Fixing several issues with library being out of date

.gitignore

@@ -92,3 +92,4 @@ ENV/
 
 # PyCharm project setting
 .idea
+.DS_Store

contributed/batch_represent.py

@@ -1,10 +1,6 @@
 #!/usr/bin/env python
 # coding=utf-8
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 """
 Allows you to generate embeddings from a directory of images in the format:
 
@@ -41,7 +37,7 @@
 The concept is inspired by Brandon Amos' github.com/cmusatyalab/openface/blob/master/batch-represent/batch-represent.lua
 """
 
-#----------------------------------------------------
+# ----------------------------------------------------
 # MIT License
 #
 # Copyright (c) 2017 Rakshak Talwar
@@ -63,86 +59,119 @@
 # 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.
-#----------------------------------------------------
+# ----------------------------------------------------
 
-import os
-import sys
 import argparse
 import importlib
+import os
+import sys
 import time
 
 sys.path.insert(1, "../src")
-import facenet
 import numpy as np
-from sklearn.datasets import load_files
 import tensorflow as tf
 from six.moves import xrange
+from sklearn.datasets import load_files
+
+import facenet
+
 
 def main(args):
+    with tf.Graph().as_default():
+        with tf.Session() as sess:
+            # create output directory if it doesn't exist
+            output_dir = os.path.expanduser(args.output_dir)
+            if not os.path.isdir(output_dir):
+                os.makedirs(output_dir)
+
+            # load the model
+            print("Loading trained model...\n")
+            meta_file, ckpt_file = facenet.get_model_filenames(
+                os.path.expanduser(args.trained_model_dir)
+            )
+            facenet.load_model(args.trained_model_dir, meta_file, ckpt_file)
+
+            # grab all image paths and labels
+            print("Finding image paths and targets...\n")
+            data = load_files(args.data_dir, load_content=False, shuffle=False)
+            labels_array = data["target"]
+            paths = data["filenames"]
+
+            # Get input and output tensors
+            images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
+            embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
+            phase_train_placeholder = tf.get_default_graph().get_tensor_by_name(
+                "phase_train:0"
+            )
+
+            image_size = images_placeholder.get_shape()[1]
+            embedding_size = embeddings.get_shape()[1]
+
+            # Run forward pass to calculate embeddings
+            print("Generating embeddings from images...\n")
+            start_time = time.time()
+            batch_size = args.batch_size
+            nrof_images = len(paths)
+            nrof_batches = int(np.ceil(1.0 * nrof_images / batch_size))
+            emb_array = np.zeros((nrof_images, embedding_size))
+            for i in xrange(nrof_batches):
+                start_index = i * batch_size
+                end_index = min((i + 1) * batch_size, nrof_images)
+                paths_batch = paths[start_index:end_index]
+                images = facenet.load_data(
+                    paths_batch,
+                    do_random_crop=False,
+                    do_random_flip=False,
+                    image_size=image_size,
+                    do_prewhiten=True,
+                )
+                feed_dict = {images_placeholder: images, phase_train_placeholder: False}
+                emb_array[start_index:end_index, :] = sess.run(
+                    embeddings, feed_dict=feed_dict
+                )
+
+            time_avg_forward_pass = (time.time() - start_time) / float(nrof_images)
+            print(
+                "Forward pass took avg of %.3f[seconds/image] for %d images\n"
+                % (time_avg_forward_pass, nrof_images)
+            )
+
+            print("Finally saving embeddings and gallery to: %s" % (output_dir))
+            # save the gallery and embeddings (signatures) as numpy arrays to disk
+            np.save(os.path.join(output_dir, "gallery.npy"), labels_array)
+            np.save(os.path.join(output_dir, "signatures.npy"), emb_array)
 
-	with tf.Graph().as_default():
-
-		with tf.Session() as sess:
-
-			# create output directory if it doesn't exist
-			output_dir = os.path.expanduser(args.output_dir)
-			if not os.path.isdir(output_dir):
-				os.makedirs(output_dir)
-
-			# load the model
-			print("Loading trained model...\n")
-			meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.trained_model_dir))
-			facenet.load_model(args.trained_model_dir, meta_file, ckpt_file)
-
-			# grab all image paths and labels
-			print("Finding image paths and targets...\n")
-			data = load_files(args.data_dir, load_content=False, shuffle=False)
-			labels_array = data['target']
-			paths = data['filenames']
-
-			# Get input and output tensors
-			images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
-			embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
-			phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
-
-			image_size = images_placeholder.get_shape()[1]
-			embedding_size = embeddings.get_shape()[1]
-
-			# Run forward pass to calculate embeddings
-			print('Generating embeddings from images...\n')
-			start_time = time.time()
-			batch_size = args.batch_size
-			nrof_images = len(paths)
-			nrof_batches = int(np.ceil(1.0*nrof_images / batch_size))
-			emb_array = np.zeros((nrof_images, embedding_size))
-			for i in xrange(nrof_batches):
-				start_index = i*batch_size
-				end_index = min((i+1)*batch_size, nrof_images)
-				paths_batch = paths[start_index:end_index]
-				images = facenet.load_data(paths_batch, do_random_crop=False, do_random_flip=False, image_size=image_size, do_prewhiten=True)
-				feed_dict = { images_placeholder:images, phase_train_placeholder:False}
-				emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
-
-			time_avg_forward_pass = (time.time() - start_time) / float(nrof_images)
-			print("Forward pass took avg of %.3f[seconds/image] for %d images\n" % (time_avg_forward_pass, nrof_images))
-
-			print("Finally saving embeddings and gallery to: %s" % (output_dir))
-			# save the gallery and embeddings (signatures) as numpy arrays to disk
-			np.save(os.path.join(output_dir, "gallery.npy"), labels_array)
-			np.save(os.path.join(output_dir, "signatures.npy"), emb_array)
 
 def parse_arguments(argv):
-	parser = argparse.ArgumentParser(description="Batch-represent face embeddings from a given data directory")
-	parser.add_argument('-d', '--data_dir', type=str,
-		help='directory of images with structure as seen at the top of this file.')
-	parser.add_argument('-o', '--output_dir', type=str,
-		help='directory containing aligned face patches with file structure as seen at the top of this file.')
-	parser.add_argument('--trained_model_dir', type=str,
-        help='Load a trained model before training starts.')
-	parser.add_argument('--batch_size', type=int, help='Number of images to process in a batch.', default=50)
-
-	return parser.parse_args(argv)
+    parser = argparse.ArgumentParser(
+        description="Batch-represent face embeddings from a given data directory"
+    )
+    parser.add_argument(
+        "-d",
+        "--data_dir",
+        type=str,
+        help="directory of images with structure as seen at the top of this file.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output_dir",
+        type=str,
+        help="directory containing aligned face patches with file structure as seen at the top of this file.",
+    )
+    parser.add_argument(
+        "--trained_model_dir",
+        type=str,
+        help="Load a trained model before training starts.",
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        help="Number of images to process in a batch.",
+        default=50,
+    )
+
+    return parser.parse_args(argv)
 
 
 if __name__ == "__main__":
-	main(parse_arguments(sys.argv[1:]))
+    main(parse_arguments(sys.argv[1:]))