4_feature_extraction_as_csv.py

# All the facial features will be stored in the csv file

import os
import dlib
from skimage import io
import csv
import numpy as np


path_images_from_camera = "models/model_faces_from_camera/"


detector = dlib.get_frontal_face_detector()


predictor = dlib.shape_predictor('models/model_dlib/shape_predictor_68_face_landmarks.dat')


face_reco_model = dlib.face_recognition_model_v1("models/model_dlib/dlib_face_recognition_resnet_model_v1.dat")



# Input:    path_img           <class 'str'>
# Output:   face_descriptor    <class 'dlib.vector'>
def return_128d_features(path_img):
    img_rd = io.imread(path_img)
    faces = detector(img_rd, 1)

    print("%-40s %-20s" % (" Image with faces detected:", path_img), '\n')



    if len(faces) != 0:
        shape = predictor(img_rd, faces[0])
        face_descriptor = face_reco_model.compute_face_descriptor(img_rd, shape)
    else:
        face_descriptor = 0
        print("no face")
    return face_descriptor



def return_features_mean_personX(path_faces_personX):
    features_list_personX = []
    photos_list = os.listdir(path_faces_personX)
    if photos_list:
        for i in range(len(photos_list)):

            print("%-40s %-20s" % (" Reading image:", path_faces_personX + "/" + photos_list[i]))
            features_128d = return_128d_features(path_faces_personX + "/" + photos_list[i])

            if features_128d == 0:
                i += 1
            else:
                features_list_personX.append(features_128d)
    else:
        print("Warning: No images in " + path_faces_personX + '/', '\n')

   
    if features_list_personX:
        features_mean_personX = np.array(features_list_personX).mean(axis=0)
    else:
        features_mean_personX = np.zeros(128, dtype=int, order='C')
    print(type(features_mean_personX))
    return features_mean_personX



person_list = os.listdir("models/model_faces_from_camera/")
person_num_list = []
for person in person_list:
    person_num_list.append(int(person.split('_')[-1]))
person_cnt = max(person_num_list)

with open("models/features_all.csv", "w", newline="") as csvfile:
    writer = csv.writer(csvfile)
    for person in range(person_cnt):
        # Get the mean/average features of face/personX, it will be a list with a length of 128D
        print(path_images_from_camera + "person_" + str(person + 1))
        features_mean_personX = return_features_mean_personX(path_images_from_camera + "person_" + str(person + 1))
        writer.writerow(features_mean_personX)
        print("The mean of features:", list(features_mean_personX))
        print('\n')
    print("Saved all the features of faces registered into: model/features_all.csv")