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fit_300VW_closed_eyes.py
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fit_300VW_closed_eyes.py
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import os
import multiprocessing as mp
from pathlib import Path
from numba.core.serialize import custom_rebuild
from face3d.face_model import FaceModel
import tqdm
import cv2
import scipy.io as sio
import glob
import numpy as np
import shutil
import utils
from face3d.utils import draw_landmarks
import random
if __name__=='__main__':
shutil.rmtree(f'300VW-3D_closed_eyes_3ddfa', ignore_errors=True)
os.makedirs(f'300VW-3D_closed_eyes_3ddfa', exist_ok=True)
for folder_path in glob.glob('GANnotation/300VW-3D_closed_eyes/*'):
folder_img_name = folder_path.split('/')[-1]
os.makedirs(
os.path.join(f'300VW-3D_closed_eyes_3ddfa', folder_img_name),
exist_ok=True
)
model = FaceModel(n_shape=40, n_exp=20)
img_list = list(Path('GANnotation/300VW-3D_closed_eyes').glob('**/*.jpg'))
bag = []
print(f'Push item to bag: ')
for img_path in tqdm.tqdm(img_list):
pts_path = str(img_path).replace('jpg', 'mat')
bag.append((str(img_path), pts_path))
def task(item, debug):
img_path, pts_path = item
img_name = img_path.split('/')[-1].split('.')[0]
folder_name = img_path.split('/')[-2]
original_img = cv2.imread(img_path)
original_pts = sio.loadmat(pts_path)['pt3d']
expand_ratio = 1.
yaw = random.uniform(-30,30)
pitch = random.uniform(-20,20)
output = model.generate_3ddfa_params_plus(original_img, original_pts, expand_ratio=expand_ratio, preprocess=False, yaw=yaw, pitch=pitch)
if output is None:
return
for idx in range(len(output)):
ori_img = output[idx][0]
ori_params = output[idx][1]
img_out_path = os.path.join(f'300VW-3D_closed_eyes_3ddfa/{folder_name}', f'{img_name}_{idx}.jpg')
params_out_path = os.path.join(f'300VW-3D_closed_eyes_3ddfa/{folder_name}', f'{img_name}_{idx}.mat')
cv2.imwrite(img_out_path, ori_img)
sio.savemat(params_out_path, ori_params)
if debug:
vertex = model.reconstruct_vertex(ori_img, ori_params['params'], de_normalize=False)[:,:2][model.bfm.kpt_ind]
draw_landmarks(ori_img.copy(), vertex.copy(), f'debug/1_{folder_name}_{img_name}_{idx}.jpg')
# fliplr_img, fliplr_pts = utils.fliplr_face_landmarks(original_img, original_pts, reverse=False)
# # draw_landmarks(fliplr_img.copy(), fliplr_pts.copy(), f'intermediate.jpg')
# expand_ratio = random.uniform(1,1.4)
# yaw = random.uniform(-30,30)
# pitch = random.uniform(-20,20)
# fliplr_output = model.generate_3ddfa_params_plus(fliplr_img, fliplr_pts, expand_ratio=expand_ratio, preprocess=True, yaw=yaw, pitch=pitch)
# for idx in range(len(fliplr_output)):
# fliplr_img = fliplr_output[idx][0]
# fliplr_params = fliplr_output[idx][1]
# fliplr_img_out_path = os.path.join(f'300VW-3D_closed_eyes_3ddfa/{folder_name}', f'{img_name}_{idx}_fliplr.jpg')
# fliplr_params_out_path = os.path.join(f'300VW-3D_closed_eyes_3ddfa/{folder_name}', f'{img_name}_{idx}_fliplr.mat')
# cv2.imwrite(fliplr_img_out_path, fliplr_img)
# sio.savemat(fliplr_params_out_path, fliplr_params)
# if debug:
# vertex = model.reconstruct_vertex(fliplr_img, fliplr_params['params'], de_normalize=False)[:,:2][model.bfm.kpt_ind]
# draw_landmarks(fliplr_img.copy(), vertex.copy(), f'debug/2_{folder_name}_{img_name}_{idx}_fliplr.jpg')
# custom_item = ('300VW-3D_cropped_closed_eyes/203/0818.jpg', '300VW-3D_cropped_closed_eyes/203/0818.mat')
# task(custom_item, True)
# import ipdb; ipdb.set_trace(context=10)
debug = True
shutil.rmtree('debug', ignore_errors=True)
os.makedirs('debug', exist_ok=True)
for idx in tqdm.tqdm(range(len(bag)), total=len(bag)):
if idx % 1000 == 0:
debug = True
task(bag[idx], debug)
debug = False
else:
task(bag[idx], debug)