10 examples of 'imresize python' in Python

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def imresize(im, h, w):
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    if im.shape[:2] == (h, w):
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        return im
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    # This has problems re-reading a numpy array if it's not 8-bit anymore.
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    assert im.max() > 1, "PIL has problems resizing images after they've been changed to e.g. [0-1] range. Either install OpenCV or resize right after reading the image."
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    img = _Image.fromarray(im.astype(_np.uint8))
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    return _np.array(img.resize((w,h), _Image.BILINEAR), dtype=im.dtype)

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def imresize(im, h, w):
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    raise ImportError(
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        "Neither OpenCV nor the Python Imaging Library (PIL) is "
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        "installed. Please install either for resizing images."
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    )

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def resize(im, res):
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    return np.array([cv2.resize(im[i],res) for i in range(im.shape[0])])

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def resize_keepdims(im, size):
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    # Opencv's resize remove the extra dimension for grayscale images. We add it back.
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    ret = cv2.resize(im, size)
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    if im.ndim == 3 and ret.ndim == 2:
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        ret = ret[:, :, np.newaxis]
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    return ret

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def imresize(im, size, interp='bilinear'):
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    if interp == 'nearest':
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        resample = Image.NEAREST
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    elif interp == 'bilinear':
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        resample = Image.BILINEAR
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    elif interp == 'bicubic':
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        resample = Image.BICUBIC
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    else:
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        raise Exception('resample method undefined!')
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    return im.resize(size, resample)

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def resize(im, short, max_size):
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    """Return resized image (NDArray) and scale (float)"""
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    im_shape = im.shape
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    im_size_min = min(im_shape[0:2])
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    im_size_max = max(im_shape[0:2])
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    im_scale = float(short) / float(im_size_min)
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    # prevent bigger axis from being more than max_size:
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    if round(im_scale * im_size_max) > max_size:
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        im_scale = float(max_size) / float(im_size_max)
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        short = int(im_size_min * im_scale)
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    im = mx.image.resize_short(im, short)
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    return im, im_scale

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@curry
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def resize(pil_img, size, interpolation=Image.BILINEAR):
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    return pil_img.resize(size, interpolation)

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def imresize(img, size, return_scale=False, interpolation='bilinear'):
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    """Resize image to a given size.
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    Args:
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        img (ndarray): The input image.
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        size (tuple): Target (w, h).
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        return_scale (bool): Whether to return `w_scale` and `h_scale`.
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        interpolation (str): Interpolation method, accepted values are
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            "nearest", "bilinear", "bicubic", "area", "lanczos".
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    Returns:
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        tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or
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            `resized_img`.
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    """
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    h, w = img.shape[:2]
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    resized_img = cv2.resize(
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        img, size, interpolation=interp_codes[interpolation])
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    if not return_scale:
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        return resized_img
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    else:
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        w_scale = size[0] / w
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        h_scale = size[1] / h
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        return resized_img, w_scale, h_scale

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def _resize(frame):
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    """Resizing function utilizing OpenCV.
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    Args:
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      frame: A frame from a cv2.video_reader object to process
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    Returns:
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      resized_frame: the frame, resized
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    """
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    resized_frame = cv2.resize(frame,
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                               None,
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                               fx=RESIZE_FACTOR,
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                               fy=RESIZE_FACTOR,
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                               interpolation=cv2.INTER_AREA)
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    return resized_frame

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def resize_img(img, size, interpolation=PIL.Image.BILINEAR):
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    """Resize image to match the given shape.
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    This method uses :mod:`cv2` or :mod:`PIL` for the backend.
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    If :mod:`cv2` is installed, this legacy uses the implementation in
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    :mod:`cv2`. This implementation is faster than the implementation in
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    :mod:`PIL`. Under Anaconda environment,
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    :mod:`cv2` can be installed by the following command.
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    .. code::
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        $ conda install -c menpo opencv3=3.2.0
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    Args:
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        img (~numpy.ndarray): An array to be transformed.
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            This is in CHW format and the type should be :obj:`numpy.float32`.
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        size (tuple): This is a tuple of length 2. Its elements are
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            ordered as (height, width).
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        interpolation (int): Determines sampling strategy. This is one of
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            :obj:`PIL.Image.NEAREST`, :obj:`PIL.Image.BILINEAR`,
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            :obj:`PIL.Image.BICUBIC`, :obj:`PIL.Image.LANCZOS`.
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            Bilinear interpolation is the default strategy.
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    Returns:
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        ~numpy.ndarray: A resize array in CHW format.
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    """
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    img = _resize(img, size, interpolation)
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    return img