File size: 11,487 Bytes
690f890 |
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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 |
# -*- coding: utf-8 -*-
# Copyright (c) Alibaba, Inc. and its affiliates.
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as TF
class VaceImageProcessor(object):
def __init__(self, downsample=None, seq_len=None):
self.downsample = downsample
self.seq_len = seq_len
def _pillow_convert(self, image, cvt_type='RGB'):
if image.mode != cvt_type:
if image.mode == 'P':
image = image.convert(f'{cvt_type}A')
if image.mode == f'{cvt_type}A':
bg = Image.new(cvt_type,
size=(image.width, image.height),
color=(255, 255, 255))
bg.paste(image, (0, 0), mask=image)
image = bg
else:
image = image.convert(cvt_type)
return image
def _load_image(self, img_path):
if img_path is None or img_path == '':
return None
img = Image.open(img_path)
img = self._pillow_convert(img)
return img
def _resize_crop(self, img, oh, ow, normalize=True):
"""
Resize, center crop, convert to tensor, and normalize.
"""
# resize and crop
iw, ih = img.size
if iw != ow or ih != oh:
# resize
scale = max(ow / iw, oh / ih)
img = img.resize(
(round(scale * iw), round(scale * ih)),
resample=Image.Resampling.LANCZOS
)
assert img.width >= ow and img.height >= oh
# center crop
x1 = (img.width - ow) // 2
y1 = (img.height - oh) // 2
img = img.crop((x1, y1, x1 + ow, y1 + oh))
# normalize
if normalize:
img = TF.to_tensor(img).sub_(0.5).div_(0.5).unsqueeze(1)
return img
def _image_preprocess(self, img, oh, ow, normalize=True, **kwargs):
return self._resize_crop(img, oh, ow, normalize)
def load_image(self, data_key, **kwargs):
return self.load_image_batch(data_key, **kwargs)
def load_image_pair(self, data_key, data_key2, **kwargs):
return self.load_image_batch(data_key, data_key2, **kwargs)
def load_image_batch(self, *data_key_batch, normalize=True, seq_len=None, **kwargs):
seq_len = self.seq_len if seq_len is None else seq_len
imgs = []
for data_key in data_key_batch:
img = self._load_image(data_key)
imgs.append(img)
w, h = imgs[0].size
dh, dw = self.downsample[1:]
# compute output size
scale = min(1., np.sqrt(seq_len / ((h / dh) * (w / dw))))
oh = int(h * scale) // dh * dh
ow = int(w * scale) // dw * dw
assert (oh // dh) * (ow // dw) <= seq_len
imgs = [self._image_preprocess(img, oh, ow, normalize) for img in imgs]
return *imgs, (oh, ow)
class VaceVideoProcessor(object):
def __init__(self, downsample, min_area, max_area, min_fps, max_fps, zero_start, seq_len, keep_last, **kwargs):
self.downsample = downsample
self.min_area = min_area
self.max_area = max_area
self.min_fps = min_fps
self.max_fps = max_fps
self.zero_start = zero_start
self.keep_last = keep_last
self.seq_len = seq_len
assert seq_len >= min_area / (self.downsample[1] * self.downsample[2])
@staticmethod
def resize_crop(video: torch.Tensor, oh: int, ow: int):
"""
Resize, center crop and normalize for decord loaded video (torch.Tensor type)
Parameters:
video - video to process (torch.Tensor): Tensor from `reader.get_batch(frame_ids)`, in shape of (T, H, W, C)
oh - target height (int)
ow - target width (int)
Returns:
The processed video (torch.Tensor): Normalized tensor range [-1, 1], in shape of (C, T, H, W)
Raises:
"""
# permute ([t, h, w, c] -> [t, c, h, w])
video = video.permute(0, 3, 1, 2)
# resize and crop
ih, iw = video.shape[2:]
if ih != oh or iw != ow:
# resize
scale = max(ow / iw, oh / ih)
video = F.interpolate(
video,
size=(round(scale * ih), round(scale * iw)),
mode='bicubic',
antialias=True
)
assert video.size(3) >= ow and video.size(2) >= oh
# center crop
x1 = (video.size(3) - ow) // 2
y1 = (video.size(2) - oh) // 2
video = video[:, :, y1:y1 + oh, x1:x1 + ow]
# permute ([t, c, h, w] -> [c, t, h, w]) and normalize
video = video.transpose(0, 1).float().div_(127.5).sub_(1.)
return video
def _video_preprocess(self, video, oh, ow):
return self.resize_crop(video, oh, ow)
def _get_frameid_bbox_default(self, fps, frame_timestamps, h, w, crop_box, rng):
target_fps = min(fps, self.max_fps)
duration = frame_timestamps[-1].mean()
x1, x2, y1, y2 = [0, w, 0, h] if crop_box is None else crop_box
h, w = y2 - y1, x2 - x1
ratio = h / w
df, dh, dw = self.downsample
# min/max area of the [latent video]
min_area_z = self.min_area / (dh * dw)
max_area_z = min(self.seq_len, self.max_area / (dh * dw), (h // dh) * (w // dw))
# sample a frame number of the [latent video]
rand_area_z = np.square(np.power(2, rng.uniform(
np.log2(np.sqrt(min_area_z)),
np.log2(np.sqrt(max_area_z))
)))
of = min(
(int(duration * target_fps) - 1) // df + 1,
int(self.seq_len / rand_area_z)
)
# deduce target shape of the [latent video]
target_area_z = min(max_area_z, int(self.seq_len / of))
oh = round(np.sqrt(target_area_z * ratio))
ow = int(target_area_z / oh)
of = (of - 1) * df + 1
oh *= dh
ow *= dw
# sample frame ids
target_duration = of / target_fps
begin = 0. if self.zero_start else rng.uniform(0, duration - target_duration)
timestamps = np.linspace(begin, begin + target_duration, of)
frame_ids = np.argmax(np.logical_and(
timestamps[:, None] >= frame_timestamps[None, :, 0],
timestamps[:, None] < frame_timestamps[None, :, 1]
), axis=1).tolist()
return frame_ids, (x1, x2, y1, y2), (oh, ow), target_fps
def _get_frameid_bbox_adjust_last(self, fps, frame_timestamps, h, w, crop_box, rng):
duration = frame_timestamps[-1].mean()
x1, x2, y1, y2 = [0, w, 0, h] if crop_box is None else crop_box
h, w = y2 - y1, x2 - x1
ratio = h / w
df, dh, dw = self.downsample
# min/max area of the [latent video]
min_area_z = self.min_area / (dh * dw)
max_area_z = min(self.seq_len, self.max_area / (dh * dw), (h // dh) * (w // dw))
# sample a frame number of the [latent video]
rand_area_z = np.square(np.power(2, rng.uniform(
np.log2(np.sqrt(min_area_z)),
np.log2(np.sqrt(max_area_z))
)))
of = min(
(len(frame_timestamps) - 1) // df + 1,
int(self.seq_len / rand_area_z)
)
# deduce target shape of the [latent video]
target_area_z = min(max_area_z, int(self.seq_len / of))
oh = round(np.sqrt(target_area_z * ratio))
ow = int(target_area_z / oh)
of = (of - 1) * df + 1
oh *= dh
ow *= dw
# sample frame ids
target_duration = duration
target_fps = of / target_duration
timestamps = np.linspace(0., target_duration, of)
frame_ids = np.argmax(np.logical_and(
timestamps[:, None] >= frame_timestamps[None, :, 0],
timestamps[:, None] <= frame_timestamps[None, :, 1]
), axis=1).tolist()
# print(oh, ow, of, target_duration, target_fps, len(frame_timestamps), len(frame_ids))
return frame_ids, (x1, x2, y1, y2), (oh, ow), target_fps
def _get_frameid_bbox(self, fps, frame_timestamps, h, w, crop_box, rng):
if self.keep_last:
return self._get_frameid_bbox_adjust_last(fps, frame_timestamps, h, w, crop_box, rng)
else:
return self._get_frameid_bbox_default(fps, frame_timestamps, h, w, crop_box, rng)
def load_video(self, data_key, crop_box=None, seed=2024, **kwargs):
return self.load_video_batch(data_key, crop_box=crop_box, seed=seed, **kwargs)
def load_video_pair(self, data_key, data_key2, crop_box=None, seed=2024, **kwargs):
return self.load_video_batch(data_key, data_key2, crop_box=crop_box, seed=seed, **kwargs)
def load_video_batch(self, *data_key_batch, crop_box=None, seed=2024, **kwargs):
rng = np.random.default_rng(seed + hash(data_key_batch[0]) % 10000)
# read video
import decord
decord.bridge.set_bridge('torch')
readers = []
for data_k in data_key_batch:
reader = decord.VideoReader(data_k)
readers.append(reader)
fps = readers[0].get_avg_fps()
length = min([len(r) for r in readers])
frame_timestamps = [readers[0].get_frame_timestamp(i) for i in range(length)]
frame_timestamps = np.array(frame_timestamps, dtype=np.float32)
h, w = readers[0].next().shape[:2]
frame_ids, (x1, x2, y1, y2), (oh, ow), fps = self._get_frameid_bbox(fps, frame_timestamps, h, w, crop_box, rng)
# preprocess video
videos = [reader.get_batch(frame_ids)[:, y1:y2, x1:x2, :] for reader in readers]
videos = [self._video_preprocess(video, oh, ow) for video in videos]
return *videos, frame_ids, (oh, ow), fps
# return videos if len(videos) > 1 else videos[0]
def prepare_source(src_video, src_mask, src_ref_images, num_frames, image_size, device):
for i, (sub_src_video, sub_src_mask) in enumerate(zip(src_video, src_mask)):
if sub_src_video is None and sub_src_mask is None:
src_video[i] = torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device)
src_mask[i] = torch.ones((1, num_frames, image_size[0], image_size[1]), device=device)
for i, ref_images in enumerate(src_ref_images):
if ref_images is not None:
for j, ref_img in enumerate(ref_images):
if ref_img is not None and ref_img.shape[-2:] != image_size:
canvas_height, canvas_width = image_size
ref_height, ref_width = ref_img.shape[-2:]
white_canvas = torch.ones((3, 1, canvas_height, canvas_width), device=device) # [-1, 1]
scale = min(canvas_height / ref_height, canvas_width / ref_width)
new_height = int(ref_height * scale)
new_width = int(ref_width * scale)
resized_image = F.interpolate(ref_img.squeeze(1).unsqueeze(0), size=(new_height, new_width), mode='bilinear', align_corners=False).squeeze(0).unsqueeze(1)
top = (canvas_height - new_height) // 2
left = (canvas_width - new_width) // 2
white_canvas[:, :, top:top + new_height, left:left + new_width] = resized_image
src_ref_images[i][j] = white_canvas
return src_video, src_mask, src_ref_images
|