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"""This script defines the custom dataset for Deep3DFaceRecon_pytorch |
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""" |
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import json |
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import os.path |
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import pickle |
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import random |
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import numpy as np |
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import torch |
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import util.util as util |
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from data.base_dataset import apply_img_affine |
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from data.base_dataset import apply_lm_affine |
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from data.base_dataset import BaseDataset |
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from data.base_dataset import get_affine_mat |
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from data.base_dataset import get_transform |
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from data.image_folder import make_dataset |
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from PIL import Image |
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from scipy.io import loadmat |
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from scipy.io import savemat |
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from util.load_mats import load_lm3d |
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from util.preprocess import align_img |
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from util.preprocess import estimate_norm |
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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 { |
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"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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} |
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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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return self.size |
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