forked from TensorLayer/tensorlayer3
642 lines
17 KiB
ReStructuredText
642 lines
17 KiB
ReStructuredText
API - Data Pre-Processing
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=========================
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.. automodule:: tensorlayer.prepro
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.. autosummary::
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affine_rotation_matrix
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affine_horizontal_flip_matrix
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affine_vertical_flip_matrix
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affine_shift_matrix
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affine_shear_matrix
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affine_zoom_matrix
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affine_respective_zoom_matrix
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transform_matrix_offset_center
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affine_transform
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affine_transform_cv2
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affine_transform_keypoints
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projective_transform_by_points
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rotation
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rotation_multi
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crop
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crop_multi
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flip_axis
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flip_axis_multi
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shift
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shift_multi
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shear
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shear_multi
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shear2
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shear_multi2
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swirl
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swirl_multi
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elastic_transform
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elastic_transform_multi
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zoom
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respective_zoom
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zoom_multi
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brightness
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brightness_multi
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illumination
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rgb_to_hsv
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hsv_to_rgb
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adjust_hue
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imresize
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pixel_value_scale
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samplewise_norm
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featurewise_norm
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channel_shift
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channel_shift_multi
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drop
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array_to_img
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find_contours
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pt2map
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binary_dilation
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dilation
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binary_erosion
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erosion
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obj_box_coord_rescale
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obj_box_coords_rescale
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obj_box_coord_scale_to_pixelunit
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obj_box_coord_centroid_to_upleft_butright
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obj_box_coord_upleft_butright_to_centroid
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obj_box_coord_centroid_to_upleft
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obj_box_coord_upleft_to_centroid
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parse_darknet_ann_str_to_list
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parse_darknet_ann_list_to_cls_box
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obj_box_horizontal_flip
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obj_box_imresize
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obj_box_crop
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obj_box_shift
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obj_box_zoom
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keypoint_random_crop
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keypoint_resize_random_crop
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keypoint_random_rotate
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keypoint_random_flip
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keypoint_random_resize
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keypoint_random_resize_shortestedge
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pad_sequences
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remove_pad_sequences
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process_sequences
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sequences_add_start_id
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sequences_add_end_id
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sequences_add_end_id_after_pad
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sequences_get_mask
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..
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Threading
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------------
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.. autofunction:: threading_data
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Affine Transform
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----------------
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Python can be FAST
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^^^^^^^^^^^^^^^^^^
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Image augmentation is a critical step in deep learning.
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Though TensorFlow has provided ``tf.image``,
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image augmentation often remains as a key bottleneck.
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``tf.image`` has three limitations:
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- Real-world visual tasks such as object detection, segmentation, and pose estimation
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must cope with image meta-data (e.g., coordinates).
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These data are beyond ``tf.image``
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which processes images as tensors.
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- ``tf.image`` operators
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breaks the pure Python programing experience (i.e., users have to
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use ``tf.py_func`` in order to call image functions written in Python); however,
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frequent uses of ``tf.py_func`` slow down TensorFlow,
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making users hard to balance flexibility and performance.
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- ``tf.image`` API is inflexible. Image operations are
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performed in an order. They are hard to jointly optimize. More importantly,
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sequential image operations can significantly
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reduces the quality of images, thus affecting training accuracy.
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TensorLayer addresses these limitations by providing a
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high-performance image augmentation API in Python.
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This API bases on affine transformation and ``cv2.wrapAffine``.
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It allows you to combine multiple image processing functions into
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a single matrix operation. This combined operation
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is executed by the fast ``cv2`` library, offering 78x performance improvement (observed in
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`openpose-plus <https://github.com/tensorlayer/openpose-plus>`_ for example).
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The following example illustrates the rationale
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behind this tremendous speed up.
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Example
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^^^^^^^
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The source code of complete examples can be found \
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`here <https://github.com/tensorlayer/tensorlayer/tree/master/examples/data_process/tutorial_fast_affine_transform.py>`__.
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The following is a typical Python program that applies rotation, shifting, flipping, zooming and shearing to an image,
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.. code-block:: python
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image = tl.vis.read_image('tiger.jpeg')
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xx = tl.prepro.rotation(image, rg=-20, is_random=False)
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xx = tl.prepro.flip_axis(xx, axis=1, is_random=False)
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xx = tl.prepro.shear2(xx, shear=(0., -0.2), is_random=False)
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xx = tl.prepro.zoom(xx, zoom_range=0.8)
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xx = tl.prepro.shift(xx, wrg=-0.1, hrg=0, is_random=False)
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tl.vis.save_image(xx, '_result_slow.png')
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However, by leveraging affine transformation, image operations can be combined into one:
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.. code-block:: python
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# 1. Create required affine transformation matrices
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M_rotate = tl.prepro.affine_rotation_matrix(angle=20)
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M_flip = tl.prepro.affine_horizontal_flip_matrix(prob=1)
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M_shift = tl.prepro.affine_shift_matrix(wrg=0.1, hrg=0, h=h, w=w)
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M_shear = tl.prepro.affine_shear_matrix(x_shear=0.2, y_shear=0)
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M_zoom = tl.prepro.affine_zoom_matrix(zoom_range=0.8)
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# 2. Combine matrices
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# NOTE: operations are applied in a reversed order (i.e., rotation is performed first)
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M_combined = M_shift.dot(M_zoom).dot(M_shear).dot(M_flip).dot(M_rotate)
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# 3. Convert the matrix from Cartesian coordinates (the origin in the middle of image)
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# to image coordinates (the origin on the top-left of image)
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transform_matrix = tl.prepro.transform_matrix_offset_center(M_combined, x=w, y=h)
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# 4. Transform the image using a single operation
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result = tl.prepro.affine_transform_cv2(image, transform_matrix) # 76 times faster
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tl.vis.save_image(result, '_result_fast.png')
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The following figure illustrates the rational behind combined affine transformation.
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.. image:: ../images/affine_transform_why.jpg
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:width: 100 %
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:align: center
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Using combined affine transformation has two key benefits. First, it allows \
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you to leverage a pure Python API to achieve orders of magnitudes of speed up in image augmentation,
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and thus prevent data pre-processing from becoming a bottleneck in training. \
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Second, performing sequential image transformation requires multiple image interpolations. \
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This produces low-quality input images. In contrast, a combined transformation performs the \
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interpolation only once, and thus
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preserve the content in an image. The following figure illustrates these two benefits:
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.. image:: ../images/affine_transform_comparison.jpg
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:width: 100 %
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:align: center
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The major reason for combined affine transformation being fast is because it has lower computational complexity.
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Assume we have ``k`` affine transformations ``T1, ..., Tk``, where ``Ti`` can be represented by 3x3 matrixes.
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The sequential transformation can be represented as ``y = Tk (... T1(x))``,
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and the time complexity is ``O(k N)`` where ``N`` is the cost of applying one transformation to image ``x``.
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``N`` is linear to the size of ``x``.
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For the combined transformation ``y = (Tk ... T1) (x)``
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the time complexity is ``O(27(k - 1) + N) = max{O(27k), O(N)} = O(N)`` (assuming 27k << N) where 27 = 3^3 is the cost for combining two transformations.
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Get rotation matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_rotation_matrix
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Get horizontal flipping matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_horizontal_flip_matrix
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Get vertical flipping matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_vertical_flip_matrix
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Get shifting matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_shift_matrix
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Get shearing matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_shear_matrix
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Get zooming matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_zoom_matrix
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Get respective zooming matrix
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_respective_zoom_matrix
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Cartesian to image coordinates
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: transform_matrix_offset_center
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..
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Apply image transform
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_transform
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Apply image transform
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^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_transform_cv2
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Apply keypoint transform
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: affine_transform_keypoints
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Images
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-----------
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Projective transform by points
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: projective_transform_by_points
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Rotation
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^^^^^^^^^
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.. autofunction:: rotation
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.. autofunction:: rotation_multi
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Crop
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^^^^^^^^^
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.. autofunction:: crop
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.. autofunction:: crop_multi
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Flip
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^^^^^^^^^
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.. autofunction:: flip_axis
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.. autofunction:: flip_axis_multi
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Shift
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^^^^^^^^^
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.. autofunction:: shift
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.. autofunction:: shift_multi
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Shear
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^^^^^^^^^
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.. autofunction:: shear
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.. autofunction:: shear_multi
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Shear V2
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^^^^^^^^^^^
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.. autofunction:: shear2
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.. autofunction:: shear_multi2
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Swirl
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^^^^^^^^^
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.. autofunction:: swirl
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.. autofunction:: swirl_multi
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Elastic transform
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^^^^^^^^^^^^^^^^^^
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.. autofunction:: elastic_transform
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.. autofunction:: elastic_transform_multi
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Zoom
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^^^^^^^^^
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.. autofunction:: zoom
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.. autofunction:: zoom_multi
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Respective Zoom
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^^^^^^^^^^^^^^^^^
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.. autofunction:: respective_zoom
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Brightness
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^^^^^^^^^^^^
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.. autofunction:: brightness
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.. autofunction:: brightness_multi
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Brightness, contrast and saturation
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: illumination
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RGB to HSV
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^^^^^^^^^^^^^^
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.. autofunction:: rgb_to_hsv
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HSV to RGB
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^^^^^^^^^^^^^^
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.. autofunction:: hsv_to_rgb
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Adjust Hue
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^^^^^^^^^^^^^^
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.. autofunction:: adjust_hue
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Resize
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^^^^^^^^^^^^
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.. autofunction:: imresize
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Pixel value scale
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^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: pixel_value_scale
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Normalization
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^^^^^^^^^^^^^^^
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.. autofunction:: samplewise_norm
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.. autofunction:: featurewise_norm
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Channel shift
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^^^^^^^^^^^^^^
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.. autofunction:: channel_shift
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.. autofunction:: channel_shift_multi
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Noise
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^^^^^^^^^^^^^^
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.. autofunction:: drop
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Numpy and PIL
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^^^^^^^^^^^^^^
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.. autofunction:: array_to_img
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Find contours
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^^^^^^^^^^^^^^
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.. autofunction:: find_contours
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Points to Image
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^^^^^^^^^^^^^^^^^
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.. autofunction:: pt2map
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Binary dilation
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^^^^^^^^^^^^^^^^^
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.. autofunction:: binary_dilation
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Greyscale dilation
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^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: dilation
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Binary erosion
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^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: binary_erosion
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Greyscale erosion
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^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: erosion
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Object detection
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-------------------
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Tutorial for Image Aug
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^^^^^^^^^^^^^^^^^^^^^^^
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Hi, here is an example for image augmentation on VOC dataset.
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.. code-block:: python
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import tensorlayer as tl
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## download VOC 2012 dataset
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imgs_file_list, _, _, _, classes, _, _,\
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_, objs_info_list, _ = tl.files.load_voc_dataset(dataset="2012")
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## parse annotation and convert it into list format
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ann_list = []
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for info in objs_info_list:
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ann = tl.prepro.parse_darknet_ann_str_to_list(info)
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c, b = tl.prepro.parse_darknet_ann_list_to_cls_box(ann)
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ann_list.append([c, b])
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# read and save one image
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idx = 2 # you can select your own image
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image = tl.vis.read_image(imgs_file_list[idx])
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tl.vis.draw_boxes_and_labels_to_image(image, ann_list[idx][0],
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ann_list[idx][1], [], classes, True, save_name='_im_original.png')
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# left right flip
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im_flip, coords = tl.prepro.obj_box_horizontal_flip(image,
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ann_list[idx][1], is_rescale=True, is_center=True, is_random=False)
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tl.vis.draw_boxes_and_labels_to_image(im_flip, ann_list[idx][0],
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coords, [], classes, True, save_name='_im_flip.png')
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# resize
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im_resize, coords = tl.prepro.obj_box_imresize(image,
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coords=ann_list[idx][1], size=[300, 200], is_rescale=True)
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tl.vis.draw_boxes_and_labels_to_image(im_resize, ann_list[idx][0],
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coords, [], classes, True, save_name='_im_resize.png')
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# crop
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im_crop, clas, coords = tl.prepro.obj_box_crop(image, ann_list[idx][0],
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ann_list[idx][1], wrg=200, hrg=200,
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is_rescale=True, is_center=True, is_random=False)
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tl.vis.draw_boxes_and_labels_to_image(im_crop, clas, coords, [],
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classes, True, save_name='_im_crop.png')
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# shift
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im_shfit, clas, coords = tl.prepro.obj_box_shift(image, ann_list[idx][0],
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ann_list[idx][1], wrg=0.1, hrg=0.1,
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is_rescale=True, is_center=True, is_random=False)
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tl.vis.draw_boxes_and_labels_to_image(im_shfit, clas, coords, [],
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classes, True, save_name='_im_shift.png')
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# zoom
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im_zoom, clas, coords = tl.prepro.obj_box_zoom(image, ann_list[idx][0],
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ann_list[idx][1], zoom_range=(1.3, 0.7),
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is_rescale=True, is_center=True, is_random=False)
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tl.vis.draw_boxes_and_labels_to_image(im_zoom, clas, coords, [],
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classes, True, save_name='_im_zoom.png')
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In practice, you may want to use threading method to process a batch of images as follows.
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.. code-block:: python
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import tensorlayer as tl
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import random
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batch_size = 64
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im_size = [416, 416]
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n_data = len(imgs_file_list)
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jitter = 0.2
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def _data_pre_aug_fn(data):
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im, ann = data
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clas, coords = ann
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## change image brightness, contrast and saturation randomly
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im = tl.prepro.illumination(im, gamma=(0.5, 1.5),
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contrast=(0.5, 1.5), saturation=(0.5, 1.5), is_random=True)
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## flip randomly
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im, coords = tl.prepro.obj_box_horizontal_flip(im, coords,
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is_rescale=True, is_center=True, is_random=True)
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## randomly resize and crop image, it can have same effect as random zoom
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tmp0 = random.randint(1, int(im_size[0]*jitter))
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tmp1 = random.randint(1, int(im_size[1]*jitter))
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im, coords = tl.prepro.obj_box_imresize(im, coords,
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[im_size[0]+tmp0, im_size[1]+tmp1], is_rescale=True,
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interp='bicubic')
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im, clas, coords = tl.prepro.obj_box_crop(im, clas, coords,
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wrg=im_size[1], hrg=im_size[0], is_rescale=True,
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is_center=True, is_random=True)
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## rescale value from [0, 255] to [-1, 1] (optional)
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im = im / 127.5 - 1
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return im, [clas, coords]
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# randomly read a batch of image and the corresponding annotations
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idexs = tl.utils.get_random_int(min=0, max=n_data-1, number=batch_size)
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b_im_path = [imgs_file_list[i] for i in idexs]
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b_images = tl.prepro.threading_data(b_im_path, fn=tl.vis.read_image)
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b_ann = [ann_list[i] for i in idexs]
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# threading process
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data = tl.prepro.threading_data([_ for _ in zip(b_images, b_ann)],
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_data_pre_aug_fn)
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b_images2 = [d[0] for d in data]
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b_ann = [d[1] for d in data]
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# save all images
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for i in range(len(b_images)):
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tl.vis.draw_boxes_and_labels_to_image(b_images[i],
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ann_list[idexs[i]][0], ann_list[idexs[i]][1], [],
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classes, True, save_name='_bbox_vis_%d_original.png' % i)
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tl.vis.draw_boxes_and_labels_to_image((b_images2[i]+1)*127.5,
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b_ann[i][0], b_ann[i][1], [], classes, True,
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save_name='_bbox_vis_%d.png' % i)
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Image Aug with TF Dataset API
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- Example code for VOC `here <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_tf_dataset_voc.py>`__.
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Coordinate pixel unit to percentage
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coord_rescale
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Coordinates pixel unit to percentage
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coords_rescale
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Coordinate percentage to pixel unit
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coord_scale_to_pixelunit
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Coordinate [x_center, x_center, w, h] to up-left button-right
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coord_centroid_to_upleft_butright
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Coordinate up-left button-right to [x_center, x_center, w, h]
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coord_upleft_butright_to_centroid
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Coordinate [x_center, x_center, w, h] to up-left-width-high
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coord_centroid_to_upleft
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Coordinate up-left-width-high to [x_center, x_center, w, h]
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|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_coord_upleft_to_centroid
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|
|
Darknet format string to list
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|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: parse_darknet_ann_str_to_list
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|
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Darknet format split class and coordinate
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|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: parse_darknet_ann_list_to_cls_box
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|
|
|
Image Aug - Flip
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|
^^^^^^^^^^^^^^^^^^^^^^^^^
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.. autofunction:: obj_box_horizontal_flip
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|
|
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Image Aug - Resize
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|
^^^^^^^^^^^^^^^^^^^^^^^^^
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|
.. autofunction:: obj_box_imresize
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|
|
|
Image Aug - Crop
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|
^^^^^^^^^^^^^^^^^^^^^^^^^
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|
.. autofunction:: obj_box_crop
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|
|
|
Image Aug - Shift
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|
^^^^^^^^^^^^^^^^^^^^^^^^^
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|
.. autofunction:: obj_box_shift
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|
|
|
Image Aug - Zoom
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: obj_box_zoom
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|
|
|
Keypoints
|
|
------------
|
|
|
|
Image Aug - Crop
|
|
^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: keypoint_random_crop
|
|
|
|
Image Aug - Resize then Crop
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: keypoint_resize_random_crop
|
|
|
|
Image Aug - Rotate
|
|
^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: keypoint_random_rotate
|
|
|
|
Image Aug - Flip
|
|
^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: keypoint_random_flip
|
|
|
|
Image Aug - Resize
|
|
^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: keypoint_random_resize
|
|
|
|
Image Aug - Resize Shortest Edge
|
|
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: keypoint_random_resize_shortestedge
|
|
|
|
|
|
Sequence
|
|
---------
|
|
|
|
More related functions can be found in ``tensorlayer.nlp``.
|
|
|
|
Padding
|
|
^^^^^^^^^
|
|
.. autofunction:: pad_sequences
|
|
|
|
Remove Padding
|
|
^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: remove_pad_sequences
|
|
|
|
|
|
Process
|
|
^^^^^^^^^
|
|
.. autofunction:: process_sequences
|
|
|
|
Add Start ID
|
|
^^^^^^^^^^^^^^^
|
|
.. autofunction:: sequences_add_start_id
|
|
|
|
|
|
Add End ID
|
|
^^^^^^^^^^^^^^^
|
|
.. autofunction:: sequences_add_end_id
|
|
|
|
Add End ID after pad
|
|
^^^^^^^^^^^^^^^^^^^^^^^
|
|
.. autofunction:: sequences_add_end_id_after_pad
|
|
|
|
Get Mask
|
|
^^^^^^^^^
|
|
.. autofunction:: sequences_get_mask
|