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Repository: davidsandberg/facenet
Branch: master
Commit: 096ed770f163
Files: 94
Total size: 669.0 KB
Directory structure:
gitextract_mncf1i0j/
├── .gitignore
├── .project
├── .pydevproject
├── .pylintrc
├── .travis.yml
├── LICENSE.md
├── README.md
├── __init__.py
├── contributed/
│ ├── __init__.py
│ ├── batch_represent.py
│ ├── cluster.py
│ ├── clustering.py
│ ├── export_embeddings.py
│ ├── face.py
│ ├── predict.py
│ └── real_time_face_recognition.py
├── data/
│ ├── learning_rate_retrain_tripletloss.txt
│ ├── learning_rate_schedule_classifier_casia.txt
│ ├── learning_rate_schedule_classifier_msceleb.txt
│ ├── learning_rate_schedule_classifier_vggface2.txt
│ └── pairs.txt
├── requirements.txt
├── src/
│ ├── __init__.py
│ ├── align/
│ │ ├── __init__.py
│ │ ├── align_dataset_mtcnn.py
│ │ ├── det1.npy
│ │ ├── det2.npy
│ │ ├── det3.npy
│ │ └── detect_face.py
│ ├── calculate_filtering_metrics.py
│ ├── classifier.py
│ ├── compare.py
│ ├── decode_msceleb_dataset.py
│ ├── download_and_extract.py
│ ├── facenet.py
│ ├── freeze_graph.py
│ ├── generative/
│ │ ├── __init__.py
│ │ ├── calculate_attribute_vectors.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ ├── dfc_vae.py
│ │ │ ├── dfc_vae_large.py
│ │ │ ├── dfc_vae_resnet.py
│ │ │ └── vae_base.py
│ │ ├── modify_attribute.py
│ │ └── train_vae.py
│ ├── lfw.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── dummy.py
│ │ ├── inception_resnet_v1.py
│ │ ├── inception_resnet_v2.py
│ │ └── squeezenet.py
│ ├── train_softmax.py
│ ├── train_tripletloss.py
│ └── validate_on_lfw.py
├── test/
│ ├── batch_norm_test.py
│ ├── center_loss_test.py
│ ├── restore_test.py
│ ├── train_test.py
│ └── triplet_loss_test.py
├── tmp/
│ ├── __init__.py
│ ├── align_dataset.m
│ ├── align_dataset.py
│ ├── align_dlib.py
│ ├── cacd2000_split_identities.py
│ ├── dataset_read_speed.py
│ ├── deepdream.py
│ ├── detect_face_v1.m
│ ├── detect_face_v2.m
│ ├── download_vgg_face_dataset.py
│ ├── funnel_dataset.py
│ ├── invariance_test.txt
│ ├── mnist_center_loss.py
│ ├── mnist_noise_labels.py
│ ├── mtcnn.py
│ ├── mtcnn_test.py
│ ├── mtcnn_test_pnet_dbg.py
│ ├── network.py
│ ├── nn2.py
│ ├── nn3.py
│ ├── nn4.py
│ ├── nn4_small2_v1.py
│ ├── random_test.py
│ ├── rename_casia_directories.py
│ ├── seed_test.py
│ ├── select_triplets_test.py
│ ├── test1.py
│ ├── test_align.py
│ ├── test_invariance_on_lfw.py
│ ├── vggface16.py
│ ├── vggverydeep19.py
│ ├── visualize.py
│ ├── visualize_vgg_model.py
│ └── visualize_vggface.py
└── util/
└── plot_learning_curves.m
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================================================
FILE: .travis.yml
================================================
language: python
sudo: required
python:
- "2.7"
- "3.5"
# command to install dependencies
install:
# numpy not using wheel to avoid problem described in
# https://github.com/tensorflow/tensorflow/issues/6968
- pip install --no-binary numpy --upgrade numpy
- pip install -r requirements.txt
# command to run tests
script:
- export PYTHONPATH=./src:./src/models:./src/align
- python -m unittest discover -s test --pattern=*.py 1>&2
dist: trusty
================================================
FILE: LICENSE.md
================================================
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.
================================================
FILE: README.md
================================================
# Face Recognition using Tensorflow [![Build Status][travis-image]][travis]
[travis-image]: http://travis-ci.org/davidsandberg/facenet.svg?branch=master
[travis]: http://travis-ci.org/davidsandberg/facenet
This is a TensorFlow implementation of the face recognizer described in the paper
["FaceNet: A Unified Embedding for Face Recognition and Clustering"](http://arxiv.org/abs/1503.03832). The project also uses ideas from the paper ["Deep Face Recognition"](http://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/parkhi15.pdf) from the [Visual Geometry Group](http://www.robots.ox.ac.uk/~vgg/) at Oxford.
## Compatibility
The code is tested using Tensorflow r1.7 under Ubuntu 14.04 with Python 2.7 and Python 3.5. The test cases can be found [here](https://github.com/davidsandberg/facenet/tree/master/test) and the results can be found [here](http://travis-ci.org/davidsandberg/facenet).
## News
| Date | Update |
|----------|--------|
| 2018-04-10 | Added new models trained on Casia-WebFace and VGGFace2 (see below). Note that the models uses fixed image standardization (see [wiki](https://github.com/davidsandberg/facenet/wiki/Training-using-the-VGGFace2-dataset)). |
| 2018-03-31 | Added a new, more flexible input pipeline as well as a bunch of minor updates. |
| 2017-05-13 | Removed a bunch of older non-slim models. Moved the last bottleneck layer into the respective models. Corrected normalization of Center Loss. |
| 2017-05-06 | Added code to [train a classifier on your own images](https://github.com/davidsandberg/facenet/wiki/Train-a-classifier-on-own-images). Renamed facenet_train.py to train_tripletloss.py and facenet_train_classifier.py to train_softmax.py. |
| 2017-03-02 | Added pretrained models that generate 128-dimensional embeddings.|
| 2017-02-22 | Updated to Tensorflow r1.0. Added Continuous Integration using Travis-CI.|
| 2017-02-03 | Added models where only trainable variables has been stored in the checkpoint. These are therefore significantly smaller. |
| 2017-01-27 | Added a model trained on a subset of the MS-Celeb-1M dataset. The LFW accuracy of this model is around 0.994. |
| 2017‑01‑02 | Updated to run with Tensorflow r0.12. Not sure if it runs with older versions of Tensorflow though. |
## Pre-trained models
| Model name | LFW accuracy | Training dataset | Architecture |
|-----------------|--------------|------------------|-------------|
| [20180408-102900](https://drive.google.com/open?id=1R77HmFADxe87GmoLwzfgMu_HY0IhcyBz) | 0.9905 | CASIA-WebFace | [Inception ResNet v1](https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py) |
| [20180402-114759](https://drive.google.com/open?id=1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-) | 0.9965 | VGGFace2 | [Inception ResNet v1](https://github.com/davidsandberg/facenet/blob/master/src/models/inception_resnet_v1.py) |
NOTE: If you use any of the models, please do not forget to give proper credit to those providing the training dataset as well.
## Inspiration
The code is heavily inspired by the [OpenFace](https://github.com/cmusatyalab/openface) implementation.
## Training data
The [CASIA-WebFace](http://www.cbsr.ia.ac.cn/english/CASIA-WebFace-Database.html) dataset has been used for training. This training set consists of total of 453 453 images over 10 575 identities after face detection. Some performance improvement has been seen if the dataset has been filtered before training. Some more information about how this was done will come later.
The best performing model has been trained on the [VGGFace2](https://www.robots.ox.ac.uk/~vgg/data/vgg_face2/) dataset consisting of ~3.3M faces and ~9000 classes.
## Pre-processing
### Face alignment using MTCNN
One problem with the above approach seems to be that the Dlib face detector misses some of the hard examples (partial occlusion, silhouettes, etc). This makes the training set too "easy" which causes the model to perform worse on other benchmarks.
To solve this, other face landmark detectors has been tested. One face landmark detector that has proven to work very well in this setting is the
[Multi-task CNN](https://kpzhang93.github.io/MTCNN_face_detection_alignment/index.html). A Matlab/Caffe implementation can be found [here](https://github.com/kpzhang93/MTCNN_face_detection_alignment) and this has been used for face alignment with very good results. A Python/Tensorflow implementation of MTCNN can be found [here](https://github.com/davidsandberg/facenet/tree/master/src/align). This implementation does not give identical results to the Matlab/Caffe implementation but the performance is very similar.
## Running training
Currently, the best results are achieved by training the model using softmax loss. Details on how to train a model using softmax loss on the CASIA-WebFace dataset can be found on the page [Classifier training of Inception-ResNet-v1](https://github.com/davidsandberg/facenet/wiki/Classifier-training-of-inception-resnet-v1) and .
## Pre-trained models
### Inception-ResNet-v1 model
A couple of pretrained models are provided. They are trained using softmax loss with the Inception-Resnet-v1 model. The datasets has been aligned using [MTCNN](https://github.com/davidsandberg/facenet/tree/master/src/align).
## Performance
The accuracy on LFW for the model [20180402-114759](https://drive.google.com/open?id=1EXPBSXwTaqrSC0OhUdXNmKSh9qJUQ55-) is 0.99650+-0.00252. A description of how to run the test can be found on the page [Validate on LFW](https://github.com/davidsandberg/facenet/wiki/Validate-on-lfw). Note that the input images to the model need to be standardized using fixed image standardization (use the option `--use_fixed_image_standardization` when running e.g. `validate_on_lfw.py`).
================================================
FILE: __init__.py
================================================
================================================
FILE: contributed/__init__.py
================================================
================================================
FILE: contributed/batch_represent.py
================================================
#!/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:
Instructions:
Image data directory should look like the following figure:
person-1
├── image-1.jpg
├── image-2.png
...
└── image-p.png
...
person-m
├── image-1.png
├── image-2.jpg
...
└── image-q.png
Trained Model:
- Both the trained model metagraph and the model parameters need to exist
in the same directory, and the metagraph should have the extension '.meta'.
####
USAGE:
$ python batch_represent.py -d <YOUR IMAGE DATA DIRECTORY> -o <DIRECTORY TO STORE OUTPUT ARRAYS> --trained_model_dir <DIRECTORY CONTAINING PRETRAINED MODEL>
###
"""
"""
Attributions:
The code is heavily inspired by the code from by David Sandberg's ../src/validate_on_lfw.py
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
#
# 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.
#----------------------------------------------------
import os
import sys
import argparse
import importlib
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
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)
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)
if __name__ == "__main__":
main(parse_arguments(sys.argv[1:]))
================================================
FILE: contributed/cluster.py
================================================
# MIT License
#
# Copyright (c) 2017 PXL University College
#
# 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.
# Clusters similar faces from input folder together in folders based on euclidean distance matrix
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from scipy import misc
import tensorflow as tf
import numpy as np
import os
import sys
import argparse
import facenet
import align.detect_face
from sklearn.cluster import DBSCAN
def main(args):
pnet, rnet, onet = create_network_face_detection(args.gpu_memory_fraction)
with tf.Graph().as_default():
with tf.Session() as sess:
facenet.load_model(args.model)
image_list = load_images_from_folder(args.data_dir)
images = align_data(image_list, args.image_size, args.margin, pnet, rnet, onet)
images_placeholder = sess.graph.get_tensor_by_name("input:0")
embeddings = sess.graph.get_tensor_by_name("embeddings:0")
phase_train_placeholder = sess.graph.get_tensor_by_name("phase_train:0")
feed_dict = {images_placeholder: images, phase_train_placeholder: False}
emb = sess.run(embeddings, feed_dict=feed_dict)
nrof_images = len(images)
matrix = np.zeros((nrof_images, nrof_images))
print('')
# Print distance matrix
print('Distance matrix')
print(' ', end='')
for i in range(nrof_images):
print(' %1d ' % i, end='')
print('')
for i in range(nrof_images):
print('%1d ' % i, end='')
for j in range(nrof_images):
dist = np.sqrt(np.sum(np.square(np.subtract(emb[i, :], emb[j, :]))))
matrix[i][j] = dist
print(' %1.4f ' % dist, end='')
print('')
print('')
# DBSCAN is the only algorithm that doesn't require the number of clusters to be defined.
db = DBSCAN(eps=args.cluster_threshold, min_samples=args.min_cluster_size, metric='precomputed')
db.fit(matrix)
labels = db.labels_
# get number of clusters
no_clusters = len(set(labels)) - (1 if -1 in labels else 0)
print('No of clusters:', no_clusters)
if no_clusters > 0:
if args.largest_cluster_only:
largest_cluster = 0
for i in range(no_clusters):
print('Cluster {}: {}'.format(i, np.nonzero(labels == i)[0]))
if len(np.nonzero(labels == i)[0]) > len(np.nonzero(labels == largest_cluster)[0]):
largest_cluster = i
print('Saving largest cluster (Cluster: {})'.format(largest_cluster))
cnt = 1
for i in np.nonzero(labels == largest_cluster)[0]:
misc.imsave(os.path.join(args.out_dir, str(cnt) + '.png'), images[i])
cnt += 1
else:
print('Saving all clusters')
for i in range(no_clusters):
cnt = 1
print('Cluster {}: {}'.format(i, np.nonzero(labels == i)[0]))
path = os.path.join(args.out_dir, str(i))
if not os.path.exists(path):
os.makedirs(path)
for j in np.nonzero(labels == i)[0]:
misc.imsave(os.path.join(path, str(cnt) + '.png'), images[j])
cnt += 1
else:
for j in np.nonzero(labels == i)[0]:
misc.imsave(os.path.join(path, str(cnt) + '.png'), images[j])
cnt += 1
def align_data(image_list, image_size, margin, pnet, rnet, onet):
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
img_list = []
for x in xrange(len(image_list)):
img_size = np.asarray(image_list[x].shape)[0:2]
bounding_boxes, _ = align.detect_face.detect_face(image_list[x], minsize, pnet, rnet, onet, threshold, factor)
nrof_samples = len(bounding_boxes)
if nrof_samples > 0:
for i in xrange(nrof_samples):
if bounding_boxes[i][4] > 0.95:
det = np.squeeze(bounding_boxes[i, 0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2, img_size[1])
bb[3] = np.minimum(det[3] + margin / 2, img_size[0])
cropped = image_list[x][bb[1]:bb[3], bb[0]:bb[2], :]
aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
prewhitened = facenet.prewhiten(aligned)
img_list.append(prewhitened)
if len(img_list) > 0:
images = np.stack(img_list)
return images
else:
return None
def create_network_face_detection(gpu_memory_fraction):
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
return pnet, rnet, onet
def load_images_from_folder(folder):
images = []
for filename in os.listdir(folder):
img = misc.imread(os.path.join(folder, filename))
if img is not None:
images.append(img)
return images
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('model', type=str,
help='Either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
parser.add_argument('data_dir', type=str,
help='The directory containing the images to cluster into folders.')
parser.add_argument('out_dir', type=str,
help='The output directory where the image clusters will be saved.')
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
parser.add_argument('--min_cluster_size', type=int,
help='The minimum amount of pictures required for a cluster.', default=1)
parser.add_argument('--cluster_threshold', type=float,
help='The minimum distance for faces to be in the same cluster', default=1.0)
parser.add_argument('--largest_cluster_only', action='store_true',
help='This argument will make that only the biggest cluster is saved.')
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: contributed/clustering.py
================================================
""" Face Cluster """
import tensorflow as tf
import numpy as np
import importlib
import argparse
import facenet
import os
import math
def face_distance(face_encodings, face_to_compare):
"""
Given a list of face encodings, compare them to a known face encoding and get a euclidean distance
for each comparison face. The distance tells you how similar the faces are.
:param faces: List of face encodings to compare
:param face_to_compare: A face encoding to compare against
:return: A numpy ndarray with the distance for each face in the same order as the 'faces' array
"""
import numpy as np
if len(face_encodings) == 0:
return np.empty((0))
#return 1/np.linalg.norm(face_encodings - face_to_compare, axis=1)
return np.sum(face_encodings*face_to_compare,axis=1)
def load_model(model_dir, meta_file, ckpt_file):
model_dir_exp = os.path.expanduser(model_dir)
saver = tf.train.import_meta_graph(os.path.join(model_dir_exp, meta_file))
saver.restore(tf.get_default_session(), os.path.join(model_dir_exp, ckpt_file))
def _chinese_whispers(encoding_list, threshold=0.55, iterations=20):
""" Chinese Whispers Algorithm
Modified from Alex Loveless' implementation,
http://alexloveless.co.uk/data/chinese-whispers-graph-clustering-in-python/
Inputs:
encoding_list: a list of facial encodings from face_recognition
threshold: facial match threshold,default 0.6
iterations: since chinese whispers is an iterative algorithm, number of times to iterate
Outputs:
sorted_clusters: a list of clusters, a cluster being a list of imagepaths,
sorted by largest cluster to smallest
"""
#from face_recognition.api import _face_distance
from random import shuffle
import networkx as nx
# Create graph
nodes = []
edges = []
image_paths, encodings = zip(*encoding_list)
if len(encodings) <= 1:
print ("No enough encodings to cluster!")
return []
for idx, face_encoding_to_check in enumerate(encodings):
# Adding node of facial encoding
node_id = idx+1
# Initialize 'cluster' to unique value (cluster of itself)
node = (node_id, {'cluster': image_paths[idx], 'path': image_paths[idx]})
nodes.append(node)
# Facial encodings to compare
if (idx+1) >= len(encodings):
# Node is last element, don't create edge
break
compare_encodings = encodings[idx+1:]
distances = face_distance(compare_encodings, face_encoding_to_check)
encoding_edges = []
for i, distance in enumerate(distances):
if distance > threshold:
# Add edge if facial match
edge_id = idx+i+2
encoding_edges.append((node_id, edge_id, {'weight': distance}))
edges = edges + encoding_edges
G = nx.Graph()
G.add_nodes_from(nodes)
G.add_edges_from(edges)
# Iterate
for _ in range(0, iterations):
cluster_nodes = G.nodes()
shuffle(cluster_nodes)
for node in cluster_nodes:
neighbors = G[node]
clusters = {}
for ne in neighbors:
if isinstance(ne, int):
if G.node[ne]['cluster'] in clusters:
clusters[G.node[ne]['cluster']] += G[node][ne]['weight']
else:
clusters[G.node[ne]['cluster']] = G[node][ne]['weight']
# find the class with the highest edge weight sum
edge_weight_sum = 0
max_cluster = 0
#use the max sum of neighbor weights class as current node's class
for cluster in clusters:
if clusters[cluster] > edge_weight_sum:
edge_weight_sum = clusters[cluster]
max_cluster = cluster
# set the class of target node to the winning local class
G.node[node]['cluster'] = max_cluster
clusters = {}
# Prepare cluster output
for (_, data) in G.node.items():
cluster = data['cluster']
path = data['path']
if cluster:
if cluster not in clusters:
clusters[cluster] = []
clusters[cluster].append(path)
# Sort cluster output
sorted_clusters = sorted(clusters.values(), key=len, reverse=True)
return sorted_clusters
def cluster_facial_encodings(facial_encodings):
""" Cluster facial encodings
Intended to be an optional switch for different clustering algorithms, as of right now
only chinese whispers is available.
Input:
facial_encodings: (image_path, facial_encoding) dictionary of facial encodings
Output:
sorted_clusters: a list of clusters, a cluster being a list of imagepaths,
sorted by largest cluster to smallest
"""
if len(facial_encodings) <= 1:
print ("Number of facial encodings must be greater than one, can't cluster")
return []
# Only use the chinese whispers algorithm for now
sorted_clusters = _chinese_whispers(facial_encodings.items())
return sorted_clusters
def compute_facial_encodings(sess,images_placeholder,embeddings,phase_train_placeholder,image_size,
embedding_size,nrof_images,nrof_batches,emb_array,batch_size,paths):
""" Compute Facial Encodings
Given a set of images, compute the facial encodings of each face detected in the images and
return them. If no faces, or more than one face found, return nothing for that image.
Inputs:
image_paths: a list of image paths
Outputs:
facial_encodings: (image_path, facial_encoding) dictionary of facial encodings
"""
for i in range(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, False, False, image_size)
feed_dict = { images_placeholder:images, phase_train_placeholder:False }
emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
facial_encodings = {}
for x in range(nrof_images):
facial_encodings[paths[x]] = emb_array[x,:]
return facial_encodings
def get_onedir(paths):
dataset = []
path_exp = os.path.expanduser(paths)
if os.path.isdir(path_exp):
images = os.listdir(path_exp)
image_paths = [os.path.join(path_exp,img) for img in images]
for x in image_paths:
if os.path.getsize(x)>0:
dataset.append(x)
return dataset
def main(args):
""" Main
Given a list of images, save out facial encoding data files and copy
images into folders of face clusters.
"""
from os.path import join, basename, exists
from os import makedirs
import numpy as np
import shutil
import sys
if not exists(args.output):
makedirs(args.output)
with tf.Graph().as_default():
with tf.Session() as sess:
image_paths = get_onedir(args.input)
#image_list, label_list = facenet.get_image_paths_and_labels(train_set)
meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
load_model(args.model_dir, meta_file, ckpt_file)
# 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]
print("image_size:",image_size)
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on images')
nrof_images = len(image_paths)
nrof_batches = int(math.ceil(1.0*nrof_images / args.batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
facial_encodings = compute_facial_encodings(sess,images_placeholder,embeddings,phase_train_placeholder,image_size,
embedding_size,nrof_images,nrof_batches,emb_array,args.batch_size,image_paths)
sorted_clusters = cluster_facial_encodings(facial_encodings)
num_cluster = len(sorted_clusters)
# Copy image files to cluster folders
for idx, cluster in enumerate(sorted_clusters):
#save all the cluster
cluster_dir = join(args.output, str(idx))
if not exists(cluster_dir):
makedirs(cluster_dir)
for path in cluster:
shutil.copy(path, join(cluster_dir, basename(path)))
def parse_args():
"""Parse input arguments."""
import argparse
parser = argparse.ArgumentParser(description='Get a shape mesh (t-pose)')
parser.add_argument('--model_dir', type=str, help='model dir', required=True)
parser.add_argument('--batch_size', type=int, help='batch size', required=30)
parser.add_argument('--input', type=str, help='Input dir of images', required=True)
parser.add_argument('--output', type=str, help='Output dir of clusters', required=True)
args = parser.parse_args()
return args
if __name__ == '__main__':
""" Entry point """
main(parse_args())
================================================
FILE: contributed/export_embeddings.py
================================================
"""
Exports the embeddings and labels of a directory of images as numpy arrays.
Typicall usage expect the image directory to be of the openface/facenet form and
the images to be aligned. Simply point to your model and your image directory:
python facenet/contributed/export_embeddings.py ~/models/facenet/20170216-091149/ ~/datasets/lfw/mylfw
Output:
embeddings.npy -- Embeddings as np array, Use --embeddings_name to change name
labels.npy -- Integer labels as np array, Use --labels_name to change name
label_strings.npy -- Strings from folders names, --labels_strings_name to change name
Use --image_batch to dictacte how many images to load in memory at a time.
If your images aren't already pre-aligned, use --is_aligned False
I started with compare.py from David Sandberg, and modified it to export
the embeddings. The image loading is done use the facenet library if the image
is pre-aligned. If the image isn't pre-aligned, I use the compare.py function.
I've found working with the embeddings useful for classifications models.
Charles Jekel 2017
"""
# 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 time
from scipy import misc
import tensorflow as tf
import numpy as np
import sys
import os
import argparse
import facenet
import align.detect_face
import glob
from six.moves import xrange
def main(args):
train_set = facenet.get_dataset(args.data_dir)
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
# fetch the classes (labels as strings) exactly as it's done in get_dataset
path_exp = os.path.expanduser(args.data_dir)
classes = [path for path in os.listdir(path_exp) \
if os.path.isdir(os.path.join(path_exp, path))]
classes.sort()
# get the label strings
label_strings = [name for name in classes if \
os.path.isdir(os.path.join(path_exp, name))]
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
facenet.load_model(args.model_dir)
# 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")
# Run forward pass to calculate embeddings
nrof_images = len(image_list)
print('Number of images: ', nrof_images)
batch_size = args.image_batch
if nrof_images % batch_size == 0:
nrof_batches = nrof_images // batch_size
else:
nrof_batches = (nrof_images // batch_size) + 1
print('Number of batches: ', nrof_batches)
embedding_size = embeddings.get_shape()[1]
emb_array = np.zeros((nrof_images, embedding_size))
start_time = time.time()
for i in range(nrof_batches):
if i == nrof_batches -1:
n = nrof_images
else:
n = i*batch_size + batch_size
# Get images for the batch
if args.is_aligned is True:
images = facenet.load_data(image_list[i*batch_size:n], False, False, args.image_size)
else:
images = load_and_align_data(image_list[i*batch_size:n], args.image_size, args.margin, args.gpu_memory_fraction)
feed_dict = { images_placeholder: images, phase_train_placeholder:False }
# Use the facenet model to calcualte embeddings
embed = sess.run(embeddings, feed_dict=feed_dict)
emb_array[i*batch_size:n, :] = embed
print('Completed batch', i+1, 'of', nrof_batches)
run_time = time.time() - start_time
print('Run time: ', run_time)
# export emedings and labels
label_list = np.array(label_list)
np.save(args.embeddings_name, emb_array)
np.save(args.labels_name, label_list)
label_strings = np.array(label_strings)
np.save(args.labels_strings_name, label_strings[label_list])
def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
minsize = 20 # minimum size of face
threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
factor = 0.709 # scale factor
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
nrof_samples = len(image_paths)
img_list = [None] * nrof_samples
for i in xrange(nrof_samples):
print(image_paths[i])
img = misc.imread(os.path.expanduser(image_paths[i]))
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
det = np.squeeze(bounding_boxes[0,0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img_size[1])
bb[3] = np.minimum(det[3]+margin/2, img_size[0])
cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
prewhitened = facenet.prewhiten(aligned)
img_list[i] = prewhitened
images = np.stack(img_list)
return images
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('model_dir', type=str,
help='Directory containing the meta_file and ckpt_file')
parser.add_argument('data_dir', type=str,
help='Directory containing images. If images are not already aligned and cropped include --is_aligned False.')
parser.add_argument('--is_aligned', type=str,
help='Is the data directory already aligned and cropped?', default=True)
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.',
default=44)
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.',
default=1.0)
parser.add_argument('--image_batch', type=int,
help='Number of images stored in memory at a time. Default 500.',
default=500)
# numpy file Names
parser.add_argument('--embeddings_name', type=str,
help='Enter string of which the embeddings numpy array is saved as.',
default='embeddings.npy')
parser.add_argument('--labels_name', type=str,
help='Enter string of which the labels numpy array is saved as.',
default='labels.npy')
parser.add_argument('--labels_strings_name', type=str,
help='Enter string of which the labels as strings numpy array is saved as.',
default='label_strings.npy')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: contributed/face.py
================================================
# coding=utf-8
"""Face Detection and Recognition"""
# MIT License
#
# Copyright (c) 2017 François Gervais
#
# This is the work of David Sandberg and shanren7 remodelled into a
# high level container. It's an attempt to simplify the use of such
# technology and provide an easy to use facial recognition package.
#
# https://github.com/davidsandberg/facenet
# https://github.com/shanren7/real_time_face_recognition
#
# 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.
import pickle
import os
import cv2
import numpy as np
import tensorflow as tf
from scipy import misc
import align.detect_face
import facenet
gpu_memory_fraction = 0.3
facenet_model_checkpoint = os.path.dirname(__file__) + "/../model_checkpoints/20170512-110547"
classifier_model = os.path.dirname(__file__) + "/../model_checkpoints/my_classifier_1.pkl"
debug = False
class Face:
def __init__(self):
self.name = None
self.bounding_box = None
self.image = None
self.container_image = None
self.embedding = None
class Recognition:
def __init__(self):
self.detect = Detection()
self.encoder = Encoder()
self.identifier = Identifier()
def add_identity(self, image, person_name):
faces = self.detect.find_faces(image)
if len(faces) == 1:
face = faces[0]
face.name = person_name
face.embedding = self.encoder.generate_embedding(face)
return faces
def identify(self, image):
faces = self.detect.find_faces(image)
for i, face in enumerate(faces):
if debug:
cv2.imshow("Face: " + str(i), face.image)
face.embedding = self.encoder.generate_embedding(face)
face.name = self.identifier.identify(face)
return faces
class Identifier:
def __init__(self):
with open(classifier_model, 'rb') as infile:
self.model, self.class_names = pickle.load(infile)
def identify(self, face):
if face.embedding is not None:
predictions = self.model.predict_proba([face.embedding])
best_class_indices = np.argmax(predictions, axis=1)
return self.class_names[best_class_indices[0]]
class Encoder:
def __init__(self):
self.sess = tf.Session()
with self.sess.as_default():
facenet.load_model(facenet_model_checkpoint)
def generate_embedding(self, face):
# 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")
prewhiten_face = facenet.prewhiten(face.image)
# Run forward pass to calculate embeddings
feed_dict = {images_placeholder: [prewhiten_face], phase_train_placeholder: False}
return self.sess.run(embeddings, feed_dict=feed_dict)[0]
class Detection:
# face detection parameters
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
def __init__(self, face_crop_size=160, face_crop_margin=32):
self.pnet, self.rnet, self.onet = self._setup_mtcnn()
self.face_crop_size = face_crop_size
self.face_crop_margin = face_crop_margin
def _setup_mtcnn(self):
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
return align.detect_face.create_mtcnn(sess, None)
def find_faces(self, image):
faces = []
bounding_boxes, _ = align.detect_face.detect_face(image, self.minsize,
self.pnet, self.rnet, self.onet,
self.threshold, self.factor)
for bb in bounding_boxes:
face = Face()
face.container_image = image
face.bounding_box = np.zeros(4, dtype=np.int32)
img_size = np.asarray(image.shape)[0:2]
face.bounding_box[0] = np.maximum(bb[0] - self.face_crop_margin / 2, 0)
face.bounding_box[1] = np.maximum(bb[1] - self.face_crop_margin / 2, 0)
face.bounding_box[2] = np.minimum(bb[2] + self.face_crop_margin / 2, img_size[1])
face.bounding_box[3] = np.minimum(bb[3] + self.face_crop_margin / 2, img_size[0])
cropped = image[face.bounding_box[1]:face.bounding_box[3], face.bounding_box[0]:face.bounding_box[2], :]
face.image = misc.imresize(cropped, (self.face_crop_size, self.face_crop_size), interp='bilinear')
faces.append(face)
return faces
================================================
FILE: contributed/predict.py
================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
#----------------------------------------------------
# MIT License
#
# Copyright (c) 2017 Rishi Rai
#
# 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.
#----------------------------------------------------
import tensorflow as tf
import numpy as np
import argparse
import facenet
import os
import sys
import math
import pickle
from sklearn.svm import SVC
from scipy import misc
import align.detect_face
from six.moves import xrange
def main(args):
images, cout_per_image, nrof_samples = load_and_align_data(args.image_files,args.image_size, args.margin, args.gpu_memory_fraction)
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
facenet.load_model(args.model)
# 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")
# Run forward pass to calculate embeddings
feed_dict = { images_placeholder: images , phase_train_placeholder:False}
emb = sess.run(embeddings, feed_dict=feed_dict)
classifier_filename_exp = os.path.expanduser(args.classifier_filename)
with open(classifier_filename_exp, 'rb') as infile:
(model, class_names) = pickle.load(infile)
print('Loaded classifier model from file "%s"\n' % classifier_filename_exp)
predictions = model.predict_proba(emb)
best_class_indices = np.argmax(predictions, axis=1)
best_class_probabilities = predictions[np.arange(len(best_class_indices)), best_class_indices]
k=0
#print predictions
for i in range(nrof_samples):
print("\npeople in image %s :" %(args.image_files[i]))
for j in range(cout_per_image[i]):
print('%s: %.3f' % (class_names[best_class_indices[k]], best_class_probabilities[k]))
k+=1
def load_and_align_data(image_paths, image_size, margin, gpu_memory_fraction):
minsize = 20 # minimum size of face
threshold = [ 0.6, 0.7, 0.7 ] # three steps's threshold
factor = 0.709 # scale factor
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = align.detect_face.create_mtcnn(sess, None)
nrof_samples = len(image_paths)
img_list = []
count_per_image = []
for i in xrange(nrof_samples):
img = misc.imread(os.path.expanduser(image_paths[i]))
img_size = np.asarray(img.shape)[0:2]
bounding_boxes, _ = align.detect_face.detect_face(img, minsize, pnet, rnet, onet, threshold, factor)
count_per_image.append(len(bounding_boxes))
for j in range(len(bounding_boxes)):
det = np.squeeze(bounding_boxes[j,0:4])
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0]-margin/2, 0)
bb[1] = np.maximum(det[1]-margin/2, 0)
bb[2] = np.minimum(det[2]+margin/2, img_size[1])
bb[3] = np.minimum(det[3]+margin/2, img_size[0])
cropped = img[bb[1]:bb[3],bb[0]:bb[2],:]
aligned = misc.imresize(cropped, (image_size, image_size), interp='bilinear')
prewhitened = facenet.prewhiten(aligned)
img_list.append(prewhitened)
images = np.stack(img_list)
return images, count_per_image, nrof_samples
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('image_files', type=str, nargs='+', help='Path(s) of the image(s)')
parser.add_argument('model', type=str,
help='Could be either a directory containing the meta_file and ckpt_file or a model protobuf (.pb) file')
parser.add_argument('classifier_filename',
help='Classifier model file name as a pickle (.pkl) file. ' +
'For training this is the output and for classification this is an input.')
parser.add_argument('--image_size', type=int,
help='Image size (height, width) in pixels.', default=160)
parser.add_argument('--seed', type=int,
help='Random seed.', default=666)
parser.add_argument('--margin', type=int,
help='Margin for the crop around the bounding box (height, width) in pixels.', default=44)
parser.add_argument('--gpu_memory_fraction', type=float,
help='Upper bound on the amount of GPU memory that will be used by the process.', default=1.0)
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: contributed/real_time_face_recognition.py
================================================
# coding=utf-8
"""Performs face detection in realtime.
Based on code from https://github.com/shanren7/real_time_face_recognition
"""
# MIT License
#
# Copyright (c) 2017 François Gervais
#
# 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.
import argparse
import sys
import time
import cv2
import face
def add_overlays(frame, faces, frame_rate):
if faces is not None:
for face in faces:
face_bb = face.bounding_box.astype(int)
cv2.rectangle(frame,
(face_bb[0], face_bb[1]), (face_bb[2], face_bb[3]),
(0, 255, 0), 2)
if face.name is not None:
cv2.putText(frame, face.name, (face_bb[0], face_bb[3]),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0),
thickness=2, lineType=2)
cv2.putText(frame, str(frame_rate) + " fps", (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0),
thickness=2, lineType=2)
def main(args):
frame_interval = 3 # Number of frames after which to run face detection
fps_display_interval = 5 # seconds
frame_rate = 0
frame_count = 0
video_capture = cv2.VideoCapture(0)
face_recognition = face.Recognition()
start_time = time.time()
if args.debug:
print("Debug enabled")
face.debug = True
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
if (frame_count % frame_interval) == 0:
faces = face_recognition.identify(frame)
# Check our current fps
end_time = time.time()
if (end_time - start_time) > fps_display_interval:
frame_rate = int(frame_count / (end_time - start_time))
start_time = time.time()
frame_count = 0
add_overlays(frame, faces, frame_rate)
frame_count += 1
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()
def parse_arguments(argv):
parser = argparse.ArgumentParser()
parser.add_argument('--debug', action='store_true',
help='Enable some debug outputs.')
return parser.parse_args(argv)
if __name__ == '__main__':
main(parse_arguments(sys.argv[1:]))
================================================
FILE: data/learning_rate_retrain_tripletloss.txt
================================================
# Learning rate schedule
# Maps an epoch number to a learning rate
0: 0.1
300: 0.01
400: 0.001
1000: 0.0001
================================================
FILE: data/learning_rate_schedule_classifier_casia.txt
================================================
# Learning rate schedule
# Maps an epoch number to a learning rate
0: 0.05
60: 0.005
80: 0.0005
91: -1
================================================
FILE: data/learning_rate_schedule_classifier_msceleb.txt
================================================
# Learning rate schedule
# Maps an epoch number to a learning rate
0: 0.1
150: 0.01
180: 0.001
251: 0.0001
================================================
FILE: data/learning_rate_schedule_classifier_vggface2.txt
================================================
# Learning rate schedule
# Maps an epoch number to a learning rate
0: 0.05
100: 0.005
200: 0.0005
276: -1
================================================
FILE: data/pairs.txt
================================================
10 300
Abel_Pacheco 1 4
Akhmed_Zakayev 1 3
Akhmed_Zakayev 2 3
Amber_Tamblyn 1 2
Anders_Fogh_Rasmussen 1 3
Anders_Fogh_Rasmussen 1 4
Angela_Bassett 1 5
Angela_Bassett 2 5
Angela_Bassett 3 4
Ann_Veneman 3 5
Ann_Veneman 6 10
Ann_Veneman 10 11
Anthony_Fauci 1 2
Antony_Leung 1 2
Antony_Leung 2 3
Anwar_Ibrahim 1 2
Augusto_Pinochet 1 2
Barbara_Brezigar 1 2
Benjamin_Netanyahu 1 4
Benjamin_Netanyahu 4 5
Bernard_Law 2 3
Bernard_Law 3 4
Bertrand_Bonello 1 2
Bill_Frist 2 9
Bill_Frist 4 5
Bob_Graham 2 4
Bob_Graham 3 6
Bob_Graham 4 6
Bob_Graham 5 6
Boris_Becker 2 6
Brad_Johnson 1 3
Brad_Johnson 2 3
Brad_Johnson 2 4
Brian_Griese 1 2
Candice_Bergen 1 2
Candice_Bergen 2 3
Carla_Myers 1 2
Cathy_Freeman 1 2
Chang_Dae-whan 1 2
Charles_Grassley 1 2
Clare_Short 1 3
Clare_Short 1 4
Corinne_Coman 1 2
Dai_Bachtiar 1 2
Dale_Earnhardt_Jr 1 3
David_Caruso 1 2
Demi_Moore 1 3
Dick_Vermeil 1 2
Doris_Roberts 1 2
Doris_Roberts 1 3
Drew_Barrymore 1 2
Edmund_Stoiber 4 7
Edmund_Stoiber 5 7
Edmund_Stoiber 7 12
Edmund_Stoiber 9 11
Elinor_Caplan 1 2
Emmanuelle_Beart 1 2
Emmanuelle_Beart 1 3
Federico_Trillo 1 2
Federico_Trillo 1 3
Federico_Trillo 2 3
Francis_Ford_Coppola 1 2
Fred_Thompson 1 2
Fred_Thompson 1 3
Fred_Thompson 2 3
Garry_Trudeau 1 2
Gary_Williams 1 2
Gene_Robinson 1 2
Gene_Robinson 1 3
Gene_Robinson 2 3
George_Galloway 1 3
George_Galloway 2 3
George_Galloway 2 4
Geraldine_Chaplin 1 2
Geraldine_Chaplin 1 4
Geraldine_Chaplin 3 4
Gerardo_Gambala 1 2
Gian_Marco 1 2
Gian_Marco 2 3
Gillian_Anderson 1 2
Gordon_Brown 6 9
Gordon_Brown 10 13
Grant_Hackett 1 2
Grant_Hackett 1 3
Grant_Hackett 1 5
Gray_Davis 7 22
Gray_Davis 21 26
Gregg_Popovich 1 5
Gregg_Popovich 3 4
Guy_Ritchie 1 2
Hamzah_Haz 1 2
Harbhajan_Singh 1 2
Hootie_Johnson 1 2
Hugo_Chavez 2 27
Hugo_Chavez 27 53
Hugo_Chavez 36 51
Hugo_Chavez 41 50
Hugo_Chavez 45 56
Isaiah_Washington 1 2
Jack_Grubman 1 2
Jacques_Rogge 1 9
Jacques_Rogge 3 7
Jacques_Rogge 3 10
Jacques_Rogge 4 6
Jacques_Rogge 4 9
Jacques_Rogge 5 8
Jacques_Rogge 6 8
James_Caan 1 2
James_Caan 1 3
James_Caan 2 3
James_Jones 1 2
Jamling_Norgay 1 2
Janica_Kostelic 1 2
Jeanne_Moreau 1 2
Jeffrey_Archer 1 2
Jennifer_Garner 1 3
Jennifer_Garner 4 9
Jennifer_Garner 5 6
Jennifer_Garner 5 9
Jennifer_Garner 7 12
Jennifer_Garner 8 11
Jeremy_Greenstock 2 24
Jeremy_Greenstock 3 22
Jeremy_Greenstock 5 11
Jeremy_Greenstock 10 14
Jeremy_Greenstock 17 21
Jessica_Lange 1 2
Jimmy_Carter 1 7
Jimmy_Carter 4 6
Jimmy_Carter 5 8
Jimmy_Carter 6 7
Jodie_Foster 1 2
John_Manley 1 6
John_Manley 2 5
John_McCain 2 3
John_McCain 3 7
John_McCain 6 7
John_Snow 3 15
John_Snow 4 16
John_Snow 9 11
Johnson_Panjaitan 1 2
Jorge_Castaneda 1 2
Jose_Serra 1 9
Jose_Serra 2 8
Jose_Serra 3 5
Jose_Serra 3 7
Jose_Serra 6 9
Jose_Theodore 1 2
Joseph_Blatter 1 2
Joseph_Ralston 1 2
Juan_Pablo_Montoya 1 8
Juan_Pablo_Montoya 2 4
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Michael_Doleac 1 Nur_Jaafar 1
Michael_Goldrich 1 Suzanne_Somers 1
Michael_Kahn 1 Rick_Caruso 1
Michel_Minard 1 Suzanne_Gaudet 1
Michelle_Bachelet 1 Sami_Al-Arian 1
Miguel_Angel_Rodriguez 1 Sasha_Cohen 1
Mike_Bryan 1 Shanna_Zolman 1
Mike_Montgomery 1 Ray_Lewis 1
Milton_Wynants 1 Stuart_Townsend 1
Miyako_Miyazaki 2 Munir_Akram 2
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Nicole_Hiltz 1 Zaini_Abdullah 1
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Oscar_Bolanos 1 Tatiana_Kennedy_Schlossberg 1
Pascal_Quignard 3 Patrick_Ewing 2
Pat_Rochester 1 Phoenix_Chang 1
Pat_Rochester 1 Will_Ofenheusle 1
Paul_Farley 1 Platon_Lebedev 1
Paula_Abdul 1 Robert_Vowler 1
Pharrell_Williams 1 Tyrone_Medley 1
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Robert_Vowler 1 Tab_Baldwin 1
Roy_Rogers 1 Steven_Feldman 1
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Sofia_Milos 1 Will_Ofenheusle 1
Sonja_Kesselschlager 1 Tim_Robbins 3
Takeo_Hiranuma 1 Ty_Votaw 1
Ted_Washington 1 Ximena_Bohorquez 1
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Christine_Greg
gitextract_mncf1i0j/
├── .gitignore
├── .project
├── .pydevproject
├── .pylintrc
├── .travis.yml
├── LICENSE.md
├── README.md
├── __init__.py
├── contributed/
│ ├── __init__.py
│ ├── batch_represent.py
│ ├── cluster.py
│ ├── clustering.py
│ ├── export_embeddings.py
│ ├── face.py
│ ├── predict.py
│ └── real_time_face_recognition.py
├── data/
│ ├── learning_rate_retrain_tripletloss.txt
│ ├── learning_rate_schedule_classifier_casia.txt
│ ├── learning_rate_schedule_classifier_msceleb.txt
│ ├── learning_rate_schedule_classifier_vggface2.txt
│ └── pairs.txt
├── requirements.txt
├── src/
│ ├── __init__.py
│ ├── align/
│ │ ├── __init__.py
│ │ ├── align_dataset_mtcnn.py
│ │ ├── det1.npy
│ │ ├── det2.npy
│ │ ├── det3.npy
│ │ └── detect_face.py
│ ├── calculate_filtering_metrics.py
│ ├── classifier.py
│ ├── compare.py
│ ├── decode_msceleb_dataset.py
│ ├── download_and_extract.py
│ ├── facenet.py
│ ├── freeze_graph.py
│ ├── generative/
│ │ ├── __init__.py
│ │ ├── calculate_attribute_vectors.py
│ │ ├── models/
│ │ │ ├── __init__.py
│ │ │ ├── dfc_vae.py
│ │ │ ├── dfc_vae_large.py
│ │ │ ├── dfc_vae_resnet.py
│ │ │ └── vae_base.py
│ │ ├── modify_attribute.py
│ │ └── train_vae.py
│ ├── lfw.py
│ ├── models/
│ │ ├── __init__.py
│ │ ├── dummy.py
│ │ ├── inception_resnet_v1.py
│ │ ├── inception_resnet_v2.py
│ │ └── squeezenet.py
│ ├── train_softmax.py
│ ├── train_tripletloss.py
│ └── validate_on_lfw.py
├── test/
│ ├── batch_norm_test.py
│ ├── center_loss_test.py
│ ├── restore_test.py
│ ├── train_test.py
│ └── triplet_loss_test.py
├── tmp/
│ ├── __init__.py
│ ├── align_dataset.m
│ ├── align_dataset.py
│ ├── align_dlib.py
│ ├── cacd2000_split_identities.py
│ ├── dataset_read_speed.py
│ ├── deepdream.py
│ ├── detect_face_v1.m
│ ├── detect_face_v2.m
│ ├── download_vgg_face_dataset.py
│ ├── funnel_dataset.py
│ ├── invariance_test.txt
│ ├── mnist_center_loss.py
│ ├── mnist_noise_labels.py
│ ├── mtcnn.py
│ ├── mtcnn_test.py
│ ├── mtcnn_test_pnet_dbg.py
│ ├── network.py
│ ├── nn2.py
│ ├── nn3.py
│ ├── nn4.py
│ ├── nn4_small2_v1.py
│ ├── random_test.py
│ ├── rename_casia_directories.py
│ ├── seed_test.py
│ ├── select_triplets_test.py
│ ├── test1.py
│ ├── test_align.py
│ ├── test_invariance_on_lfw.py
│ ├── vggface16.py
│ ├── vggverydeep19.py
│ ├── visualize.py
│ ├── visualize_vgg_model.py
│ └── visualize_vggface.py
└── util/
└── plot_learning_curves.m
SYMBOL INDEX (299 symbols across 59 files)
FILE: contributed/batch_represent.py
function main (line 81) | def main(args):
function parse_arguments (line 134) | def parse_arguments(argv):
FILE: contributed/cluster.py
function main (line 40) | def main(args):
function align_data (line 117) | def align_data(image_list, image_size, margin, pnet, rnet, onet):
function create_network_face_detection (line 149) | def create_network_face_detection(gpu_memory_fraction):
function load_images_from_folder (line 158) | def load_images_from_folder(folder):
function parse_arguments (line 167) | def parse_arguments(argv):
FILE: contributed/clustering.py
function face_distance (line 9) | def face_distance(face_encodings, face_to_compare):
function load_model (line 24) | def load_model(model_dir, meta_file, ckpt_file):
function _chinese_whispers (line 29) | def _chinese_whispers(encoding_list, threshold=0.55, iterations=20):
function cluster_facial_encodings (line 130) | def cluster_facial_encodings(facial_encodings):
function compute_facial_encodings (line 153) | def compute_facial_encodings(sess,images_placeholder,embeddings,phase_tr...
function get_onedir (line 183) | def get_onedir(paths):
function main (line 197) | def main(args):
function parse_args (line 253) | def parse_args():
FILE: contributed/export_embeddings.py
function main (line 66) | def main(args):
function load_and_align_data (line 131) | def load_and_align_data(image_paths, image_size, margin, gpu_memory_frac...
function parse_arguments (line 164) | def parse_arguments(argv):
FILE: contributed/face.py
class Face (line 50) | class Face:
method __init__ (line 51) | def __init__(self):
class Recognition (line 59) | class Recognition:
method __init__ (line 60) | def __init__(self):
method add_identity (line 65) | def add_identity(self, image, person_name):
method identify (line 74) | def identify(self, image):
class Identifier (line 86) | class Identifier:
method __init__ (line 87) | def __init__(self):
method identify (line 91) | def identify(self, face):
class Encoder (line 98) | class Encoder:
method __init__ (line 99) | def __init__(self):
method generate_embedding (line 104) | def generate_embedding(self, face):
class Detection (line 117) | class Detection:
method __init__ (line 123) | def __init__(self, face_crop_size=160, face_crop_margin=32):
method _setup_mtcnn (line 128) | def _setup_mtcnn(self):
method find_faces (line 135) | def find_faces(self, image):
FILE: contributed/predict.py
function main (line 45) | def main(args):
function load_and_align_data (line 77) | def load_and_align_data(image_paths, image_size, margin, gpu_memory_frac...
function parse_arguments (line 112) | def parse_arguments(argv):
FILE: contributed/real_time_face_recognition.py
function add_overlays (line 36) | def add_overlays(frame, faces, frame_rate):
function main (line 53) | def main(args):
function parse_arguments (line 94) | def parse_arguments(argv):
FILE: src/align/align_dataset_mtcnn.py
function main (line 39) | def main(args):
function parse_arguments (line 141) | def parse_arguments(argv):
FILE: src/align/detect_face.py
function layer (line 37) | def layer(op):
class Network (line 61) | class Network(object):
method __init__ (line 63) | def __init__(self, inputs, trainable=True):
method setup (line 75) | def setup(self):
method load (line 79) | def load(self, data_path, session, ignore_missing=False):
method feed (line 97) | def feed(self, *args):
method get_output (line 112) | def get_output(self):
method get_unique_name (line 116) | def get_unique_name(self, prefix):
method make_var (line 123) | def make_var(self, name, shape):
method validate_padding (line 127) | def validate_padding(self, padding):
method conv (line 132) | def conv(self,
method prelu (line 167) | def prelu(self, inp, name):
method max_pool (line 175) | def max_pool(self, inp, k_h, k_w, s_h, s_w, name, padding='SAME'):
method fc (line 184) | def fc(self, inp, num_out, name, relu=True):
method softmax (line 209) | def softmax(self, target, axis, name=None):
class PNet (line 216) | class PNet(Network):
method setup (line 217) | def setup(self):
class RNet (line 232) | class RNet(Network):
method setup (line 233) | def setup(self):
class ONet (line 251) | class ONet(Network):
method setup (line 252) | def setup(self):
function create_mtcnn (line 276) | def create_mtcnn(sess, model_path):
function detect_face (line 298) | def detect_face(img, minsize, pnet, rnet, onet, threshold, factor):
function bulk_detect_face (line 421) | def bulk_detect_face(images, detection_window_size_ratio, pnet, rnet, on...
function bbreg (line 646) | def bbreg(boundingbox,reg):
function generateBoundingBox (line 660) | def generateBoundingBox(imap, reg, scale, t):
function nms (line 687) | def nms(boxes, threshold, method):
function pad (line 720) | def pad(total_boxes, w, h):
function rerec (line 755) | def rerec(bboxA):
function imresample (line 765) | def imresample(img, sz):
FILE: src/calculate_filtering_metrics.py
function main (line 41) | def main(args):
function parse_arguments (line 112) | def parse_arguments(argv):
FILE: src/classifier.py
function main (line 39) | def main(args):
function split_dataset (line 125) | def split_dataset(dataset, min_nrof_images_per_class, nrof_train_images_...
function parse_arguments (line 138) | def parse_arguments(argv):
FILE: src/compare.py
function main (line 39) | def main(args):
function load_and_align_data (line 79) | def load_and_align_data(image_paths, image_size, margin, gpu_memory_frac...
function parse_arguments (line 115) | def parse_arguments(argv):
FILE: src/decode_msceleb_dataset.py
function main (line 48) | def main(args):
FILE: src/download_and_extract.py
function download_and_extract_file (line 13) | def download_and_extract_file(model_name, data_dir):
function download_file_from_google_drive (line 23) | def download_file_from_google_drive(file_id, destination):
function get_confirm_token (line 38) | def get_confirm_token(response):
function save_response_content (line 45) | def save_response_content(response, destination):
FILE: src/facenet.py
function triplet_loss (line 44) | def triplet_loss(anchor, positive, negative, alpha):
function center_loss (line 64) | def center_loss(features, label, alfa, nrof_classes):
function get_image_paths_and_labels (line 79) | def get_image_paths_and_labels(dataset):
function shuffle_examples (line 87) | def shuffle_examples(image_paths, labels):
function random_rotate_image (line 93) | def random_rotate_image(image):
function create_input_pipeline (line 103) | def create_input_pipeline(input_queue, image_size, nrof_preprocess_threa...
function get_control_flag (line 139) | def get_control_flag(control, field):
function _add_loss_summaries (line 142) | def _add_loss_summaries(total_loss):
function train (line 168) | def train(total_loss, global_step, optimizer, learning_rate, moving_aver...
function prewhiten (line 213) | def prewhiten(x):
function crop (line 220) | def crop(image, random_crop, image_size):
function flip (line 232) | def flip(image, random_flip):
function to_rgb (line 237) | def to_rgb(img):
function load_data (line 243) | def load_data(image_paths, do_random_crop, do_random_flip, image_size, d...
function get_label_batch (line 257) | def get_label_batch(label_data, batch_size, batch_index):
function get_batch (line 269) | def get_batch(image_data, batch_size, batch_index):
function get_triplet_batch (line 281) | def get_triplet_batch(triplets, batch_index, batch_size):
function get_learning_rate_from_file (line 289) | def get_learning_rate_from_file(filename, epoch):
class ImageClass (line 305) | class ImageClass():
method __init__ (line 307) | def __init__(self, name, image_paths):
method __str__ (line 311) | def __str__(self):
method __len__ (line 314) | def __len__(self):
function get_dataset (line 317) | def get_dataset(path, has_class_directories=True):
function get_image_paths (line 332) | def get_image_paths(facedir):
function split_dataset (line 339) | def split_dataset(dataset, split_ratio, min_nrof_images_per_class, mode):
function load_model (line 364) | def load_model(model, input_map=None):
function get_model_filenames (line 384) | def get_model_filenames(model_dir):
function distance (line 408) | def distance(embeddings1, embeddings2, distance_metric=0):
function calculate_roc (line 424) | def calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, n...
function calculate_accuracy (line 457) | def calculate_accuracy(threshold, dist, actual_issame):
function calculate_val (line 471) | def calculate_val(thresholds, embeddings1, embeddings2, actual_issame, f...
function calculate_val_far (line 508) | def calculate_val_far(threshold, dist, actual_issame):
function store_revision_info (line 518) | def store_revision_info(src_path, output_dir, arg_string):
function list_variables (line 545) | def list_variables(filename):
function put_images_on_grid (line 551) | def put_images_on_grid(images, shape=(16,8)):
function write_arguments_to_file (line 568) | def write_arguments_to_file(args, filename):
FILE: src/freeze_graph.py
function main (line 38) | def main(args):
function freeze_graph_def (line 65) | def freeze_graph_def(sess, input_graph_def, output_node_names):
function parse_arguments (line 93) | def parse_arguments(argv):
FILE: src/generative/calculate_attribute_vectors.py
function main (line 42) | def main(args):
function read_annotations (line 157) | def read_annotations(filename):
function parse_arguments (line 172) | def parse_arguments(argv):
FILE: src/generative/models/dfc_vae.py
class Vae (line 37) | class Vae(generative.models.vae_base.Vae):
method __init__ (line 39) | def __init__(self, latent_variable_dim):
method encoder (line 42) | def encoder(self, images, is_training):
method decoder (line 62) | def decoder(self, latent_var, is_training):
function leaky_relu (line 90) | def leaky_relu(x):
FILE: src/generative/models/dfc_vae_large.py
class Vae (line 37) | class Vae(generative.models.vae_base.Vae):
method __init__ (line 39) | def __init__(self, latent_variable_dim):
method encoder (line 43) | def encoder(self, images, is_training):
method decoder (line 64) | def decoder(self, latent_var, is_training):
function leaky_relu (line 94) | def leaky_relu(x):
FILE: src/generative/models/dfc_vae_resnet.py
class Vae (line 37) | class Vae(generative.models.vae_base.Vae):
method __init__ (line 39) | def __init__(self, latent_variable_dim):
method encoder (line 42) | def encoder(self, images, is_training):
method decoder (line 72) | def decoder(self, latent_var, is_training):
function conv2d_block (line 105) | def conv2d_block(inp, scale, *args, **kwargs):
function leaky_relu (line 108) | def leaky_relu(x):
FILE: src/generative/models/vae_base.py
class Vae (line 32) | class Vae(object):
method __init__ (line 34) | def __init__(self, latent_variable_dim, image_size):
method encoder (line 48) | def encoder(self, images, is_training):
method decoder (line 52) | def decoder(self, latent_var, is_training):
method get_image_size (line 56) | def get_image_size(self):
FILE: src/generative/modify_attribute.py
function main (line 42) | def main(args):
function parse_arguments (line 122) | def parse_arguments(argv):
FILE: src/generative/train_vae.py
function main (line 43) | def main(args):
function get_variables_to_train (line 219) | def get_variables_to_train():
function get_facenet_variables_to_restore (line 226) | def get_facenet_variables_to_restore():
function kl_divergence_loss (line 234) | def kl_divergence_loss(mean, log_variance):
function parse_arguments (line 238) | def parse_arguments(argv):
FILE: src/lfw.py
function evaluate (line 34) | def evaluate(embeddings, actual_issame, nrof_folds=10, distance_metric=0...
function get_paths (line 46) | def get_paths(lfw_dir, pairs):
function add_extension (line 69) | def add_extension(path):
function read_pairs (line 77) | def read_pairs(pairs_filename):
FILE: src/models/dummy.py
function inference (line 33) | def inference(images, keep_probability, phase_train=True, # @UnusedVari...
FILE: src/models/inception_resnet_v1.py
function block35 (line 30) | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=...
function block17 (line 51) | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=...
function block8 (line 72) | def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=N...
function reduction_a (line 91) | def reduction_a(net, k, l, m, n):
function reduction_b (line 108) | def reduction_b(net):
function inference (line 130) | def inference(images, keep_probability, phase_train=True,
function inception_resnet_v1 (line 152) | def inception_resnet_v1(inputs, is_training=True,
FILE: src/models/inception_resnet_v2.py
function block35 (line 30) | def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=...
function block17 (line 51) | def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=...
function block8 (line 72) | def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=N...
function inference (line 91) | def inference(images, keep_probability, phase_train=True,
function inception_resnet_v2 (line 112) | def inception_resnet_v2(inputs, is_training=True,
FILE: src/models/squeezenet.py
function fire_module (line 8) | def fire_module(inputs,
function squeeze (line 21) | def squeeze(inputs, num_outputs):
function expand (line 24) | def expand(inputs, num_outputs):
function inference (line 30) | def inference(images, keep_probability, phase_train=True, bottleneck_lay...
FILE: src/train_softmax.py
function main (line 47) | def main(args):
function find_threshold (line 265) | def find_threshold(var, percentile):
function filter_dataset (line 273) | def filter_dataset(dataset, data_filename, percentile, min_nrof_images_p...
function train (line 296) | def train(args, sess, epoch, image_list, label_list, index_dequeue_op, e...
function validate (line 356) | def validate(args, sess, epoch, image_list, label_list, enqueue_op, imag...
function evaluate (line 397) | def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placehold...
function save_variables_and_metagraph (line 457) | def save_variables_and_metagraph(sess, saver, summary_writer, model_dir,...
function parse_arguments (line 480) | def parse_arguments(argv):
FILE: src/train_tripletloss.py
function main (line 46) | def main(args):
function train (line 200) | def train(args, sess, dataset, epoch, image_paths_placeholder, labels_pl...
function select_triplets (line 271) | def select_triplets(embeddings, nrof_images_per_class, image_paths, peop...
function sample_people (line 313) | def sample_people(dataset, people_per_batch, images_per_person):
function evaluate (line 341) | def evaluate(sess, image_paths, embeddings, labels_batch, image_paths_pl...
function save_variables_and_metagraph (line 381) | def save_variables_and_metagraph(sess, saver, summary_writer, model_dir,...
function get_learning_rate_from_file (line 404) | def get_learning_rate_from_file(filename, epoch):
function parse_arguments (line 418) | def parse_arguments(argv):
FILE: src/validate_on_lfw.py
function main (line 44) | def main(args):
function evaluate (line 86) | def evaluate(sess, enqueue_op, image_paths_placeholder, labels_placehold...
function parse_arguments (line 138) | def parse_arguments(argv):
FILE: test/batch_norm_test.py
class BatchNormTest (line 29) | class BatchNormTest(unittest.TestCase):
method testBatchNorm (line 33) | def testBatchNorm(self):
FILE: test/center_loss_test.py
class CenterLossTest (line 28) | class CenterLossTest(unittest.TestCase):
method testCenterLoss (line 32) | def testCenterLoss(self):
function create_features (line 70) | def create_features(label_to_center, batch_size, nrof_features, labels):
FILE: test/restore_test.py
class TrainTest (line 30) | class TrainTest(unittest.TestCase):
method setUpClass (line 33) | def setUpClass(self):
method tearDownClass (line 37) | def tearDownClass(self):
method test_restore_noema (line 41) | def test_restore_noema(self):
method test_restore_ema (line 91) | def test_restore_ema(self):
function create_checkpoint_file (line 172) | def create_checkpoint_file(model_dir, model_file):
FILE: test/train_test.py
function memory_usage_psutil (line 32) | def memory_usage_psutil():
function align_dataset_if_needed (line 39) | def align_dataset_if_needed(self):
class TrainTest (line 50) | class TrainTest(unittest.TestCase):
method setUpClass (line 53) | def setUpClass(self):
method tearDownClass (line 68) | def tearDownClass(self):
method tearDown (line 72) | def tearDown(self):
method test_training_classifier_inception_resnet_v1 (line 75) | def test_training_classifier_inception_resnet_v1(self):
method test_training_classifier_inception_resnet_v2 (line 93) | def test_training_classifier_inception_resnet_v2(self):
method test_training_classifier_squeezenet (line 110) | def test_training_classifier_squeezenet(self):
method test_train_tripletloss_inception_resnet_v1 (line 128) | def test_train_tripletloss_inception_resnet_v1(self):
method test_finetune_tripletloss_inception_resnet_v1 (line 146) | def test_finetune_tripletloss_inception_resnet_v1(self):
method test_compare (line 166) | def test_compare(self):
method test_validate_on_lfw (line 175) | def test_validate_on_lfw(self):
method test_validate_on_lfw_frozen_graph (line 187) | def test_validate_on_lfw_frozen_graph(self):
method test_freeze_graph (line 200) | def test_freeze_graph(self):
function create_mock_dataset (line 209) | def create_mock_dataset(dataset_dir, image_size):
function create_mock_lfw_pairs (line 226) | def create_mock_lfw_pairs(tmp_dir):
FILE: test/triplet_loss_test.py
class DemuxEmbeddingsTest (line 28) | class DemuxEmbeddingsTest(unittest.TestCase):
method testDemuxEmbeddings (line 30) | def testDemuxEmbeddings(self):
FILE: tmp/align_dataset.py
function main (line 37) | def main(args):
function parse_arguments (line 117) | def parse_arguments(argv):
FILE: tmp/align_dlib.py
class AlignDlib (line 69) | class AlignDlib:
method __init__ (line 89) | def __init__(self, facePredictor):
method getAllFaceBoundingBoxes (line 102) | def getAllFaceBoundingBoxes(self, rgbImg):
method getLargestFaceBoundingBox (line 120) | def getLargestFaceBoundingBox(self, rgbImg, skipMulti=False):
method findLandmarks (line 139) | def findLandmarks(self, rgbImg, bb):
method align (line 158) | def align(self, imgDim, rgbImg, bb=None,
FILE: tmp/cacd2000_split_identities.py
function main (line 6) | def main(args):
function parse_arguments (line 24) | def parse_arguments(argv):
FILE: tmp/dataset_read_speed.py
function main (line 7) | def main(args):
function parse_arguments (line 23) | def parse_arguments(argv):
FILE: tmp/deepdream.py
function main (line 12) | def main():
FILE: tmp/download_vgg_face_dataset.py
function main (line 39) | def main(args):
function save_error_message_file (line 83) | def save_error_message_file(filename, error_message):
function to_rgb (line 88) | def to_rgb(img):
function parse_arguments (line 94) | def parse_arguments(argv):
FILE: tmp/funnel_dataset.py
function TemporaryDirectory (line 19) | def TemporaryDirectory():
function main (line 27) | def main(args):
function parse_arguments (line 83) | def parse_arguments(argv):
FILE: tmp/mnist_center_loss.py
function data_type (line 58) | def data_type():
function maybe_download (line 66) | def maybe_download(filename):
function extract_data (line 79) | def extract_data(filename, num_images):
function extract_labels (line 93) | def extract_labels(filename, num_images):
function fake_data (line 103) | def fake_data(num_images):
function error_rate (line 116) | def error_rate(predictions, labels):
function main (line 124) | def main(argv=None): # pylint: disable=unused-argument
FILE: tmp/mnist_noise_labels.py
function data_type (line 57) | def data_type():
function maybe_download (line 65) | def maybe_download(filename):
function extract_data (line 78) | def extract_data(filename, num_images):
function extract_labels (line 92) | def extract_labels(filename, num_images):
function fake_data (line 102) | def fake_data(num_images):
function error_rate (line 115) | def error_rate(predictions, labels):
function main (line 123) | def main(argv=None): # pylint: disable=unused-argument
FILE: tmp/network.py
function conv (line 35) | def conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType, name, phase_train=Tr...
function affine (line 52) | def affine(inpOp, nIn, nOut, name, weight_decay=0.0):
function l2_loss (line 62) | def l2_loss(tensor, weight=1.0, scope=None):
function lppool (line 78) | def lppool(inpOp, pnorm, kH, kW, dH, dW, padding, name):
function mpool (line 98) | def mpool(inpOp, kH, kW, dH, dW, padding, name):
function apool (line 106) | def apool(inpOp, kH, kW, dH, dW, padding, name):
function batch_norm (line 114) | def batch_norm(x, phase_train):
function inception (line 148) | def inception(inp, inSize, ks, o1s, o2s1, o2s2, o3s1, o3s2, o4s1, o4s2, ...
FILE: tmp/nn2.py
function inference (line 31) | def inference(images, keep_probability, phase_train=True, weight_decay=0...
FILE: tmp/nn3.py
function inference (line 31) | def inference(images, keep_probability, phase_train=True, weight_decay=0...
FILE: tmp/nn4.py
function inference (line 31) | def inference(images, keep_probability, phase_train=True, weight_decay=0...
FILE: tmp/nn4_small2_v1.py
function inference (line 31) | def inference(images, keep_probability, phase_train=True, weight_decay=0...
FILE: tmp/rename_casia_directories.py
function main (line 6) | def main(args):
function parse_arguments (line 29) | def parse_arguments(argv):
FILE: tmp/seed_test.py
function run_train (line 25) | def run_train():
function _conv (line 90) | def _conv(inpOp, nIn, nOut, kH, kW, dH, dW, padType):
function _affine (line 102) | def _affine(inpOp, nIn, nOut):
function inference_conv_test (line 111) | def inference_conv_test(images):
function inference_affine_test (line 117) | def inference_affine_test(images):
FILE: tmp/test_align.py
function main (line 7) | def main():
FILE: tmp/test_invariance_on_lfw.py
function main (line 41) | def main(args):
function save_result (line 134) | def save_result(aug, acc, filename):
function evaluate_accuracy (line 139) | def evaluate_accuracy(sess, images_placeholder, phase_train_placeholder,...
function scale_images (line 160) | def scale_images(images, scale, image_size):
function rotate_images (line 170) | def rotate_images(images, angle, image_size):
function translate_images (line 180) | def translate_images(images, offset, image_size):
function parse_arguments (line 187) | def parse_arguments(argv):
FILE: tmp/vggface16.py
function load (line 9) | def load(filename, images):
FILE: tmp/vggverydeep19.py
function load (line 9) | def load(filename, images):
FILE: tmp/visualize.py
function main (line 38) | def main(args):
function T (line 86) | def T(layer):
function visstd (line 90) | def visstd(a, s=0.1):
function render_naive (line 94) | def render_naive(sess, t_input, t_obj, img0, iter_n=20, step=1.0):
function parse_arguments (line 106) | def parse_arguments(argv):
FILE: tmp/visualize_vgg_model.py
function sqErrorLossContent (line 31) | def sqErrorLossContent(sess, modelGraph, layer):
function sqErrorLossStyle (line 46) | def sqErrorLossStyle(sess, modelGraph):
FILE: tmp/visualize_vggface.py
function main (line 6) | def main():
function showarray (line 26) | def showarray(a):
function visstd (line 31) | def visstd(a, s=0.1):
function render_naive (line 35) | def render_naive(sess, t_input, t_obj, img0, iter_n=20, step=1.0):
Condensed preview — 94 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (725K chars).
[
{
"path": ".gitignore",
"chars": 1080,
"preview": "led / optimized / DLL files\n__pycache__/\n*.py[cod]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\ne"
},
{
"path": ".project",
"chars": 361,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<projectDescription>\n\t<name>facenet</name>\n\t<comment></comment>\n\t<projects>\n\t</pr"
},
{
"path": ".pydevproject",
"chars": 461,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n<?eclipse-pydev version=\"1.0\"?><pydev_project>\n<pydev_pathpropert"
},
{
"path": ".pylintrc",
"chars": 13765,
"preview": "[MASTER]\n\n# Specify a configuration file.\n#rcfile=\n\n# Python code to execute, usually for sys.path manipulation such as\n"
},
{
"path": ".travis.yml",
"chars": 458,
"preview": "language: python\nsudo: required\npython:\n - \"2.7\"\n - \"3.5\"\n# command to install dependencies\ninstall:\n# numpy not using"
},
{
"path": "LICENSE.md",
"chars": 1071,
"preview": "MIT License\n\nCopyright (c) 2016 David Sandberg\n\nPermission is hereby granted, free of charge, to any person obtaining a "
},
{
"path": "README.md",
"chars": 5784,
"preview": "# Face Recognition using Tensorflow [![Build Status][travis-image]][travis]\n\n[travis-image]: http://travis-ci.org/davids"
},
{
"path": "__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "contributed/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "contributed/batch_represent.py",
"chars": 5483,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __futu"
},
{
"path": "contributed/cluster.py",
"chars": 8541,
"preview": "# MIT License\n#\n# Copyright (c) 2017 PXL University College\n#\n# Permission is hereby granted, free of charge, to any per"
},
{
"path": "contributed/clustering.py",
"chars": 9733,
"preview": "\"\"\" Face Cluster \"\"\"\nimport tensorflow as tf\nimport numpy as np\nimport importlib\nimport argparse\nimport facenet\nimport o"
},
{
"path": "contributed/export_embeddings.py",
"chars": 8585,
"preview": "\"\"\"\nExports the embeddings and labels of a directory of images as numpy arrays.\n\nTypicall usage expect the image directo"
},
{
"path": "contributed/face.py",
"chars": 5933,
"preview": "# coding=utf-8\n\"\"\"Face Detection and Recognition\"\"\"\n# MIT License\n#\n# Copyright (c) 2017 François Gervais\n#\n# This is th"
},
{
"path": "contributed/predict.py",
"chars": 6182,
"preview": "\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n#-------"
},
{
"path": "contributed/real_time_face_recognition.py",
"chars": 3412,
"preview": "# coding=utf-8\n\"\"\"Performs face detection in realtime.\n\nBased on code from https://github.com/shanren7/real_time_face_re"
},
{
"path": "data/learning_rate_retrain_tripletloss.txt",
"chars": 108,
"preview": "# Learning rate schedule\n# Maps an epoch number to a learning rate\n0: 0.1\n300: 0.01\n400: 0.001\n1000: 0.0001"
},
{
"path": "data/learning_rate_schedule_classifier_casia.txt",
"chars": 104,
"preview": "# Learning rate schedule\n# Maps an epoch number to a learning rate\n0: 0.05\n60: 0.005\n80: 0.0005\n91: -1\n"
},
{
"path": "data/learning_rate_schedule_classifier_msceleb.txt",
"chars": 107,
"preview": "# Learning rate schedule\n# Maps an epoch number to a learning rate\n0: 0.1\n150: 0.01\n180: 0.001\n251: 0.0001"
},
{
"path": "data/learning_rate_schedule_classifier_vggface2.txt",
"chars": 107,
"preview": "# Learning rate schedule\n# Maps an epoch number to a learning rate\n0: 0.05\n100: 0.005\n200: 0.0005\n276: -1"
},
{
"path": "data/pairs.txt",
"chars": 155335,
"preview": "10\t300\nAbel_Pacheco\t1\t4\nAkhmed_Zakayev\t1\t3\nAkhmed_Zakayev\t2\t3\nAmber_Tamblyn\t1\t2\nAnders_Fogh_Rasmussen\t1\t3\nAnders_Fogh_Ra"
},
{
"path": "requirements.txt",
"chars": 88,
"preview": "tensorflow==1.7\nscipy\nscikit-learn\nopencv-python\nh5py\nmatplotlib\nPillow\nrequests\npsutil\n"
},
{
"path": "src/__init__.py",
"chars": 16,
"preview": "# flake8: noqa\n\n"
},
{
"path": "src/align/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/align/align_dataset_mtcnn.py",
"chars": 8302,
"preview": "\"\"\"Performs face alignment and stores face thumbnails in the output directory.\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 "
},
{
"path": "src/align/detect_face.py",
"chars": 31695,
"preview": "\"\"\" Tensorflow implementation of the face detection / alignment algorithm found at\nhttps://github.com/kpzhang93/MTCNN_fa"
},
{
"path": "src/calculate_filtering_metrics.py",
"chars": 6024,
"preview": "\"\"\"Calculate filtering metrics for a dataset and store in a .hdf file.\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David S"
},
{
"path": "src/classifier.py",
"chars": 8113,
"preview": "\"\"\"An example of how to use your own dataset to train a classifier that recognizes people.\n\"\"\"\n# MIT License\n# \n# Copyri"
},
{
"path": "src/compare.py",
"chars": 5510,
"preview": "\"\"\"Performs face alignment and calculates L2 distance between the embeddings of images.\"\"\"\n\n# MIT License\n# \n# Copyright"
},
{
"path": "src/decode_msceleb_dataset.py",
"chars": 3630,
"preview": "\"\"\"Decode the MsCelebV1 dataset in TSV (tab separated values) format downloaded from\nhttps://www.microsoft.com/en-us/res"
},
{
"path": "src/download_and_extract.py",
"chars": 1752,
"preview": "import requests\nimport zipfile\nimport os\n\nmodel_dict = {\n 'lfw-subset': '1B5BQUZuJO-paxdN8UclxeHAR1WnR_Tzi', \n "
},
{
"path": "src/facenet.py",
"chars": 23366,
"preview": "\"\"\"Functions for building the face recognition network.\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Pe"
},
{
"path": "src/freeze_graph.py",
"chars": 4735,
"preview": "\"\"\"Imports a model metagraph and checkpoint file, converts the variables to constants\nand exports the model as a graphde"
},
{
"path": "src/generative/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/generative/calculate_attribute_vectors.py",
"chars": 8896,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/generative/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/generative/models/dfc_vae.py",
"chars": 4916,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/generative/models/dfc_vae_large.py",
"chars": 5279,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/generative/models/dfc_vae_resnet.py",
"chars": 6257,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/generative/models/vae_base.py",
"chars": 2275,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/generative/modify_attribute.py",
"chars": 6129,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/generative/train_vae.py",
"chars": 13938,
"preview": "# MIT License\n# \n# Copyright (c) 2017 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "src/lfw.py",
"chars": 3568,
"preview": "\"\"\"Helper for evaluation on the Labeled Faces in the Wild dataset \n\"\"\"\n\n# MIT License\n# \n# Copyright (c) 2016 David Sand"
},
{
"path": "src/models/__init__.py",
"chars": 16,
"preview": "# flake8: noqa\n\n"
},
{
"path": "src/models/dummy.py",
"chars": 2556,
"preview": "\"\"\"Dummy model used only for testing\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby "
},
{
"path": "src/models/inception_resnet_v1.py",
"chars": 11768,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "src/models/inception_resnet_v2.py",
"chars": 13364,
"preview": "# Copyright 2016 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "src/models/squeezenet.py",
"chars": 3403,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport ten"
},
{
"path": "src/train_softmax.py",
"chars": 32282,
"preview": "\"\"\"Training a face recognizer with TensorFlow using softmax cross entropy loss\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016"
},
{
"path": "src/train_tripletloss.py",
"chars": 24907,
"preview": "\"\"\"Training a face recognizer with TensorFlow based on the FaceNet paper\nFaceNet: A Unified Embedding for Face Recogniti"
},
{
"path": "src/validate_on_lfw.py",
"chars": 8795,
"preview": "\"\"\"Validate a face recognizer on the \"Labeled Faces in the Wild\" dataset (http://vis-www.cs.umass.edu/lfw/).\nEmbeddings "
},
{
"path": "test/batch_norm_test.py",
"chars": 2466,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "test/center_loss_test.py",
"chars": 3706,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "test/restore_test.py",
"chars": 7104,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "test/train_test.py",
"chars": 9848,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "test/triplet_loss_test.py",
"chars": 2449,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/__init__.py",
"chars": 16,
"preview": "# flake8: noqa\n\n"
},
{
"path": "tmp/align_dataset.m",
"chars": 8428,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/align_dataset.py",
"chars": 7002,
"preview": "\"\"\"Performs face alignment and stores face thumbnails in the output directory.\"\"\"\n\n# MIT License\n# \n# Copyright (c) 2016"
},
{
"path": "tmp/align_dlib.py",
"chars": 8790,
"preview": "# Copyright 2015-2016 Carnegie Mellon University\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# y"
},
{
"path": "tmp/cacd2000_split_identities.py",
"chars": 1130,
"preview": "import shutil\nimport argparse\nimport os\nimport sys\n\ndef main(args):\n src_path_exp = os.path.expanduser(args.src_path)"
},
{
"path": "tmp/dataset_read_speed.py",
"chars": 903,
"preview": "import facenet\nimport argparse\nimport sys\nimport time\nimport numpy as np\n\ndef main(args):\n\n dataset = facenet.get_dat"
},
{
"path": "tmp/deepdream.py",
"chars": 10431,
"preview": "# boilerplate code\nimport numpy as np\nfrom functools import partial\nimport PIL.Image\n\nimport tensorflow as tf\nimport mat"
},
{
"path": "tmp/detect_face_v1.m",
"chars": 7954,
"preview": "% MIT License\n% \n% Copyright (c) 2016 Kaipeng Zhang\n% \n% Permission is hereby granted, free of charge, to any person obt"
},
{
"path": "tmp/detect_face_v2.m",
"chars": 9016,
"preview": "% MIT License\n% \n% Copyright (c) 2016 Kaipeng Zhang\n% \n% Permission is hereby granted, free of charge, to any person obt"
},
{
"path": "tmp/download_vgg_face_dataset.py",
"chars": 5016,
"preview": "\"\"\"Download the VGG face dataset from URLs given by http://www.robots.ox.ac.uk/~vgg/data/vgg_face/vgg_face_dataset.tar.g"
},
{
"path": "tmp/funnel_dataset.py",
"chars": 4264,
"preview": "\"\"\"Performs face alignment and stores face thumbnails in the output directory.\"\"\"\n\nfrom __future__ import absolute_impor"
},
{
"path": "tmp/invariance_test.txt",
"chars": 1598,
"preview": "Accuracy: 0.860±0.009\nAccuracy: 0.861±0.008\nAccuracy: 0.870±0.011\nAccuracy: 0.885±0.012\nAccuracy: 0.896±0.013\nAccuracy: "
},
{
"path": "tmp/mnist_center_loss.py",
"chars": 18603,
"preview": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "tmp/mnist_noise_labels.py",
"chars": 15432,
"preview": "# Copyright 2015 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"L"
},
{
"path": "tmp/mtcnn.py",
"chars": 2831,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/mtcnn_test.py",
"chars": 4376,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/mtcnn_test_pnet_dbg.py",
"chars": 4550,
"preview": "from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport ten"
},
{
"path": "tmp/network.py",
"chars": 8807,
"preview": "\"\"\"Functions for building the face recognition network.\n\"\"\"\n# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Pe"
},
{
"path": "tmp/nn2.py",
"chars": 4671,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/nn3.py",
"chars": 4671,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/nn4.py",
"chars": 4663,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/nn4_small2_v1.py",
"chars": 4093,
"preview": "# MIT License\n# \n# Copyright (c) 2016 David Sandberg\n# \n# Permission is hereby granted, free of charge, to any person ob"
},
{
"path": "tmp/random_test.py",
"chars": 3770,
"preview": "import tensorflow as tf\nimport numpy as np\nfrom six.moves import xrange\n\n\nwith tf.Graph().as_default():\n tf.set_random_"
},
{
"path": "tmp/rename_casia_directories.py",
"chars": 1350,
"preview": "import shutil\nimport argparse\nimport os\nimport sys\n\ndef main(args):\n \n identity_map = {}\n with open(os.path.expan"
},
{
"path": "tmp/seed_test.py",
"chars": 5549,
"preview": "import tensorflow as tf\nimport numpy as np\nimport sys\nimport time\nsys.path.append('../src')\nimport facenet\nfrom tensorfl"
},
{
"path": "tmp/select_triplets_test.py",
"chars": 774,
"preview": "import facenet\nimport numpy as np\nimport tensorflow as tf\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_integer('peop"
},
{
"path": "tmp/test1.py",
"chars": 21,
"preview": "print('Hello world')\n"
},
{
"path": "tmp/test_align.py",
"chars": 1503,
"preview": "import facenet\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef main():\n image_size = 96\n old_da"
},
{
"path": "tmp/test_invariance_on_lfw.py",
"chars": 11123,
"preview": "\"\"\"Test invariance to translation, scaling and rotation on the \"Labeled Faces in the Wild\" dataset (http://vis-www.cs.um"
},
{
"path": "tmp/vggface16.py",
"chars": 4807,
"preview": "\"\"\"Load the VGG Face model into TensorFlow.\nDownload the model from http://www.robots.ox.ac.uk/~vgg/software/vgg_face/\na"
},
{
"path": "tmp/vggverydeep19.py",
"chars": 4024,
"preview": "\"\"\"Load the VGG imagenet model into TensorFlow.\nDownload the model from http://www.robots.ox.ac.uk/~vgg/research/very_de"
},
{
"path": "tmp/visualize.py",
"chars": 5036,
"preview": "\"\"\"Visualize individual feature channels and their combinations to explore the space of patterns learned by the neural n"
},
{
"path": "tmp/visualize_vgg_model.py",
"chars": 3543,
"preview": "import numpy as np\nfrom scipy import misc\nimport tensorflow as tf\nfrom matplotlib import pyplot, image\nimport vggverydee"
},
{
"path": "tmp/visualize_vggface.py",
"chars": 1640,
"preview": "import numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport tmp.vggface16\n\ndef main():\n \n sess"
},
{
"path": "util/plot_learning_curves.m",
"chars": 10884,
"preview": "% Plots the lerning curves for the specified training runs from data in the\n% file \"lfw_result.txt\" stored in the log di"
}
]
// ... and 3 more files (download for full content)
About this extraction
This page contains the full source code of the davidsandberg/facenet GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 94 files (669.0 KB), approximately 211.0k tokens, and a symbol index with 299 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.