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84 lines
2.5 KiB
Python
84 lines
2.5 KiB
Python
# encoding: utf-8
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import cv2
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import os
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import numpy as np
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import torch
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from torch.utils.data import Dataset
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from cvtransforms import HorizontalFlip, ColorNormalize
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class dataset_visual(Dataset):
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def __init__(
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self,
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video_path,
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anno_path,
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file_list,
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vid_padding,
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phase,
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):
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self.anno_path = anno_path
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self.vid_padding = vid_padding
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self.phase = phase
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with open(file_list, "r") as f:
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self.videos = [
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os.path.join(video_path, line.strip()) for line in f.readlines()
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]
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self.data = []
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for vid in self.videos:
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items = vid.split(os.path.sep)
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aud = (
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vid.replace("lip", "audio_25k").replace("/video/mpg_6000", "")
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+ ".wav"
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)
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self.data.append((vid, aud, items[-4], items[-1]))
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def __getitem__(self, idx):
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(vid, aud, spk, name) = self.data[idx]
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vid = self._load_vid(vid)
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anno = self._load_anno(
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os.path.join(self.anno_path, spk, "align", name + ".align")
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)
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if self.phase == "train":
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vid = HorizontalFlip(vid)
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vid = ColorNormalize(vid)
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vid = self._padding(vid, self.vid_padding)
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return {
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"vid": torch.FloatTensor(vid.transpose(3, 0, 1, 2)),
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"txt": anno.upper(),
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}
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def __len__(self):
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return len(self.data)
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def _load_vid(self, p):
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files = os.listdir(p)
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files = list(filter(lambda file: file.find(".jpg") != -1, files))
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files = sorted(files, key=lambda file: int(os.path.splitext(file)[0]))
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array = [cv2.imread(os.path.join(p, file)) for file in files]
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array = list(filter(lambda im: im is not None, array))
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array = [
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cv2.resize(im, (128, 64), interpolation=cv2.INTER_LANCZOS4)
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for im in array
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]
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array = np.stack(array, axis=0).astype(np.float32)
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return array
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def _load_anno(self, name):
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with open(name, "r") as f:
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lines = [line.strip().split(" ") for line in f.readlines()]
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txt = [line[2] for line in lines]
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txt = list(filter(lambda s: not s.upper() in ["SIL", "SP"], txt))
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txt = " ".join(txt)
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return txt
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def _padding(self, array, length):
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array = [array[_] for _ in range(array.shape[0])]
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size = array[0].shape
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for i in range(length - len(array)):
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array.append(np.zeros(size))
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return np.stack(array, axis=0)
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