face_recognition/face_recognition_svm.py

import face_recognition
from sklearn import svm
import joblib
import os

# Training the SVC classifier

# The training data would be all the face encodings from all the known images and the labels are their names
encodings = []
names = []
train_list = []

is_train = False

if not is_train:
    try:
        clf = joblib.load('saved_model.pkl')
    except:
        clf = None
    if clf is None:
        is_train = True

if is_train:
    # Training directory
    train_str = os.path.join('data', 'peeps', 'anchor')
    train_dir = os.listdir(os.path.join('data', 'peeps', 'anchor'))

    # Loop through each person in the training directory
    for person in train_dir:
        pix_str = os.path.join(train_str, person)
        pix = os.listdir(os.path.join(train_str, person))

        # Loop through each training image for the current person
        for person_img in pix:
            # Get the face encodings for the face in each image file
            face = face_recognition.load_image_file(pix_str + "/" + person_img)
            face_bounding_boxes = face_recognition.face_locations(face)

            # If training image contains exactly one face
            if len(face_bounding_boxes) == 1:
                face_enc = face_recognition.face_encodings(face)[0]
                # Add face encoding for current image with corresponding label (name) to the training data
                encodings.append(face_enc)
                names.append(person)
            else:
                print(person + "/" + person_img + " was skipped and can't be used for training")

    # Create and train the SVC classifier

    clf = svm.SVC(gamma='scale', probability=True)
    clf.fit(encodings, names)
    joblib.dump(clf, 'saved_model.pkl')

# Load the test image with unknown faces into a numpy array
test_image = face_recognition.load_image_file(os.path.join('application_data', 'input_image', 'input_image.jpg'))

# Find all the faces in the test image using the default HOG-based model
face_locations = face_recognition.face_locations(test_image)
no = len(face_locations)
print("Number of faces detected: ", no)

# Predict all the faces in the test image using the trained classifier
print("Found:")
for i in range(no):
    test_image_enc = face_recognition.face_encodings(test_image)[i]
    name = clf.predict([test_image_enc])
    print(*name)