# 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 = dlib.load_rgb_image(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)
