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import os
import shutil
from threading import local
import uuid
from pathlib import Path
from typing import Literal
import numpy as np
from PIL import Image as PILImage
import mediapy as mp
try: # absolute imports when installed
from trackio.file_storage import FileStorage
from trackio.utils import TRACKIO_DIR
except ImportError: # relative imports for local execution on Spaces
from file_storage import FileStorage
from utils import TRACKIO_DIR
class TrackioImage:
"""
Creates an image that can be logged with trackio.
Demo: fake-training-images
"""
TYPE = "trackio.image"
def __init__(
self, value: str | np.ndarray | PILImage.Image, caption: str | None = None
):
"""
Parameters:
value: A string path to an image, a numpy array, or a PIL Image.
caption: A string caption for the image.
"""
self.caption = caption
self._pil = TrackioImage._as_pil(value)
self._file_path: Path | None = None
self._file_format: str | None = None
@staticmethod
def _as_pil(value: str | np.ndarray | PILImage.Image) -> PILImage.Image:
try:
if isinstance(value, str):
return PILImage.open(value).convert("RGBA")
elif isinstance(value, np.ndarray):
arr = np.asarray(value).astype("uint8")
return PILImage.fromarray(arr).convert("RGBA")
elif isinstance(value, PILImage.Image):
return value.convert("RGBA")
except Exception as e:
raise ValueError(f"Failed to process image data: {value}") from e
def _save(self, project: str, run: str, step: int = 0, format: str = "PNG") -> str:
if not self._file_path:
# Save image as {TRACKIO_DIR}/media/{project}/{run}/{step}/{uuid}.{ext}
filename = f"{uuid.uuid4()}.{format.lower()}"
path = FileStorage.save_image(
self._pil, project, run, step, filename, format=format
)
self._file_path = path.relative_to(TRACKIO_DIR)
self._file_format = format
return str(self._file_path)
def _get_relative_file_path(self) -> Path | None:
return self._file_path
def _get_absolute_file_path(self) -> Path | None:
return TRACKIO_DIR / self._file_path
def _to_dict(self) -> dict:
if not self._file_path:
raise ValueError("Image must be saved to file before serialization")
return {
"_type": self.TYPE,
"file_path": str(self._get_relative_file_path()),
"file_format": self._file_format,
"caption": self.caption,
}
@classmethod
def _from_dict(cls, obj: dict) -> "TrackioImage":
if not isinstance(obj, dict):
raise TypeError(f"Expected dict, got {type(obj).__name__}")
if obj.get("_type") != cls.TYPE:
raise ValueError(f"Wrong _type: {obj.get('_type')!r}")
file_path = obj.get("file_path")
if not isinstance(file_path, str):
raise TypeError(
f"'file_path' must be string, got {type(file_path).__name__}"
)
absolute_path = TRACKIO_DIR / file_path
try:
if not absolute_path.is_file():
raise ValueError(f"Image file not found: {file_path}")
pil = PILImage.open(absolute_path).convert("RGBA")
instance = cls(pil, caption=obj.get("caption"))
instance._file_path = Path(file_path)
instance._file_format = obj.get("file_format")
return instance
except Exception as e:
raise ValueError(f"Failed to load image from file: {absolute_path}") from e
TrackioVideoSourceType = str | Path | np.ndarray
TrackioVideoFormatType = Literal["gif", "mp4", "webm", "ogg"]
class TrackioVideo:
"""
Creates a video that can be logged with trackio.
Demo: video-demo
"""
TYPE = "trackio.video"
def __init__(self,
value: TrackioVideoSourceType,
caption: str | None = None,
fps: int | None = None,
format: TrackioVideoFormatType | None = None,
):
self._value = value
self._caption = caption
self._fps = fps
self._format = format
self._file_path: Path | None = None
@property
def _codec(self) -> str | None:
match self._format:
case "gif":
return "gif"
case "mp4":
return "h264"
case "webm" | "ogg":
return "vp9"
case _:
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()}"
media_path = media_dir / filename
if isinstance(self._value, np.ndarray):
video = TrackioVideo._process_ndarray(self._value)
mp.write_video(media_path, video, fps=self._fps, codec=self._codec)
elif isinstance(self._value, str | Path):
if os.path.isfile(self._value):
shutil.copy(self._value, media_path)
else:
raise ValueError(f"File not found: {self._value}")
self._file_path = media_path.relative_to(TRACKIO_DIR)
def _get_absolute_file_path(self) -> Path | None:
return TRACKIO_DIR / self._file_path
def _file_extension(self) -> str:
if self._format is None:
if self._file_path is None:
raise ValueError("File format not specified and no file path provided")
return self._file_path.suffix[1:].lower()
return self._format
# def _gen_upload_file_path(self, project: str, run: str, step: int) -> Path:
# if self._upload_file_path:
# return self._upload_file_path
# filename = f"{uuid.uuid4()}.{self._file_extension()}"
# dir = FileStorage.init_project_media_path(project, run, step)
# return Path.home() / dir.relative_to(TRACKIO_DIR) / filename
@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) for 4D or (batch, frames, channels, height, width) for 5D
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)
# Convert final result from (F, H, W, C) to (F, C, H, W) for mediapy
value = np.transpose(value, (0, 3, 1, 2))
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
def _to_dict(self, upload: bool = False) -> dict:
return {
"_type": self.TYPE,
"file_path": str(self._file_path),
"caption": self._caption,
"upload": upload,
} |