1]
    factor: the factor used to create a scaling pyramid of face sizes to detect in the image.
    '''
    all_scales = [None] * len(images)
    images_with_boxes = [None] * len(images)
  
    for i in range(len(images)):
      images_with_boxes[i] = {'total_boxes': np.empty((0, 9))}
  
    # create scale pyramid
    for index, img in enumerate(images):
      all_scales[index] = []
      h = img.shape[0]
      w = img.shape[1]
      minsize = int(detection_window_size_ratio * np.minimum(w, h))
      factor_count = 0
      minl = np.amin([h, w])
      if minsize <= 12:
        minsize = 12
  
      m = 12.0 / minsize
      minl = minl * m
      while minl >= 12:
        all_scales[index].append(m * np.power(factor, factor_count))
        minl = minl * factor
        factor_count += 1
  
    # # # # # # # # # # # # #
    # first stage - fast proposal network (pnet) to obtain face candidates
    # # # # # # # # # # # # #
  
    images_obj_per_resolution = {}
  
    # TODO: use some type of rounding to number modulo 8 to increase probability that pyramid images will have the same resolution across input images
  
    for index, scales in enumerate(all_scales):
      h = images[index].shape[0]
      w = images[index].shape[1]
  
      for scale in scales:
        hs = int(np.ceil(h * scale))
        ws = int(np.ceil(w * scale))
  
        if (ws, hs) not in images_obj_per_resolution:
          images_obj_per_resolution[(ws, hs)] = []
  
        im_data = imresample(images[index], (hs, ws))
        im_data = (im_data - 127.5) * 0.0078125
        img_y = np.transpose(im_data, (1, 0, 2))  # caffe uses different dimensions ordering
        images_obj_per_resolution[(ws, hs)].append({'scale': scale, 'image': img_y, 'index': index})
  
    for resolution in images_obj_per_resolution:
      images_per_resolution = [i['image'] for i in images_obj_per_resolution[resolution]]
      outs = pnet(images_per_resolution)
  
      for index in range(len(outs[0])):
        scale = images_obj_per_resolution[resolution][index]['scale']
        image_index = images_obj_per_resolution[resolution][index]['index']
        out0 = np.transpose(outs[0][index], (1, 0, 2))
        out1 = np.transpose(outs[1][index], (1, 0, 2))
  
        boxes, _ = generateBoundingBox(out1[:, :, 1].copy(), out0[:, :, :].copy(), scale, threshold[0])
  
        # inter-scale nms
        pick = nms(boxes.copy(), 0.5, 'Union')
        if boxes.size > 0 and pick.size > 0:
          boxes = boxes[pick, :]
          images_with_boxes[image_index]['total_boxes'] = np.append(images_with_boxes[image_index]['total_boxes'],
                                        boxes,
                                        axis=0)
  
    for index, image_obj in enumerate(images_with_boxes):
      numbox = image_obj['total_boxes'].shape[0]
      if numbox > 0:
        h = images[index].shape[0]
        w = images[index].shape[1]
        pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Union')
        image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
        regw = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0]
        regh = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1]
        qq1 = image_obj['total_boxes'][:, 0] + image_obj['total_boxes'][:, 5] * regw
        qq2 = image_obj['total_boxes'][:, 1] + image_obj['total_boxes'][:, 6] * regh
        qq3 = image_obj['total_boxes'][:, 2] + image_obj['total_boxes'][:, 7] * regw
        qq4 = image_obj['total_boxes'][:, 3] + image_obj['total_boxes'][:, 8] * regh
        image_obj['total_boxes'] = np.transpose(np.vstack([qq1, qq2, qq3, qq4, image_obj['total_boxes'][:, 4]]))
        image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
        image_obj['total_boxes'][:, 0:4] = np.fix(image_obj['total_boxes'][:, 0:4]).astype(np.int32)
        dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
  
        numbox = image_obj['total_boxes'].shape[0]
        tempimg = np.zeros((24, 24, 3, numbox))
  
        if numbox > 0:
          for k in range(0, numbox):
            tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
            tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
            if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
              tempimg[:, :, :, k] = imresample(tmp, (24, 24))
            else:
              return np.empty()
  
          tempimg = (tempimg - 127.5) * 0.0078125
          image_obj['rnet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
  
    # # # # # # # # # # # # #
    # second stage - refinement of face candidates with rnet
    # # # # # # # # # # # # #
  
    bulk_rnet_input = np.empty((0, 24, 24, 3))
    for index, image_obj in enumerate(images_with_boxes):
      if 'rnet_input' in image_obj:
        bulk_rnet_input = np.append(bulk_rnet_input, image_obj['rnet_input'], axis=0)
  
    out = rnet(bulk_rnet_input)
    out0 = np.transpose(out[0])
    out1 = np.transpose(out[1])
    score = out1[1, :]
  
    i = 0
    for index, image_obj in enumerate(images_with_boxes):
      if 'rnet_input' not in image_obj:
        continue
  
      rnet_input_count = image_obj['rnet_input'].shape[0]
      score_per_image = score[i:i + rnet_input_count]
      out0_per_image = out0[:, i:i + rnet_input_count]
  
      ipass = np.where(score_per_image > threshold[1])
      image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
                          np.expand_dims(score_per_image[ipass].copy(), 1)])
  
      mv = out0_per_image[:, ipass[0]]
  
      if image_obj['total_boxes'].shape[0] > 0:
        h = images[index].shape[0]
        w = images[index].shape[1]
        pick = nms(image_obj['total_boxes'], 0.7, 'Union')
        image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
        image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv[:, pick]))
        image_obj['total_boxes'] = rerec(image_obj['total_boxes'].copy())
  
        numbox = image_obj['total_boxes'].shape[0]
  
        if numbox > 0:
          tempimg = np.zeros((48, 48, 3, numbox))
          image_obj['total_boxes'] = np.fix(image_obj['total_boxes']).astype(np.int32)
          dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph = pad(image_obj['total_boxes'].copy(), w, h)
  
          for k in range(0, numbox):
            tmp = np.zeros((int(tmph[k]), int(tmpw[k]), 3))
            tmp[dy[k] - 1:edy[k], dx[k] - 1:edx[k], :] = images[index][y[k] - 1:ey[k], x[k] - 1:ex[k], :]
            if tmp.shape[0] > 0 and tmp.shape[1] > 0 or tmp.shape[0] == 0 and tmp.shape[1] == 0:
              tempimg[:, :, :, k] = imresample(tmp, (48, 48))
            else:
              return np.empty()
          tempimg = (tempimg - 127.5) * 0.0078125
          image_obj['onet_input'] = np.transpose(tempimg, (3, 1, 0, 2))
  
      i += rnet_input_count
  
    # # # # # # # # # # # # #
    # third stage - further refinement and facial landmarks positions with onet
    # # # # # # # # # # # # #
  
    bulk_onet_input = np.empty((0, 48, 48, 3))
    for index, image_obj in enumerate(images_with_boxes):
      if 'onet_input' in image_obj:
        bulk_onet_input = np.append(bulk_onet_input, image_obj['onet_input'], axis=0)
  
    out = onet(bulk_onet_input)
  
    out0 = np.transpose(out[0])
    out1 = np.transpose(out[1])
    out2 = np.transpose(out[2])
    score = out2[1, :]
    points = out1
  
    i = 0
    ret = []
    for index, image_obj in enumerate(images_with_boxes):
      if 'onet_input' not in image_obj:
        ret.append(None)
        continue
  
      onet_input_count = image_obj['onet_input'].shape[0]
  
      out0_per_image = out0[:, i:i + onet_input_count]
      score_per_image = score[i:i + onet_input_count]
      points_per_image = points[:, i:i + onet_input_count]
  
      ipass = np.where(score_per_image > threshold[2])
      points_per_image = points_per_image[:, ipass[0]]
  
      image_obj['total_boxes'] = np.hstack([image_obj['total_boxes'][ipass[0], 0:4].copy(),
                          np.expand_dims(score_per_image[ipass].copy(), 1)])
      mv = out0_per_image[:, ipass[0]]
  
      w = image_obj['total_boxes'][:, 2] - image_obj['total_boxes'][:, 0] + 1
      h = image_obj['total_boxes'][:, 3] - image_obj['total_boxes'][:, 1] + 1
      points_per_image[0:5, :] = np.tile(w, (5, 1)) * points_per_image[0:5, :] + np.tile(
        image_obj['total_boxes'][:, 0], (5, 1)) - 1
      points_per_image[5:10, :] = np.tile(h, (5, 1)) * points_per_image[5:10, :] + np.tile(
        image_obj['total_boxes'][:, 1], (5, 1)) - 1
  
      if image_obj['total_boxes'].shape[0] > 0:
        image_obj['total_boxes'] = bbreg(image_obj['total_boxes'].copy(), np.transpose(mv))
        pick = nms(image_obj['total_boxes'].copy(), 0.7, 'Min')
        image_obj['total_boxes'] = image_obj['total_boxes'][pick, :]
        points_per_image = points_per_image[:, pick]
  
        ret.append((image_obj['total_boxes'], points_per_image))
      else:
        ret.append(None)
  
      i += onet_input_count
  
    return ret
  
  
  # function [boundingbox] = bbreg(boundingbox,reg)
  def bbreg(boundingbox,reg):
    '''Calibrate bounding boxes'''
    if reg.shape[1]==1:
      reg = np.reshape(reg, (reg.shape[2], reg.shape[3]))
  
    w = boundingbox[:,2]-boundingbox[:,0]+1
    h = boundingbox[:,3]-boundingbox[:,1]+1
    b1 = boundingbox[:,0]+reg[:,0]*w
    b2 = boundingbox[:,1]+reg[:,1]*h
    b3 = boundingbox[:,2]+reg[:,2]*w
    b4 = boundingbox[:,3]+reg[:,3]*h
    boundingbox[:,0:4] = np.transpose(np.vstack([b1, b2, b3, b4 ]))
    return boundingbox
  
  def generateBoundingBox(imap, reg, scale, t):
    '''Use heatmap to generate bounding boxes'''
    stride=2
    cellsize=12
  
    imap = np.transpose(imap)
    dx1 = np.transpose(reg[:,:,0])
    dy1 = np.transpose(reg[:,:,1])
    dx2 = np.transpose(reg[:,:,2])
    dy2 = np.transpose(reg[:,:,3])
    y, x = np.where(imap >= t)
    if y.shape[0]==1:
      dx1 = np.flipud(dx1)
      dy1 = np.flipud(dy1)
      dx2 = np.flipud(dx2)
      dy2 = np.flipud(dy2)
    score = imap[(y,x)]
    reg = np.transpose(np.vstack([ dx1[(y,x)], dy1[(y,x)], dx2[(y,x)], dy2[(y,x)] ]))
    if reg.size==0:
      reg = np.empty((0,3))
    bb = np.transpose(np.vstack([y,x]))
    q1 = np.fix((stride*bb+1)/scale)
    q2 = np.fix((stride*bb+cellsize-1+1)/scale)
    boundingbox = np.hstack([q1, q2, np.expand_dims(score,1), reg])
    return boundingbox, reg
  
  # function pick = nms(boxes,threshold,type)
  def nms(boxes, threshold, method):
    if boxes.size==0:
      return np.empty((0,3))
    x1 = boxes[:,0]
    y1 = boxes[:,1]
    x2 = boxes[:,2]
    y2 = boxes[:,3]
    s = boxes[:,4]
    area = (x2-x1+1) * (y2-y1+1)
    I = np.argsort(s)
    pick = np.zeros_like(s, dtype=np.int16)
    counter = 0
    while I.size>0:
      i = I[-1]
      pick[counter] = i
      counter += 1
      idx = I[0:-1]
      xx1 = np.maximum(x1[i], x1[idx])
      yy1 = np.maximum(y1[i], y1[idx])
      xx2 = np.minimum(x2[i], x2[idx])
      yy2 = np.minimum(y2[i], y2[idx])
      w = np.maximum(0.0, xx2-xx1+1)
      h = np.maximum(0.0, yy2-yy1+1)
      inter = w * h
      if method is 'Min':
        o = inter / np.minimum(area[i], area[idx])
      else:
        o = inter / (area[i] + area[idx] - inter)
      I = I[np.where(o<=threshold)]
    pick = pick[0:counter]
    return pick
  
  # function [dy edy dx edx y ey x ex tmpw tmph] = pad(total_boxes,w,h)
  def pad(total_boxes, w, h):
    '''Compute the padding coordinates (pad the bounding boxes to square)'''
    tmpw = (total_boxes[:,2]-total_boxes[:,0]+1).astype(np.int32)
    tmph = (total_boxes[:,3]-total_boxes[:,1]+1).astype(np.int32)
    numbox = total_boxes.shape[0]
  
    dx = np.ones((numbox), dtype=np.int32)
    dy = np.ones((numbox), dtype=np.int32)
    edx = tmpw.copy().astype(np.int32)
    edy = tmph.copy().astype(np.int32)
  
    x = total_boxes[:,0].copy().astype(np.int32)
    y = total_boxes[:,1].copy().astype(np.int32)
    ex = total_boxes[:,2].copy().astype(np.int32)
    ey = total_boxes[:,3].copy().astype(np.int32)
  
    tmp = np.where(ex>w)
    edx.flat[tmp] = np.expand_dims(-ex[tmp]+w+tmpw[tmp],1)
    ex[tmp] = w
  
    tmp = np.where(ey>h)
    edy.flat[tmp] = np.expand_dims(-ey[tmp]+h+tmph[tmp],1)
    ey[tmp] = h
  
    tmp = np.where(x<1)
    dx.flat[tmp] = np.expand_dims(2-x[tmp],1)
    x[tmp] = 1
  
    tmp = np.where(y<1)
    dy.flat[tmp] = np.expand_dims(2-y[tmp],1)
    y[tmp] = 1
  
    return dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph
  
  # function [bboxA] = rerec(bboxA)
  def rerec(bboxA):
    '''Convert bboxA to square.'''
    h = bboxA[:,3]-bboxA[:,1]
    w = bboxA[:,2]-bboxA[:,0]
    l = np.maximum(w, h)
    bboxA[:,0] = bboxA[:,0]+w*0.5-l*0.5
    bboxA[:,1] = bboxA[:,1]+h*0.5-l*0.5
    bboxA[:,2:4] = bboxA[:,0:2] + np.transpose(np.tile(l,(2,1)))
    return bboxA
  
  def imresample(img, sz):
    return misc.imresize(img, sz)
  