File size: 3,527 Bytes
127bcdc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import os
import cv2
import torch
import torchvision.transforms as transforms
from torch.utils.data import Dataset
import utils
class AVLip(Dataset):
def __init__(self, opt):
assert opt.data_label in ["train", "val"]
self.data_label = opt.data_label
self.real_list = utils.get_list(opt.real_list_path)
self.fake_list = utils.get_list(opt.fake_list_path)
self.label_dict = dict()
for i in self.real_list:
self.label_dict[i] = 0
for i in self.fake_list:
self.label_dict[i] = 1
self.total_list = self.real_list + self.fake_list
def __len__(self):
return len(self.total_list)
def __getitem__(self, idx):
# 防止无限递归：记录已尝试的索引
tried_indices = set()
return self._get_item_with_skip(idx, tried_indices)
def _get_item_with_skip(self, idx, tried_indices):
# 如果已尝试所有样本，抛出异常
if len(tried_indices) >= len(self.total_list):
raise RuntimeError("All samples are corrupted or cannot be read!")
tried_indices.add(idx)
img_path = self.total_list[idx]
label = self.label_dict[img_path]
# 尝试读取图像，如果失败则跳过该文件
try:
# 检查文件是否存在
if not os.path.exists(img_path):
print(f"WARNING: File not found, skipping: {img_path}")
# 跳过当前文件，尝试下一个样本
return self._get_item_with_skip((idx + 1) % len(self.total_list), tried_indices)
# 读取图像
img_cv = cv2.imread(img_path)
if img_cv is None:
print(f"WARNING: Failed to read image, skipping: {img_path}")
# 跳过当前文件，尝试下一个样本
return self._get_item_with_skip((idx + 1) % len(self.total_list), tried_indices)
img = torch.tensor(img_cv, dtype=torch.float32)
img = img.permute(2, 0, 1)
except Exception as e:
print(f"WARNING: Error processing {img_path}: {e}, skipping...")
# 跳过当前文件，尝试下一个样本
return self._get_item_with_skip((idx + 1) % len(self.total_list), tried_indices)
crops = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
std=[0.26862954, 0.26130258, 0.27577711])(img)
# crop images
# crops[0]: 1.0x, crops[1]: 0.65x, crops[2]: 0.45x
# NB: bottom strip layout is [face0|face1|face2|face3|face4] each 500x500.
# Original code used `i:i+500 for i in range(5)` which only sampled the
# left-most 504 columns (5 near-identical 1-px-shifted views of face0).
# Fixed to `i*500:(i+1)*500` so 5 distinct face patches reach the model.
crops = [[transforms.Resize((224, 224))(img[:, 500:, i*500:(i+1)*500]) for i in range(5)], [], []]
crop_idx = [(28, 196), (61, 163)]
for i in range(len(crops[0])):
crops[1].append(transforms.Resize((224, 224))
(crops[0][i][:, crop_idx[0][0]:crop_idx[0][1], crop_idx[0][0]:crop_idx[0][1]]))
crops[2].append(transforms.Resize((224, 224))
(crops[0][i][:, crop_idx[1][0]:crop_idx[1][1], crop_idx[1][0]:crop_idx[1][1]]))
img = transforms.Resize((1120, 1120))(img)
return img, crops, label, img_path
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