image_generator.py

from PIL import Image, ImageFile
from os.path import join, isfile, dirname, splitext
from os import listdir
import face_recognition
import numpy as np
import os

DIR_PATH = os.path.abspath(dirname(__file__))


def create_mask_images(s_dir, t_dir, m_dir):
    s_dir = join(DIR_PATH, s_dir)
    t_dir = join(DIR_PATH, t_dir)
    m_dir = join(DIR_PATH, m_dir)

    existing_files = [join(t_dir, f) for f in listdir(t_dir)]
    for existing_file in existing_files:
        os.remove(existing_file)

    images = [join(s_dir, f) for f in listdir(s_dir) if isfile(
        join(s_dir, f)) and splitext(join(s_dir, f))[1] == '.jpg']
    masks = [join(m_dir, f)
             for f in listdir(m_dir) if isfile(join(m_dir, f))]

    for image in images:
        mask_path = masks.pop(0)
        FaceMasker(image, mask_path, t_dir).mask()
        masks.append(mask_path)


class FaceMasker:
    KEY_FACIAL_FEATURES = ('nose_bridge', 'chin')

    def __init__(self, face_path, mask_path, target_dir, model='hog'):
        self.face_path = face_path
        self.mask_path = mask_path
        self.target_dir = target_dir
        self.model = model
        self._face_img: ImageFile = None
        self._mask_img: ImageFile = None

    def mask(self):
        face_image_np = face_recognition.load_image_file(self.face_path)
        face_locations = face_recognition.face_locations(
            face_image_np, model=self.model)
        face_landmarks = face_recognition.face_landmarks(
            face_image_np, face_locations)
        self._face_img = Image.fromarray(face_image_np)
        self._mask_img = Image.open(self.mask_path)

        found_face = False
        for face_landmark in face_landmarks:
            # check whether facial features meet requirement
            skip = False
            for facial_feature in self.KEY_FACIAL_FEATURES:
                if facial_feature not in face_landmark:
                    skip = True
                    break
            if skip:
                continue

            # mask face
            found_face = True
            self._mask_face(face_landmark)

        if found_face:
            # save
            self._save()
        else:
            print('Found no face.')

    def _mask_face(self, face_landmark: dict):
        nose_bridge = face_landmark['nose_bridge']
        nose_point = nose_bridge[len(nose_bridge) * 1 // 4]
        nose_v = np.array(nose_point)

        chin = face_landmark['chin']
        chin_len = len(chin)
        chin_bottom_point = chin[chin_len // 2]
        chin_bottom_v = np.array(chin_bottom_point)
        chin_left_point = chin[chin_len // 8]
        chin_right_point = chin[chin_len * 7 // 8]

        # split mask and resize
        width = self._mask_img.width
        height = self._mask_img.height
        width_ratio = 1.2
        new_height = int(np.linalg.norm(nose_v - chin_bottom_v))

        # left
        mask_left_img = self._mask_img.crop((0, 0, width // 2, height))
        mask_left_width = self.get_distance_from_point_to_line(
            chin_left_point, nose_point, chin_bottom_point)
        mask_left_width = int(mask_left_width * width_ratio)
        mask_left_img = mask_left_img.resize((mask_left_width, new_height))

        # right
        mask_right_img = self._mask_img.crop((width // 2, 0, width, height))
        mask_right_width = self.get_distance_from_point_to_line(
            chin_right_point, nose_point, chin_bottom_point)
        mask_right_width = int(mask_right_width * width_ratio)
        mask_right_img = mask_right_img.resize((mask_right_width, new_height))

        # merge mask
        size = (mask_left_img.width + mask_right_img.width, new_height)
        mask_img = Image.new('RGBA', size)
        mask_img.paste(mask_left_img, (0, 0), mask_left_img)
        mask_img.paste(
            mask_right_img, (mask_left_img.width, 0), mask_right_img)

        # rotate mask
        angle = np.arctan2(
            chin_bottom_point[1] - nose_point[1], chin_bottom_point[0] - nose_point[0])
        rotated_mask_img = mask_img.rotate(angle, expand=True)

        # calculate mask location
        center_x = (nose_point[0] + chin_bottom_point[0]) // 2
        center_y = (nose_point[1] + chin_bottom_point[1]) // 2

        offset = mask_img.width // 2 - mask_left_img.width
        radian = angle * np.pi / 180
        box_x = center_x + int(offset * np.cos(radian)) - \
            rotated_mask_img.width // 2
        box_y = center_y + int(offset * np.sin(radian)) - \
            rotated_mask_img.height // 2

        # add mask
        self._face_img.paste(mask_img, (box_x, box_y), mask_img)

    def _save(self):
        file_name = self.face_path.split('/')[-1]
        new_face_path = join(self.target_dir, file_name)
        self._face_img.save(new_face_path)
        print(f'Save to {new_face_path}')

    @staticmethod
    def get_distance_from_point_to_line(point, line_point1, line_point2):
        distance = np.abs((line_point2[1] - line_point1[1]) * point[0] +
                          (line_point1[0] - line_point2[0]) * point[1] +
                          (line_point2[0] - line_point1[0]) * line_point1[1] +
                          (line_point1[1] - line_point2[1]) * line_point1[0]) / \
            np.sqrt((line_point2[1] - line_point1[1]) * (line_point2[1] - line_point1[1]) +
                    (line_point1[0] - line_point2[0]) * (line_point1[0] - line_point2[0]))
        return int(distance)


if __name__ == '__main__':
    create_mask_images('images/without_mask',
                       'images/with_mask', 'images/masks')