import numpy as np


def filter_small_boxes(boxes, min_width, min_height):
    """Filters all boxes with side smaller than min size. 

    Args:
        boxes: a numpy array with shape [N, 4] holding N boxes.
        min_width (float): minimum width
        min_height (float): minimum height

    Returns:
        keep: indices of the boxes that have width larger than
            min_width and height larger than min_height.

    References:
        `_filter_boxes` in py-faster-rcnn
        `prune_small_boxes` in TensorFlow object detection API.
        `structures.Boxes.nonempty` in detectron2
        `ops.boxes.remove_small_boxes` in torchvision
    """
    widths = boxes[:, 2] - boxes[:, 0]
    heights = boxes[:, 3] - boxes[:, 1] 
    keep = (widths >= min_width)
    keep &= (heights >= min_height)
    return np.nonzero(keep)[0]
    

def filter_boxes_outside(boxes, reference_box):
    """Filters bounding boxes that fall outside reference box.
    
    References:
        `prune_outside_window` in TensorFlow object detection API.
    """
    x_min, y_min, x_max, y_max = reference_box[:4]
    keep = ((boxes[:, 0] >= x_min) & (boxes[:, 1] >= y_min) &
            (boxes[:, 2] <= x_max) & (boxes[:, 3] <= y_max))
    return np.nonzero(keep)[0]


def filter_boxes_completely_outside(boxes, reference_box):
    """Filters bounding boxes that fall completely outside of reference box.
    
    References:
        `prune_completely_outside_window` in TensorFlow object detection API.
    """
    x_min, y_min, x_max, y_max = reference_box[:4]
    keep = ((boxes[:, 0] < x_max) & (boxes[:, 1] < y_max) &
            (boxes[:, 2] > x_min) & (boxes[:, 3] > y_min))
    return np.nonzero(keep)[0]