vtoonify/model/encoder/align_all_parallel.py

from argparse import ArgumentParser
import time
import numpy as np
import PIL
import PIL.Image
import os
import scipy
import scipy.ndimage
import insightface
import multiprocessing as mp
import math

def get_landmark(filepath, face_detector):
    """get landmark with InsightFace
    :return: np.array shape=(68, 2)
    """
    if isinstance(filepath, str):
        img = PIL.Image.open(filepath)
        img = np.array(img)
    else:
        img = filepath

    faces = face_detector.get(img)
    
    if len(faces) == 0:
        print('Error: no face detected!')
        return None
    
    # Assume the first detected face is the target
    face = faces[0]
    lm = face.landmark_2d_106[:, :2]  # Use 106-point landmarks
    return lm

def align_face(filepath, face_detector):
    """
    :param filepath: str
    :return: PIL Image
    """
    lm = get_landmark(filepath, face_detector)
    if lm is None:
        return None    
    
    # Use the same landmark indices as before
    lm_eye_left = lm[36: 42]  # left-clockwise
    lm_eye_right = lm[42: 48]  # left-clockwise
    lm_mouth_outer = lm[48: 60]  # 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
    if isinstance(filepath, str):
        img = PIL.Image.open(filepath)
    else:
        img = PIL.Image.fromarray(filepath)

    output_size = 256
    transform_size = 256
    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.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 = 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.ANTIALIAS)

    return img

def chunks(lst, n):
    """Yield successive n-sized chunks from lst."""
    for i in range(0, len(lst), n):
        yield lst[i:i + n]

def extract_on_paths(file_paths, face_detector):
    pid = mp.current_process().name
    print('\t{} is starting to extract on #{} images'.format(pid, len(file_paths)))
    tot_count = len(file_paths)
    count = 0
    for file_path, res_path in file_paths:
        count += 1
        if count % 100 == 0:
            print('{} done with {}/{}'.format(pid, count, tot_count))
        try:
            res = align_face(file_path, face_detector)
            res = res.convert('RGB')
            os.makedirs(os.path.dirname(res_path), exist_ok=True)
            res.save(res_path)
        except Exception:
            continue
    print('\tDone!')

def parse_args():
    parser = ArgumentParser(add_help=False)
    parser.add_argument('--num_threads', type=int, default=1)
    parser.add_argument('--root_path', type=str, default='')
    args = parser.parse_args()
    return args

def run(args):
    root_path = args.root_path
    out_crops_path = root_path + '_crops'
    if not os.path.exists(out_crops_path):
        os.makedirs(out_crops_path, exist_ok=True)

    file_paths = []
    for root, dirs, files in os.walk(root_path):
        for file in files:
            file_path = os.path.join(root, file)
            fname = os.path.join(out_crops_path, os.path.relpath(file_path, root_path))
            res_path = '{}.jpg'.format(os.path.splitext(fname)[0])
            if os.path.splitext(file_path)[1] == '.txt' or os.path.exists(res_path):
                continue
            file_paths.append((file_path, res_path))

    file_chunks = list(chunks(file_paths, int(math.ceil(len(file_paths) / args.num_threads))))
    print(len(file_chunks))
    pool = mp.Pool(args.num_threads)
    print('Running on {} paths\nHere we goooo'.format(len(file_paths)))
    tic = time.time()
    pool.starmap(extract_on_paths, [(chunk, face_detector) for chunk in file_chunks])
    toc = time.time()
    print('Mischief managed in {}s'.format(toc - tic))

if __name__ == '__main__':
    # Initialize InsightFace
    face_detector = insightface.app.FaceAnalysis()
    face_detector.prepare(ctx_id=-1, det_size=(640, 640))  # ctx_id=-1 for CPU

    args = parse_args()
    run(args)