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Python cfg.BBOX_XFORM_CLIP属性代码示例

本文整理汇总了Python中detectron.core.config.cfg.BBOX_XFORM_CLIP属性的典型用法代码示例。如果您正苦于以下问题:Python cfg.BBOX_XFORM_CLIP属性的具体用法?Python cfg.BBOX_XFORM_CLIP怎么用?Python cfg.BBOX_XFORM_CLIP使用的例子?那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在detectron.core.config.cfg的用法示例。


在下文中一共展示了cfg.BBOX_XFORM_CLIP属性的2个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: bbox_transform

# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import BBOX_XFORM_CLIP [as 别名]
def bbox_transform(boxes, deltas, weights=(1.0, 1.0, 1.0, 1.0)):
    """Forward transform that maps proposal boxes to predicted ground-truth
    boxes using bounding-box regression deltas. See bbox_transform_inv for a
    description of the weights argument.
    """
    if boxes.shape[0] == 0:
        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)

    boxes = boxes.astype(deltas.dtype, copy=False)

    widths = boxes[:, 2] - boxes[:, 0] + 1.0
    heights = boxes[:, 3] - boxes[:, 1] + 1.0
    ctr_x = boxes[:, 0] + 0.5 * widths
    ctr_y = boxes[:, 1] + 0.5 * heights

    wx, wy, ww, wh = weights
    dx = deltas[:, 0::4] / wx
    dy = deltas[:, 1::4] / wy
    dw = deltas[:, 2::4] / ww
    dh = deltas[:, 3::4] / wh

    # Prevent sending too large values into np.exp()
    dw = np.minimum(dw, cfg.BBOX_XFORM_CLIP)
    dh = np.minimum(dh, cfg.BBOX_XFORM_CLIP)

    pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
    pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
    pred_w = np.exp(dw) * widths[:, np.newaxis]
    pred_h = np.exp(dh) * heights[:, np.newaxis]

    pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
    # x1
    pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
    # y1
    pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
    # x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
    # y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1

    return pred_boxes 
开发者ID:fyangneil,项目名称:Clustered-Object-Detection-in-Aerial-Image,代码行数:43,代码来源:boxes.py

示例2: bbox_transform

# 需要导入模块: from detectron.core.config import cfg [as 别名]
# 或者: from detectron.core.config.cfg import BBOX_XFORM_CLIP [as 别名]
def bbox_transform(boxes, deltas, weights=(1.0, 1.0, 1.0, 1.0)):
    """Forward transform that maps proposal boxes to predicted ground-truth
    boxes using bounding-box regression deltas. See bbox_transform_inv for a
    description of the weights argument.
    Args:
	boxes: xyxy
    Return:
        xyxy
    """
    if cfg.XYXY:
        return bbox_transform_xyxy(boxes, deltas, weights)
    if boxes.shape[0] == 0:
        return np.zeros((0, deltas.shape[1]), dtype=deltas.dtype)

    boxes = boxes.astype(deltas.dtype, copy=False)

    widths = boxes[:, 2] - boxes[:, 0] + 1.0
    heights = boxes[:, 3] - boxes[:, 1] + 1.0
    ctr_x = boxes[:, 0] + 0.5 * widths
    ctr_y = boxes[:, 1] + 0.5 * heights

    wx, wy, ww, wh = weights
    dx = deltas[:, 0::4] / wx
    dy = deltas[:, 1::4] / wy
    dw = deltas[:, 2::4] / ww
    dh = deltas[:, 3::4] / wh

    # Prevent sending too large values into np.exp()
    dw = np.minimum(dw, cfg.BBOX_XFORM_CLIP)
    dh = np.minimum(dh, cfg.BBOX_XFORM_CLIP)

    pred_ctr_x = dx * widths[:, np.newaxis] + ctr_x[:, np.newaxis]
    pred_ctr_y = dy * heights[:, np.newaxis] + ctr_y[:, np.newaxis]
    pred_w = np.exp(dw) * widths[:, np.newaxis]
    pred_h = np.exp(dh) * heights[:, np.newaxis]

    pred_boxes = np.zeros(deltas.shape, dtype=deltas.dtype)
    # x1
    pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * pred_w
    # y1
    pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * pred_h
    # x2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * pred_w - 1
    # y2 (note: "- 1" is correct; don't be fooled by the asymmetry)
    pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * pred_h - 1

    return pred_boxes 
开发者ID:yihui-he,项目名称:KL-Loss,代码行数:49,代码来源:boxes.py


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