本文整理汇总了Python中layer_utils.generate_anchors.generate_anchors方法的典型用法代码示例。如果您正苦于以下问题：Python generate_anchors.generate_anchors方法的具体用法？Python generate_anchors.generate_anchors怎么用？Python generate_anchors.generate_anchors使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类layer_utils.generate_anchors的用法示例。
在下文中一共展示了generate_anchors.generate_anchors方法的4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: generate_anchors_pre
# 需要导入模块: from layer_utils import generate_anchors [as 别名]
# 或者: from layer_utils.generate_anchors import generate_anchors [as 别名]
def generate_anchors_pre(height, width, feat_stride, anchor_scales=(8,16,32), anchor_ratios=(0.5,1,2)):
""" A wrapper function to generate anchors given different scales
Also return the number of anchors in variable 'length'
"""
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
K = shifts.shape[0]
# width changes faster, so here it is H, W, C
anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
length = np.int32(anchors.shape[0])
return anchors, length 开发者ID:Sunarker，项目名称:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代码行数:19，代码来源:snippets.py
示例2: generate_anchors_pre
# 需要导入模块: from layer_utils import generate_anchors [as 别名]
# 或者: from layer_utils.generate_anchors import generate_anchors [as 别名]
def generate_anchors_pre(rpn_cls_score, feat_stride, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
""" A wrapper function to generate anchors given different scales
Also return the number of anchors in variable 'length'
"""
height, width = rpn_cls_score.shape[1:3]
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
shift_x = np.arange(0, width) * feat_stride
shift_y = np.arange(0, height) * feat_stride
shift_x, shift_y = np.meshgrid(shift_x, shift_y)
shifts = np.vstack((shift_x.ravel(), shift_y.ravel(), shift_x.ravel(), shift_y.ravel())).transpose()
K = shifts.shape[0]
# width changes faster, so here it is H, W, C
anchors = anchors.reshape((1, A, 4)) + shifts.reshape((1, K, 4)).transpose((1, 0, 2))
anchors = anchors.reshape((K * A, 4)).astype(np.float32, copy=False)
length = np.int32(anchors.shape[0])
return anchors, length 示例3: generate_anchors_pre_tf
# 需要导入模块: from layer_utils import generate_anchors [as 别名]
# 或者: from layer_utils.generate_anchors import generate_anchors [as 别名]
def generate_anchors_pre_tf(rpn_cls_score, feat_stride=16, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
height = tf.shape(rpn_cls_score)[1]
width = tf.shape(rpn_cls_score)[2]
shift_x = tf.range(width) * feat_stride # width
shift_y = tf.range(height) * feat_stride # height
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
sx = tf.reshape(shift_x, shape=(-1,))
sy = tf.reshape(shift_y, shape=(-1,))
shifts = tf.transpose(tf.stack([sx, sy, sx, sy]))
K = tf.multiply(width, height)
shifts = tf.transpose(tf.reshape(shifts, shape=[1, K, 4]), perm=(1, 0, 2))
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
anchor_constant = tf.constant(anchors.reshape((1, A, 4)), dtype=tf.int32)
length = K * A
anchors_tf = tf.reshape(tf.add(anchor_constant, shifts), shape=(length, 4))
return tf.cast(anchors_tf, dtype=tf.float32), length 示例4: generate_anchors_pre_tf
# 需要导入模块: from layer_utils import generate_anchors [as 别名]
# 或者: from layer_utils.generate_anchors import generate_anchors [as 别名]
def generate_anchors_pre_tf(height, width, feat_stride=16, anchor_scales=(8, 16, 32), anchor_ratios=(0.5, 1, 2)):
shift_x = tf.range(width) * feat_stride # width
shift_y = tf.range(height) * feat_stride # height
shift_x, shift_y = tf.meshgrid(shift_x, shift_y)
sx = tf.reshape(shift_x, shape=(-1,))
sy = tf.reshape(shift_y, shape=(-1,))
shifts = tf.transpose(tf.stack([sx, sy, sx, sy]))
K = tf.multiply(width, height)
shifts = tf.transpose(tf.reshape(shifts, shape=[1, K, 4]), perm=(1, 0, 2))
anchors = generate_anchors(ratios=np.array(anchor_ratios), scales=np.array(anchor_scales))
A = anchors.shape[0]
anchor_constant = tf.constant(anchors.reshape((1, A, 4)), dtype=tf.int32)
length = K * A
anchors_tf = tf.reshape(tf.add(anchor_constant, shifts), shape=(length, 4))
return tf.cast(anchors_tf, dtype=tf.float32), length