def from_distance_maps(distance_maps, inverted=False, if_not_found_coords={"x": -1, "y": -1}, threshold=None, # pylint: disable=locally-disabled, dangerous-default-value, line-too-long
                           nb_channels=None):
        """
        Converts maps generated by ``to_distance_maps()`` back to a KeypointsOnImage object.

        Parameters
        ----------
        distance_maps : (H,W,N) ndarray
            The distance maps. N is the number of keypoints.

        inverted : bool, optional
            Whether the given distance maps were generated in inverted or normal mode.

        if_not_found_coords : tuple or list or dict or None, optional
            Coordinates to use for keypoints that cannot be found in ``distance_maps``.
            If this is a list/tuple, it must have two integer values.
            If it is a dictionary, it must have the keys ``x`` and ``y``, with each
            containing one integer value.
            If this is None, then the keypoint will not be added to the final
            KeypointsOnImage object.

        threshold : float, optional
            The search for keypoints works by searching for the argmin (non-inverted) or
            argmax (inverted) in each channel. This parameters contains the maximum (non-inverted)
            or minimum (inverted) value to accept in order to view a hit as a keypoint.
            Use None to use no min/max.

        nb_channels : None or int, optional
            Number of channels of the image on which the keypoints are placed.
            Some keypoint augmenters require that information.
            If set to None, the keypoint's shape will be set
            to ``(height, width)``, otherwise ``(height, width, nb_channels)``.

        Returns
        -------
        imgaug.KeypointsOnImage
            The extracted keypoints.

        """
        ia.do_assert(len(distance_maps.shape) == 3)
        height, width, nb_keypoints = distance_maps.shape

        drop_if_not_found = False
        if if_not_found_coords is None:
            drop_if_not_found = True
            if_not_found_x = -1
            if_not_found_y = -1
        elif isinstance(if_not_found_coords, (tuple, list)):
            ia.do_assert(len(if_not_found_coords) == 2)
            if_not_found_x = if_not_found_coords[0]
            if_not_found_y = if_not_found_coords[1]
        elif isinstance(if_not_found_coords, dict):
            if_not_found_x = if_not_found_coords["x"]
            if_not_found_y = if_not_found_coords["y"]
        else:
            raise Exception("Expected if_not_found_coords to be None or tuple or list or dict, got %s." % (
                type(if_not_found_coords),))

        keypoints = []
        for i in sm.xrange(nb_keypoints):
            # TODO introduce voting here among all distance values that have min/max values
            if inverted:
                hitidx_flat = np.argmax(distance_maps[..., i])
            else:
                hitidx_flat = np.argmin(distance_maps[..., i])
            hitidx_ndim = np.unravel_index(hitidx_flat, (height, width))
            if not inverted and threshold is not None:
                found = (distance_maps[hitidx_ndim[0], hitidx_ndim[1], i] < threshold)
            elif inverted and threshold is not None:
                found = (distance_maps[hitidx_ndim[0], hitidx_ndim[1], i] >= threshold)
            else:
                found = True
            if found:
                keypoints.append(Keypoint(x=hitidx_ndim[1], y=hitidx_ndim[0]))
            else:
                if drop_if_not_found:
                    pass  # dont add the keypoint to the result list, i.e. drop it
                else:
                    keypoints.append(Keypoint(x=if_not_found_x, y=if_not_found_y))

        out_shape = (height, width)
        if nb_channels is not None:
            out_shape += (nb_channels,)
        return KeypointsOnImage(keypoints, shape=out_shape)
def from_distance_maps(distance_maps, inverted=False, if_not_found_coords={"x": -1, "y": -1}, threshold=None, # pylint: disable=locally-disabled, dangerous-default-value, line-too-long
                           nb_channels=None):
        """
        Converts maps generated by ``to_distance_maps()`` back to a KeypointsOnImage object.

        Parameters
        ----------
        distance_maps : (H,W,N) ndarray
            The distance maps. N is the number of keypoints.

        inverted : bool, optional
            Whether the given distance maps were generated in inverted or normal mode.

        if_not_found_coords : tuple or list or dict or None, optional
            Coordinates to use for keypoints that cannot be found in ``distance_maps``.
            If this is a list/tuple, it must have two integer values.
            If it is a dictionary, it must have the keys ``x`` and ``y``, with each
            containing one integer value.
            If this is None, then the keypoint will not be added to the final
            KeypointsOnImage object.

        threshold : float, optional
            The search for keypoints works by searching for the argmin (non-inverted) or
            argmax (inverted) in each channel. This parameters contains the maximum (non-inverted)
            or minimum (inverted) value to accept in order to view a hit as a keypoint.
            Use None to use no min/max.

        nb_channels : None or int, optional
            Number of channels of the image on which the keypoints are placed.
            Some keypoint augmenters require that information.
            If set to None, the keypoint's shape will be set
            to ``(height, width)``, otherwise ``(height, width, nb_channels)``.

        Returns
        -------
        imgaug.KeypointsOnImage
            The extracted keypoints.

        """
        ia.do_assert(len(distance_maps.shape) == 3)
        height, width, nb_keypoints = distance_maps.shape

        drop_if_not_found = False
        if if_not_found_coords is None:
            drop_if_not_found = True
            if_not_found_x = -1
            if_not_found_y = -1
        elif isinstance(if_not_found_coords, (tuple, list)):
            ia.do_assert(len(if_not_found_coords) == 2)
            if_not_found_x = if_not_found_coords[0]
            if_not_found_y = if_not_found_coords[1]
        elif isinstance(if_not_found_coords, dict):
            if_not_found_x = if_not_found_coords["x"]
            if_not_found_y = if_not_found_coords["y"]
        else:
            raise Exception("Expected if_not_found_coords to be None or tuple or list or dict, got %s." % (
                type(if_not_found_coords),))

        keypoints = []
        for i in sm.xrange(nb_keypoints):
            # TODO introduce voting here among all distance values that have min/max values
            if inverted:
                hitidx_flat = np.argmax(distance_maps[..., i])
            else:
                hitidx_flat = np.argmin(distance_maps[..., i])
            hitidx_ndim = np.unravel_index(hitidx_flat, (height, width))
            if not inverted and threshold is not None:
                found = (distance_maps[hitidx_ndim[0], hitidx_ndim[1], i] < threshold)
            elif inverted and threshold is not None:
                found = (distance_maps[hitidx_ndim[0], hitidx_ndim[1], i] >= threshold)
            else:
                found = True
            if found:
                keypoints.append(Keypoint(x=hitidx_ndim[1], y=hitidx_ndim[0]))
            else:
                if drop_if_not_found:
                    pass  # dont add the keypoint to the result list, i.e. drop it
                else:
                    keypoints.append(Keypoint(x=if_not_found_x, y=if_not_found_y))

        out_shape = (height, width)
        if nb_channels is not None:
            out_shape += (nb_channels,)
        return KeypointsOnImage(keypoints, shape=out_shape)
def from_distance_maps ( distance_maps , inverted = False , if_not_found_coords = { "x" : - 1 , "y" : - 1 } , threshold = None , # pylint: disable=locally-disabled, dangerous-default-value, line-too-long nb_channels = None ) : ia . do_assert ( len ( distance_maps . shape ) == 3 ) height , width , nb_keypoints = distance_maps . shape drop_if_not_found = False if if_not_found_coords is None : drop_if_not_found = True if_not_found_x = - 1 if_not_found_y = - 1 elif isinstance ( if_not_found_coords , ( tuple , list ) ) : ia . do_assert ( len ( if_not_found_coords ) == 2 ) if_not_found_x = if_not_found_coords [ 0 ] if_not_found_y = if_not_found_coords [ 1 ] elif isinstance ( if_not_found_coords , dict ) : if_not_found_x = if_not_found_coords [ "x" ] if_not_found_y = if_not_found_coords [ "y" ] else : raise Exception ( "Expected if_not_found_coords to be None or tuple or list or dict, got %s." % ( type ( if_not_found_coords ) , ) ) keypoints = [ ] for i in sm . xrange ( nb_keypoints ) : # TODO introduce voting here among all distance values that have min/max values if inverted : hitidx_flat = np . argmax ( distance_maps [ ... , i ] ) else : hitidx_flat = np . argmin ( distance_maps [ ... , i ] ) hitidx_ndim = np . unravel_index ( hitidx_flat , ( height , width ) ) if not inverted and threshold is not None : found = ( distance_maps [ hitidx_ndim [ 0 ] , hitidx_ndim [ 1 ] , i ] < threshold ) elif inverted and threshold is not None : found = ( distance_maps [ hitidx_ndim [ 0 ] , hitidx_ndim [ 1 ] , i ] >= threshold ) else : found = True if found : keypoints . append ( Keypoint ( x = hitidx_ndim [ 1 ] , y = hitidx_ndim [ 0 ] ) ) else : if drop_if_not_found : pass # dont add the keypoint to the result list, i.e. drop it else : keypoints . append ( Keypoint ( x = if_not_found_x , y = if_not_found_y ) ) out_shape = ( height , width ) if nb_channels is not None : out_shape += ( nb_channels , ) return KeypointsOnImage ( keypoints , shape = out_shape )
Converts maps generated by ``to_distance_maps()`` back to a KeypointsOnImage object.

        Parameters
        ----------
        distance_maps : (H,W,N) ndarray
            The distance maps. N is the number of keypoints.

        inverted : bool, optional
            Whether the given distance maps were generated in inverted or normal mode.

        if_not_found_coords : tuple or list or dict or None, optional
            Coordinates to use for keypoints that cannot be found in ``distance_maps``.
            If this is a list/tuple, it must have two integer values.
            If it is a dictionary, it must have the keys ``x`` and ``y``, with each
            containing one integer value.
            If this is None, then the keypoint will not be added to the final
            KeypointsOnImage object.

        threshold : float, optional
            The search for keypoints works by searching for the argmin (non-inverted) or
            argmax (inverted) in each channel. This parameters contains the maximum (non-inverted)
            or minimum (inverted) value to accept in order to view a hit as a keypoint.
            Use None to use no min/max.

        nb_channels : None or int, optional
            Number of channels of the image on which the keypoints are placed.
            Some keypoint augmenters require that information.
            If set to None, the keypoint's shape will be set
            to ``(height, width)``, otherwise ``(height, width, nb_channels)``.

        Returns
        -------
        imgaug.KeypointsOnImage
            The extracted keypoints.
Converts maps generated by to_distance_maps () back to a KeypointsOnImage object .