diff options
Diffstat (limited to 'tensorflow/contrib/image/python/ops/image_ops.py')
| -rw-r--r-- | tensorflow/contrib/image/python/ops/image_ops.py | 294 |
1 files changed, 196 insertions, 98 deletions
diff --git a/tensorflow/contrib/image/python/ops/image_ops.py b/tensorflow/contrib/image/python/ops/image_ops.py index aef3e385b5..011ddeaa9a 100644 --- a/tensorflow/contrib/image/python/ops/image_ops.py +++ b/tensorflow/contrib/image/python/ops/image_ops.py @@ -37,16 +37,18 @@ _IMAGE_DTYPES = set( ops.RegisterShape("ImageProjectiveTransform")(common_shapes.call_cpp_shape_fn) -def rotate(images, angles, interpolation="NEAREST"): +def rotate(images, angles, interpolation="NEAREST", name=None): """Rotate image(s) by the passed angle(s) in radians. Args: images: A tensor of shape (num_images, num_rows, num_columns, num_channels) (NHWC), (num_rows, num_columns, num_channels) (HWC), or - (num_rows, num_columns) (HW). + (num_rows, num_columns) (HW). The rank must be statically known (the + shape is not `TensorShape(None)`. angles: A scalar angle to rotate all images by, or (if images has rank 4) a vector of length num_images, with an angle for each image in the batch. interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR". + name: The name of the op. Returns: Image(s) with the same type and shape as `images`, rotated by the given @@ -55,38 +57,77 @@ def rotate(images, angles, interpolation="NEAREST"): Raises: TypeError: If `image` is an invalid type. """ - image_or_images = ops.convert_to_tensor(images, name="images") - if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES: - raise TypeError("Invalid dtype %s." % image_or_images.dtype) - if len(image_or_images.get_shape()) == 2: - images = image_or_images[None, :, :, None] - elif len(image_or_images.get_shape()) == 3: - images = image_or_images[None, :, :, :] - elif len(image_or_images.get_shape()) == 4: - images = image_or_images - else: - raise TypeError("Images should have rank between 2 and 4.") - - image_height = math_ops.cast(array_ops.shape(images)[1], dtypes.float32)[None] - image_width = math_ops.cast(array_ops.shape(images)[2], dtypes.float32)[None] - output = transform( - images, - angles_to_projective_transforms(angles, image_height, image_width), - interpolation=interpolation) - if len(image_or_images.get_shape()) == 2: - return output[0, :, :, 0] - elif len(image_or_images.get_shape()) == 3: - return output[0, :, :, :] - else: - return output + with ops.name_scope(name, "rotate"): + image_or_images = ops.convert_to_tensor(images) + if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES: + raise TypeError("Invalid dtype %s." % image_or_images.dtype) + elif image_or_images.get_shape().ndims is None: + raise TypeError("image_or_images rank must be statically known") + elif len(image_or_images.get_shape()) == 2: + images = image_or_images[None, :, :, None] + elif len(image_or_images.get_shape()) == 3: + images = image_or_images[None, :, :, :] + elif len(image_or_images.get_shape()) == 4: + images = image_or_images + else: + raise TypeError("Images should have rank between 2 and 4.") + + image_height = math_ops.cast(array_ops.shape(images)[1], + dtypes.float32)[None] + image_width = math_ops.cast(array_ops.shape(images)[2], + dtypes.float32)[None] + output = transform( + images, + angles_to_projective_transforms(angles, image_height, image_width), + interpolation=interpolation) + if image_or_images.get_shape().ndims is None: + raise TypeError("image_or_images rank must be statically known") + elif len(image_or_images.get_shape()) == 2: + return output[0, :, :, 0] + elif len(image_or_images.get_shape()) == 3: + return output[0, :, :, :] + else: + return output + + +def translate(images, translations, interpolation="NEAREST", name=None): + """Translate image(s) by the passed vectors(s). + Args: + images: A tensor of shape (num_images, num_rows, num_columns, num_channels) + (NHWC), (num_rows, num_columns, num_channels) (HWC), or + (num_rows, num_columns) (HW). The rank must be statically known (the + shape is not `TensorShape(None)`. + translations: A vector representing [dx, dy] or (if images has rank 4) + a matrix of length num_images, with a [dx, dy] vector for each image in + the batch. + interpolation: Interpolation mode. Supported values: "NEAREST", "BILINEAR". + name: The name of the op. -def angles_to_projective_transforms(angles, image_height, image_width): + Returns: + Image(s) with the same type and shape as `images`, translated by the given + vector(s). Empty space due to the translation will be filled with zeros. + + Raises: + TypeError: If `image` is an invalid type. + """ + with ops.name_scope(name, "translate"): + return transform( + images, + translations_to_projective_transforms(translations), + interpolation=interpolation) + + +def angles_to_projective_transforms(angles, + image_height, + image_width, + name=None): """Returns projective transform(s) for the given angle(s). Args: angles: A scalar angle to rotate all images by, or (for batches of images) - a vector with an angle to rotate each image in the batch. + a vector with an angle to rotate each image in the batch. The rank must + be statically known (the shape is not `TensorShape(None)`. image_height: Height of the image(s) to be transformed. image_width: Width of the image(s) to be transformed. @@ -94,41 +135,89 @@ def angles_to_projective_transforms(angles, image_height, image_width): A tensor of shape (num_images, 8). Projective transforms which can be given to `tf.contrib.image.transform`. """ - angle_or_angles = ops.convert_to_tensor( - angles, name="angles", dtype=dtypes.float32) - if len(angle_or_angles.get_shape()) == 0: # pylint: disable=g-explicit-length-test - angles = angle_or_angles[None] - elif len(angle_or_angles.get_shape()) == 1: - angles = angle_or_angles - else: - raise TypeError("Angles should have rank 0 or 1.") - x_offset = ((image_width - 1) - (math_ops.cos(angles) * - (image_width - 1) - math_ops.sin(angles) * - (image_height - 1))) / 2.0 - y_offset = ((image_height - 1) - (math_ops.sin(angles) * - (image_width - 1) + math_ops.cos(angles) * - (image_height - 1))) / 2.0 - num_angles = array_ops.shape(angles)[0] - return array_ops.concat( - values=[ - math_ops.cos(angles)[:, None], - -math_ops.sin(angles)[:, None], - x_offset[:, None], - math_ops.sin(angles)[:, None], - math_ops.cos(angles)[:, None], - y_offset[:, None], - array_ops.zeros((num_angles, 2), dtypes.float32), - ], - axis=1) - - -def transform(images, transforms, interpolation="NEAREST"): + with ops.name_scope(name, "angles_to_projective_transforms"): + angle_or_angles = ops.convert_to_tensor( + angles, name="angles", dtype=dtypes.float32) + if len(angle_or_angles.get_shape()) == 0: # pylint: disable=g-explicit-length-test + angles = angle_or_angles[None] + elif len(angle_or_angles.get_shape()) == 1: + angles = angle_or_angles + else: + raise TypeError("Angles should have rank 0 or 1.") + x_offset = ((image_width - 1) - (math_ops.cos(angles) * + (image_width - 1) - math_ops.sin(angles) * + (image_height - 1))) / 2.0 + y_offset = ((image_height - 1) - (math_ops.sin(angles) * + (image_width - 1) + math_ops.cos(angles) * + (image_height - 1))) / 2.0 + num_angles = array_ops.shape(angles)[0] + return array_ops.concat( + values=[ + math_ops.cos(angles)[:, None], + -math_ops.sin(angles)[:, None], + x_offset[:, None], + math_ops.sin(angles)[:, None], + math_ops.cos(angles)[:, None], + y_offset[:, None], + array_ops.zeros((num_angles, 2), dtypes.float32), + ], + axis=1) + + +def translations_to_projective_transforms(translations, name=None): + """Returns projective transform(s) for the given translation(s). + + Args: + translations: A 2-element list representing [dx, dy] or a matrix of + 2-element lists representing [dx, dy] to translate for each image + (for a batch of images). The rank must be statically known (the shape + is not `TensorShape(None)`. + name: The name of the op. + + Returns: + A tensor of shape (num_images, 8) projective transforms which can be given + to `tf.contrib.image.transform`. + """ + with ops.name_scope(name, "translations_to_projective_transforms"): + translation_or_translations = ops.convert_to_tensor( + translations, name="translations", dtype=dtypes.float32) + if translation_or_translations.get_shape().ndims is None: + raise TypeError( + "translation_or_translations rank must be statically known") + elif len(translation_or_translations.get_shape()) == 1: + translations = translation_or_translations[None] + elif len(translation_or_translations.get_shape()) == 2: + translations = translation_or_translations + else: + raise TypeError("Translations should have rank 1 or 2.") + num_translations = array_ops.shape(translations)[0] + # The translation matrix looks like: + # [[1 0 -dx] + # [0 1 -dy] + # [0 0 1]] + # where the last entry is implicit. + # Translation matrices are always float32. + return array_ops.concat( + values=[ + array_ops.ones((num_translations, 1), dtypes.float32), + array_ops.zeros((num_translations, 1), dtypes.float32), + -translations[:, 0, None], + array_ops.zeros((num_translations, 1), dtypes.float32), + array_ops.ones((num_translations, 1), dtypes.float32), + -translations[:, 1, None], + array_ops.zeros((num_translations, 2), dtypes.float32), + ], + axis=1) + + +def transform(images, transforms, interpolation="NEAREST", name=None): """Applies the given transform(s) to the image(s). Args: images: A tensor of shape (num_images, num_rows, num_columns, num_channels) (NHWC), (num_rows, num_columns, num_channels) (HWC), or - (num_rows, num_columns) (HW). + (num_rows, num_columns) (HW). The rank must be statically known (the + shape is not `TensorShape(None)`. transforms: Projective transform matrix/matrices. A vector of length 8 or tensor of size N x 8. If one row of transforms is [a0, a1, a2, b0, b1, b2, c0, c1], then it maps the *output* point @@ -146,34 +235,40 @@ def transform(images, transforms, interpolation="NEAREST"): Raises: TypeError: If `image` is an invalid type. """ - image_or_images = ops.convert_to_tensor(images, name="images") - transform_or_transforms = ops.convert_to_tensor( - transforms, name="transforms", dtype=dtypes.float32) - if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES: - raise TypeError("Invalid dtype %s." % image_or_images.dtype) - if len(image_or_images.get_shape()) == 2: - images = image_or_images[None, :, :, None] - elif len(image_or_images.get_shape()) == 3: - images = image_or_images[None, :, :, :] - elif len(image_or_images.get_shape()) == 4: - images = image_or_images - else: - raise TypeError("Images should have rank between 2 and 4.") - - if len(transform_or_transforms.get_shape()) == 1: - transforms = transform_or_transforms[None] - elif len(transform_or_transforms.get_shape()) == 2: - transforms = transform_or_transforms - else: - raise TypeError("Transforms should have rank 1 or 2.") - output = gen_image_ops.image_projective_transform( - images, transforms, interpolation=interpolation.upper()) - if len(image_or_images.get_shape()) == 2: - return output[0, :, :, 0] - elif len(image_or_images.get_shape()) == 3: - return output[0, :, :, :] - else: - return output + with ops.name_scope(name, "transform"): + image_or_images = ops.convert_to_tensor(images, name="images") + transform_or_transforms = ops.convert_to_tensor( + transforms, name="transforms", dtype=dtypes.float32) + if image_or_images.dtype.base_dtype not in _IMAGE_DTYPES: + raise TypeError("Invalid dtype %s." % image_or_images.dtype) + elif image_or_images.get_shape().ndims is None: + raise TypeError("image_or_images rank must be statically known") + elif len(image_or_images.get_shape()) == 2: + images = image_or_images[None, :, :, None] + elif len(image_or_images.get_shape()) == 3: + images = image_or_images[None, :, :, :] + elif len(image_or_images.get_shape()) == 4: + images = image_or_images + else: + raise TypeError("Images should have rank between 2 and 4.") + + if len(transform_or_transforms.get_shape()) == 1: + transforms = transform_or_transforms[None] + elif transform_or_transforms.get_shape().ndims is None: + raise TypeError( + "transform_or_transforms rank must be statically known") + elif len(transform_or_transforms.get_shape()) == 2: + transforms = transform_or_transforms + else: + raise TypeError("Transforms should have rank 1 or 2.") + output = gen_image_ops.image_projective_transform( + images, transforms, interpolation=interpolation.upper()) + if len(image_or_images.get_shape()) == 2: + return output[0, :, :, 0] + elif len(image_or_images.get_shape()) == 3: + return output[0, :, :, :] + else: + return output def compose_transforms(*transforms): @@ -191,11 +286,12 @@ def compose_transforms(*transforms): order. """ assert transforms, "transforms cannot be empty" - composed = _flat_transforms_to_matrices(transforms[0]) - for tr in transforms[1:]: - # Multiply batches of matrices. - composed = math_ops.matmul(composed, _flat_transforms_to_matrices(tr)) - return _transform_matrices_to_flat(composed) + with ops.name_scope("compose_transforms"): + composed = _flat_transforms_to_matrices(transforms[0]) + for tr in transforms[1:]: + # Multiply batches of matrices. + composed = math_ops.matmul(composed, _flat_transforms_to_matrices(tr)) + return _transform_matrices_to_flat(composed) def _flat_transforms_to_matrices(transforms): @@ -211,8 +307,8 @@ def _flat_transforms_to_matrices(transforms): def _transform_matrices_to_flat(transform_matrices): # Flatten each matrix. - transforms = array_ops.reshape( - transform_matrices, constant_op.constant([-1, 9])) + transforms = array_ops.reshape(transform_matrices, + constant_op.constant([-1, 9])) # Divide each matrix by the last entry (normally 1). transforms /= transforms[:, 8:9] return transforms[:, :8] @@ -260,10 +356,10 @@ def _image_projective_transform_grad(op, grad): return [output, None] -def bipartite_match( - distance_mat, - num_valid_rows, - top_k=-1): +def bipartite_match(distance_mat, + num_valid_rows, + top_k=-1, + name="bipartite_match"): """Find bipartite matching based on a given distance matrix. A greedy bi-partite matching algorithm is used to obtain the matching with @@ -282,6 +378,7 @@ def bipartite_match( top_k: A scalar that specifies the number of top-k matches to retrieve. If set to be negative, then is set according to the maximum number of matches from `distance_mat`. + name: The name of the op. Returns: row_to_col_match_indices: A vector of length num_rows, which is the number @@ -292,7 +389,8 @@ def bipartite_match( If `col_to_row_match_indices[j]` is not -1, column j is matched to row `col_to_row_match_indices[j]`. """ - result = gen_image_ops.bipartite_match(distance_mat, num_valid_rows, top_k) + result = gen_image_ops.bipartite_match( + distance_mat, num_valid_rows, top_k, name=name) return result |