Cannot open tif images when using COCO parser

[AlexandreBrown]

🐛 Bug

Describe the bug COCO parser uses PIL which cannot open tif images with 3 float32 channels. Stacktrace from a unit test that caught the bug for me :

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/opt/conda/lib/python3.9/site-packages/pytorch_lightning/core/datamodule.py:474: in wrapped_fn
    fn(*args, **kwargs)
../mdai_core/datasets/image/coco_data_module.py:158: in setup
    self.test_dataset = self.create_dataset(
../mdai_core/datasets/image/coco_data_module.py:186: in create_dataset
    records, *_ = parser.parse(data_splitter=data_splitter, autofix=True)
/opt/conda/lib/python3.9/site-packages/icevision/parsers/parser.py:119: in parse
    records = self.parse_dicted(show_pbar=show_pbar)
/opt/conda/lib/python3.9/site-packages/icevision/parsers/parser.py:77: in parse_dicted
    self.parse_fields(sample, record=record, is_new=record.is_new)
/opt/conda/lib/python3.9/site-packages/icevision/parsers/coco_parser.py:101: in parse_fields
    super().parse_fields(o, record, is_new=is_new)
/opt/conda/lib/python3.9/site-packages/icevision/parsers/coco_parser.py:82: in parse_fields
    record.set_img_size(self.img_size(o))
/opt/conda/lib/python3.9/site-packages/icevision/parsers/coco_parser.py:68: in img_size
    return get_img_size(self.filepath(o))
/opt/conda/lib/python3.9/site-packages/icevision/utils/imageio.py:94: in get_img_size
    with PIL.Image.open(str(filepath)) as image:
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

fp = <_io.BufferedReader name='temp-9ba11f6f-a8e0-4102-9c07-d6eaafffef76/img-fc426b36-8bc4-4cd3-a15d-5b2c4504f661.tiff'>, mode = 'r'
formats = ['PNG', 'BMP', 'DIB', 'GIF', 'TIFF', 'JPEG', ...]

    def open(fp, mode="r", formats=None):
        """
        Opens and identifies the given image file.
    
        This is a lazy operation; this function identifies the file, but
        the file remains open and the actual image data is not read from
        the file until you try to process the data (or call the
        :py:meth:`~PIL.Image.Image.load` method).  See
        :py:func:`~PIL.Image.new`. See :ref:`file-handling`.
    
        :param fp: A filename (string), pathlib.Path object or a file object.
           The file object must implement ``file.read``,
           ``file.seek``, and ``file.tell`` methods,
           and be opened in binary mode.
        :param mode: The mode.  If given, this argument must be "r".
        :param formats: A list or tuple of formats to attempt to load the file in.
           This can be used to restrict the set of formats checked.
           Pass ``None`` to try all supported formats. You can print the set of
           available formats by running ``python3 -m PIL`` or using
           the :py:func:`PIL.features.pilinfo` function.
        :returns: An :py:class:`~PIL.Image.Image` object.
        :exception FileNotFoundError: If the file cannot be found.
        :exception PIL.UnidentifiedImageError: If the image cannot be opened and
           identified.
        :exception ValueError: If the ``mode`` is not "r", or if a ``StringIO``
           instance is used for ``fp``.
        :exception TypeError: If ``formats`` is not ``None``, a list or a tuple.
        """
    
        if mode != "r":
            raise ValueError(f"bad mode {repr(mode)}")
        elif isinstance(fp, io.StringIO):
            raise ValueError(
                "StringIO cannot be used to open an image. "
                "Binary data must be used instead."
            )
    
        if formats is None:
            formats = ID
        elif not isinstance(formats, (list, tuple)):
            raise TypeError("formats must be a list or tuple")
    
        exclusive_fp = False
        filename = ""
        if isinstance(fp, Path):
            filename = str(fp.resolve())
        elif isPath(fp):
            filename = fp
    
        if filename:
            fp = builtins.open(filename, "rb")
            exclusive_fp = True
    
        try:
            fp.seek(0)
        except (AttributeError, io.UnsupportedOperation):
            fp = io.BytesIO(fp.read())
            exclusive_fp = True
    
        prefix = fp.read(16)
    
        preinit()
    
        accept_warnings = []
    
        def _open_core(fp, filename, prefix, formats):
            for i in formats:
                i = i.upper()
                if i not in OPEN:
                    init()
                try:
                    factory, accept = OPEN[i]
                    result = not accept or accept(prefix)
                    if type(result) in [str, bytes]:
                        accept_warnings.append(result)
                    elif result:
                        fp.seek(0)
                        im = factory(fp, filename)
                        _decompression_bomb_check(im.size)
                        return im
                except (SyntaxError, IndexError, TypeError, struct.error):
                    # Leave disabled by default, spams the logs with image
                    # opening failures that are entirely expected.
                    # logger.debug("", exc_info=True)
                    continue
                except BaseException:
                    if exclusive_fp:
                        fp.close()
                    raise
            return None
    
        im = _open_core(fp, filename, prefix, formats)
    
        if im is None:
            if init():
                im = _open_core(fp, filename, prefix, formats)
    
        if im:
            im._exclusive_fp = exclusive_fp
            return im
    
        if exclusive_fp:
            fp.close()
        for message in accept_warnings:
            warnings.warn(message)
>       raise UnidentifiedImageError(
            "cannot identify image file %r" % (filename if filename else fp)
        )
E       PIL.UnidentifiedImageError: cannot identify image file 'temp-9ba11f6f-a8e0-4102-9c07-d6eaafffef76/img-fc426b36-8bc4-4cd3-a15d-5b2c4504f661.tiff'

To Reproduce Steps to reproduce the behavior:

1. Create a tif image with 3 bands of dtype float32 (eg: using rasterio)
2. Create a COCO dataset for this image
3. Use coco parser to parse the data

Expected behavior I expect to be able to parse COCO dataset with tif image format

Desktop (please complete the following information):

• OS: ubuntu 18.04