def crop(images, boxes, batch_inds, stride = 1, pooled_height = 7, pooled_width = 7, scope='ROIAlign'):
"""Cropping areas of features into fixed size
Params:
--------
images: a 4-d Tensor of shape (N, H, W, C)
boxes: rois in the original image, of shape (N, ..., 4), [x1, y1, x2, y2]
batch_inds:
Returns:
--------
A Tensor of shape (N, pooled_height, pooled_width, C)
"""
with tf.name_scope(scope):
#
boxes = boxes / (stride + 0.0)
boxes = tf.reshape(boxes, [-1, 4])
# normalize the boxes and swap x y dimensions
shape = tf.shape(images)
boxes = tf.reshape(boxes, [-1, 2]) # to (x, y)
xs = boxes[:, 0]
ys = boxes[:, 1]
xs = xs / tf.cast(shape[2], tf.float32)
ys = ys / tf.cast(shape[1], tf.float32)
boxes = tf.concat([ys[:, tf.newaxis], xs[:, tf.newaxis]], axis=1)
boxes = tf.reshape(boxes, [-1, 4]) # to (y1, x1, y2, x2)
# if batch_inds is False:
# num_boxes = tf.shape(boxes)[0]
# batch_inds = tf.zeros([num_boxes], dtype=tf.int32, name='batch_inds')
# batch_inds = boxes[:, 0] * 0
# batch_inds = tf.cast(batch_inds, tf.int32)
# assert_op = tf.Assert(tf.greater(tf.shape(images)[0], tf.reduce_max(batch_inds)), [images, batch_inds])
assert_op = tf.Assert(tf.greater(tf.size(images), 0), [images, batch_inds])
with tf.control_dependencies([assert_op, images, batch_inds]):
return tf.image.crop_and_resize(images, boxes, batch_inds,
[pooled_height, pooled_width],
method='bilinear',
name='Crop')
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