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| """Operations for image patches."""
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| from __future__ import absolute_import
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| from __future__ import division
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| from __future__ import print_function
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| import tensorflow.compat.v1 as tf
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| def get_patch_mask(y, x, patch_size, image_shape):
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| """Creates a 2D mask array for a square patch of a given size and location.
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| The mask is created with its center at the y and x coordinates, which must be
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| within the image. While the mask center must be within the image, the mask
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| itself can be partially outside of it. If patch_size is an even number, then
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| the mask is created with lower-valued coordinates first (top and left).
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| Args:
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| y: An integer or scalar int32 tensor. The vertical coordinate of the
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| patch mask center. Must be within the range [0, image_height).
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| x: An integer or scalar int32 tensor. The horizontal coordinate of the
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| patch mask center. Must be within the range [0, image_width).
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| patch_size: An integer or scalar int32 tensor. The square size of the
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| patch mask. Must be at least 1.
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| image_shape: A list or 1D int32 tensor representing the shape of the image
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| to which the mask will correspond, with the first two values being image
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| height and width. For example, [image_height, image_width] or
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| [image_height, image_width, image_channels].
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| Returns:
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| Boolean mask tensor of shape [image_height, image_width] with True values
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| for the patch.
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| Raises:
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| tf.errors.InvalidArgumentError: if x is not in the range [0, image_width), y
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| is not in the range [0, image_height), or patch_size is not at least 1.
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| """
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| image_hw = image_shape[:2]
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| mask_center_yx = tf.stack([y, x])
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| with tf.control_dependencies([
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| tf.debugging.assert_greater_equal(
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| patch_size, 1,
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| message='Patch size must be >= 1'),
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| tf.debugging.assert_greater_equal(
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| mask_center_yx, 0,
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| message='Patch center (y, x) must be >= (0, 0)'),
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| tf.debugging.assert_less(
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| mask_center_yx, image_hw,
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| message='Patch center (y, x) must be < image (h, w)')
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| ]):
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| mask_center_yx = tf.identity(mask_center_yx)
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| half_patch_size = tf.cast(patch_size, dtype=tf.float32) / 2
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| start_yx = mask_center_yx - tf.cast(tf.floor(half_patch_size), dtype=tf.int32)
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| end_yx = mask_center_yx + tf.cast(tf.ceil(half_patch_size), dtype=tf.int32)
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| start_yx = tf.maximum(start_yx, 0)
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| end_yx = tf.minimum(end_yx, image_hw)
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| start_y = start_yx[0]
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| start_x = start_yx[1]
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| end_y = end_yx[0]
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| end_x = end_yx[1]
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| lower_pad = image_hw[0] - end_y
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| upper_pad = start_y
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| left_pad = start_x
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| right_pad = image_hw[1] - end_x
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| mask = tf.ones([end_y - start_y, end_x - start_x], dtype=tf.bool)
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| return tf.pad(mask, [[upper_pad, lower_pad], [left_pad, right_pad]])
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|
