Spaces:
Sleeping
Sleeping
File size: 8,546 Bytes
3bba4f1 |
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 |
import os
import shutil
import uuid
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Literal
import mediapy as mp
import numpy as np
from PIL import Image as PILImage
try: # absolute imports when installed
from trackio.file_storage import FileStorage
from trackio.utils import MEDIA_DIR
except ImportError: # relative imports for local execution on Spaces
from file_storage import FileStorage
from utils import MEDIA_DIR
class TrackioMedia(ABC):
"""
Abstract base class for Trackio media objects
Provides shared functionality for file handling and serialization.
"""
TYPE: str
def __init_subclass__(cls, **kwargs):
"""Ensure subclasses define the TYPE attribute."""
super().__init_subclass__(**kwargs)
if not hasattr(cls, "TYPE") or cls.TYPE is None:
raise TypeError(f"Class {cls.__name__} must define TYPE attribute")
def __init__(self, value, caption: str | None = None):
self.caption = caption
self._value = value
self._file_path: Path | None = None
# Validate file existence for string/Path inputs
if isinstance(self._value, str | Path):
if not os.path.isfile(self._value):
raise ValueError(f"File not found: {self._value}")
def _file_extension(self) -> str:
if self._file_path:
return self._file_path.suffix[1:].lower()
if isinstance(self._value, str | Path):
path = Path(self._value)
return path.suffix[1:].lower()
if hasattr(self, "_format") and self._format:
return self._format
return "unknown"
def _get_relative_file_path(self) -> Path | None:
return self._file_path
def _get_absolute_file_path(self) -> Path | None:
if self._file_path:
return MEDIA_DIR / self._file_path
return None
def _save(self, project: str, run: str, step: int = 0):
if self._file_path:
return
media_dir = FileStorage.init_project_media_path(project, run, step)
filename = f"{uuid.uuid4()}.{self._file_extension()}"
file_path = media_dir / filename
# Delegate to subclass-specific save logic
self._save_media(file_path)
self._file_path = file_path.relative_to(MEDIA_DIR)
@abstractmethod
def _save_media(self, file_path: Path):
"""
Performs the actual media saving logic.
"""
pass
def _to_dict(self) -> dict:
if not self._file_path:
raise ValueError("Media must be saved to file before serialization")
return {
"_type": self.TYPE,
"file_path": str(self._get_relative_file_path()),
"caption": self.caption,
}
TrackioImageSourceType = str | Path | np.ndarray | PILImage.Image
class TrackioImage(TrackioMedia):
"""
Initializes an Image object.
Args:
value (`str`, `Path`, `numpy.ndarray`, or `PIL.Image`, *optional*, defaults to `None`):
A path to an image, a PIL Image, or a numpy array of shape (height, width, channels).
caption (`str`, *optional*, defaults to `None`):
A string caption for the image.
"""
TYPE = "trackio.image"
def __init__(self, value: TrackioImageSourceType, caption: str | None = None):
super().__init__(value, caption)
self._format: str | None = None
if (
isinstance(self._value, np.ndarray | PILImage.Image)
and self._format is None
):
self._format = "png"
def _as_pil(self) -> PILImage.Image | None:
try:
if isinstance(self._value, np.ndarray):
arr = np.asarray(self._value).astype("uint8")
return PILImage.fromarray(arr).convert("RGBA")
if isinstance(self._value, PILImage.Image):
return self._value.convert("RGBA")
except Exception as e:
raise ValueError(f"Failed to process image data: {self._value}") from e
return None
def _save_media(self, file_path: Path):
if pil := self._as_pil():
pil.save(file_path, format=self._format)
elif isinstance(self._value, str | Path):
if os.path.isfile(self._value):
shutil.copy(self._value, file_path)
else:
raise ValueError(f"File not found: {self._value}")
TrackioVideoSourceType = str | Path | np.ndarray
TrackioVideoFormatType = Literal["gif", "mp4", "webm"]
class TrackioVideo(TrackioMedia):
"""
Initializes a Video object.
Args:
value (`str`, `Path`, or `numpy.ndarray`, *optional*, defaults to `None`):
A path to a video file, or a numpy array of shape (frames, channels, height, width) or (batch, frames, channels, height, width).
caption (`str`, *optional*, defaults to `None`):
A string caption for the video.
fps (`int`, *optional*, defaults to `None`):
Frames per second for the video. Only relevant when using a numpy array.
format (`Literal["gif", "mp4", "webm"]`, *optional*, defaults to `None`):
Video format ("gif", "mp4", or "webm"). Only relevant when using a numpy array.
"""
TYPE = "trackio.video"
def __init__(
self,
value: TrackioVideoSourceType,
caption: str | None = None,
fps: int | None = None,
format: TrackioVideoFormatType | None = None,
):
super().__init__(value, caption)
self._fps = fps
self._format = format
if isinstance(self._value, np.ndarray) and self._format is None:
self._format = "gif"
@property
def _codec(self) -> str | None:
match self._format:
case "gif":
return "gif"
case "mp4":
return "h264"
case "webm":
return "vp9"
case _:
return None
def _save_media(self, file_path: Path):
if isinstance(self._value, np.ndarray):
video = TrackioVideo._process_ndarray(self._value)
mp.write_video(file_path, video, fps=self._fps, codec=self._codec)
elif isinstance(self._value, str | Path):
if os.path.isfile(self._value):
shutil.copy(self._value, file_path)
else:
raise ValueError(f"File not found: {self._value}")
@staticmethod
def _process_ndarray(value: np.ndarray) -> np.ndarray:
# Verify value is either 4D (single video) or 5D array (batched videos).
# Expected format: (frames, channels, height, width) or (batch, frames, channels, height, width)
if value.ndim < 4:
raise ValueError(
"Video requires at least 4 dimensions (frames, channels, height, width)"
)
if value.ndim > 5:
raise ValueError(
"Videos can have at most 5 dimensions (batch, frames, channels, height, width)"
)
if value.ndim == 4:
# Reshape to 5D with single batch: (1, frames, channels, height, width)
value = value[np.newaxis, ...]
value = TrackioVideo._tile_batched_videos(value)
return value
@staticmethod
def _tile_batched_videos(video: np.ndarray) -> np.ndarray:
"""
Tiles a batch of videos into a grid of videos.
Input format: (batch, frames, channels, height, width) - original FCHW format
Output format: (frames, total_height, total_width, channels)
"""
batch_size, frames, channels, height, width = video.shape
next_pow2 = 1 << (batch_size - 1).bit_length()
if batch_size != next_pow2:
pad_len = next_pow2 - batch_size
pad_shape = (pad_len, frames, channels, height, width)
padding = np.zeros(pad_shape, dtype=video.dtype)
video = np.concatenate((video, padding), axis=0)
batch_size = next_pow2
n_rows = 1 << ((batch_size.bit_length() - 1) // 2)
n_cols = batch_size // n_rows
# Reshape to grid layout: (n_rows, n_cols, frames, channels, height, width)
video = video.reshape(n_rows, n_cols, frames, channels, height, width)
# Rearrange dimensions to (frames, total_height, total_width, channels)
video = video.transpose(2, 0, 4, 1, 5, 3)
video = video.reshape(frames, n_rows * height, n_cols * width, channels)
return video
|