1 files changed, 127 insertions, 0 deletions
diff --git a/ml/dlib/python_examples/face_clustering.py b/ml/dlib/python_examples/face_clustering.py
new file mode 100755
index 000000000..362613871
--- /dev/null
+++ b/ml/dlib/python_examples/face_clustering.py
@@ -0,0 +1,127 @@
+#!/usr/bin/python
+# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+#
+#   This example shows how to use dlib's face recognition tool for clustering using chinese_whispers.
+#   This is useful when you have a collection of photographs which you know are linked to
+#   a particular person, but the person may be photographed with multiple other people.
+#   In this example, we assume the largest cluster will contain photos of the common person in the
+#   collection of photographs. Then, we save extracted images of the face in the largest cluster in
+#   a 150x150 px format which is suitable for jittering and loading to perform metric learning (as shown
+#   in the dnn_metric_learning_on_images_ex.cpp example.
+#   https://github.com/davisking/dlib/blob/master/examples/dnn_metric_learning_on_images_ex.cpp
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  This code will also use CUDA if you have CUDA and cuDNN
+#   installed.
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake installed.  On Ubuntu, this can be done easily by running the
+#   command:
+#       sudo apt-get install cmake
+#
+#   Also note that this example requires scikit-image which can be installed
+#   via the command:
+#       pip install scikit-image
+#   Or downloaded from http://scikit-image.org/download.html. 
+
+import sys
+import os
+import dlib
+import glob
+from skimage import io
+
+if len(sys.argv) != 5:
+    print(
+        "Call this program like this:\n"
+        "   ./face_clustering.py shape_predictor_5_face_landmarks.dat dlib_face_recognition_resnet_model_v1.dat ../examples/faces output_folder\n"
+        "You can download a trained facial shape predictor and recognition model from:\n"
+        "    http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n"
+        "    http://dlib.net/files/dlib_face_recognition_resnet_model_v1.dat.bz2")
+    exit()
+
+predictor_path = sys.argv[1]
+face_rec_model_path = sys.argv[2]
+faces_folder_path = sys.argv[3]
+output_folder_path = sys.argv[4]
+
+# Load all the models we need: a detector to find the faces, a shape predictor
+# to find face landmarks so we can precisely localize the face, and finally the
+# face recognition model.
+detector = dlib.get_frontal_face_detector()
+sp = dlib.shape_predictor(predictor_path)
+facerec = dlib.face_recognition_model_v1(face_rec_model_path)
+
+descriptors = []
+images = []
+
+# Now find all the faces and compute 128D face descriptors for each face.
+for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
+    print("Processing file: {}".format(f))
+    img = io.imread(f)
+
+    # Ask the detector to find the bounding boxes of each face. The 1 in the
+    # second argument indicates that we should upsample the image 1 time. This
+    # will make everything bigger and allow us to detect more faces.
+    dets = detector(img, 1)
+    print("Number of faces detected: {}".format(len(dets)))
+
+    # Now process each face we found.
+    for k, d in enumerate(dets):
+        # Get the landmarks/parts for the face in box d.
+        shape = sp(img, d)
+
+        # Compute the 128D vector that describes the face in img identified by
+        # shape.  
+        face_descriptor = facerec.compute_face_descriptor(img, shape)
+        descriptors.append(face_descriptor)
+        images.append((img, shape))
+
+# Now let's cluster the faces.  
+labels = dlib.chinese_whispers_clustering(descriptors, 0.5)
+num_classes = len(set(labels))
+print("Number of clusters: {}".format(num_classes))
+
+# Find biggest class
+biggest_class = None
+biggest_class_length = 0
+for i in range(0, num_classes):
+    class_length = len([label for label in labels if label == i])
+    if class_length > biggest_class_length:
+        biggest_class_length = class_length
+        biggest_class = i
+
+print("Biggest cluster id number: {}".format(biggest_class))
+print("Number of faces in biggest cluster: {}".format(biggest_class_length))
+
+# Find the indices for the biggest class
+indices = []
+for i, label in enumerate(labels):
+    if label == biggest_class:
+        indices.append(i)
+
+print("Indices of images in the biggest cluster: {}".format(str(indices)))
+
+# Ensure output directory exists
+if not os.path.isdir(output_folder_path):
+    os.makedirs(output_folder_path)
+
+# Save the extracted faces
+print("Saving faces in largest cluster to output folder...")
+for i, index in enumerate(indices):
+    img, shape = images[index]
+    file_path = os.path.join(output_folder_path, "face_" + str(i))
+    # The size and padding arguments are optional with default size=150x150 and padding=0.25
+    dlib.save_face_chip(img, shape, file_path, size=150, padding=0.25)
+    
+    
+
+