def to_keypoint_image(self, size=1):
        """
        Draws a new black image of shape ``(H,W,N)`` in which all keypoint coordinates are set to 255.
        (H=shape height, W=shape width, N=number of keypoints)

        This function can be used as a helper when augmenting keypoints with a method that only supports the
        augmentation of images.

        Parameters
        -------
        size : int
            Size of each (squared) point.

        Returns
        -------
        image : (H,W,N) ndarray
            Image in which the keypoints are marked. H is the height,
            defined in KeypointsOnImage.shape[0] (analogous W). N is the
            number of keypoints.

        """
        ia.do_assert(len(self.keypoints) > 0)
        height, width = self.shape[0:2]
        image = np.zeros((height, width, len(self.keypoints)), dtype=np.uint8)
        ia.do_assert(size % 2 != 0)
        sizeh = max(0, (size-1)//2)
        for i, keypoint in enumerate(self.keypoints):
            # TODO for float values spread activation over several cells
            # here and do voting at the end
            y = keypoint.y_int
            x = keypoint.x_int

            x1 = np.clip(x - sizeh, 0, width-1)
            x2 = np.clip(x + sizeh + 1, 0, width)
            y1 = np.clip(y - sizeh, 0, height-1)
            y2 = np.clip(y + sizeh + 1, 0, height)

            if x1 < x2 and y1 < y2:
                image[y1:y2, x1:x2, i] = 128
            if 0 <= y < height and 0 <= x < width:
                image[y, x, i] = 255
        return image
def to_keypoint_image(self, size=1):
        
        ia.do_assert(len(self.keypoints) > 0)
        height, width = self.shape[0:2]
        image = np.zeros((height, width, len(self.keypoints)), dtype=np.uint8)
        ia.do_assert(size % 2 != 0)
        sizeh = max(0, (size-1)//2)
        for i, keypoint in enumerate(self.keypoints):
            
            
            y = keypoint.y_int
            x = keypoint.x_int

            x1 = np.clip(x - sizeh, 0, width-1)
            x2 = np.clip(x + sizeh + 1, 0, width)
            y1 = np.clip(y - sizeh, 0, height-1)
            y2 = np.clip(y + sizeh + 1, 0, height)

            if x1 < x2 and y1 < y2:
                image[y1:y2, x1:x2, i] = 128
            if 0 <= y < height and 0 <= x < width:
                image[y, x, i] = 255
        return image
def to_keypoint_image ( self , size = 1 ) : ia . do_assert ( len ( self . keypoints ) > 0 ) height , width = self . shape [ 0 : 2 ] image = np . zeros ( ( height , width , len ( self . keypoints ) ) , dtype = np . uint8 ) ia . do_assert ( size % 2 != 0 ) sizeh = max ( 0 , ( size - 1 ) // 2 ) for i , keypoint in enumerate ( self . keypoints ) : # TODO for float values spread activation over several cells # here and do voting at the end y = keypoint . y_int x = keypoint . x_int x1 = np . clip ( x - sizeh , 0 , width - 1 ) x2 = np . clip ( x + sizeh + 1 , 0 , width ) y1 = np . clip ( y - sizeh , 0 , height - 1 ) y2 = np . clip ( y + sizeh + 1 , 0 , height ) if x1 < x2 and y1 < y2 : image [ y1 : y2 , x1 : x2 , i ] = 128 if 0 <= y < height and 0 <= x < width : image [ y , x , i ] = 255 return image
Draws a new black image of shape ``(H,W,N)`` in which all keypoint coordinates are set to 255.
        (H=shape height, W=shape width, N=number of keypoints)

        This function can be used as a helper when augmenting keypoints with a method that only supports the
        augmentation of images.

        Parameters
        -------
        size : int
            Size of each (squared) point.

        Returns
        -------
        image : (H,W,N) ndarray
            Image in which the keypoints are marked. H is the height,
            defined in KeypointsOnImage.shape[0] (analogous W). N is the
            number of keypoints.
Draws a new black image of shape ( H W N ) in which all keypoint coordinates are set to 255 . ( H = shape height W = shape width N = number of keypoints )