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Image utils

SimplePreprocessor and AspectAwarePreprocessor are directly taked from the great series of Books `Deep Learning for Computer Vision by Adrian. Therefore, all credit for the code in the image_utils module goes to Adrian Rosebrock.

AspectAwarePreprocessor

Class to resize an image to a certain width and height taking into account the image aspect ratio

Parameters:

Name Type Description Default
width int

output width

 required
height int

output height

 required
inter

opencv interpolation method. See opencv InterpolationFlags.

 INTER_AREA
Source code in pytorch_widedeep/utils/image_utils.py 
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class AspectAwarePreprocessor:
    r"""Class to resize an image to a certain width and height taking into account
    the image aspect ratio

    Parameters
    ----------
    width: int
        output width
    height: int
        output height
    inter: interpolation method,  default = ``cv2.INTER_AREA``
        ``opencv`` interpolation method. See ``opencv``
        `InterpolationFlags`.
    """

    def __init__(self, width: int, height: int, inter=cv2.INTER_AREA):
        self.width = width
        self.height = height
        self.inter = inter

    def preprocess(self, image: np.ndarray) -> np.ndarray:
        r"""Returns the resized input image taking into account the image aspect ratio

        Parameters
        ----------
        image: np.ndarray
            Input image to be resized

        Examples
        --------
        >>> import cv2
        >>> from pytorch_widedeep.utils import AspectAwarePreprocessor
        >>> img = cv2.imread("tests/test_data_utils/images/galaxy1.png")
        >>> img.shape
        (694, 890, 3)
        >>> app = AspectAwarePreprocessor(width=224, height=224)
        >>> resized_img = app.preprocess(img)
        >>> resized_img.shape
        (224, 224, 3)

        Returns
        -------
        np.ndarray
            Resized image according to its original image aspect ratio
        """
        (h, w) = image.shape[:2]
        dW = 0
        dH = 0

        if w < h:
            image = resize(image, width=self.width, inter=self.inter)
            dH = int((image.shape[0] - self.height) / 2.0)
        else:
            image = resize(image, height=self.height, inter=self.inter)
            dW = int((image.shape[1] - self.width) / 2.0)

        (h, w) = image.shape[:2]
        image = image[dH : h - dH, dW : w - dW]

        resized_image = cv2.resize(
            image, (self.width, self.height), interpolation=self.inter
        )

        return resized_image

preprocess 

preprocess(image)

Returns the resized input image taking into account the image aspect ratio

Parameters:

Name Type Description Default
image ndarray

Input image to be resized

 required

Examples:

>>> import cv2
>>> from pytorch_widedeep.utils import AspectAwarePreprocessor
>>> img = cv2.imread("tests/test_data_utils/images/galaxy1.png")
>>> img.shape
(694, 890, 3)
>>> app = AspectAwarePreprocessor(width=224, height=224)
>>> resized_img = app.preprocess(img)
>>> resized_img.shape
(224, 224, 3)

Returns:

Type Description
ndarray

Resized image according to its original image aspect ratio
Source code in pytorch_widedeep/utils/image_utils.py 
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def preprocess(self, image: np.ndarray) -> np.ndarray:
    r"""Returns the resized input image taking into account the image aspect ratio

    Parameters
    ----------
    image: np.ndarray
        Input image to be resized

    Examples
    --------
    >>> import cv2
    >>> from pytorch_widedeep.utils import AspectAwarePreprocessor
    >>> img = cv2.imread("tests/test_data_utils/images/galaxy1.png")
    >>> img.shape
    (694, 890, 3)
    >>> app = AspectAwarePreprocessor(width=224, height=224)
    >>> resized_img = app.preprocess(img)
    >>> resized_img.shape
    (224, 224, 3)

    Returns
    -------
    np.ndarray
        Resized image according to its original image aspect ratio
    """
    (h, w) = image.shape[:2]
    dW = 0
    dH = 0

    if w < h:
        image = resize(image, width=self.width, inter=self.inter)
        dH = int((image.shape[0] - self.height) / 2.0)
    else:
        image = resize(image, height=self.height, inter=self.inter)
        dW = int((image.shape[1] - self.width) / 2.0)

    (h, w) = image.shape[:2]
    image = image[dH : h - dH, dW : w - dW]

    resized_image = cv2.resize(
        image, (self.width, self.height), interpolation=self.inter
    )

    return resized_image

SimplePreprocessor

Class to resize an image to a certain width and height

Parameters:

Name Type Description Default
width int

output width

 required
height int

output height

 required
inter

opencv interpolation method. See opencv InterpolationFlags.

 INTER_AREA
Source code in pytorch_widedeep/utils/image_utils.py 
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class SimplePreprocessor:
    r"""Class to resize an image to a certain width and height

    Parameters
    ----------
    width: int
        output width
    height: int
        output height
    inter: interpolation method, default = ``cv2.INTER_AREA``
        ``opencv`` interpolation method. See ``opencv``
        `InterpolationFlags`.
    """

    def __init__(self, width: int, height: int, inter=cv2.INTER_AREA):
        self.width = width
        self.height = height
        self.inter = inter

    def preprocess(self, image: np.ndarray) -> np.ndarray:
        r"""Returns the resized input image

        Parameters
        ----------
        image: np.ndarray
            Input image to be resized

        Returns
        -------
        np.ndarray
            Resized image

        """
        resized_image = cv2.resize(
            image, (self.width, self.height), interpolation=self.inter
        )

        return resized_image

preprocess 

preprocess(image)

Returns the resized input image

Parameters:

Name Type Description Default
image ndarray

Input image to be resized

 required

Returns:

Type Description
ndarray

Resized image
Source code in pytorch_widedeep/utils/image_utils.py 
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def preprocess(self, image: np.ndarray) -> np.ndarray:
    r"""Returns the resized input image

    Parameters
    ----------
    image: np.ndarray
        Input image to be resized

    Returns
    -------
    np.ndarray
        Resized image

    """
    resized_image = cv2.resize(
        image, (self.width, self.height), interpolation=self.inter
    )

    return resized_image