Module deepposekit.io.BaseGenerator

Classes

class BaseGenerator (**kwargs)

BaseGenerator class for abstracting data loading and saving. Attributes that should be defined before use: init len compute_image_shape compute_keypoints_shape get_images get_keypoints set_keypoints (only needed for saving data)

See docstrings for further details.

Initializes the BaseGenerator class. If graph and swap_index are not defined, they are set to a vector of -1 corresponding to keypoints shape

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class BaseGenerator(Sequence):
    """
    BaseGenerator class for abstracting data loading and saving.
    Attributes that should be defined before use:
    __init__
    __len__
    compute_image_shape
    compute_keypoints_shape
    get_images
    get_keypoints
    set_keypoints (only needed for saving data)
    
    See docstrings for further details.
    """

    def __init__(self, **kwargs):
        """
        Initializes the BaseGenerator class.
        If graph and swap_index are not defined,
        they are set to a vector of -1 corresponding
        to keypoints shape
        """
        if not hasattr(self, "graph"):
            self.graph = -np.ones(self.keypoints_shape[0])
        if not hasattr(self, "swap_index"):
            self.swap_index = -np.ones(self.keypoints_shape[0])
        return

    def __len__(self):
        """
        Returns the number of samples in the generator as an integer (int64)
        """
        raise NotImplementedError()

    def compute_image_shape(self):
        """
        Returns a tuple of integers describing
        the image shape in the form:
        (height, width, n_channels)
        """
        raise NotImplementedError()

    def compute_keypoints_shape(self):
        """
        Returns a tuple of integers describing the
        keypoints shape in the form:
        (n_keypoints, 2), where 2 is the x,y coordinates
        """
        raise NotImplementedError()

    def get_images(self, indexes):
        """
        Takes a list or array of indexes corresponding
        to image-keypoint pairs in the dataset.
        Returns a numpy array of images with the shape:
        (1, height, width, n_channels)
        """
        raise NotImplementedError()

    def get_keypoints(self, indexes):
        """
        Takes a list or array of indexes corresponding to
        image-keypoint pairs in the dataset.
        Returns a numpy array of keypoints with the shape:
        (1, n_keypoints, 2), where 2 is the x,y coordinates
        """
        raise NotImplementedError()

    def set_keypoints(self, indexes, keypoints):
        """
        Takes a list or array of indexes and corresponding
        to keypoints.
        Sets the values of the keypoints corresponding to the indexes
        in the dataset.
        """
        raise NotImplementedError()

    def __call__(self):
        return NotImplementedError()

    @property
    def image_shape(self):
        return self.compute_image_shape()

    def replace_nan(self, keypoints):
        keypoints[np.isnan(keypoints)] = -99999
        return keypoints

    @property
    def keypoints_shape(self):
        return self.compute_keypoints_shape()

    @property
    def shape(self):
        """
        Returns a tuple of tuples describing the data shapes
        in the form:
        ((height, width, n_channels), (n_keypoints, 2))
        """
        return (self.image_shape, self.keypoints_shape)

    def get_data(self, indexes):
        keypoints = self.get_keypoints(indexes)
        keypoints = self.replace_nan(keypoints)
        return (self.get_images(indexes), keypoints)

    def set_data(self, indexes, keypoints):
        self.set_keypoints(indexes, keypoints)

    def _check_index(self, key):
        if isinstance(key, slice):
            start = key.start
            stop = key.stop
            if start is None:
                start = 0
            if stop is None:
                stop = len(self)
            if stop <= len(self):
                indexes = range(start, stop)
            else:
                raise IndexError()
        elif isinstance(key, (int, np.integer)):
            if key < len(self):
                indexes = [key]
            else:
                raise IndexError()
        elif isinstance(key, np.ndarray):
            if np.max(key) < len(self):
                indexes = key.tolist()
            else:
                raise IndexError
        elif isinstance(key, list):
            if max(key) < len(self):
                indexes = key
            else:
                raise IndexError()
        else:
            raise IndexError()
        return indexes

    def __getitem__(self, key):
        indexes = self._check_index(key)
        return self.get_data(indexes)

    def __setitem__(self, key, keypoints):
        indexes = self._check_index(key)
        if len(keypoints) != len(indexes):
            raise IndexError("data shape and index do not match")
        self.set_data(indexes, keypoints[..., :2])

    def get_config(self):
        config = {
            "generator": self.__class__.__name__,
            "n_samples": len(self),
            "image_shape": self.image_shape,
            "keypoints_shape": self.keypoints_shape,
        }
        return config

Ancestors

• tensorflow.python.keras.utils.data_utils.Sequence

Subclasses

• DataGenerator
• ImageGenerator
• DLCDataGenerator

Instance variables

var image_shape

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@property
def image_shape(self):
    return self.compute_image_shape()

var keypoints_shape

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@property
def keypoints_shape(self):
    return self.compute_keypoints_shape()

var shape

Returns a tuple of tuples describing the data shapes in the form: ((height, width, n_channels), (n_keypoints, 2))

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@property
def shape(self):
    """
    Returns a tuple of tuples describing the data shapes
    in the form:
    ((height, width, n_channels), (n_keypoints, 2))
    """
    return (self.image_shape, self.keypoints_shape)

Methods

def compute_image_shape(self)

Returns a tuple of integers describing the image shape in the form: (height, width, n_channels)

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def compute_image_shape(self):
    """
    Returns a tuple of integers describing
    the image shape in the form:
    (height, width, n_channels)
    """
    raise NotImplementedError()

def compute_keypoints_shape(self)

Returns a tuple of integers describing the keypoints shape in the form: (n_keypoints, 2), where 2 is the x,y coordinates

Expand source code

def compute_keypoints_shape(self):
    """
    Returns a tuple of integers describing the
    keypoints shape in the form:
    (n_keypoints, 2), where 2 is the x,y coordinates
    """
    raise NotImplementedError()

def get_config(self)

Expand source code

def get_config(self):
    config = {
        "generator": self.__class__.__name__,
        "n_samples": len(self),
        "image_shape": self.image_shape,
        "keypoints_shape": self.keypoints_shape,
    }
    return config

def get_data(self, indexes)

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def get_data(self, indexes):
    keypoints = self.get_keypoints(indexes)
    keypoints = self.replace_nan(keypoints)
    return (self.get_images(indexes), keypoints)

def get_images(self, indexes)

Takes a list or array of indexes corresponding to image-keypoint pairs in the dataset. Returns a numpy array of images with the shape: (1, height, width, n_channels)

Expand source code

def get_images(self, indexes):
    """
    Takes a list or array of indexes corresponding
    to image-keypoint pairs in the dataset.
    Returns a numpy array of images with the shape:
    (1, height, width, n_channels)
    """
    raise NotImplementedError()

def get_keypoints(self, indexes)

Takes a list or array of indexes corresponding to image-keypoint pairs in the dataset. Returns a numpy array of keypoints with the shape: (1, n_keypoints, 2), where 2 is the x,y coordinates

Expand source code

def get_keypoints(self, indexes):
    """
    Takes a list or array of indexes corresponding to
    image-keypoint pairs in the dataset.
    Returns a numpy array of keypoints with the shape:
    (1, n_keypoints, 2), where 2 is the x,y coordinates
    """
    raise NotImplementedError()

def replace_nan(self, keypoints)

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def replace_nan(self, keypoints):
    keypoints[np.isnan(keypoints)] = -99999
    return keypoints

def set_data(self, indexes, keypoints)

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def set_data(self, indexes, keypoints):
    self.set_keypoints(indexes, keypoints)

def set_keypoints(self, indexes, keypoints)

Takes a list or array of indexes and corresponding to keypoints. Sets the values of the keypoints corresponding to the indexes in the dataset.

Expand source code

def set_keypoints(self, indexes, keypoints):
    """
    Takes a list or array of indexes and corresponding
    to keypoints.
    Sets the values of the keypoints corresponding to the indexes
    in the dataset.
    """
    raise NotImplementedError()