align_crop.py

"""
brief: face alignment with FFHQ method (https://github.com/NVlabs/ffhq-dataset)
author: lzhbrian (https://lzhbrian.me)
date: 2020.1.5
note: code is heavily borrowed from 
    https://github.com/NVlabs/ffhq-dataset
    http://dlib.net/face_landmark_detection.py.html
requirements:
    apt install cmake
    conda install Pillow numpy scipy
    pip install dlib
    # download face landmark model from: 
    # http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
"""

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

def get_landmark(filepath, args, face_alignment_path, face_detector_path):
    """get landmark with and without dlib
    :return: np.array shape=(68, 2)
    """
    if args.use_dlib:
        import dlib

        detector = dlib.get_frontal_face_detector()
        # download model from: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
        if not os.path.exists('./shape_predictor_68_face_landmarks.dat'):
            print('Downloading files for aligning face image...')
            os.system('wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2')
            os.system('bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2')
            os.system('rm shape_predictor_68_face_landmarks.dat.bz2')

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

        img = dlib.load_rgb_image(filepath)
        dets = detector(img, 1)

        # print("Number of faces detected: {}".format(len(dets)))
        shape = None
        for k, d in enumerate(dets):
            # print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
                # k, d.left(), d.top(), d.right(), d.bottom()))
            # Get the landmarks/parts for the face in box d.
            shape = predictor(img, d)
            # print("Part 0: {}, Part 1: {} ...".format(shape.part(0), shape.part(1)))

        if shape is not None:
            t = list(shape.parts())
            a = []
            for tt in t:
                a.append([tt.x, tt.y])
            lm = np.array(a)
            # lm is a shape=(68,2) np.array
    else: 
        from psgan.preprocess import PreProcess

        preprocess = PreProcess(
                args, None, face_alignment_path, face_detector_path, need_parser=False, return_landmarks=True
            )

        image = PIL.Image.open(filepath).convert("RGB")
        image = image.resize((256,256))
        lm = preprocess(image)

    return lm

    
def align_face(filepath, args, face_alignment_path, face_detector_path):
    """
    :param filepath: str
    :return: PIL Image
    """
    lm = get_landmark(filepath, args, face_alignment_path, face_detector_path)

    if lm is not None:
        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)
        y = np.flipud(x) * [-1, 1]
        c = eye_avg + eye_to_mouth * 0.1
        quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
        qsize = np.hypot(*x) * 2


        # read image
        img = PIL.Image.open(filepath)

        if args.use_dlib:
            output_size=1024
            transform_size=4096
        else:
            img = img.resize((256,256))
            output_size=256
            transform_size=1024
        enable_padding=True

        # 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.LANCZOS)
            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 = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), '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.LANCZOS)

        # Save aligned image.
        return img