pixel3dmmA

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# -*- coding: utf-8 -*-

# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2023 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: mica@tue.mpg.de


import argparse
import os
import random
import traceback
from glob import glob
from pathlib import Path
from PIL import Image

import cv2
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import trimesh
from insightface.app.common import Face
from insightface.utils import face_align
from loguru import logger
from skimage.io import imread
from tqdm import tqdm
#from retinaface.pre_trained_models import get_model
#from retinaface.utils import vis_annotations
#from matplotlib import pyplot as plt


from pixel3dmm.preprocessing.MICA.configs.config import get_cfg_defaults
from pixel3dmm.preprocessing.MICA.datasets.creation.util import get_arcface_input, get_center, draw_on
from pixel3dmm.preprocessing.MICA.utils import util
from pixel3dmm.preprocessing.MICA.utils.landmark_detector import LandmarksDetector, detectors
from pixel3dmm import env_paths


#model = get_model("resnet50_2020-07-20", max_size=512)
#model.eval()


def deterministic(rank):
    torch.manual_seed(rank)
    torch.cuda.manual_seed(rank)
    np.random.seed(rank)
    random.seed(rank)

    cudnn.deterministic = True
    cudnn.benchmark = False


def process(args, app, image_size=224, draw_bbox=False):
    dst = Path(args.a)
    dst.mkdir(parents=True, exist_ok=True)
    processes = []
    image_paths = sorted(glob(args.i + '/*.*'))#[:1]
    image_paths = image_paths[::max(1, len(image_paths)//10)]
    for image_path in tqdm(image_paths):
        name = Path(image_path).stem
        img = cv2.imread(image_path)


        # FOR pytorch retinaface use this: img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        # I had issues with onnxruntime!
        bboxes, kpss = app.detect(img)

        #annotation = model.predict_jsons(img)
        #Image.fromarray(vis_annotations(img, annotation)).show()

        #bboxes = np.stack([np.array( annotation[0]['bbox'] + [annotation[0]['score']] ) for i in range(len(annotation))], axis=0)
        #kpss = np.stack([np.array( annotation[0]['landmarks'] ) for i in range(len(annotation))], axis=0)
        if bboxes.shape[0] == 0:
            logger.error(f'[ERROR] Face not detected for {image_path}')
            continue
        i = get_center(bboxes, img)
        bbox = bboxes[i, 0:4]
        det_score = bboxes[i, 4]
        kps = None
        if kpss is not None:
            kps = kpss[i]

        ##for ikp in range(kps.shape[0]):
        #    img[int(kps[ikp][1]), int(kps[ikp][0]), 0] = 255
        #    img[int(kpss_[0][ikp][1]), int(kpss_[0][ikp][0]), 1] = 255
        #Image.fromarray(img).show()
        face = Face(bbox=bbox, kps=kps, det_score=det_score)
        blob, aimg = get_arcface_input(face, img)
        file = str(Path(dst, name))
        np.save(file, blob)
        processes.append(file + '.npy')
        cv2.imwrite(file + '.jpg', face_align.norm_crop(img, landmark=face.kps, image_size=image_size))
        if draw_bbox:
            dimg = draw_on(img, [face])
            cv2.imwrite(file + '_bbox.jpg', dimg)

    return processes


def to_batch(path):
    src = path.replace('npy', 'jpg')
    if not os.path.exists(src):
        src = path.replace('npy', 'png')

    image = imread(src)[:, :, :3]
    image = image / 255.
    image = cv2.resize(image, (224, 224)).transpose(2, 0, 1)
    image = torch.tensor(image).cuda()[None]

    arcface = np.load(path)
    arcface = torch.tensor(arcface).cuda()[None]

    return image, arcface


def load_checkpoint(args, mica):
    checkpoint = torch.load(args.m, weights_only=False)
    if 'arcface' in checkpoint:
        mica.arcface.load_state_dict(checkpoint['arcface'])
    if 'flameModel' in checkpoint:
        mica.flameModel.load_state_dict(checkpoint['flameModel'])


def main(cfg, args):
    device = 'cuda:0'
    cfg.model.testing = True
    mica = util.find_model_using_name(model_dir='micalib.models', model_name=cfg.model.name)(cfg, device)
    load_checkpoint(args, mica)
    mica.eval()

    faces = mica.flameModel.generator.faces_tensor.cpu()
    Path(args.o).mkdir(exist_ok=True, parents=True)

    app = LandmarksDetector(model=detectors.RETINAFACE)

    with torch.no_grad():
        logger.info(f'Processing has started...')
        paths = process(args, app, draw_bbox=False)
        for path in tqdm(paths):
            name = Path(path).stem
            images, arcface = to_batch(path)
            codedict = mica.encode(images, arcface)
            opdict = mica.decode(codedict)
            meshes = opdict['pred_canonical_shape_vertices']
            code = opdict['pred_shape_code']
            lmk = mica.flameModel.generator.compute_landmarks(meshes)

            mesh = meshes[0]
            landmark_51 = lmk[0, 17:]
            landmark_7 = landmark_51[[19, 22, 25, 28, 16, 31, 37]]

            dst = Path(args.o, name)
            dst.mkdir(parents=True, exist_ok=True)
            trimesh.Trimesh(vertices=mesh.cpu() * 1000.0, faces=faces, process=False).export(f'{dst}/mesh.ply')  # save in millimeters
            trimesh.Trimesh(vertices=mesh.cpu() * 1000.0, faces=faces, process=False).export(f'{dst}/mesh.obj')
            np.save(f'{dst}/identity', code[0].cpu().numpy())
            np.save(f'{dst}/kpt7', landmark_7.cpu().numpy() * 1000.0)
            np.save(f'{dst}/kpt68', lmk.cpu().numpy() * 1000.0)

        logger.info(f'Processing finished. Results has been saved in {args.o}')


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='MICA - Towards Metrical Reconstruction of Human Faces')
    parser.add_argument('-video_name', required=True, type=str)
    parser.add_argument('-a', default='demo/arcface', type=str, help='Processed images for MICA input')
    parser.add_argument('-m', default='data/pretrained/mica.tar', type=str, help='Pretrained model path')

    args = parser.parse_args()
    cfg = get_cfg_defaults()
    args.i = f'{env_paths.PREPROCESSED_DATA}/{args.video_name}/cropped/'
    args.o = f'{env_paths.PREPROCESSED_DATA}/{args.video_name}/mica/'
    if os.path.exists(f'{env_paths.PREPROCESSED_DATA}/{args.video_name}/mica/'):
        if len(os.listdir(f'{env_paths.PREPROCESSED_DATA}/{args.video_name}/mica/')) >= 10:
            print(f'''
                            <<<<<<<< ALREADY COMPLETE MICA PREDICTION FOR {args.video_name}, SKIPPING >>>>>>>>
                            ''')
            exit()
    main(cfg, args)