pyiqa.utils.img_util
====================

.. py:module:: pyiqa.utils.img_util




Module Contents
---------------

.. py:function:: is_image_file(filename)

.. py:function:: scandir_images(dir, max_dataset_size=float('inf'), followlinks=True)

   Get all image files from a directory and return a sorted list of fullpath.


.. py:function:: imread2pil(img_source, rgb=False)

   Read image to tensor.

   :param img_source: image filepath string, image contents as a bytearray or a PIL Image instance
   :type img_source: str, bytes, or PIL.Image
   :param rgb: convert input to RGB if true


.. py:function:: imread2tensor(img_source, rgb=False)

   Read image to tensor.

   :param img_source: image filepath string, image contents as a bytearray or a PIL Image instance
   :type img_source: str, bytes, or PIL.Image
   :param rgb: convert input to RGB if true


.. py:function:: img2tensor(imgs, bgr2rgb=True, float32=True)

   Numpy array to tensor.

   :param imgs: Input images.
   :type imgs: list[ndarray] | ndarray
   :param bgr2rgb: Whether to change bgr to rgb.
   :type bgr2rgb: bool
   :param float32: Whether to change to float32.
   :type float32: bool

   :returns:

             Tensor images. If returned results only have
                 one element, just return tensor.
   :rtype: list[tensor] | tensor


.. py:function:: tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1))

   Convert torch Tensors into image numpy arrays.

   After clamping to [min, max], values will be normalized to [0, 1].

   :param tensor: Accept shapes:
                  1) 4D mini-batch Tensor of shape (B x 3/1 x H x W);
                  2) 3D Tensor of shape (3/1 x H x W);
                  3) 2D Tensor of shape (H x W).
                  Tensor channel should be in RGB order.
   :type tensor: Tensor or list[Tensor]
   :param rgb2bgr: Whether to change rgb to bgr.
   :type rgb2bgr: bool
   :param out_type: output types. If ``np.uint8``, transform outputs
                    to uint8 type with range [0, 255]; otherwise, float type with
                    range [0, 1]. Default: ``np.uint8``.
   :type out_type: numpy type
   :param min_max: min and max values for clamp.
   :type min_max: tuple[int]

   :returns: 3D ndarray of shape (H x W x C) OR 2D ndarray of
             shape (H x W). The channel order is BGR.
   :rtype: (Tensor or list)


.. py:function:: tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1))

   This implementation is slightly faster than tensor2img.
   It now only supports torch tensor with shape (1, c, h, w).

   :param tensor: Now only support torch tensor with (1, c, h, w).
   :type tensor: Tensor
   :param rgb2bgr: Whether to change rgb to bgr. Default: True.
   :type rgb2bgr: bool
   :param min_max: min and max values for clamp.
   :type min_max: tuple[int]


.. py:function:: imfrombytes(content, flag='color', float32=False)

   Read an image from bytes.

   :param content: Image bytes got from files or other streams.
   :type content: bytes
   :param flag: Flags specifying the color type of a loaded image,
                candidates are `color`, `grayscale` and `unchanged`.
   :type flag: str
   :param float32: Whether to change to float32., If True, will also norm
                   to [0, 1]. Default: False.
   :type float32: bool

   :returns: Loaded image array.
   :rtype: ndarray


.. py:function:: imwrite(img, file_path, params=None, auto_mkdir=True)

   Write image to file.

   :param img: Image array to be written.
   :type img: ndarray
   :param file_path: Image file path.
   :type file_path: str
   :param params: Same as opencv's :func:`imwrite` interface.
   :type params: None or list
   :param auto_mkdir: If the parent folder of `file_path` does not exist,
                      whether to create it automatically.
   :type auto_mkdir: bool

   :returns: Successful or not.
   :rtype: bool


.. py:function:: crop_border(imgs, crop_border)

   Crop borders of images.

   :param imgs: Images with shape (h, w, c).
   :type imgs: list[ndarray] | ndarray
   :param crop_border: Crop border for each end of height and weight.
   :type crop_border: int

   :returns: Cropped images.
   :rtype: list[ndarray]