本文整理匯總了Python中detectron.core.config.cfg.BBOX_XFORM_CLIP屬性的典型用法代碼示例。如果您正苦於以下問題：Python cfg.BBOX_XFORM_CLIP屬性的具體用法？Python cfg.BBOX_XFORM_CLIP怎麽用？Python cfg.BBOX_XFORM_CLIP使用的例子？那麽, 這裏精選的屬性代碼示例或許可以為您提供幫助。您也可以進一步了解該屬性所在類detectron.core.config.cfg的用法示例。
在下文中一共展示了cfg.BBOX_XFORM_CLIP屬性的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: bbox_transform
# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import BBOX_XFORM_CLIP [as 別名]
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 示例2: bbox_transform
# 需要導入模塊: from detectron.core.config import cfg [as 別名]
# 或者: from detectron.core.config.cfg import BBOX_XFORM_CLIP [as 別名]
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