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"""This script defines the custom dataset for Deep3DFaceRecon_pytorch
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"""
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import os.path
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from data.base_dataset import BaseDataset, get_transform, get_affine_mat, apply_img_affine, apply_lm_affine
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from data.image_folder import make_dataset
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from PIL import Image
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import random
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import util.util as util
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import numpy as np
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import json
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import torch
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from scipy.io import loadmat, savemat
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import pickle
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from util.preprocess import align_img, estimate_norm
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from util.load_mats import load_lm3d
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def default_flist_reader(flist):
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"""
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flist format: impath label\nimpath label\n ...(same to caffe's filelist)
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"""
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imlist = []
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with open(flist, 'r') as rf:
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for line in rf.readlines():
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impath = line.strip()
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imlist.append(impath)
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return imlist
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def jason_flist_reader(flist):
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with open(flist, 'r') as fp:
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info = json.load(fp)
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return info
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def parse_label(label):
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return torch.tensor(np.array(label).astype(np.float32))
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class FlistDataset(BaseDataset):
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"""
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It requires one directories to host training images '/path/to/data/train'
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You can train the model with the dataset flag '--dataroot /path/to/data'.
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"""
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def __init__(self, opt):
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"""Initialize this dataset class.
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Parameters:
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opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
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"""
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BaseDataset.__init__(self, opt)
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self.lm3d_std = load_lm3d(opt.bfm_folder)
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msk_names = default_flist_reader(opt.flist)
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self.msk_paths = [os.path.join(opt.data_root, i) for i in msk_names]
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self.size = len(self.msk_paths)
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self.opt = opt
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self.name = 'train' if opt.isTrain else 'val'
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if '_' in opt.flist:
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self.name += '_' + opt.flist.split(os.sep)[-1].split('_')[0]
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def __getitem__(self, index):
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"""Return a data point and its metadata information.
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Parameters:
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index (int) -- a random integer for data indexing
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Returns a dictionary that contains A, B, A_paths and B_paths
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img (tensor) -- an image in the input domain
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msk (tensor) -- its corresponding attention mask
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lm (tensor) -- its corresponding 3d landmarks
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im_paths (str) -- image paths
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aug_flag (bool) -- a flag used to tell whether its raw or augmented
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"""
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msk_path = self.msk_paths[index % self.size]
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img_path = msk_path.replace('mask/', '')
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lm_path = '.'.join(msk_path.replace('mask', 'landmarks').split('.')[:-1]) + '.txt'
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raw_img = Image.open(img_path).convert('RGB')
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raw_msk = Image.open(msk_path).convert('RGB')
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raw_lm = np.loadtxt(lm_path).astype(np.float32)
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_, img, lm, msk = align_img(raw_img, raw_lm, self.lm3d_std, raw_msk)
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aug_flag = self.opt.use_aug and self.opt.isTrain
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if aug_flag:
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img, lm, msk = self._augmentation(img, lm, self.opt, msk)
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_, H = img.size
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M = estimate_norm(lm, H)
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transform = get_transform()
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img_tensor = transform(img)
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msk_tensor = transform(msk)[:1, ...]
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lm_tensor = parse_label(lm)
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M_tensor = parse_label(M)
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return {'imgs': img_tensor,
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'lms': lm_tensor,
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'msks': msk_tensor,
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'M': M_tensor,
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'im_paths': img_path,
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'aug_flag': aug_flag,
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'dataset': self.name}
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def _augmentation(self, img, lm, opt, msk=None):
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affine, affine_inv, flip = get_affine_mat(opt, img.size)
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img = apply_img_affine(img, affine_inv)
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lm = apply_lm_affine(lm, affine, flip, img.size)
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if msk is not None:
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msk = apply_img_affine(msk, affine_inv, method=Image.BILINEAR)
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return img, lm, msk
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def __len__(self):
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"""Return the total number of images in the dataset.
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"""
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return self.size
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