Docker File

Dockerfile

@@ -0,0 +1,12 @@
+FROM python:3.5-slim
+# Installing curl to download the pre-trained model, det1.npy, det2.npy, det3.npy and requirements.txt - Installing g++, libglib2.0-0, libsm6, libxrender1, libxext6 which are required for the installment of grpcio, which is required for the installment of python packages by pip - Installing libgl1 which is required by cv2 package.
+RUN apt-get update && apt-get install -y curl g++ libgl1 libglib2.0-0 libsm6 libxrender1 libxext6 && rm -rf /var/cache/apt/archives /var/lib/apt/lists/*
+# Downloading pre-trained model, required .npy files and requirements.txt.
+RUN curl -L -O https://github.com/barisgecer/facegan/raw/master/facenet_model/20170511-185253.pb && curl -L -O https://github.com/davidsandberg/facenet/raw/master/src/align/det[1-3].npy && curl -L -O https://github.com/davidsandberg/facenet/raw/master/requirements.txt
+RUN python3 -m pip install -r requirements.txt
+# misc.imresize is deprecated in scipy package with versions newer than 1.1.0.
+RUN pip uninstall -y scipy && pip install scipy==1.1.0
+COPY . .
+RUN python3 Make_aligndata_git.py && python3 Make_classifier_git.py
+ENTRYPOINT ["python3"]
+CMD ["realtime_facenet_git.py"]

Make_aligndata_git.py

@@ -13,20 +13,20 @@
 import random
 from time import sleep
 
-output_dir_path = '/..Path to output folder../'
+output_dir_path = './output_dir'    # Path to output folder
 output_dir = os.path.expanduser(output_dir_path)
 if not os.path.exists(output_dir):
         os.makedirs(output_dir)
 
-datadir = '/..Path to human img data folder../'
+datadir = './data'  # Path to human img data folder
 dataset = facenet.get_dataset(datadir)
 
 print('Creating networks and loading parameters')
 with tf.Graph().as_default():
     gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
     sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
     with sess.as_default():
-        pnet, rnet, onet = detect_face.create_mtcnn(sess, './Path to det1.npy,..')
+        pnet, rnet, onet = detect_face.create_mtcnn(sess, './') # './Path to det1.npy,..' as the second argument
 
 minsize = 20  # minimum size of face
 threshold = [0.6, 0.7, 0.7]  # three steps's threshold

Make_classifier_git.py

@@ -18,14 +18,14 @@
 
     with tf.Session() as sess:
 
-        datadir = '/..Path to align face data../'
+        datadir = './output_dir'    # Path to aligned face data
         dataset = facenet.get_dataset(datadir)
         paths, labels = facenet.get_image_paths_and_labels(dataset)
         print('Number of classes: %d' % len(dataset))
         print('Number of images: %d' % len(paths))
 
         print('Loading feature extraction model')
-        modeldir = '/..Path to Pre-trained model../20170512-110547/20170512-110547.pb'
+        modeldir = './20170511-185253.pb'   # Pre-trained model's path
         facenet.load_model(modeldir)
 
         images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
@@ -48,7 +48,7 @@
             feed_dict = {images_placeholder: images, phase_train_placeholder: False}
             emb_array[start_index:end_index, :] = sess.run(embeddings, feed_dict=feed_dict)
 
-        classifier_filename = '/..Path to save classifier../my_classifier.pkl'
+        classifier_filename = './classifier/my_classifier.pkl'  # Path to classifier
         classifier_filename_exp = os.path.expanduser(classifier_filename)
 
         # Train classifier

README.md

@@ -20,5 +20,7 @@ Real-time face recognition program using Google's facenet.
 </br>Your own classifier is a ~.pkl file that loads the previously mentioned pre-trained model ('[20170511-185253.pb](https://drive.google.com/file/d/0B5MzpY9kBtDVOTVnU3NIaUdySFE/edit)') and embeds the face for each person.<br/>All of these can be obtained by running 'Make_classifier.py'.<br/>
 * Finally, we load our own 'my_classifier.pkl' obtained above and then open the sensor and start recognition.
 </br> (Note that, look carefully at the paths of files and folders in all .py)
+### Docker
+* First, clone this repo and change current directory to its directory on your device. Next, put the images of each person in separated directories with the names that you want to be shown for each person inside the '*data*' directory; for example, Daehyun's photos are in: '*./data/Daehyun*' and Byeonggil's are in: '*./data/Byeonggil*'. Then, you can run it in two ways: <br/>1. Build the Docker image by running: <br/>`docker build -t "your_image_name":"your_image_tag" .` <br/>After the build is done, run: <br/>`docker run --device /dev/video0:/dev/video0 "your_image_name":"your_image_tag` <br/>2. Run: <br/>`docker compose up`. <br/> (Note that Windows doesn’t give camera access to Docker containers. So, you can just run this container on Linux.)
 ## Result
-<img src="https://github.com/bearsprogrammer/real-time-deep-face-recogniton/blob/master/realtime_demo_pic.jpg" width="60%">
+<img src="https://github.com/bearsprogrammer/real-time-deep-face-recogniton/blob/master/realtime_demo_pic.jpg" width="60%">

classifier/.gitignore

@@ -0,0 +1,4 @@
+# Ignore every file in this folder
+*
+# Except this one
+!.gitignore

data/.gitignore

@@ -0,0 +1,4 @@
+# Ignore every file in this folder
+*
+# Except this one
+!.gitignore

detect_face.py

@@ -82,7 +82,7 @@ def load(self, data_path, session, ignore_missing=False):
         session: The current TensorFlow session
         ignore_missing: If true, serialized weights for missing layers are ignored.
         '''
-        data_dict = np.load(data_path, encoding='latin1').item() #pylint: disable=no-member
+        data_dict = np.load(data_path, encoding='latin1', allow_pickle=True).item() #pylint: disable=no-member
 
         for op_name in data_dict:
             with tf.variable_scope(op_name, reuse=True):

docker-compose.yml

@@ -0,0 +1,5 @@
+services:
+    real-time-deep-face-recognition:
+        build: .
+        devices:
+            - /dev/video0:/dev/video0

output_dir/.gitignore

@@ -0,0 +1,4 @@
+# Ignore every file in this folder
+*
+# Except this one
+!.gitignore

realtime_facenet_git.py

@@ -25,7 +25,7 @@
     gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.6)
     sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
     with sess.as_default():
-        pnet, rnet, onet = detect_face.create_mtcnn(sess, './Path to det1.npy,..')
+        pnet, rnet, onet = detect_face.create_mtcnn(sess, './') # './Path to det1.npy,..' as the second argument
 
         minsize = 20  # minimum size of face
         threshold = [0.6, 0.7, 0.7]  # three steps's threshold
@@ -36,18 +36,18 @@
         image_size = 182
         input_image_size = 160
 
-        HumanNames = ['Human_a','Human_b','Human_c','...','Human_h']    #train human name
+        HumanNames = [Human for Human in os.listdir("./data")]    #training human name
 
         print('Loading feature extraction model')
-        modeldir = '/..Path to pre-trained model../20170512-110547/20170512-110547.pb'
+        modeldir = './20170511-185253.pb'   # Pre-trained model's path
         facenet.load_model(modeldir)
 
         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")
         embedding_size = embeddings.get_shape()[1]
 
-        classifier_filename = '/..Path to classifier model../my_classifier.pkl'
+        classifier_filename = './classifier/my_classifier.pkl'  # Classifier Path
         classifier_filename_exp = os.path.expanduser(classifier_filename)
         with open(classifier_filename_exp, 'rb') as infile:
             (model, class_names) = pickle.load(infile)