third_party/ml-depth-pro/src/depth_pro/utils.py

# Copyright (C) 2024 Apple Inc. All Rights Reserved.

import logging
from pathlib import Path
from typing import Any, Dict, List, Tuple, Union

import numpy as np
import pillow_heif
from PIL import ExifTags, Image, TiffTags
from pillow_heif import register_heif_opener

register_heif_opener()
LOGGER = logging.getLogger(__name__)


def extract_exif(img_pil: Image) -> Dict[str, Any]:
    """Return exif information as a dictionary.

    Args:
    ----
        img_pil: A Pillow image.

    Returns:
    -------
        A dictionary with extracted EXIF information.

    """
    # Get full exif description from get_ifd(0x8769):
    # cf https://pillow.readthedocs.io/en/stable/releasenotes/8.2.0.html#image-getexif-exif-and-gps-ifd
    img_exif = img_pil.getexif().get_ifd(0x8769)
    exif_dict = {ExifTags.TAGS[k]: v for k, v in img_exif.items() if k in ExifTags.TAGS}

    tiff_tags = img_pil.getexif()
    tiff_dict = {
        TiffTags.TAGS_V2[k].name: v
        for k, v in tiff_tags.items()
        if k in TiffTags.TAGS_V2
    }
    return {**exif_dict, **tiff_dict}


def fpx_from_f35(width: float, height: float, f_mm: float = 50) -> float:
    """Convert a focal length given in mm (35mm film equivalent) to pixels."""
    return f_mm * np.sqrt(width**2.0 + height**2.0) / np.sqrt(36**2 + 24**2)


def load_rgb(
    path: Union[Path, str], auto_rotate: bool = True, remove_alpha: bool = True
) -> Tuple[np.ndarray, List[bytes], float]:
    """Load an RGB image.

    Args:
    ----
        path: The url to the image to load.
        auto_rotate: Rotate the image based on the EXIF data, default is True.
        remove_alpha: Remove the alpha channel, default is True.

    Returns:
    -------
        img: The image loaded as a numpy array.
        icc_profile: The color profile of the image.
        f_px: The optional focal length in pixels, extracting from the exif data.

    """
    LOGGER.debug(f"Loading image {path} ...")

    path = Path(path)
    if path.suffix.lower() in [".heic"]:
        heif_file = pillow_heif.open_heif(path, convert_hdr_to_8bit=True)
        img_pil = heif_file.to_pillow()
    else:
        img_pil = Image.open(path)

    img_exif = extract_exif(img_pil)
    icc_profile = img_pil.info.get("icc_profile", None)

    # Rotate the image.
    if auto_rotate:
        exif_orientation = img_exif.get("Orientation", 1)
        if exif_orientation == 3:
            img_pil = img_pil.transpose(Image.ROTATE_180)
        elif exif_orientation == 6:
            img_pil = img_pil.transpose(Image.ROTATE_270)
        elif exif_orientation == 8:
            img_pil = img_pil.transpose(Image.ROTATE_90)
        elif exif_orientation != 1:
            LOGGER.warning(f"Ignoring image orientation {exif_orientation}.")

    img = np.array(img_pil)
    # Convert to RGB if single channel.
    if img.ndim < 3 or img.shape[2] == 1:
        img = np.dstack((img, img, img))

    if remove_alpha:
        img = img[:, :, :3]

    LOGGER.debug(f"\tHxW: {img.shape[0]}x{img.shape[1]}")

    # Extract the focal length from exif data.
    f_35mm = img_exif.get(
        "FocalLengthIn35mmFilm",
        img_exif.get(
            "FocalLenIn35mmFilm", img_exif.get("FocalLengthIn35mmFormat", None)
        ),
    )
    if f_35mm is not None and f_35mm > 0:
        LOGGER.debug(f"\tfocal length @ 35mm film: {f_35mm}mm")
        f_px = fpx_from_f35(img.shape[1], img.shape[0], f_35mm)
    else:
        f_px = None

    return img, icc_profile, f_px