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import json
import random
from typing import List
import numpy as np
import pandas as pd
import torch
import torchvision.transforms as transforms
from decord import VideoReader
from PIL import Image
from torch.utils.data import Dataset
from transformers import CLIPImageProcessor
class HumanDanceVideoDataset(Dataset):
def __init__(
self,
sample_rate,
n_sample_frames,
width,
height,
img_scale=(1.0, 1.0),
img_ratio=(0.9, 1.0),
drop_ratio=0.1,
data_meta_paths=["./data/fashion_meta.json"],
):
super().__init__()
self.sample_rate = sample_rate
self.n_sample_frames = n_sample_frames
self.width = width
self.height = height
self.img_scale = img_scale
self.img_ratio = img_ratio
vid_meta = []
for data_meta_path in data_meta_paths:
vid_meta.extend(json.load(open(data_meta_path, "r")))
self.vid_meta = vid_meta
self.clip_image_processor = CLIPImageProcessor()
self.pixel_transform = transforms.Compose(
[
# transforms.RandomResizedCrop(
# (height, width),
# scale=self.img_scale,
# ratio=self.img_ratio,
# interpolation=transforms.InterpolationMode.BILINEAR,
# ),
transforms.Resize(
(height, width),
),
transforms.ToTensor(),
transforms.Normalize([0.5], [0.5]),
]
)
self.cond_transform = transforms.Compose(
[
# transforms.RandomResizedCrop(
# (height, width),
# scale=self.img_scale,
# ratio=self.img_ratio,
# interpolation=transforms.InterpolationMode.BILINEAR,
# ),
transforms.Resize(
(height, width),
),
transforms.ToTensor(),
]
)
self.drop_ratio = drop_ratio
def augmentation(self, images, transform, state=None):
if state is not None:
torch.set_rng_state(state)
if isinstance(images, List):
transformed_images = [transform(img) for img in images]
ret_tensor = torch.stack(transformed_images, dim=0) # (f, c, h, w)
else:
ret_tensor = transform(images) # (c, h, w)
return ret_tensor
def __getitem__(self, index):
video_meta = self.vid_meta[index]
video_path = video_meta["video_path"]
kps_path = video_meta["kps_path"]
video_reader = VideoReader(video_path)
kps_reader = VideoReader(kps_path)
assert len(video_reader) == len(
kps_reader
), f"{len(video_reader) = } != {len(kps_reader) = } in {video_path}"
video_length = len(video_reader)
video_fps = video_reader.get_avg_fps()
# print("fps", video_fps)
if video_fps > 30: # 30-60
sample_rate = self.sample_rate*2
else:
sample_rate = self.sample_rate
clip_length = min(
video_length, (self.n_sample_frames - 1) * sample_rate + 1
)
start_idx = random.randint(0, video_length - clip_length)
batch_index = np.linspace(
start_idx, start_idx + clip_length - 1, self.n_sample_frames, dtype=int
).tolist()
# read frames and kps
vid_pil_image_list = []
pose_pil_image_list = []
for index in batch_index:
img = video_reader[index]
vid_pil_image_list.append(Image.fromarray(img.asnumpy()))
img = kps_reader[index]
pose_pil_image_list.append(Image.fromarray(img.asnumpy()))
ref_img_idx = random.randint(0, video_length - 1)
ref_img = Image.fromarray(video_reader[ref_img_idx].asnumpy())
# transform
state = torch.get_rng_state()
pixel_values_vid = self.augmentation(
vid_pil_image_list, self.pixel_transform, state
)
pixel_values_pose = self.augmentation(
pose_pil_image_list, self.cond_transform, state
)
pixel_values_ref_img = self.augmentation(ref_img, self.pixel_transform, state)
clip_ref_img = self.clip_image_processor(
images=ref_img, return_tensors="pt"
).pixel_values[0]
sample = dict(
video_dir=video_path,
pixel_values_vid=pixel_values_vid,
pixel_values_pose=pixel_values_pose,
pixel_values_ref_img=pixel_values_ref_img,
clip_ref_img=clip_ref_img,
)
return sample
def __len__(self):
return len(self.vid_meta)
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