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import os
from os.path import join
import csv
import cv2, copy
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
from PIL import Image
# import torchaudio
import sys
from scipy.io import wavfile
import json
class DHF1KDataset(Dataset):
def __init__(self, path_data, len_snippet, mode="train", multi_frame=0, alternate=1):
self.path_data = path_data
self.len_snippet = len_snippet
self.mode = mode
self.multi_frame = multi_frame
self.alternate = alternate
self.img_transform = transforms.Compose([
transforms.Resize((224, 384)),
transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]
)
])
if self.mode == "train":
self.video_names = os.listdir(path_data)
self.list_num_frame = [len(os.listdir(os.path.join(path_data, d, 'images'))) for d in self.video_names]
elif self.mode == "val":
self.list_num_frame = []
for v in os.listdir(path_data):
for i in range(0, len(
os.listdir(os.path.join(path_data, v, 'images'))) - self.alternate * self.len_snippet,
32):
self.list_num_frame.append((v, i))
else:
self.list_num_frame = []
for v in os.listdir(path_data):
for i in range(0, len(
os.listdir(os.path.join(path_data, v, 'images'))) - self.alternate * self.len_snippet,
self.len_snippet):
self.list_num_frame.append((v, i))
self.list_num_frame.append(
(v, len(os.listdir(os.path.join(path_data, v, 'images'))) - self.len_snippet))
def __len__(self):
return len(self.list_num_frame)
def __getitem__(self, idx):
if self.mode == "train":
file_name = self.video_names[idx]
start_idx = np.random.randint(0, self.list_num_frame[idx] - self.alternate * self.len_snippet + 1)
elif self.mode == "val" or self.mode == "save":
(file_name, start_idx) = self.list_num_frame[idx]
path_clip = os.path.join(self.path_data, file_name, 'images')
path_annt = os.path.join(self.path_data, file_name, 'maps')
clip_img = []
clip_gt = []
for i in range(self.len_snippet):
img = Image.open(os.path.join(path_clip, '%04d.png' % (start_idx + self.alternate * i + 1))).convert('RGB')
sz = img.size
if self.mode != "save":
gt = np.array(
Image.open(os.path.join(path_annt, '%04d.png' % (start_idx + self.alternate * i + 1))).convert('L'))
gt = gt.astype('float')
if self.mode == "train":
gt = cv2.resize(gt, (384, 224))
if np.max(gt) > 1.0:
gt = gt / 255.0
clip_gt.append(torch.FloatTensor(gt))
clip_img.append(self.img_transform(img))
clip_img = torch.FloatTensor(torch.stack(clip_img, dim=0))
if self.mode != "save":
clip_gt = torch.FloatTensor(torch.stack(clip_gt, dim=0))
if self.mode == "save":
return clip_img, start_idx, file_name, sz
else:
if self.multi_frame == 0:
return clip_img, clip_gt[-1]
return clip_img, clip_gt
class Hollywood_UCFDataset(Dataset):
def __init__(self, path_data, len_snippet, mode="train", frame_no="last", multi_frame=0):
self.path_data = path_data
self.len_snippet = len_snippet
self.mode = mode
self.frame_no = frame_no
self.multi_frame = multi_frame
self.img_transform = transforms.Compose([
transforms.Resize((224, 384)),
transforms.ToTensor(),
transforms.Normalize(
[0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]
)
])
if self.mode == "train":
self.video_names = os.listdir(path_data)
self.list_num_frame = [len(os.listdir(os.path.join(path_data, d, 'images'))) for d in self.video_names]
elif self.mode == "val":
self.list_num_frame = []
for v in os.listdir(path_data):
for i in range(0, len(os.listdir(os.path.join(path_data, v, 'images'))) - self.len_snippet,
self.len_snippet):
self.list_num_frame.append((v, i))
if len(os.listdir(os.path.join(path_data, v, 'images'))) <= self.len_snippet:
self.list_num_frame.append((v, 0))
def __len__(self):
return len(self.list_num_frame)
def __getitem__(self, idx):
if self.mode == "train":
file_name = self.video_names[idx]
start_idx = np.random.randint(0, max(1, self.list_num_frame[idx] - self.len_snippet + 1))
elif self.mode == "val":
(file_name, start_idx) = self.list_num_frame[idx]
path_clip = os.path.join(self.path_data, file_name, 'images')
path_annt = os.path.join(self.path_data, file_name, 'maps')
clip_img = []
clip_gt = []
list_clips = os.listdir(path_clip)
list_clips.sort()
list_sal_clips = os.listdir(path_annt)
list_sal_clips.sort()
if len(list_sal_clips) < self.len_snippet:
temp = [list_clips[0] for _ in range(self.len_snippet - len(list_clips))]
temp.extend(list_clips)
list_clips = copy.deepcopy(temp)
temp = [list_sal_clips[0] for _ in range(self.len_snippet - len(list_sal_clips))]
temp.extend(list_sal_clips)
list_sal_clips = copy.deepcopy(temp)
assert len(list_sal_clips) == self.len_snippet and len(list_clips) == self.len_snippet
for i in range(self.len_snippet):
img = Image.open(os.path.join(path_clip, list_clips[start_idx + i])).convert('RGB')
clip_img.append(self.img_transform(img))
gt = np.array(Image.open(os.path.join(path_annt, list_sal_clips[start_idx + i])).convert('L'))
gt = gt.astype('float')
if self.mode == "train":
gt = cv2.resize(gt, (384, 224))
if np.max(gt) > 1.0:
gt = gt / 255.0
clip_gt.append(torch.FloatTensor(gt))
clip_img = torch.FloatTensor(torch.stack(clip_img, dim=0))
if self.multi_frame == 0:
gt = clip_gt[-1]
else:
gt = torch.FloatTensor(torch.stack(clip_gt, dim=0))
return clip_img, gt