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from dataclasses import dataclass, replace
import os
from os import path
from tempfile import TemporaryDirectory
from typing import Optional
import cv2
import progressbar
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from torchvision.transforms import InterpolationMode
import torch.nn.functional as F
from PIL import Image
import numpy as np
from dataset.range_transform import im_normalization
@dataclass
class Sample:
rgb: torch.Tensor
raw_image_pil: Image.Image
frame: str
save: bool
shape: tuple
need_resize: bool
mask: Optional[torch.Tensor] = None
class VideoReader(Dataset):
"""
This class is used to read a video, one frame at a time
"""
def __init__(
self,
vid_name,
video_path,
mask_dir,
size=-1,
to_save=None,
use_all_masks=False,
size_dir=None,
):
"""
image_dir - points to a directory of jpg images
mask_dir - points to a directory of png masks
size - resize min. side to size. Does nothing if <0.
to_save - optionally contains a list of file names without extensions
where the segmentation mask is required
use_all_mask - when true, read all available mask in mask_dir.
Default false. Set to true for YouTubeVOS validation.
"""
self.vid_name = vid_name
self.video_path = video_path
self.mask_dir = mask_dir
self.to_save = to_save
self.use_all_masks = use_all_masks
self.reference_mask = Image.open(
path.join(mask_dir, sorted(os.listdir(mask_dir))[0])
).convert('P')
self.first_gt_path = path.join(
self.mask_dir, sorted(os.listdir(self.mask_dir))[0]
)
if size < 0:
self.im_transform = transforms.Compose(
[
transforms.ToTensor(),
im_normalization,
]
)
else:
self.im_transform = transforms.Compose(
[
transforms.ToTensor(),
im_normalization,
transforms.Resize(size, interpolation=InterpolationMode.BILINEAR),
]
)
self.size = size
if os.path.isfile(self.video_path):
self.tmp_dir = TemporaryDirectory()
self.image_dir = self.tmp_dir.name
self._extract_frames()
else:
self.image_dir = video_path
if size_dir is None:
self.size_dir = self.image_dir
else:
self.size_dir = size_dir
self.frames = sorted(os.listdir(self.image_dir))
def __getitem__(self, idx) -> Sample:
data = {}
frame_name = self.frames[idx]
im_path = path.join(self.image_dir, frame_name)
img = Image.open(im_path).convert('RGB')
if self.image_dir == self.size_dir:
shape = np.array(img).shape[:2]
else:
size_path = path.join(self.size_dir, frame_name)
size_im = Image.open(size_path).convert('RGB')
shape = np.array(size_im).shape[:2]
gt_path = path.join(self.mask_dir, frame_name[:-4] + '.png')
if not os.path.exists(gt_path):
gt_path = path.join(self.mask_dir, frame_name[:-4] + '.PNG')
data['raw_image_pil'] = img
img = self.im_transform(img)
load_mask = self.use_all_masks or (gt_path == self.first_gt_path)
if load_mask and path.exists(gt_path):
mask = Image.open(gt_path).convert('P')
mask = np.array(mask, dtype=np.uint8)
data['mask'] = mask
info = {}
info['save'] = (self.to_save is None) or (frame_name[:-4] in self.to_save)
info['frame'] = frame_name
info['shape'] = shape
info['need_resize'] = not (self.size < 0)
data['rgb'] = img
data = Sample(**data, **info)
return data
def __len__(self):
return len(self.frames)
def __del__(self):
if hasattr(self, 'tmp_dir'):
self.tmp_dir.cleanup()
def _extract_frames(self):
cap = cv2.VideoCapture(self.video_path)
frame_index = 0
print(f'Extracting frames from {self.video_path} into a temporary dir...')
bar = progressbar.ProgressBar(max_value=int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))
while cap.isOpened():
_, frame = cap.read()
if frame is None:
break
if self.size > 0:
h, w = frame.shape[:2]
new_w = w * self.size // min(w, h)
new_h = h * self.size // min(w, h)
if new_w != w or new_h != h:
frame = cv2.resize(
frame, dsize=(new_w, new_h), interpolation=cv2.INTER_AREA
)
cv2.imwrite(
path.join(self.image_dir, f'frame_{frame_index:06d}.jpg'), frame
)
frame_index += 1
bar.update(frame_index)
bar.finish()
print('Done!')
def resize_mask(self, mask):
# mask transform is applied AFTER mapper, so we need to post-process it in eval.py
h, w = mask.shape[-2:]
min_hw = min(h, w)
return F.interpolate(
mask,
(int(h / min_hw * self.size), int(w / min_hw * self.size)),
mode='nearest',
)
def map_the_colors_back(self, pred_mask: Image.Image):
# https://stackoverflow.com/questions/29433243/convert-image-to-specific-palette-using-pil-without-dithering
# dither=Dither.NONE just in case
return pred_mask.quantize(
palette=self.reference_mask, dither=Image.Dither.NONE
).convert('RGB')
@staticmethod
def collate_fn_identity(x):
if x.mask is not None:
return replace(x, mask=torch.tensor(x.mask))
else:
return x
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