face_detector.py

from typing import Iterator, List, Tuple

import dlib
import numpy as np
import PIL.Image
import scipy.ndimage


class FaceAligner(object):
    def __init__(
        self,
        shape_predictor_path: str = 'shape_predictor_68_face_landmarks.dat',
        image_size: int = 512,
    ) -> None:
        self.image_size = image_size
        self.detector = dlib.get_frontal_face_detector()
        self.shape_predictor = dlib.shape_predictor(shape_predictor_path)

    def align(self, image: PIL.Image.Image) -> List[PIL.Image.Image]:
        landmarks = self.get_landmarks(image)

        return [image_align(
            image,
            face_landmarks,
            output_size=self.image_size,
            transform_size=self.image_size * 2,
        ) for face_landmarks in landmarks]

    def get_landmarks(
        self,
        image: PIL.Image.Image,
    ) -> Iterator[List[Tuple[int, int]]]:
        img = np.asarray(image.convert('L'))
        dets = self.detector(img, 1)

        for detection in dets:
            try:
                parts = self.shape_predictor(img, detection).parts()
                face_landmarks = [(point.x, point.y) for point in parts]
                yield face_landmarks
            except:
                print("Exception in get_landmarks()!")


def image_align(
    img: PIL.Image.Image,
    face_landmarks: List[Tuple[int, int]],
    output_size: int = 1024,
    transform_size: int = 4096,
    enable_padding: bool = True,
    x_scale: float = 1,
    y_scale: float = 1,
    em_scale: float = 0.1,
) -> PIL.Image.Image:
    # Align function from FFHQ dataset pre-processing step
    # https://github.com/NVlabs/ffhq-dataset/blob/master/download_ffhq.py

    lm = np.array(face_landmarks)
    lm_chin = lm[0: 17]  # left-right
    lm_eyebrow_left = lm[17: 22]  # left-right
    lm_eyebrow_right = lm[22: 27]  # left-right
    lm_nose = lm[27: 31]  # top-down
    lm_nostrils = lm[31: 36]  # top-down
    lm_eye_left = lm[36: 42]  # left-clockwise
    lm_eye_right = lm[42: 48]  # left-clockwise
    lm_mouth_outer = lm[48: 60]  # left-clockwise
    lm_mouth_inner = lm[60: 68]  # left-clockwise

    # Calculate auxiliary vectors.
    eye_left = np.mean(lm_eye_left, axis=0)
    eye_right = np.mean(lm_eye_right, axis=0)
    eye_avg = (eye_left + eye_right) * 0.5
    eye_to_eye = eye_right - eye_left
    mouth_left = lm_mouth_outer[0]
    mouth_right = lm_mouth_outer[6]
    mouth_avg = (mouth_left + mouth_right) * 0.5
    eye_to_mouth = mouth_avg - eye_avg

    # Choose oriented crop rectangle.
    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
    x /= np.hypot(*x)
    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
    x *= x_scale
    y = np.flipud(x) * [-y_scale, y_scale]
    c = eye_avg + eye_to_mouth * em_scale
    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
    qsize = np.hypot(*x) * 2

    # Shrink.
    shrink = int(np.floor(qsize / output_size * 0.5))
    if shrink > 1:
        rsize = (int(np.rint(float(img.size[0]) / shrink)),
                 int(np.rint(float(img.size[1]) / shrink)))
        img = img.resize(rsize, PIL.Image.ANTIALIAS)
        quad /= shrink
        qsize /= shrink

    # Crop.
    border = max(int(np.rint(qsize * 0.1)), 3)
    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(
        np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0),
            min(crop[2] + border, img.size[0]), min(crop[3] + border, img.size[1]))
    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
        img = img.crop(crop)
        quad -= crop[0:2]

    # Pad.
    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(
        np.ceil(max(quad[:, 0]))), int(np.ceil(max(quad[:, 1]))))
    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] -
           img.size[0] + border, 0), max(pad[3] - img.size[1] + border, 0))
    if enable_padding and max(pad) > border - 4:
        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
        img = np.pad(np.float32(img),
                     ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
        h, w, _ = img.shape
        y, x, _ = np.ogrid[:h, :w, :1]
        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(
            w-1-x) / pad[2]), 1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h-1-y) / pad[3]))
        blur = qsize * 0.02
        img += (scipy.ndimage.gaussian_filter(img,
                                              [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
        img = np.uint8(np.clip(np.rint(img), 0, 255))
        img = PIL.Image.fromarray(img, 'RGB')
        quad += pad[:2]

    # Transform.
    img = img.transform((transform_size, transform_size),
                        PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
    if output_size < transform_size:
        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)

    return img