Add facenet extractor

.gitignore

@@ -11,5 +11,4 @@ src
 develop-eggs
 sphinx
 dist
-bob/ip/caffe_extractor/data/face_verification_experiment-master/
-bob/ip/caffe_extractor/data/vgg_face_caffe/
+bob/ip/tensorflow_extractor/data/FaceNet/

bob/ip/tensorflow_extractor/FaceNet.py

+from __future__ import division
+import os
+import re
+import logging
+import numpy
+import tensorflow as tf
+from bob.ip.color import gray_to_rgb
+from bob.io.image import to_matplotlib
+from . import download_file
+
+logger = logging.getLogger(__name__)
+
+
+def prewhiten(img):
+    mean = numpy.mean(img)
+    std = numpy.std(img)
+    std_adj = numpy.maximum(std, 1.0 / numpy.sqrt(img.size))
+    y = numpy.multiply(numpy.subtract(img, mean), 1 / std_adj)
+    return y
+
+
+def get_model_filenames(model_dir):
+    # code from https://github.com/davidsandberg/facenet
+    files = os.listdir(model_dir)
+    meta_files = [s for s in files if s.endswith('.meta')]
+    if len(meta_files) == 0:
+        raise ValueError(
+            'No meta file found in the model directory (%s)' % model_dir)
+    elif len(meta_files) > 1:
+        raise ValueError(
+            'There should not be more than one meta file in the model '
+            'directory (%s)' % model_dir)
+    meta_file = meta_files[0]
+    max_step = -1
+    for f in files:
+        step_str = re.match(r'(^model-[\w\- ]+.ckpt-(\d+))', f)
+        if step_str is not None and len(step_str.groups()) >= 2:
+            step = int(step_str.groups()[1])
+            if step > max_step:
+                max_step = step
+                ckpt_file = step_str.groups()[0]
+    return meta_file, ckpt_file
+
+
+class FaceNet(object):
+    """Wrapper for the free FaceNet variant:
+    https://github.com/davidsandberg/facenet"""
+
+    def __init__(self,
+                 model_path=None,
+                 image_size=160,
+                 **kwargs):
+        super(FaceNet, self).__init__()
+        self.model_path = model_path
+        self.image_size = image_size
+        self.session = None
+        self.embeddings = None
+
+    def _check_feature(self, img):
+        img = numpy.ascontiguousarray(img)
+        if img.ndim == 2:
+            img = gray_to_rgb(img)
+        assert img.shape[-1] == self.image_size
+        assert img.shape[-2] == self.image_size
+        img = to_matplotlib(img)
+        img = prewhiten(img)
+        return img[None, ...]
+
+    def load_model(self):
+        if self.model_path is None:
+            self.model_path = self.get_modelpath()
+        if not os.path.exists(self.model_path):
+            self.download_model()
+        # code from https://github.com/davidsandberg/facenet
+        model_exp = os.path.expanduser(self.model_path)
+        if (os.path.isfile(model_exp)):
+            logger.info('Model filename: %s' % model_exp)
+            with tf.gfile.FastGFile(model_exp, 'rb') as f:
+                graph_def = tf.GraphDef()
+                graph_def.ParseFromString(f.read())
+                tf.import_graph_def(graph_def, name='')
+        else:
+            logger.info('Model directory: %s' % model_exp)
+            meta_file, ckpt_file = get_model_filenames(model_exp)
+
+            logger.info('Metagraph file: %s' % meta_file)
+            logger.info('Checkpoint file: %s' % ckpt_file)
+
+            saver = tf.train.import_meta_graph(
+                os.path.join(model_exp, meta_file))
+            saver.restore(tf.get_default_session(),
+                          os.path.join(model_exp, ckpt_file))
+        # Get input and output tensors
+        self.images_placeholder = self.graph.get_tensor_by_name("input:0")
+        self.embeddings = self.graph.get_tensor_by_name("embeddings:0")
+        self.phase_train_placeholder = self.graph.get_tensor_by_name(
+            "phase_train:0")
+        logger.info("Successfully loaded the model.")
+
+    def __call__(self, img):
+        images = self._check_feature(img)
+        if self.session is None:
+            self.session = tf.InteractiveSession()
+            self.graph = tf.get_default_graph()
+        if self.embeddings is None:
+            self.load_model()
+        feed_dict = {self.images_placeholder: images,
+                     self.phase_train_placeholder: False}
+        features = self.session.run(
+            self.embeddings, feed_dict=feed_dict)
+        return features.flatten()
+
+    def __del__(self):
+        tf.reset_default_graph()
+
+    @staticmethod
+    def get_modelpath():
+        import pkg_resources
+        return pkg_resources.resource_filename(__name__,
+                                               'data/FaceNet/20170512-110547')
+
+    @staticmethod
+    def download_model():
+        """
+        Download and extract the FaceNet files in bob/ip/tensorflow_extractor
+        """
+        import zipfile
+        zip_file = os.path.join(FaceNet.get_modelpath(),
+                                "20170512-110547.zip")
+        urls = [
+            # This is a private link at Idiap to save bandwidth.
+            "http://beatubulatest.lab.idiap.ch/private/wheels/gitlab/"
+            "facenet_model2_20170512-110547.zip",
+            # this works for everybody
+            "https://drive.google.com/uc?export=download&id="
+            "0B5MzpY9kBtDVZ2RpVDYwWmxoSUk",
+        ]
+
+        for url in urls:
+            try:
+                logger.info(
+                    "Downloading the FaceNet model from "
+                    "{} ...".format(url))
+                download_file(url, zip_file)
+                break
+            except Exception as e:
+                logger.warning(
+                    "Could not download from the %s url", url, exc_info=True)
+        else:  # else is for the for loop
+            if not os.path.isfile(zip_file):
+                raise RuntimeError("Could not download the zip file.")
+
+        # Unzip
+        logger.info("Unziping in {0}".format(FaceNet.get_modelpath()))
+        with zipfile.ZipFile(zip_file) as myzip:
+            myzip.extractall(os.path.dirname(FaceNet.get_modelpath()))
+
+        # delete extra files
+        os.unlink(zip_file)

bob/ip/tensorflow_extractor/__init__.py

 #!/usr/bin/env python
-
-
-from .Extractor import Extractor
-from .InceptionResNet_v1 import InceptionResNet_v1
-
-
-def scratch_network(inputs, end_point="fc1", reuse = False):
+def scratch_network(inputs, end_point="fc1", reuse=False):
 
     import tensorflow as tf
     slim = tf.contrib.slim
@@ -13,76 +7,86 @@ def scratch_network(inputs, end_point="fc1", reuse = False):
     # Creating a random network
     initializer = tf.contrib.layers.xavier_initializer(seed=10)
     end_points = dict()
-    
-    graph = slim.conv2d(inputs, 10, [3, 3], activation_fn=tf.nn.relu, stride=1, scope='conv1',
-                        weights_initializer=initializer, reuse=reuse)
+
+    graph = slim.conv2d(inputs, 10, [3, 3], activation_fn=tf.nn.relu, stride=1,
+                        scope='conv1', weights_initializer=initializer,
+                        reuse=reuse)
     end_points["conv1"] = graph
-                        
+
     graph = slim.max_pool2d(graph, [4, 4], scope='pool1')
     end_points["pool1"] = graph
-    
+
     graph = slim.flatten(graph, scope='flatten1')
     end_points["flatten1"] = graph
-        
+
     graph = slim.fully_connected(graph, 10, activation_fn=None, scope='fc1',
                                  weights_initializer=initializer, reuse=reuse)
-    end_points["fc1"] = graph                                 
+    end_points["fc1"] = graph
 
     return end_points[end_point]
 
 
 def download_file(url, out_file):
-  """Downloads a file from a given url
-
-  Parameters
-  ----------
-  url : str
-      The url to download form.
-  out_file : str
-      Where to save the file.
-  """
-  import sys
-  if sys.version_info[0] < 3:
-    # python2 technique for downloading a file
-    from urllib2 import urlopen
-    with open(out_file, 'wb') as f:
-      response = urlopen(url)
-      f.write(response.read())
-
-  else:
-    # python3 technique for downloading a file
-    from urllib.request import urlopen
-    from shutil import copyfileobj
-    with urlopen(url) as response:
-      with open(out_file, 'wb') as f:
-        copyfileobj(response, f)
-
+    """Downloads a file from a given url
+
+    Parameters
+    ----------
+    url : str
+        The url to download form.
+    out_file : str
+        Where to save the file.
+    """
+    from bob.io.base import create_directories_safe
+    import os
+    create_directories_safe(os.path.dirname(out_file))
+
+    import sys
+    if sys.version_info[0] < 3:
+        # python2 technique for downloading a file
+        from urllib2 import urlopen
+        with open(out_file, 'wb') as f:
+            response = urlopen(url)
+            f.write(response.read())
+
+    else:
+        # python3 technique for downloading a file
+        from urllib.request import urlopen
+        from shutil import copyfileobj
+        with urlopen(url) as response:
+            with open(out_file, 'wb') as f:
+                copyfileobj(response, f)
 
 
 def get_config():
-  """Returns a string containing the configuration information.
-  """
-  import bob.extension
-  return bob.extension.get_config(__name__)
+    """Returns a string containing the configuration information.
+    """
+    import bob.extension
+    return bob.extension.get_config(__name__)
+
+
+from .Extractor import Extractor
+from .FaceNet import FaceNet
 
 
 # gets sphinx autodoc done right - don't remove it
 def __appropriate__(*args):
-  """Says object was actually declared here, and not in the import module.
-  Fixing sphinx warnings of not being able to find classes, when path is shortened.
-  Parameters:
+    """Says object was actually declared here, and not in the import module.
+    Fixing sphinx warnings of not being able to find classes, when path is
+    shortened. Parameters:
+
+      *args: An iterable of objects to modify
 
-    *args: An iterable of objects to modify
+    Resolves `Sphinx referencing issues
+    <https://github.com/sphinx-doc/sphinx/issues/3048>`
+    """
 
-  Resolves `Sphinx referencing issues
-  <https://github.com/sphinx-doc/sphinx/issues/3048>`
-  """
+    for obj in args:
+        obj.__module__ = __name__
 
-  for obj in args:
-    obj.__module__ = __name__
 
 __appropriate__(
-    Extractor,    
+    Extractor,
+    FaceNet,
 )
 
 # gets sphinx autodoc done right - don't remove it

bob/ip/tensorflow_extractor/test.py

@@ -14,30 +14,39 @@ from . import scratch_network
 
 def test_output():
 
-    # Loading MNIST model    
-    filename = os.path.join( pkg_resources.resource_filename(__name__, 'data'), 'model.ckp')
+    # Loading MNIST model
+    filename = os.path.join(pkg_resources.resource_filename(
+        __name__, 'data'), 'model.ckp')
     inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
 
     # Testing the last output
     graph = scratch_network(inputs)
     extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-    
-    
+
     data = numpy.random.rand(2, 28, 28, 1).astype("float32")
     output = extractor(data)
     assert extractor(data).shape == (2, 10)
     del extractor
-    
+
     # Testing flatten
     inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
     graph = scratch_network(inputs, end_point="flatten1")
     extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-    
+
     data = numpy.random.rand(2, 28, 28, 1).astype("float32")
     output = extractor(data)
-    assert extractor(data).shape == (2, 1690)
+    assert output.shape == (2, 1690)
     del extractor
 
+
+def test_facenet():
+    from bob.ip.tensorflow_extractor import FaceNet
+    extractor = FaceNet()
+    data = numpy.random.rand(3, 160, 160).astype("uint8")
+    output = extractor(data)
+    assert output.size == 128, output.shape
+
+
 """
 def test_output_from_meta():
 
@@ -48,19 +57,19 @@ def test_output_from_meta():
     # Testing the last output
     graph = scratch_network(inputs)
     extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-    
+
     data = numpy.random.rand(2, 28, 28, 1).astype("float32")
     output = extractor(data)
     assert extractor(data).shape == (2, 10)
     del extractor
-    
+
     # Testing flatten
     inputs = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
     graph = scratch_network(inputs, end_point="flatten1")
     extractor = bob.ip.tensorflow_extractor.Extractor(filename, inputs, graph)
-    
+
     data = numpy.random.rand(2, 28, 28, 1).astype("float32")
     output = extractor(data)
     assert extractor(data).shape == (2, 1690)
-    del extractor    
+    del extractor
 """

requirements.txt

@@ -3,3 +3,5 @@ bob.extension
 bob.io.base
 bob.io.image
 bob.db.mnist
+bob.ip.color
+numpy