face recognition scratch

face_detection_operation.py

from os.path import join, exists
from os import mkdir, listdir
import glob

from mtcnn import MTCNN
from PIL import Image
import numpy as np
import cv2


def save_cropped_face(images_root_folder,
                      required_size=(224, 224),
                      cropped_folder='dataset'):

    if not exists(images_root_folder):
        return Exception("Input Images folder is not exist.")
    file_types = ["*.png", "*.PNG", "*.JPEG", "*.jpeg", "*.jpg", "*.JPG"]
    people = listdir(images_root_folder)

    for file_type in file_types:
        for person in people:
            for i, image_file in enumerate(glob.glob( \
                    join(images_root_folder, person, file_type) \
                    ) \
                    ):

                print(f"processing {image_file}")
                img = cv2.imread(image_file)
                detector = MTCNN()
                results = detector.detect_faces(img)
                if not results:
                    continue

                x, y, width, height = results[0]['box']
                face = img[y:y + height, x:x + width]
                try:
                    image = Image.fromarray(face)
                except ValueError:
                    continue
                image = image.resize(required_size)
                face_array = np.asarray(image)

                if not exists(cropped_folder):
                    mkdir(cropped_folder)

                if not exists(join(cropped_folder, person)):
                    mkdir(join(cropped_folder, person))

                output_file_name = f"{person}_{i}{image_file[-4:]}"
                cv2.imwrite(
                    join(cropped_folder, person, output_file_name),
                    face_array)


def get_detected_face(filename, required_size=(224, 224)):
    img = cv2.imread(filename)
    detector = MTCNN()
    results = detector.detect_faces(img)
    x, y, width, height = results[0]['box']
    face = img[y:y + height, x:x + width]
    image = Image.fromarray(face)
    image = image.resize(required_size)
    face_array = np.asarray(image)
    return face_array, face