(boxes, im_shape)
return boxes
Clip boxes to image boundaries.
    :param boxes: [N, 4* num_classes]
    :param im_shape: tuple of 2
    :return: [N, 4* num_classes]
Clip boxes to image boundaries.
    :param boxes: [N, 4* num_classes]
    :param im_shape: tuple of 2
    :return: [N, 4* num_classes]
Clip boxes to image boundaries . : param boxes : [ N 4 * num_classes ] : param im_shape : tuple of 2 : return : [ N 4 * num_classes ]
def clip_boxes(boxes, im_shape):
    """
    Clip boxes to image boundaries.
    :param boxes: [N, 4* num_classes]
    :param im_shape: tuple of 2
    :return: [N, 4* num_classes]
    """
    # x1 >= 0
    boxes[:, 0::4] = np.maximum(np.minimum(boxes[:, 0::4], im_shape[1] - 1), 0)
    # y1 >= 0
    boxes[:, 1::4] = np.maximum(np.minimum(boxes[:, 1::4], im_shape[0] - 1), 0)
    # x2 < im_shape[1]
    boxes[:, 2::4] = np.maximum(np.minimum(boxes[:, 2::4], im_shape[1] - 1), 0)
    # y2 < im_shape[0]
    boxes[:, 3::4] = np.maximum(np.minimum(boxes[:, 3::4], im_shape[0] - 1), 0)
    return boxes
def clip_boxes ( boxes , im_shape ) : # x1 >= 0 boxes [ : , 0 : : 4 ] = np . maximum ( np . minimum ( boxes [ : , 0 : : 4 ] , im_shape [ 1 ] - 1 ) , 0 ) # y1 >= 0 boxes [ : , 1 : : 4 ] = np . maximum ( np . minimum ( boxes [ : , 1 : : 4 ] , im_shape [ 0 ] - 1 ) , 0 ) # x2 < im_shape[1] boxes [ : , 2 : : 4 ] = np . maximum ( np . minimum ( boxes [ : , 2 : : 4 ] , im_shape [ 1 ] - 1 ) , 0 ) # y2 < im_shape[0] boxes [ : , 3 : : 4 ] = np . maximum ( np . minimum ( boxes [ : , 3 : : 4 ] , im_shape [ 0 ] - 1 ) , 0 ) return boxes