API - Preprocessing

We provide abundant data augmentation and processing functions by using Numpy, Scipy, Threading and Queue. However, we recommend you to use TensorFlow operation function like tf.image.central_crop, more TensorFlow data augmentation method can be found here and tutorial_cifar10_tfrecord.py. Some of the code in this package are borrowed from Keras.

threading_data([data, fn, thread_count]) Process a batch of data by given function by threading.
rotation(x[, rg, is_random, row_index, …]) Rotate an image randomly or non-randomly.
rotation_multi(x[, rg, is_random, …]) Rotate multiple images with the same arguments, randomly or non-randomly.
crop(x, wrg, hrg[, is_random, row_index, …]) Randomly or centrally crop an image.
crop_multi(x, wrg, hrg[, is_random, …]) Randomly or centrally crop multiple images.
flip_axis(x[, axis, is_random]) Flip the axis of an image, such as flip left and right, up and down, randomly or non-randomly,
flip_axis_multi(x, axis[, is_random]) Flip the axises of multiple images together, such as flip left and right, up and down, randomly or non-randomly,
shift(x[, wrg, hrg, is_random, row_index, …]) Shift an image randomly or non-randomly.
shift_multi(x[, wrg, hrg, is_random, …]) Shift images with the same arguments, randomly or non-randomly.
shear(x[, intensity, is_random, row_index, …]) Shear an image randomly or non-randomly.
shear_multi(x[, intensity, is_random, …]) Shear images with the same arguments, randomly or non-randomly.
shear2(x[, shear, is_random, row_index, …]) Shear an image randomly or non-randomly.
shear_multi2(x[, shear, is_random, …]) Shear images with the same arguments, randomly or non-randomly.
swirl(x[, center, strength, radius, …]) Swirl an image randomly or non-randomly, see scikit-image swirl API and example.
swirl_multi(x[, center, strength, radius, …]) Swirl multiple images with the same arguments, randomly or non-randomly.
elastic_transform(x, alpha, sigma[, mode, …]) Elastic transformation for image as described in [Simard2003].
elastic_transform_multi(x, alpha, sigma[, …]) Elastic transformation for images as described in [Simard2003].
zoom(x[, zoom_range, is_random, row_index, …]) Zoom in and out of a single image, randomly or non-randomly.
zoom_multi(x[, zoom_range, is_random, …]) Zoom in and out of images with the same arguments, randomly or non-randomly.
brightness(x[, gamma, gain, is_random]) Change the brightness of a single image, randomly or non-randomly.
brightness_multi(x[, gamma, gain, is_random]) Change the brightness of multiply images, randomly or non-randomly.
illumination(x[, gamma, contrast, …]) Perform illumination augmentation for a single image, randomly or non-randomly.
rgb_to_hsv(rgb) Input RGB image [0~255] return HSV image [0~1].
hsv_to_rgb(hsv) Input HSV image [0~1] return RGB image [0~255].
adjust_hue(im[, hout, is_offset, is_clip, …]) Adjust hue of an RGB image.
imresize(x[, size, interp, mode]) Resize an image by given output size and method.
pixel_value_scale(im[, val, clip, is_random]) Scales each value in the pixels of the image.
samplewise_norm(x[, rescale, …]) Normalize an image by rescale, samplewise centering and samplewise centering in order.
featurewise_norm(x[, mean, std, epsilon]) Normalize every pixels by the same given mean and std, which are usually compute from all examples.
channel_shift(x, intensity[, is_random, …]) Shift the channels of an image, randomly or non-randomly, see numpy.rollaxis.
channel_shift_multi(x, intensity[, …]) Shift the channels of images with the same arguments, randomly or non-randomly, see numpy.rollaxis.
drop(x[, keep]) Randomly set some pixels to zero by a given keeping probability.
transform_matrix_offset_center(matrix, x, y) Return transform matrix offset center.
apply_transform(x, transform_matrix[, …]) Return transformed images by given transform_matrix from transform_matrix_offset_center.
projective_transform_by_points(x, src, dst) Projective transform by given coordinates, usually 4 coordinates.
array_to_img(x[, dim_ordering, scale]) Converts a numpy array to PIL image object (uint8 format).
find_contours(x[, level, fully_connected, …]) Find iso-valued contours in a 2D array for a given level value, returns list of (n, 2)-ndarrays see skimage.measure.find_contours.
pt2map([list_points, size, val]) Inputs a list of points, return a 2D image.
binary_dilation(x[, radius]) Return fast binary morphological dilation of an image.
dilation(x[, radius]) Return greyscale morphological dilation of an image, see skimage.morphology.dilation.
binary_erosion(x[, radius]) Return binary morphological erosion of an image, see skimage.morphology.binary_erosion.
erosion(x[, radius]) Return greyscale morphological erosion of an image, see skimage.morphology.erosion.
obj_box_coord_rescale([coord, shape]) Scale down one coordinates from pixel unit to the ratio of image size i.e.
obj_box_coords_rescale([coords, shape]) Scale down a list of coordinates from pixel unit to the ratio of image size i.e.
obj_box_coord_scale_to_pixelunit(coord[, shape]) Convert one coordinate [x, y, w (or x2), h (or y2)] in ratio format to image coordinate format.
obj_box_coord_centroid_to_upleft_butright(coord) Convert one coordinate [x_center, y_center, w, h] to [x1, y1, x2, y2] in up-left and botton-right format.
obj_box_coord_upleft_butright_to_centroid(coord) Convert one coordinate [x1, y1, x2, y2] to [x_center, y_center, w, h].
obj_box_coord_centroid_to_upleft(coord) Convert one coordinate [x_center, y_center, w, h] to [x, y, w, h].
obj_box_coord_upleft_to_centroid(coord) Convert one coordinate [x, y, w, h] to [x_center, y_center, w, h].
parse_darknet_ann_str_to_list(annotations) Input string format of class, x, y, w, h, return list of list format.
parse_darknet_ann_list_to_cls_box(annotations) Parse darknet annotation format into two lists for class and bounding box.
obj_box_horizontal_flip(im[, coords, …]) Left-right flip the image and coordinates for object detection.
obj_box_imresize(im[, coords, size, interp, …]) Resize an image, and compute the new bounding box coordinates.
obj_box_crop(im[, classes, coords, wrg, …]) Randomly or centrally crop an image, and compute the new bounding box coordinates.
obj_box_shift(im[, classes, coords, wrg, …]) Shift an image randomly or non-randomly, and compute the new bounding box coordinates.
obj_box_zoom(im[, classes, coords, …]) Zoom in and out of a single image, randomly or non-randomly, and compute the new bounding box coordinates.
pad_sequences(sequences[, maxlen, dtype, …]) Pads each sequence to the same length: the length of the longest sequence.
remove_pad_sequences(sequences[, pad_id]) Remove padding.
process_sequences(sequences[, end_id, …]) Set all tokens(ids) after END token to the padding value, and then shorten (option) it to the maximum sequence length in this batch.
sequences_add_start_id(sequences[, …]) Add special start token(id) in the beginning of each sequence.
sequences_add_end_id(sequences[, end_id]) Add special end token(id) in the end of each sequence.
sequences_add_end_id_after_pad(sequences[, …]) Add special end token(id) in the end of each sequence.
sequences_get_mask(sequences[, pad_val]) Return mask for sequences.

Threading

tensorlayer.prepro.threading_data(data=None, fn=None, thread_count=None, **kwargs)[source]

Process a batch of data by given function by threading.

Usually be used for data augmentation.

Parameters:
  • data (numpy.array or others) – The data to be processed.
  • thread_count (int) – The number of threads to use.
  • fn (function) – The function for data processing.
  • args (more) – Ssee Examples below.

Examples

Process images.

>>> images, _, _, _ = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3))
>>> images = tl.prepro.threading_data(images[0:32], tl.prepro.zoom, zoom_range=[0.5, 1])

Customized image preprocessing function.

>>> def distort_img(x):
>>>     x = tl.prepro.flip_axis(x, axis=0, is_random=True)
>>>     x = tl.prepro.flip_axis(x, axis=1, is_random=True)
>>>     x = tl.prepro.crop(x, 100, 100, is_random=True)
>>>     return x
>>> images = tl.prepro.threading_data(images, distort_img)

Process images and masks together (Usually be used for image segmentation).

>>> X, Y --> [batch_size, row, col, 1]
>>> data = tl.prepro.threading_data([_ for _ in zip(X, Y)], tl.prepro.zoom_multi, zoom_range=[0.5, 1], is_random=True)
data --> [batch_size, 2, row, col, 1]
>>> X_, Y_ = data.transpose((1,0,2,3,4))
X_, Y_ --> [batch_size, row, col, 1]
>>> tl.vis.save_image(X_, 'images.png')
>>> tl.vis.save_image(Y_, 'masks.png')

Process images and masks together by using thread_count.

>>> X, Y --> [batch_size, row, col, 1]
>>> data = tl.prepro.threading_data(X, tl.prepro.zoom_multi, 8, zoom_range=[0.5, 1], is_random=True)
data --> [batch_size, 2, row, col, 1]
>>> X_, Y_ = data.transpose((1,0,2,3,4))
X_, Y_ --> [batch_size, row, col, 1]
>>> tl.vis.save_image(X_, 'after.png')
>>> tl.vis.save_image(Y_, 'before.png')

Customized function for processing images and masks together.

>>> def distort_img(data):
>>>    x, y = data
>>>    x, y = tl.prepro.flip_axis_multi([x, y], axis=0, is_random=True)
>>>    x, y = tl.prepro.flip_axis_multi([x, y], axis=1, is_random=True)
>>>    x, y = tl.prepro.crop_multi([x, y], 100, 100, is_random=True)
>>>    return x, y
>>> X, Y --> [batch_size, row, col, channel]
>>> data = tl.prepro.threading_data([_ for _ in zip(X, Y)], distort_img)
>>> X_, Y_ = data.transpose((1,0,2,3,4))
Returns:The processed results.
Return type:list or numpyarray

References

Images

  • These functions only apply on a single image, use threading_data to apply multiple threading see tutorial_image_preprocess.py.
  • All functions have argument is_random.
  • All functions end with *_multi process all images together, usually be used for image segmentation i.e. the input and output image should be matched.

Rotation

tensorlayer.prepro.rotation(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Rotate an image randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • rg (int or float) – Degree to rotate, usually 0 ~ 180.
  • is_random (boolean) – If True, randomly rotate. Default is False
  • col_index and channel_index (row_index) – Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
  • fill_mode (str) – Method to fill missing pixel, default nearest, more options constant, reflect or wrap, see scipy ndimage affine_transform
  • cval (float) – Value used for points outside the boundaries of the input if mode=`constant`. Default is 0.0
  • order (int) – The order of interpolation. The order has to be in the range 0-5. See tl.prepro.apply_transform and scipy ndimage affine_transform
Returns:

A processed image.

Return type:

numpy.array

Examples

>>> x --> [row, col, 1]
>>> x = tl.prepro.rotation(x, rg=40, is_random=False)
>>> tl.vis.save_image(x, 'im.png')
tensorlayer.prepro.rotation_multi(x, rg=20, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Rotate multiple images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.rotation.
Returns:

A list of processed images.

Return type:

numpy.array

Examples

>>> x, y --> [row, col, 1]  greyscale
>>> x, y = tl.prepro.rotation_multi([x, y], rg=90, is_random=False)

Crop

tensorlayer.prepro.crop(x, wrg, hrg, is_random=False, row_index=0, col_index=1)[source]

Randomly or centrally crop an image.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • wrg (int) – Size of width.
  • hrg (int) – Size of height.
  • is_random (boolean,) – If True, randomly crop, else central crop. Default is False.
  • row_index (int) – index of row.
  • col_index (int) – index of column.
Returns:

A processed image.

Return type:

numpy.array

tensorlayer.prepro.crop_multi(x, wrg, hrg, is_random=False, row_index=0, col_index=1)[source]

Randomly or centrally crop multiple images.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.crop.
Returns:

A list of processed images.

Return type:

numpy.array

Flip

tensorlayer.prepro.flip_axis(x, axis=1, is_random=False)[source]

Flip the axis of an image, such as flip left and right, up and down, randomly or non-randomly,

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • axis (int) –
    Which axis to flip.
    • 0, flip up and down
    • 1, flip left and right
    • 2, flip channel
  • is_random (boolean) – If True, randomly flip. Default is False.
Returns:

A processed image.

Return type:

numpy.array

tensorlayer.prepro.flip_axis_multi(x, axis, is_random=False)[source]

Flip the axises of multiple images together, such as flip left and right, up and down, randomly or non-randomly,

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.flip_axis.
Returns:

A list of processed images.

Return type:

numpy.array

Shift

tensorlayer.prepro.shift(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Shift an image randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • wrg (float) – Percentage of shift in axis x, usually -0.25 ~ 0.25.
  • hrg (float) – Percentage of shift in axis y, usually -0.25 ~ 0.25.
  • is_random (boolean) – If True, randomly shift. Default is False.
  • col_index and channel_index (row_index) – Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
  • fill_mode (str) – Method to fill missing pixel, default nearest, more options constant, reflect or wrap, see scipy ndimage affine_transform
  • cval (float) – Value used for points outside the boundaries of the input if mode=’constant’. Default is 0.0.
  • order (int) – The order of interpolation. The order has to be in the range 0-5. See tl.prepro.apply_transform and scipy ndimage affine_transform
Returns:

A processed image.

Return type:

numpy.array

tensorlayer.prepro.shift_multi(x, wrg=0.1, hrg=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Shift images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.shift.
Returns:

A list of processed images.

Return type:

numpy.array

Shear

tensorlayer.prepro.shear(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Shear an image randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • intensity (float) – Percentage of shear, usually -0.5 ~ 0.5 (is_random==True), 0 ~ 0.5 (is_random==False), you can have a quick try by shear(X, 1).
  • is_random (boolean) – If True, randomly shear. Default is False.
  • col_index and channel_index (row_index) – Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
  • fill_mode (str) – Method to fill missing pixel, default nearest, more options constant, reflect or wrap, see and scipy ndimage affine_transform
  • cval (float) – Value used for points outside the boundaries of the input if mode=’constant’. Default is 0.0.
  • order (int) – The order of interpolation. The order has to be in the range 0-5. See tl.prepro.apply_transform and scipy ndimage affine_transform
Returns:

A processed image.

Return type:

numpy.array

References

tensorlayer.prepro.shear_multi(x, intensity=0.1, is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Shear images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.shear.
Returns:

A list of processed images.

Return type:

numpy.array

Shear V2

tensorlayer.prepro.shear2(x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Shear an image randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • shear (tuple of two floats) – Percentage of shear for height and width direction (0, 1).
  • is_random (boolean) – If True, randomly shear. Default is False.
  • col_index and channel_index (row_index) – Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
  • fill_mode (str) – Method to fill missing pixel, default nearest, more options constant, reflect or wrap, see scipy ndimage affine_transform
  • cval (float) – Value used for points outside the boundaries of the input if mode=’constant’. Default is 0.0.
  • order (int) – The order of interpolation. The order has to be in the range 0-5. See tl.prepro.apply_transform and scipy ndimage affine_transform
Returns:

A processed image.

Return type:

numpy.array

References

tensorlayer.prepro.shear_multi2(x, shear=(0.1, 0.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Shear images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.shear2.
Returns:

A list of processed images.

Return type:

numpy.array

Swirl

tensorlayer.prepro.swirl(x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False)[source]

Swirl an image randomly or non-randomly, see scikit-image swirl API and example.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • center (tuple or 2 int or None) – Center coordinate of transformation (optional).
  • strength (float) – The amount of swirling applied.
  • radius (float) – The extent of the swirl in pixels. The effect dies out rapidly beyond radius.
  • rotation (float) – Additional rotation applied to the image, usually [0, 360], relates to center.
  • output_shape (tuple of 2 int or None) – Shape of the output image generated (height, width). By default the shape of the input image is preserved.
  • order (int, optional) – The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See skimage.transform.warp for detail.
  • mode (str) – One of constant (default), edge, symmetric reflect and wrap. Points outside the boundaries of the input are filled according to the given mode, with constant used as the default. Modes match the behaviour of numpy.pad.
  • cval (float) – Used in conjunction with mode constant, the value outside the image boundaries.
  • clip (boolean) – Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
  • preserve_range (boolean) – Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
  • is_random (boolean,) –
    If True, random swirl. Default is False.
    • random center = [(0 ~ x.shape[0]), (0 ~ x.shape[1])]
    • random strength = [0, strength]
    • random radius = [1e-10, radius]
    • random rotation = [-rotation, rotation]
Returns:

A processed image.

Return type:

numpy.array

Examples

>>> x --> [row, col, 1] greyscale
>>> x = tl.prepro.swirl(x, strength=4, radius=100)
tensorlayer.prepro.swirl_multi(x, center=None, strength=1, radius=100, rotation=0, output_shape=None, order=1, mode='constant', cval=0, clip=True, preserve_range=False, is_random=False)[source]

Swirl multiple images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.swirl.
Returns:

A list of processed images.

Return type:

numpy.array

Elastic transform

tensorlayer.prepro.elastic_transform(x, alpha, sigma, mode='constant', cval=0, is_random=False)[source]

Elastic transformation for image as described in [Simard2003].

Parameters:
  • x (numpy.array) – A greyscale image.
  • alpha (float) – Alpha value for elastic transformation.
  • sigma (float or sequence of float) – The smaller the sigma, the more transformation. Standard deviation for Gaussian kernel. The standard deviations of the Gaussian filter are given for each axis as a sequence, or as a single number, in which case it is equal for all axes.
  • mode (str) – See scipy.ndimage.filters.gaussian_filter. Default is constant.
  • cval (float,) – Used in conjunction with mode of constant, the value outside the image boundaries.
  • is_random (boolean) – Default is False.
Returns:

A processed image.

Return type:

numpy.array

Examples

>>> x = tl.prepro.elastic_transform(x, alpha=x.shape[1]*3, sigma=x.shape[1]*0.07)

References

tensorlayer.prepro.elastic_transform_multi(x, alpha, sigma, mode='constant', cval=0, is_random=False)[source]

Elastic transformation for images as described in [Simard2003].

Parameters:
  • x (list of numpy.array) – List of greyscale images.
  • others (args) – See tl.prepro.elastic_transform.
Returns:

A list of processed images.

Return type:

numpy.array

Zoom

tensorlayer.prepro.zoom(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Zoom in and out of a single image, randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • zoom_range (list or tuple) –
    Zoom range for height and width.
    • If is_random=False, (h, w) are the fixed zoom factor for row and column axies, factor small than one is zoom in.
    • If is_random=True, (h, w) are (min zoom out, max zoom out) for x and y with different random zoom in/out factor, e.g (0.5, 1) zoom in 1~2 times.
  • is_random (boolean) – If True, randomly zoom. Default is False.
  • col_index and channel_index (row_index) – Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
  • fill_mode (str) – Method to fill missing pixel, default nearest, more options constant, reflect or wrap, see scipy ndimage affine_transform
  • cval (float) – Value used for points outside the boundaries of the input if mode=’constant’. Default is 0.0.
  • order (int) – The order of interpolation. The order has to be in the range 0-5. See tl.prepro.apply_transform and scipy ndimage affine_transform
Returns:

A processed image.

Return type:

numpy.array

tensorlayer.prepro.zoom_multi(x, zoom_range=(0.9, 1.1), is_random=False, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Zoom in and out of images with the same arguments, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.zoom.
Returns:

A list of processed images.

Return type:

numpy.array

Brightness

tensorlayer.prepro.brightness(x, gamma=1, gain=1, is_random=False)[source]

Change the brightness of a single image, randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • gamma (float) –
    Non negative real number. Default value is 1.
    • Small than 1 means brighter.
    • If is_random is True, gamma in a range of (1-gamma, 1+gamma).
  • gain (float) – The constant multiplier. Default value is 1.
  • is_random (boolean) – If True, randomly change brightness. Default is False.
Returns:

A processed image.

Return type:

numpy.array

References

tensorlayer.prepro.brightness_multi(x, gamma=1, gain=1, is_random=False)[source]

Change the brightness of multiply images, randomly or non-randomly. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpyarray) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.brightness.
Returns:

A list of processed images.

Return type:

numpy.array

Brightness, contrast and saturation

tensorlayer.prepro.illumination(x, gamma=1.0, contrast=1.0, saturation=1.0, is_random=False)[source]

Perform illumination augmentation for a single image, randomly or non-randomly.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • gamma (float) –
    Change brightness (the same with tl.prepro.brightness)
    • if is_random=False, one float number, small than one means brighter, greater than one means darker.
    • if is_random=True, tuple of two float numbers, (min, max).
  • contrast (float) –
    Change contrast.
    • if is_random=False, one float number, small than one means blur.
    • if is_random=True, tuple of two float numbers, (min, max).
  • saturation (float) –
    Change saturation.
    • if is_random=False, one float number, small than one means unsaturation.
    • if is_random=True, tuple of two float numbers, (min, max).
  • is_random (boolean) – If True, randomly change illumination. Default is False.
Returns:

A processed image.

Return type:

numpy.array

Examples

Random

>>> x = tl.prepro.illumination(x, gamma=(0.5, 5.0), contrast=(0.3, 1.0), saturation=(0.7, 1.0), is_random=True)

Non-random

>>> x = tl.prepro.illumination(x, 0.5, 0.6, 0.8, is_random=False)

RGB to HSV

tensorlayer.prepro.rgb_to_hsv(rgb)[source]

Input RGB image [0~255] return HSV image [0~1].

Parameters:rgb (numpy.array) – An image with values between 0 and 255.
Returns:A processed image.
Return type:numpy.array

HSV to RGB

tensorlayer.prepro.hsv_to_rgb(hsv)[source]

Input HSV image [0~1] return RGB image [0~255].

Parameters:hsv (numpy.array) – An image with values between 0.0 and 1.0
Returns:A processed image.
Return type:numpy.array

Adjust Hue

tensorlayer.prepro.adjust_hue(im, hout=0.66, is_offset=True, is_clip=True, is_random=False)[source]

Adjust hue of an RGB image.

This is a convenience method that converts an RGB image to float representation, converts it to HSV, add an offset to the hue channel, converts back to RGB and then back to the original data type. For TF, see tf.image.adjust_hue.and tf.image.random_hue.

Parameters:
  • im (numpy.array) – An image with values between 0 and 255.
  • hout (float) –
    The scale value for adjusting hue.
    • If is_offset is False, set all hue values to this value. 0 is red; 0.33 is green; 0.66 is blue.
    • If is_offset is True, add this value as the offset to the hue channel.
  • is_offset (boolean) – Whether hout is added on HSV as offset or not. Default is True.
  • is_clip (boolean) – If HSV value smaller than 0, set to 0. Default is True.
  • is_random (boolean) – If True, randomly change hue. Default is False.
Returns:

A processed image.

Return type:

numpy.array

Examples

Random, add a random value between -0.2 and 0.2 as the offset to every hue values.

>>> im_hue = tl.prepro.adjust_hue(image, hout=0.2, is_offset=True, is_random=False)

Non-random, make all hue to green.

>>> im_green = tl.prepro.adjust_hue(image, hout=0.66, is_offset=False, is_random=False)

References

Resize

tensorlayer.prepro.imresize(x, size=None, interp='bicubic', mode=None)[source]

Resize an image by given output size and method.

Warning, this function will rescale the value to [0, 255].

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • size (list of 2 int or None) – For height and width.
  • interp (str) – Interpolation method for re-sizing (nearest, lanczos, bilinear, bicubic (default) or cubic).
  • mode (str) – The PIL image mode (P, L, etc.) to convert arr before resizing.
Returns:

A processed image.

Return type:

numpy.array

References

Pixel value scale

tensorlayer.prepro.pixel_value_scale(im, val=0.9, clip=None, is_random=False)[source]

Scales each value in the pixels of the image.

Parameters:
  • im (numpy.array) – An image.
  • val (float) –
    The scale value for changing pixel value.
    • If is_random=False, multiply this value with all pixels.
    • If is_random=True, multiply a value between [1-val, 1+val] with all pixels.
  • clip (tuple of 2 numbers) – The minimum and maximum value.
  • is_random (boolean) – If True, see val.
Returns:

A processed image.

Return type:

numpy.array

Examples

Random

>>> im = pixel_value_scale(im, 0.1, [0, 255], is_random=True)

Non-random

>>> im = pixel_value_scale(im, 0.9, [0, 255], is_random=False)

Normalization

tensorlayer.prepro.samplewise_norm(x, rescale=None, samplewise_center=False, samplewise_std_normalization=False, channel_index=2, epsilon=1e-07)[source]

Normalize an image by rescale, samplewise centering and samplewise centering in order.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • rescale (float) – Rescaling factor. If None or 0, no rescaling is applied, otherwise we multiply the data by the value provided (before applying any other transformation)
  • samplewise_center (boolean) – If True, set each sample mean to 0.
  • samplewise_std_normalization (boolean) – If True, divide each input by its std.
  • epsilon (float) – A small position value for dividing standard deviation.
Returns:

A processed image.

Return type:

numpy.array

Examples

>>> x = samplewise_norm(x, samplewise_center=True, samplewise_std_normalization=True)
>>> print(x.shape, np.mean(x), np.std(x))
(160, 176, 1), 0.0, 1.0

Notes

When samplewise_center and samplewise_std_normalization are True. - For greyscale image, every pixels are subtracted and divided by the mean and std of whole image. - For RGB image, every pixels are subtracted and divided by the mean and std of this pixel i.e. the mean and std of a pixel is 0 and 1.

tensorlayer.prepro.featurewise_norm(x, mean=None, std=None, epsilon=1e-07)[source]

Normalize every pixels by the same given mean and std, which are usually compute from all examples.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • mean (float) – Value for subtraction.
  • std (float) – Value for division.
  • epsilon (float) – A small position value for dividing standard deviation.
Returns:

A processed image.

Return type:

numpy.array

Channel shift

tensorlayer.prepro.channel_shift(x, intensity, is_random=False, channel_index=2)[source]

Shift the channels of an image, randomly or non-randomly, see numpy.rollaxis.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • intensity (float) – Intensity of shifting.
  • is_random (boolean) – If True, randomly shift. Default is False.
  • channel_index (int) – Index of channel. Default is 2.
Returns:

A processed image.

Return type:

numpy.array

tensorlayer.prepro.channel_shift_multi(x, intensity, is_random=False, channel_index=2)[source]

Shift the channels of images with the same arguments, randomly or non-randomly, see numpy.rollaxis. Usually be used for image segmentation which x=[X, Y], X and Y should be matched.

Parameters:
  • x (list of numpy.array) – List of images with dimension of [n_images, row, col, channel] (default).
  • others (args) – See tl.prepro.channel_shift.
Returns:

A list of processed images.

Return type:

numpy.array

Noise

tensorlayer.prepro.drop(x, keep=0.5)[source]

Randomly set some pixels to zero by a given keeping probability.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] or [row, col].
  • keep (float) – The keeping probability (0, 1), the lower more values will be set to zero.
Returns:

A processed image.

Return type:

numpy.array

Transform matrix offset

tensorlayer.prepro.transform_matrix_offset_center(matrix, x, y)[source]

Return transform matrix offset center.

Parameters:
  • matrix (numpy.array) – Transform matrix.
  • and y (x) – Size of image.
Returns:

The transform matrix.

Return type:

numpy.array

Examples

  • See tl.prepro.rotation, tl.prepro.shear, tl.prepro.zoom.

Apply affine transform by matrix

tensorlayer.prepro.apply_transform(x, transform_matrix, channel_index=2, fill_mode='nearest', cval=0.0, order=1)[source]

Return transformed images by given transform_matrix from transform_matrix_offset_center.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • transform_matrix (numpy.array) – Transform matrix (offset center), can be generated by transform_matrix_offset_center
  • channel_index (int) – Index of channel, default 2.
  • fill_mode (str) – Method to fill missing pixel, default nearest, more options constant, reflect or wrap, see scipy ndimage affine_transform
  • cval (float) – Value used for points outside the boundaries of the input if mode=’constant’. Default is 0.0
  • order (int) –
    The order of interpolation. The order has to be in the range 0-5:
Returns:

A processed image.

Return type:

numpy.array

Examples

  • See tl.prepro.rotation, tl.prepro.shift, tl.prepro.shear, tl.prepro.zoom.

Projective transform by points

tensorlayer.prepro.projective_transform_by_points(x, src, dst, map_args=None, output_shape=None, order=1, mode='constant', cval=0.0, clip=True, preserve_range=False)[source]

Projective transform by given coordinates, usually 4 coordinates.

see scikit-image.

Parameters:
  • x (numpy.array) – An image with dimension of [row, col, channel] (default).
  • src (list or numpy) – The original coordinates, usually 4 coordinates of (width, height).
  • dst (list or numpy) – The coordinates after transformation, the number of coordinates is the same with src.
  • map_args (dictionary or None) – Keyword arguments passed to inverse map.
  • output_shape (tuple of 2 int) – Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified.
  • order (int) –
    The order of interpolation. The order has to be in the range 0-5:
    • 0 Nearest-neighbor
    • 1 Bi-linear (default)
    • 2 Bi-quadratic
    • 3 Bi-cubic
    • 4 Bi-quartic
    • 5 Bi-quintic
  • mode (str) – One of constant (default), edge, symmetric, reflect or wrap. Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad.
  • cval (float) – Used in conjunction with mode constant, the value outside the image boundaries.
  • clip (boolean) – Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range.
  • preserve_range (boolean) – Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.
Returns:

A processed image.

Return type:

numpy.array

Examples

Assume X is an image from CIFAR-10, i.e. shape == (32, 32, 3)

>>> src = [[0,0],[0,32],[32,0],[32,32]]     # [w, h]
>>> dst = [[10,10],[0,32],[32,0],[32,32]]
>>> x = tl.prepro.projective_transform_by_points(X, src, dst)

References

Numpy and PIL

tensorlayer.prepro.array_to_img(x, dim_ordering=(0, 1, 2), scale=True)[source]

Converts a numpy array to PIL image object (uint8 format).

Parameters:
  • x (numpy.array) – An image with dimension of 3 and channels of 1 or 3.
  • dim_ordering (tuple of 3 int) – Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).
  • scale (boolean) – If True, converts image to [0, 255] from any range of value like [-1, 2]. Default is True.
Returns:

An image.

Return type:

PIL.image

References

PIL Image.fromarray

Find contours

tensorlayer.prepro.find_contours(x, level=0.8, fully_connected='low', positive_orientation='low')[source]

Find iso-valued contours in a 2D array for a given level value, returns list of (n, 2)-ndarrays see skimage.measure.find_contours.

Parameters:
  • x (2D ndarray of double.) – Input data in which to find contours.
  • level (float) – Value along which to find contours in the array.
  • fully_connected (str) – Either low or high. Indicates whether array elements below the given level value are to be considered fully-connected (and hence elements above the value will only be face connected), or vice-versa. (See notes below for details.)
  • positive_orientation (str) – Either low or high. Indicates whether the output contours will produce positively-oriented polygons around islands of low- or high-valued elements. If low then contours will wind counter-clockwise around elements below the iso-value. Alternately, this means that low-valued elements are always on the left of the contour.
Returns:

Each contour is an ndarray of shape (n, 2), consisting of n (row, column) coordinates along the contour.

Return type:

list of (n,2)-ndarrays

Points to Image

tensorlayer.prepro.pt2map(list_points=None, size=(100, 100), val=1)[source]

Inputs a list of points, return a 2D image.

Parameters:
  • list_points (list of 2 int) – [[x, y], [x, y]..] for point coordinates.
  • size (tuple of 2 int) – (w, h) for output size.
  • val (float or int) – For the contour value.
Returns:

An image.

Return type:

numpy.array

Binary dilation

tensorlayer.prepro.binary_dilation(x, radius=3)[source]

Return fast binary morphological dilation of an image. see skimage.morphology.binary_dilation.

Parameters:
  • x (2D array) – A binary image.
  • radius (int) – For the radius of mask.
Returns:

A processed binary image.

Return type:

numpy.array

Greyscale dilation

tensorlayer.prepro.dilation(x, radius=3)[source]

Return greyscale morphological dilation of an image, see skimage.morphology.dilation.

Parameters:
  • x (2D array) – An greyscale image.
  • radius (int) – For the radius of mask.
Returns:

A processed greyscale image.

Return type:

numpy.array

Binary erosion

tensorlayer.prepro.binary_erosion(x, radius=3)[source]

Return binary morphological erosion of an image, see skimage.morphology.binary_erosion.

Parameters:
  • x (2D array) – A binary image.
  • radius (int) – For the radius of mask.
Returns:

A processed binary image.

Return type:

numpy.array

Greyscale erosion

tensorlayer.prepro.erosion(x, radius=3)[source]

Return greyscale morphological erosion of an image, see skimage.morphology.erosion.

Parameters:
  • x (2D array) – A greyscale image.
  • radius (int) – For the radius of mask.
Returns:

A processed greyscale image.

Return type:

numpy.array

Object detection

Tutorial for Image Aug

Hi, here is an example for image augmentation on VOC dataset.

import tensorlayer as tl

## download VOC 2012 dataset
imgs_file_list, _, _, _, classes, _, _,\
    _, objs_info_list, _ = tl.files.load_voc_dataset(dataset="2012")

## parse annotation and convert it into list format
ann_list = []
for info in objs_info_list:
    ann = tl.prepro.parse_darknet_ann_str_to_list(info)
    c, b = tl.prepro.parse_darknet_ann_list_to_cls_box(ann)
    ann_list.append([c, b])

# read and save one image
idx = 2  # you can select your own image
image = tl.vis.read_image(imgs_file_list[idx])
tl.vis.draw_boxes_and_labels_to_image(image, ann_list[idx][0],
     ann_list[idx][1], [], classes, True, save_name='_im_original.png')

# left right flip
im_flip, coords = tl.prepro.obj_box_horizontal_flip(image,
        ann_list[idx][1], is_rescale=True, is_center=True, is_random=False)
tl.vis.draw_boxes_and_labels_to_image(im_flip, ann_list[idx][0],
        coords, [], classes, True, save_name='_im_flip.png')

# resize
im_resize, coords = tl.prepro.obj_box_imresize(image,
        coords=ann_list[idx][1], size=[300, 200], is_rescale=True)
tl.vis.draw_boxes_and_labels_to_image(im_resize, ann_list[idx][0],
        coords, [], classes, True, save_name='_im_resize.png')

# crop
im_crop, clas, coords = tl.prepro.obj_box_crop(image, ann_list[idx][0],
         ann_list[idx][1], wrg=200, hrg=200,
         is_rescale=True, is_center=True, is_random=False)
tl.vis.draw_boxes_and_labels_to_image(im_crop, clas, coords, [],
         classes, True, save_name='_im_crop.png')

# shift
im_shfit, clas, coords = tl.prepro.obj_box_shift(image, ann_list[idx][0],
        ann_list[idx][1], wrg=0.1, hrg=0.1,
        is_rescale=True, is_center=True, is_random=False)
tl.vis.draw_boxes_and_labels_to_image(im_shfit, clas, coords, [],
        classes, True, save_name='_im_shift.png')

# zoom
im_zoom, clas, coords = tl.prepro.obj_box_zoom(image, ann_list[idx][0],
        ann_list[idx][1], zoom_range=(1.3, 0.7),
        is_rescale=True, is_center=True, is_random=False)
tl.vis.draw_boxes_and_labels_to_image(im_zoom, clas, coords, [],
        classes, True, save_name='_im_zoom.png')

In practice, you may want to use threading method to process a batch of images as follows.

import tensorlayer as tl
import random

batch_size = 64
im_size = [416, 416]
n_data = len(imgs_file_list)
jitter = 0.2
def _data_pre_aug_fn(data):
    im, ann = data
    clas, coords = ann
    ## change image brightness, contrast and saturation randomly
    im = tl.prepro.illumination(im, gamma=(0.5, 1.5),
             contrast=(0.5, 1.5), saturation=(0.5, 1.5), is_random=True)
    ## flip randomly
    im, coords = tl.prepro.obj_box_horizontal_flip(im, coords,
             is_rescale=True, is_center=True, is_random=True)
    ## randomly resize and crop image, it can have same effect as random zoom
    tmp0 = random.randint(1, int(im_size[0]*jitter))
    tmp1 = random.randint(1, int(im_size[1]*jitter))
    im, coords = tl.prepro.obj_box_imresize(im, coords,
            [im_size[0]+tmp0, im_size[1]+tmp1], is_rescale=True,
             interp='bicubic')
    im, clas, coords = tl.prepro.obj_box_crop(im, clas, coords,
             wrg=im_size[1], hrg=im_size[0], is_rescale=True,
             is_center=True, is_random=True)
    ## rescale value from [0, 255] to [-1, 1] (optional)
    im = im / 127.5 - 1
    return im, [clas, coords]

# randomly read a batch of image and the corresponding annotations
idexs = tl.utils.get_random_int(min=0, max=n_data-1, number=batch_size)
b_im_path = [imgs_file_list[i] for i in idexs]
b_images = tl.prepro.threading_data(b_im_path, fn=tl.vis.read_image)
b_ann = [ann_list[i] for i in idexs]

# threading process
data = tl.prepro.threading_data([_ for _ in zip(b_images, b_ann)],
              _data_pre_aug_fn)
b_images2 = [d[0] for d in data]
b_ann = [d[1] for d in data]

# save all images
for i in range(len(b_images)):
    tl.vis.draw_boxes_and_labels_to_image(b_images[i],
             ann_list[idexs[i]][0], ann_list[idexs[i]][1], [],
             classes, True, save_name='_bbox_vis_%d_original.png' % i)
    tl.vis.draw_boxes_and_labels_to_image((b_images2[i]+1)*127.5,
             b_ann[i][0], b_ann[i][1], [], classes, True,
             save_name='_bbox_vis_%d.png' % i)

Image Aug with TF Dataset API

  • Example code for VOC here.

Coordinate pixel unit to percentage

tensorlayer.prepro.obj_box_coord_rescale(coord=None, shape=None)[source]

Scale down one coordinates from pixel unit to the ratio of image size i.e. in the range of [0, 1]. It is the reverse process of obj_box_coord_scale_to_pixelunit.

Parameters:
  • coords (list of 4 int or None) – One coordinates of one image e.g. [x, y, w, h].
  • shape (list of 2 int or None) – For [height, width].
Returns:

New bounding box.

Return type:

list of 4 numbers

Examples

>>> coord = tl.prepro.obj_box_coord_rescale(coord=[30, 40, 50, 50], shape=[100, 100])
  [0.3, 0.4, 0.5, 0.5]

Coordinates pixel unit to percentage

tensorlayer.prepro.obj_box_coords_rescale(coords=None, shape=None)[source]

Scale down a list of coordinates from pixel unit to the ratio of image size i.e. in the range of [0, 1].

Parameters:
  • coords (list of list of 4 ints or None) – For coordinates of more than one images .e.g.[[x, y, w, h], [x, y, w, h], …].
  • shape (list of 2 int or None) – 【height, width].
Returns:

A list of new bounding boxes.

Return type:

list of list of 4 numbers

Examples

>>> coords = obj_box_coords_rescale(coords=[[30, 40, 50, 50], [10, 10, 20, 20]], shape=[100, 100])
>>> print(coords)
  [[0.3, 0.4, 0.5, 0.5], [0.1, 0.1, 0.2, 0.2]]
>>> coords = obj_box_coords_rescale(coords=[[30, 40, 50, 50]], shape=[50, 100])
>>> print(coords)
  [[0.3, 0.8, 0.5, 1.0]]
>>> coords = obj_box_coords_rescale(coords=[[30, 40, 50, 50]], shape=[100, 200])
>>> print(coords)
  [[0.15, 0.4, 0.25, 0.5]]
Returns:New coordinates.
Return type:list of 4 numbers

Coordinate percentage to pixel unit

tensorlayer.prepro.obj_box_coord_scale_to_pixelunit(coord, shape=None)[source]

Convert one coordinate [x, y, w (or x2), h (or y2)] in ratio format to image coordinate format. It is the reverse process of obj_box_coord_rescale.

Parameters:
  • coord (list of 4 float) – One coordinate of one image [x, y, w (or x2), h (or y2)] in ratio format, i.e value range [0~1].
  • shape (tuple of 2 or None) – For [height, width].
Returns:

New bounding box.

Return type:

list of 4 numbers

Examples

>>> x, y, x2, y2 = tl.prepro.obj_box_coord_scale_to_pixelunit([0.2, 0.3, 0.5, 0.7], shape=(100, 200, 3))
  [40, 30, 100, 70]

Coordinate [x_center, x_center, w, h] to up-left button-right

tensorlayer.prepro.obj_box_coord_centroid_to_upleft_butright(coord, to_int=False)[source]

Convert one coordinate [x_center, y_center, w, h] to [x1, y1, x2, y2] in up-left and botton-right format.

Parameters:
  • coord (list of 4 int/float) – One coordinate.
  • to_int (boolean) – Whether to convert output as integer.
Returns:

New bounding box.

Return type:

list of 4 numbers

Examples

>>> coord = obj_box_coord_centroid_to_upleft_butright([30, 40, 20, 20])
  [20, 30, 40, 50]

Coordinate up-left button-right to [x_center, x_center, w, h]

tensorlayer.prepro.obj_box_coord_upleft_butright_to_centroid(coord)[source]

Convert one coordinate [x1, y1, x2, y2] to [x_center, y_center, w, h]. It is the reverse process of obj_box_coord_centroid_to_upleft_butright.

Parameters:coord (list of 4 int/float) – One coordinate.
Returns:New bounding box.
Return type:list of 4 numbers

Coordinate [x_center, x_center, w, h] to up-left-width-high

tensorlayer.prepro.obj_box_coord_centroid_to_upleft(coord)[source]

Convert one coordinate [x_center, y_center, w, h] to [x, y, w, h]. It is the reverse process of obj_box_coord_upleft_to_centroid.

Parameters:coord (list of 4 int/float) – One coordinate.
Returns:New bounding box.
Return type:list of 4 numbers

Coordinate up-left-width-high to [x_center, x_center, w, h]

tensorlayer.prepro.obj_box_coord_upleft_to_centroid(coord)[source]

Convert one coordinate [x, y, w, h] to [x_center, y_center, w, h]. It is the reverse process of obj_box_coord_centroid_to_upleft.

Parameters:coord (list of 4 int/float) – One coordinate.
Returns:New bounding box.
Return type:list of 4 numbers

Darknet format string to list

tensorlayer.prepro.parse_darknet_ann_str_to_list(annotations)[source]

Input string format of class, x, y, w, h, return list of list format.

Parameters:annotations (str) – The annotations in darkent format “class, x, y, w, h ….” seperated by “\n”.
Returns:List of bounding box.
Return type:list of list of 4 numbers

Darknet format split class and coordinate

tensorlayer.prepro.parse_darknet_ann_list_to_cls_box(annotations)[source]

Parse darknet annotation format into two lists for class and bounding box.

Input list of [[class, x, y, w, h], …], return two list of [class …] and [[x, y, w, h], …].

Parameters:annotations (list of list) – A list of class and bounding boxes of images e.g. [[class, x, y, w, h], …]
Returns:
  • list of int – List of class labels.
  • list of list of 4 numbers – List of bounding box.

Image Aug - Flip

tensorlayer.prepro.obj_box_horizontal_flip(im, coords=None, is_rescale=False, is_center=False, is_random=False)[source]

Left-right flip the image and coordinates for object detection.

Parameters:
  • im (numpy.array) – An image with dimension of [row, col, channel] (default).
  • coords (list of list of 4 int/float or None) – Coordinates [[x, y, w, h], [x, y, w, h], …].
  • is_rescale (boolean) – Set to True, if the input coordinates are rescaled to [0, 1]. Default is False.
  • is_center (boolean) – Set to True, if the x and y of coordinates are the centroid (i.e. darknet format). Default is False.
  • is_random (boolean) – If True, randomly flip. Default is False.
Returns:

  • numpy.array – A processed image
  • list of list of 4 numbers – A list of new bounding boxes.

Examples

>>> im = np.zeros([80, 100])    # as an image with shape width=100, height=80
>>> im, coords = obj_box_left_right_flip(im, coords=[[0.2, 0.4, 0.3, 0.3], [0.1, 0.5, 0.2, 0.3]], is_rescale=True, is_center=True, is_random=False)
>>> print(coords)
  [[0.8, 0.4, 0.3, 0.3], [0.9, 0.5, 0.2, 0.3]]
>>> im, coords = obj_box_left_right_flip(im, coords=[[0.2, 0.4, 0.3, 0.3]], is_rescale=True, is_center=False, is_random=False)
>>> print(coords)
  [[0.5, 0.4, 0.3, 0.3]]
>>> im, coords = obj_box_left_right_flip(im, coords=[[20, 40, 30, 30]], is_rescale=False, is_center=True, is_random=False)
>>> print(coords)
  [[80, 40, 30, 30]]
>>> im, coords = obj_box_left_right_flip(im, coords=[[20, 40, 30, 30]], is_rescale=False, is_center=False, is_random=False)
>>> print(coords)
  [[50, 40, 30, 30]]

Image Aug - Resize

tensorlayer.prepro.obj_box_imresize(im, coords=None, size=None, interp='bicubic', mode=None, is_rescale=False)[source]

Resize an image, and compute the new bounding box coordinates.

Parameters:
  • im (numpy.array) – An image with dimension of [row, col, channel] (default).
  • coords (list of list of 4 int/float or None) – Coordinates [[x, y, w, h], [x, y, w, h], …]
  • interp and mode (size) – See tl.prepro.imresize.
  • is_rescale (boolean) – Set to True, if the input coordinates are rescaled to [0, 1], then return the original coordinates. Default is False.
Returns:

  • numpy.array – A processed image
  • list of list of 4 numbers – A list of new bounding boxes.

Examples

>>> im = np.zeros([80, 100, 3])    # as an image with shape width=100, height=80
>>> _, coords = obj_box_imresize(im, coords=[[20, 40, 30, 30], [10, 20, 20, 20]], size=[160, 200], is_rescale=False)
>>> print(coords)
  [[40, 80, 60, 60], [20, 40, 40, 40]]
>>> _, coords = obj_box_imresize(im, coords=[[20, 40, 30, 30]], size=[40, 100], is_rescale=False)
>>> print(coords)
  [[20, 20, 30, 15]]
>>> _, coords = obj_box_imresize(im, coords=[[20, 40, 30, 30]], size=[60, 150], is_rescale=False)
>>> print(coords)
  [[30, 30, 45, 22]]
>>> im2, coords = obj_box_imresize(im, coords=[[0.2, 0.4, 0.3, 0.3]], size=[160, 200], is_rescale=True)
>>> print(coords, im2.shape)
  [[0.2, 0.4, 0.3, 0.3]] (160, 200, 3)

Image Aug - Crop

tensorlayer.prepro.obj_box_crop(im, classes=None, coords=None, wrg=100, hrg=100, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12.0)[source]

Randomly or centrally crop an image, and compute the new bounding box coordinates. Objects outside the cropped image will be removed.

Parameters:
  • im (numpy.array) – An image with dimension of [row, col, channel] (default).
  • classes (list of int or None) – Class IDs.
  • coords (list of list of 4 int/float or None) – Coordinates [[x, y, w, h], [x, y, w, h], …]
  • hrg and is_random (wrg) – See tl.prepro.crop.
  • is_rescale (boolean) – Set to True, if the input coordinates are rescaled to [0, 1]. Default is False.
  • is_center (boolean, default False) – Set to True, if the x and y of coordinates are the centroid (i.e. darknet format). Default is False.
  • thresh_wh (float) – Threshold, remove the box if its ratio of width(height) to image size less than the threshold.
  • thresh_wh2 (float) – Threshold, remove the box if its ratio of width to height or vice verse higher than the threshold.
Returns:

  • numpy.array – A processed image
  • list of int – A list of classes
  • list of list of 4 numbers – A list of new bounding boxes.

Image Aug - Shift

tensorlayer.prepro.obj_box_shift(im, classes=None, coords=None, wrg=0.1, hrg=0.1, row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12.0)[source]

Shift an image randomly or non-randomly, and compute the new bounding box coordinates. Objects outside the cropped image will be removed.

Parameters:
  • im (numpy.array) – An image with dimension of [row, col, channel] (default).
  • classes (list of int or None) – Class IDs.
  • coords (list of list of 4 int/float or None) – Coordinates [[x, y, w, h], [x, y, w, h], …]
  • hrg row_index col_index channel_index is_random fill_mode cval and order (wrg,) –
  • is_rescale (boolean) – Set to True, if the input coordinates are rescaled to [0, 1]. Default is False.
  • is_center (boolean) – Set to True, if the x and y of coordinates are the centroid (i.e. darknet format). Default is False.
  • thresh_wh (float) – Threshold, remove the box if its ratio of width(height) to image size less than the threshold.
  • thresh_wh2 (float) – Threshold, remove the box if its ratio of width to height or vice verse higher than the threshold.
Returns:

  • numpy.array – A processed image
  • list of int – A list of classes
  • list of list of 4 numbers – A list of new bounding boxes.

Image Aug - Zoom

tensorlayer.prepro.obj_box_zoom(im, classes=None, coords=None, zoom_range=(0.9, 1.1), row_index=0, col_index=1, channel_index=2, fill_mode='nearest', cval=0.0, order=1, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12.0)[source]

Zoom in and out of a single image, randomly or non-randomly, and compute the new bounding box coordinates. Objects outside the cropped image will be removed.

Parameters:
  • im (numpy.array) – An image with dimension of [row, col, channel] (default).
  • classes (list of int or None) – Class IDs.
  • coords (list of list of 4 int/float or None) – Coordinates [[x, y, w, h], [x, y, w, h], …].
  • row_index col_index channel_index is_random fill_mode cval and order (zoom_range) –
  • is_rescale (boolean) – Set to True, if the input coordinates are rescaled to [0, 1]. Default is False.
  • is_center (boolean) – Set to True, if the x and y of coordinates are the centroid. (i.e. darknet format). Default is False.
  • thresh_wh (float) – Threshold, remove the box if its ratio of width(height) to image size less than the threshold.
  • thresh_wh2 (float) – Threshold, remove the box if its ratio of width to height or vice verse higher than the threshold.
Returns:

  • numpy.array – A processed image
  • list of int – A list of classes
  • list of list of 4 numbers – A list of new bounding boxes.

Sequence

More related functions can be found in tensorlayer.nlp.

Padding

tensorlayer.prepro.pad_sequences(sequences, maxlen=None, dtype='int32', padding='post', truncating='pre', value=0.0)[source]

Pads each sequence to the same length: the length of the longest sequence. If maxlen is provided, any sequence longer than maxlen is truncated to maxlen. Truncation happens off either the beginning (default) or the end of the sequence. Supports post-padding and pre-padding (default).

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • maxlen (int) – Maximum length.
  • dtype (numpy.dtype or str) – Data type to cast the resulting sequence.
  • padding (str) – Either ‘pre’ or ‘post’, pad either before or after each sequence.
  • truncating (str) – Either ‘pre’ or ‘post’, remove values from sequences larger than maxlen either in the beginning or in the end of the sequence
  • value (float) – Value to pad the sequences to the desired value.
Returns:

x – With dimensions (number_of_sequences, maxlen)

Return type:

numpy.array

Examples

>>> sequences = [[1,1,1,1,1],[2,2,2],[3,3]]
>>> sequences = pad_sequences(sequences, maxlen=None, dtype='int32',
...                  padding='post', truncating='pre', value=0.)
[[1 1 1 1 1]
 [2 2 2 0 0]
 [3 3 0 0 0]]

Remove Padding

tensorlayer.prepro.remove_pad_sequences(sequences, pad_id=0)[source]

Remove padding.

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • pad_id (int) – The pad ID.
Returns:

The processed sequences.

Return type:

list of list of int

Examples

>>> sequences = [[2,3,4,0,0], [5,1,2,3,4,0,0,0], [4,5,0,2,4,0,0,0]]
>>> print(remove_pad_sequences(sequences, pad_id=0))
[[2, 3, 4], [5, 1, 2, 3, 4], [4, 5, 0, 2, 4]]

Process

tensorlayer.prepro.process_sequences(sequences, end_id=0, pad_val=0, is_shorten=True, remain_end_id=False)[source]

Set all tokens(ids) after END token to the padding value, and then shorten (option) it to the maximum sequence length in this batch.

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • end_id (int) – The special token for END.
  • pad_val (int) – Replace the end_id and the IDs after end_id to this value.
  • is_shorten (boolean) – Shorten the sequences. Default is True.
  • remain_end_id (boolean) – Keep an end_id in the end. Default is False.
Returns:

The processed sequences.

Return type:

list of list of int

Examples

>>> sentences_ids = [[4, 3, 5, 3, 2, 2, 2, 2],  <-- end_id is 2
...                  [5, 3, 9, 4, 9, 2, 2, 3]]  <-- end_id is 2
>>> sentences_ids = precess_sequences(sentences_ids, end_id=vocab.end_id, pad_val=0, is_shorten=True)
[[4, 3, 5, 3, 0], [5, 3, 9, 4, 9]]

Add Start ID

tensorlayer.prepro.sequences_add_start_id(sequences, start_id=0, remove_last=False)[source]

Add special start token(id) in the beginning of each sequence.

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • start_id (int) – The start ID.
  • remove_last (boolean) – Remove the last value of each sequences. Usually be used for removing the end ID.
Returns:

The processed sequences.

Return type:

list of list of int

Examples

>>> sentences_ids = [[4,3,5,3,2,2,2,2], [5,3,9,4,9,2,2,3]]
>>> sentences_ids = sequences_add_start_id(sentences_ids, start_id=2)
[[2, 4, 3, 5, 3, 2, 2, 2, 2], [2, 5, 3, 9, 4, 9, 2, 2, 3]]
>>> sentences_ids = sequences_add_start_id(sentences_ids, start_id=2, remove_last=True)
[[2, 4, 3, 5, 3, 2, 2, 2], [2, 5, 3, 9, 4, 9, 2, 2]]

For Seq2seq

>>> input = [a, b, c]
>>> target = [x, y, z]
>>> decode_seq = [start_id, a, b] <-- sequences_add_start_id(input, start_id, True)

Add End ID

tensorlayer.prepro.sequences_add_end_id(sequences, end_id=888)[source]

Add special end token(id) in the end of each sequence.

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • end_id (int) – The end ID.
Returns:

The processed sequences.

Return type:

list of list of int

Examples

>>> sequences = [[1,2,3],[4,5,6,7]]
>>> print(sequences_add_end_id(sequences, end_id=999))
[[1, 2, 3, 999], [4, 5, 6, 999]]

Add End ID after pad

tensorlayer.prepro.sequences_add_end_id_after_pad(sequences, end_id=888, pad_id=0)[source]

Add special end token(id) in the end of each sequence.

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • end_id (int) – The end ID.
  • pad_id (int) – The pad ID.
Returns:

The processed sequences.

Return type:

list of list of int

Examples

>>> sequences = [[1,2,0,0], [1,2,3,0], [1,2,3,4]]
>>> print(sequences_add_end_id_after_pad(sequences, end_id=99, pad_id=0))
[[1, 2, 99, 0], [1, 2, 3, 99], [1, 2, 3, 4]]

Get Mask

tensorlayer.prepro.sequences_get_mask(sequences, pad_val=0)[source]

Return mask for sequences.

Parameters:
  • sequences (list of list of int) – All sequences where each row is a sequence.
  • pad_val (int) – The pad value.
Returns:

The mask.

Return type:

list of list of int

Examples

>>> sentences_ids = [[4, 0, 5, 3, 0, 0],
...                  [5, 3, 9, 4, 9, 0]]
>>> mask = sequences_get_mask(sentences_ids, pad_val=0)
[[1 1 1 1 0 0]
 [1 1 1 1 1 0]]