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import numpy as np


def bbox_flip(bbox, width, flip_x=False):
    """
    invalid value in bbox_transform if this wrong (no overlap), note index 0 and 2
    also note need to save before assignment
    :param bbox: [n][x1, y1, x2, y2]
    :param width: cv2 (height, width, channel)
    :param flip_x: will flip x1 and x2
    :return: flipped box
    """
    if flip_x:
        xmax = width - bbox[:, 0]
        xmin = width - bbox[:, 2]
        bbox[:, 0] = xmin
        bbox[:, 2] = xmax
    return bbox


def bbox_overlaps(boxes, query_boxes):
    """
    determine overlaps between boxes and query_boxes
    :param boxes: n * 4 bounding boxes
    :param query_boxes: k * 4 bounding boxes
    :return: overlaps: n * k overlaps
    """
    n_ = boxes.shape[0]
    k_ = query_boxes.shape[0]
    overlaps = np.zeros((n_, k_), dtype=np.float)
    for k in range(k_):
        query_box_area = (query_boxes[k, 2] - query_boxes[k, 0] + 1) * (query_boxes[k, 3] - query_boxes[k, 1] + 1)
        for n in range(n_):
            iw = min(boxes[n, 2], query_boxes[k, 2]) - max(boxes[n, 0], query_boxes[k, 0]) + 1
            if iw > 0:
                ih = min(boxes[n, 3], query_boxes[k, 3]) - max(boxes[n, 1], query_boxes[k, 1]) + 1
                if ih > 0:
                    box_area = (boxes[n, 2] - boxes[n, 0] + 1) * (boxes[n, 3] - boxes[n, 1] + 1)
                    all_area = float(box_area + query_box_area - iw * ih)
                    overlaps[n, k] = iw * ih / all_area
    return overlaps


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 bbox_transform(ex_rois, gt_rois, box_stds):
    """
    compute bounding box regression targets from ex_rois to gt_rois
    :param ex_rois: [N, 4]
    :param gt_rois: [N, 4]
    :return: [N, 4]
    """
    assert ex_rois.shape[0] == gt_rois.shape[0], 'inconsistent rois number'

    ex_widths = ex_rois[:, 2] - ex_rois[:, 0] + 1.0
    ex_heights = ex_rois[:, 3] - ex_rois[:, 1] + 1.0
    ex_ctr_x = ex_rois[:, 0] + 0.5 * (ex_widths - 1.0)
    ex_ctr_y = ex_rois[:, 1] + 0.5 * (ex_heights - 1.0)

    gt_widths = gt_rois[:, 2] - gt_rois[:, 0] + 1.0
    gt_heights = gt_rois[:, 3] - gt_rois[:, 1] + 1.0
    gt_ctr_x = gt_rois[:, 0] + 0.5 * (gt_widths - 1.0)
    gt_ctr_y = gt_rois[:, 1] + 0.5 * (gt_heights - 1.0)

    targets_dx = (gt_ctr_x - ex_ctr_x) / (ex_widths + 1e-14) / box_stds[0]
    targets_dy = (gt_ctr_y - ex_ctr_y) / (ex_heights + 1e-14) / box_stds[1]
    targets_dw = np.log(gt_widths / ex_widths) / box_stds[2]