face-detection.py

# Face Detection with MTCNN (Multi-Task Cascaded Convolutional Neural Network)
from os import listdir
from os.path import isdir
from PIL import Image
from matplotlib import pyplot
from numpy import savez_compressed
from numpy import asarray
from mtcnn.mtcnn import MTCNN

# extract a single face from a given photograph
def extract_face(filename, required_size=(160, 160)):
    # load image from file
    image = Image.open(filename)
    # convert to RGB, if needed
    image = image.convert('RGB')
    # convert to array
    pixels = asarray(image)
    # create the detector, using default weights
    detector = MTCNN()
    # detect faces in the image
    results = detector.detect_faces(pixels)
    # check if face was detected
    if results:
        # extract the bounding box from the first face
        x1, y1, width, height = results[0]['box']
        # bug fix
        x1, y1 = abs(x1), abs(y1)
        x2, y2 = x1 + width, y1 + height
        # extract the face
        face = pixels[y1:y2, x1:x2]
        # resize pixels to the model size
        image = Image.fromarray(face)
        image = image.resize(required_size)
        face_array = asarray(image)
        return face_array
    else:
        return None

# load images and extract faces for all images in a directory
def load_faces(directory):
    faces = []
    # enumerate files
    for filename in listdir(directory):
        # path
        path = directory + filename
        # get face
        face = extract_face(path)
        # check if face was detected
        if face is not None:
            # store
            faces.append(face)
    return faces

# load a dataset that contains one subdir for each class that in turn contains images
def load_dataset(directory):
    X, y = [], []
    # enumerate folders, on per class
    for subdir in listdir(directory):
        # path
        path = directory + subdir + '/'
        # skip any files that might be in the dir
        if not isdir(path):
            continue
        # load all faces in the subdirectory
        faces = load_faces(path)
        # create labels
        labels = [subdir for _ in range(len(faces))]
        # summarize progress
        print('>loaded %d examples for class: %s' % (len(faces), subdir))
        # store
        X.extend(faces)
        y.extend(labels)
    return asarray(X), asarray(y)

# load train dataset
trainX, trainy = load_dataset('/home/jawabreh/Desktop/Face-Recognition-Test/Datasets/Team-Dataset/data/train/')
print(trainX.shape, trainy.shape)
# save arrays to one file in compressed format
savez_compressed('/home/jawabreh/Desktop/Face-Recognition-Test/Datasets/Embedding_Datasets/full-dataset-compressed', trainX, trainy)