media.py

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,
        }