diff --git a/.gitignore b/.gitignore
index 7e1405ca940260a0efafc158afe015818b539026..1d06de1f736efc0cda0e787d107d7faa974fd5ed 100644
--- a/.gitignore
+++ b/.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/
diff --git a/bob/ip/tensorflow_extractor/FaceNet.py b/bob/ip/tensorflow_extractor/FaceNet.py
new file mode 100644
index 0000000000000000000000000000000000000000..28828df2d5eb1580f137b3e26a39a88e902a7ba0
--- /dev/null
+++ b/bob/ip/tensorflow_extractor/FaceNet.py
@@ -0,0 +1,159 @@
+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)
diff --git a/bob/ip/tensorflow_extractor/__init__.py b/bob/ip/tensorflow_extractor/__init__.py
index f8bd953f2a54ec2084484df53618b82f61ec1493..2a29c5e8f9eaf8bd6a7d1b0ffd5ce9a66a7840a3 100755
--- a/bob/ip/tensorflow_extractor/__init__.py
+++ b/bob/ip/tensorflow_extractor/__init__.py
@@ -1,11 +1,5 @@
#!/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
diff --git a/bob/ip/tensorflow_extractor/test.py b/bob/ip/tensorflow_extractor/test.py
index 5628fcf7b616dd7b437d6ddfacee64846fe1d78c..d000b7b27927e8282d3419c5a20bdc6bf69de232 100644
--- a/bob/ip/tensorflow_extractor/test.py
+++ b/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
"""
diff --git a/requirements.txt b/requirements.txt
index 8dc7a9f862dd3a1dcfe2960379fd55bdaa30c36c..f2c95a248699d5901e69443f861d1c9b3cb7a3c4 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,3 +3,5 @@ bob.extension
bob.io.base
bob.io.image
bob.db.mnist
+bob.ip.color
+numpy