"""Read/Write images using OpenCV. Backend Library: `OpenCV `_ This plugin wraps OpenCV (also known as ``cv2``), a popular image processing library. Currently, it exposes OpenCVs image reading capability (no video or GIF support yet); however, this may be added in future releases. Methods ------- .. note:: Check the respective function for a list of supported kwargs and their documentation. .. autosummary:: :toctree: OpenCVPlugin.read OpenCVPlugin.iter OpenCVPlugin.write OpenCVPlugin.properties OpenCVPlugin.metadata Pixel Formats (Colorspaces) --------------------------- OpenCV is known to process images in BGR; however, most of the python ecosystem (in particular matplotlib and other pydata libraries) use the RGB. As such, images are converted to RGB, RGBA, or grayscale (where applicable) by default. """ import warnings from pathlib import Path from typing import Any, Dict, List, Optional, Union import cv2 import numpy as np from ..core import Request from ..core.request import URI_BYTES, InitializationError, IOMode from ..core.v3_plugin_api import ImageProperties, PluginV3 from ..typing import ArrayLike class OpenCVPlugin(PluginV3): def __init__(self, request: Request) -> None: super().__init__(request) self.file_handle = request.get_local_filename() if request._uri_type is URI_BYTES: self.filename = "" else: self.filename = request.raw_uri mode = request.mode.io_mode if mode == IOMode.read and not cv2.haveImageReader(self.file_handle): raise InitializationError(f"OpenCV can't read `{self.filename}`.") elif mode == IOMode.write and not cv2.haveImageWriter(self.file_handle): raise InitializationError(f"OpenCV can't write to `{self.filename}`.") def read( self, *, index: int = None, colorspace: Union[int, str] = None, flags: int = cv2.IMREAD_COLOR, ) -> np.ndarray: """Read an image from the ImageResource. Parameters ---------- index : int, Ellipsis If int, read the index-th image from the ImageResource. If ``...``, read all images from the ImageResource and stack them along a new, prepended, batch dimension. If None (default), use ``index=0`` if the image contains exactly one image and ``index=...`` otherwise. colorspace : str, int The colorspace to convert into after loading and before returning the image. If None (default) keep grayscale images as is, convert images with an alpha channel to ``RGBA`` and all other images to ``RGB``. If int, interpret ``colorspace`` as one of OpenCVs `conversion flags `_ and use it for conversion. If str, convert the image into the given colorspace. Possible string values are: ``"RGB"``, ``"BGR"``, ``"RGBA"``, ``"BGRA"``, ``"GRAY"``, ``"HSV"``, or ``"LAB"``. flags : int The OpenCV flag(s) to pass to the reader. Refer to the `OpenCV docs `_ for details. Returns ------- ndimage : np.ndarray The decoded image as a numpy array. """ if index is None: n_images = cv2.imcount(self.file_handle, flags) index = 0 if n_images == 1 else ... if index is ...: retval, img = cv2.imreadmulti(self.file_handle, flags=flags) is_batch = True else: retval, img = cv2.imreadmulti(self.file_handle, index, 1, flags=flags) is_batch = False if retval is False: raise ValueError(f"Could not read index `{index}` from `{self.filename}`.") if img[0].ndim == 2: in_colorspace = "GRAY" out_colorspace = colorspace or "GRAY" elif img[0].shape[-1] == 4: in_colorspace = "BGRA" out_colorspace = colorspace or "RGBA" else: in_colorspace = "BGR" out_colorspace = colorspace or "RGB" if isinstance(colorspace, int): cvt_space = colorspace elif in_colorspace == out_colorspace.upper(): cvt_space = None else: out_colorspace = out_colorspace.upper() cvt_space = getattr(cv2, f"COLOR_{in_colorspace}2{out_colorspace}") if cvt_space is not None: img = np.stack([cv2.cvtColor(x, cvt_space) for x in img]) else: img = np.stack(img) return img if is_batch else img[0] def iter( self, colorspace: Union[int, str] = None, flags: int = cv2.IMREAD_COLOR, ) -> np.ndarray: """Yield images from the ImageResource. Parameters ---------- colorspace : str, int The colorspace to convert into after loading and before returning the image. If None (default) keep grayscale images as is, convert images with an alpha channel to ``RGBA`` and all other images to ``RGB``. If int, interpret ``colorspace`` as one of OpenCVs `conversion flags `_ and use it for conversion. If str, convert the image into the given colorspace. Possible string values are: ``"RGB"``, ``"BGR"``, ``"RGBA"``, ``"BGRA"``, ``"GRAY"``, ``"HSV"``, or ``"LAB"``. flags : int The OpenCV flag(s) to pass to the reader. Refer to the `OpenCV docs `_ for details. Yields ------ ndimage : np.ndarray The decoded image as a numpy array. """ for idx in range(cv2.imcount(self.file_handle)): yield self.read(index=idx, flags=flags, colorspace=colorspace) def write( self, ndimage: Union[ArrayLike, List[ArrayLike]], is_batch: bool = False, params: List[int] = None, ) -> Optional[bytes]: """Save an ndimage in the ImageResource. Parameters ---------- ndimage : ArrayLike, List[ArrayLike] The image data that will be written to the file. It is either a single image, a batch of images, or a list of images. is_batch : bool If True, the provided ndimage is a batch of images. If False (default), the provided ndimage is a single image. If the provided ndimage is a list of images, this parameter has no effect. params : List[int] A list of parameters that will be passed to OpenCVs imwrite or imwritemulti functions. Possible values are documented in the `OpenCV documentation `_. Returns ------- encoded_image : bytes, None If the ImageResource is ``""`` the call to write returns the encoded image as a bytes string. Otherwise it returns None. """ if isinstance(ndimage, list): ndimage = np.stack(ndimage, axis=0) elif not is_batch: ndimage = ndimage[None, ...] if ndimage[0].ndim == 2: n_channels = 1 else: n_channels = ndimage[0].shape[-1] if n_channels == 1: ndimage_cv2 = [x for x in ndimage] elif n_channels == 4: ndimage_cv2 = [cv2.cvtColor(x, cv2.COLOR_RGBA2BGRA) for x in ndimage] else: ndimage_cv2 = [cv2.cvtColor(x, cv2.COLOR_RGB2BGR) for x in ndimage] retval = cv2.imwritemulti(self.file_handle, ndimage_cv2, params) if retval is False: # not sure what scenario would trigger this, but # it can occur theoretically. raise IOError("OpenCV failed to write.") # pragma: no cover if self.request._uri_type == URI_BYTES: return Path(self.file_handle).read_bytes() def properties( self, index: int = None, colorspace: Union[int, str] = None, flags: int = cv2.IMREAD_COLOR, ) -> ImageProperties: """Standardized image metadata. Parameters ---------- index : int, Ellipsis If int, get the properties of the index-th image in the ImageResource. If ``...``, get the properties of the image stack that contains all images. If None (default), use ``index=0`` if the image contains exactly one image and ``index=...`` otherwise. colorspace : str, int The colorspace to convert into after loading and before returning the image. If None (default) keep grayscale images as is, convert images with an alpha channel to ``RGBA`` and all other images to ``RGB``. If int, interpret ``colorspace`` as one of OpenCVs `conversion flags `_ and use it for conversion. If str, convert the image into the given colorspace. Possible string values are: ``"RGB"``, ``"BGR"``, ``"RGBA"``, ``"BGRA"``, ``"GRAY"``, ``"HSV"``, or ``"LAB"``. flags : int The OpenCV flag(s) to pass to the reader. Refer to the `OpenCV docs `_ for details. Returns ------- props : ImageProperties A dataclass filled with standardized image metadata. Notes ----- Reading properties with OpenCV involves decoding pixel data, because OpenCV doesn't provide a direct way to access metadata. """ if index is None: n_images = cv2.imcount(self.file_handle, flags) is_batch = n_images > 1 elif index is Ellipsis: n_images = cv2.imcount(self.file_handle, flags) is_batch = True else: is_batch = False # unfortunately, OpenCV doesn't allow reading shape without reading pixel data if is_batch: img = self.read(index=0, flags=flags, colorspace=colorspace) return ImageProperties( shape=(n_images, *img.shape), dtype=img.dtype, n_images=n_images, is_batch=True, ) img = self.read(index=index, flags=flags, colorspace=colorspace) return ImageProperties(shape=img.shape, dtype=img.dtype, is_batch=False) def metadata( self, index: int = None, exclude_applied: bool = True ) -> Dict[str, Any]: """Format-specific metadata. .. warning:: OpenCV does not support reading metadata. When called, this function will raise a ``NotImplementedError``. Parameters ---------- index : int This parameter has no effect. exclude_applied : bool This parameter has no effect. """ warnings.warn("OpenCV does not support reading metadata.", UserWarning) return dict()