TensorLayer官方中文文档1.7.4：API – 数据预处理

所属分类：TensorLayer

API - 数据预处理¶

我们提供大量的数据增强及处理方法，使用 Numpy, Scipy, Threading 和 Queue。
不过，我们建议你直接使用 TensorFlow 提供的 operator，如 tf.image.central_crop ，更多关于 TensorFlow 的信息请见
这里 和 tutorial_cifar10_tfrecord.py.
这个包的一部分代码来自Keras。

threading_data([data, fn, thread_count])	Return a batch of result by given data.
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 deformation of images as described in [Simard2003] .
elastic_transform_multi(x, alpha, sigma[, ...])	Elastic deformation of 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(annotation)	Input string format of class, x, y, w, h, return list of list format.
parse_darknet_ann_list_to_cls_box(annotation)	Input list of [[class, x, y, w, h], ...], return two list of [class ...] and [[x, y, w, h], ...].
obj_box_left_right_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.

并行 Threading¶

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

Return a batch of result by given data.
Usually be used for data augmentation.

Parameters:	data : numpy array, file names and etc, see Examples below. thread_count : the number of threads to use fn : the function for data processing. more args : the args for fn, see Examples below.

References

• python queue
• run with limited queue

Examples

• Single array

>>> X --> [batch_size, row, col, 1] greyscale
>>> results = threading_data(X, zoom, zoom_range=[0.5, 1], is_random=True)
... results --> [batch_size, row, col, channel]
>>> tl.visualize.images2d(images=np.asarray(results), second=0.01, saveable=True, name='after', dtype=None)
>>> tl.visualize.images2d(images=np.asarray(X), second=0.01, saveable=True, name='before', dtype=None)

• List of array (e.g. functions with multi)

>>> X, Y --> [batch_size, row, col, 1]  greyscale
>>> data = threading_data([_ for _ in zip(X, Y)], 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.visualize.images2d(images=np.asarray(X_), second=0.01, saveable=True, name='after', dtype=None)
>>> tl.visualize.images2d(images=np.asarray(Y_), second=0.01, saveable=True, name='before', dtype=None)

• Single array split across thread_count threads (e.g. functions with multi)

>>> X, Y --> [batch_size, row, col, 1]  greyscale
>>> data = threading_data(X, 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.visualize.images2d(images=np.asarray(X_), second=0.01, saveable=True, name='after', dtype=None)
>>> tl.visualize.images2d(images=np.asarray(Y_), second=0.01, saveable=True, name='before', dtype=None)

• Customized function for image segmentation

>>> def distort_img(data):
...     x, y = data
...     x, y = flip_axis_multi([x, y], axis=0, is_random=True)
...     x, y = flip_axis_multi([x, y], axis=1, is_random=True)
...     x, y = crop_multi([x, y], 100, 100, is_random=True)
...     return x, y
>>> X, Y --> [batch_size, row, col, channel]
>>> data = threading_data([_ for _ in zip(X, Y)], distort_img)
>>> X_, Y_ = data.transpose((1,0,2,3,4))

图像¶

• 这些函数只对一个图像做处理， 使用 threading_data 函数来实现多线程处理，请参考 tutorial_image_preprocess.py 。
• 所有函数都有一个 is_random 。
• 所有结尾是 multi 的函数通常用于图像分隔，因为输入和输出的图像必需是匹配的。

旋转¶

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)[源代码]¶

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, default False If True, randomly rotate. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’ scipy ndimage affine_transform cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0 order : int, optional The order of interpolation. The order has to be in the range 0-5. See apply_transform. scipy ndimage affine_transform

Examples

>>> x --> [row, col, 1] greyscale
>>> x = rotation(x, rg=40, is_random=False)
>>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='temp',cmap='gray')

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)[源代码]¶

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 : see rotation.

Examples

>>> x, y --> [row, col, 1]  greyscale
>>> x, y = rotation_multi([x, y], rg=90, is_random=False)
>>> tl.visualize.frame(x[:,:,0], second=0.01, saveable=True, name='x',cmap='gray')
>>> tl.visualize.frame(y[:,:,0], second=0.01, saveable=True, name='y',cmap='gray')

裁剪¶

tensorlayer.prepro.crop(x, wrg, hrg, is_random=False, row_index=0, col_index=1, channel_index=2)[源代码]¶

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, default False If True, randomly crop, else central crop. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0).

tensorlayer.prepro.crop_multi(x, wrg, hrg, is_random=False, row_index=0, col_index=1, channel_index=2)[源代码]¶

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 : see crop.

翻转¶

tensorlayer.prepro.flip_axis(x, axis=1, is_random=False)[源代码]¶

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 0, flip up and down 1, flip left and right 2, flip channel is_random : boolean, default False If True, randomly flip.

tensorlayer.prepro.flip_axis_multi(x, axis, is_random=False)[源代码]¶

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 : see flip_axis.

位移¶

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)[源代码]¶

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, default False If True, randomly shift. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. scipy ndimage affine_transform cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See apply_transform. scipy ndimage affine_transform

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)[源代码]¶

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 : see shift.

切变¶

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)[源代码]¶

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, default False If True, randomly shear. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. scipy ndimage affine_transform cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See apply_transform. scipy ndimage affine_transform

References

• Affine transformation

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)[源代码]¶

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 : see tl.prepro.shear.

切变 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)[源代码]¶

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, default False If True, randomly shear. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. scipy ndimage affine_transform cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See apply_transform. scipy ndimage affine_transform

References

• Affine transformation

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)[源代码]¶

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 : see tl.prepro.shear2.

漩涡¶

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)[源代码]¶

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 : (row, column) tuple or (2,) ndarray, optional Center coordinate of transformation. strength : float, optional The amount of swirling applied. radius : float, optional The extent of the swirl in pixels. The effect dies out rapidly beyond radius. rotation : float, (degree) optional Additional rotation applied to the image, usually [0, 360], relates to center. output_shape : tuple (rows, cols), optional Shape of the output image generated. 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 : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional 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, optional Used in conjunction with mode ‘constant’, the value outside the image boundaries. clip : bool, optional 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 : bool, optional 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, default False If True, random swirl. random center = [(0 ~ x.shape[0]), (0 ~ x.shape[1])] random strength = [0, strength] random radius = [1e-10, radius] random rotation = [-rotation, rotation]

Examples

>>> x --> [row, col, 1] greyscale
>>> x = 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)[源代码]¶

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 : see swirl.

局部扭曲(Elastic transform)¶

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

Elastic deformation of images as described in [Simard2003] .

Parameters:

x : numpy array, a greyscale image. alpha : scalar factor. sigma : scalar or sequence of scalars, 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 : default constant, see scipy.ndimage.filters.gaussian_filter. cval : float, optional. Used in conjunction with mode ‘constant’, the value outside the image boundaries. is_random : boolean, default False

References

• Github.
• Kaggle

Examples

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

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

Elastic deformation of images as described in [Simard2003].

Parameters:	x : list of numpy array others : see elastic_transform.

缩放¶

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)[源代码]¶

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 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, it is (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, default False If True, randomly zoom. row_index, col_index, channel_index : int Index of row, col and channel, default (0, 1, 2), for theano (1, 2, 0). fill_mode : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’. scipy ndimage affine_transform cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0. order : int, optional The order of interpolation. The order has to be in the range 0-5. See apply_transform. scipy ndimage affine_transform

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)[源代码]¶

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 : see zoom.

亮度¶

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

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, small than 1 means brighter. Non negative real number. Default value is 1, smaller 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, default False If True, randomly change brightness.

References

• skimage.exposure.adjust_gamma
• chinese blog

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

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 numpy array List of images with dimension of [n_images, row, col, channel] (default). others : see brightness.

亮度, 饱和度, 对比度¶

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

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 : 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 : 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 : 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 : whether the parameters are randomly set.

Examples

• Random

>>> x = illumination(x, gamma=(0.5, 5.0), contrast=(0.3, 1.0), saturation=(0.7, 1.0), is_random=True)
- Non-random
>>> x = illumination(x, 0.5, 0.6, 0.8, is_random=False)

RGB 转 HSV¶

tensorlayer.prepro.rgb_to_hsv(rgb)[源代码]¶

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

Parameters:	rgb : should be a numpy arrays with values between 0 and 255.

HSV 转 RGB¶

tensorlayer.prepro.hsv_to_rgb(hsv)[源代码]¶

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

Parameters:	hsv : should be a numpy arrays with values between 0.0 and 1.0

调整色调（Hue）¶

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

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 : should be a numpy arrays with values between 0 and 255. hout : float. 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, default True. is_clip : boolean, default True. If True, set negative hue values to 0. is_random : boolean, default False.

References

• tf.image.random_hue.
• tf.image.adjust_hue.
• StackOverflow: Changing image hue with python PIL.

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)

调整大小¶

tensorlayer.prepro.imresize(x, size=[100, 100], interp='bicubic', mode=None)[源代码]¶

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 : int, float or tuple (h, w) int, Percentage of current size. float, Fraction of current size. tuple, Size of the output image. interp : str, optional Interpolation to use for re-sizing (‘nearest’, ‘lanczos’, ‘bilinear’, ‘bicubic’ or ‘cubic’). mode : str, optional The PIL image mode (‘P’, ‘L’, etc.) to convert arr before resizing.

Returns:

imresize : ndarray The resized array of image.

References

• scipy.misc.imresize

像素值缩放¶

tensorlayer.prepro.pixel_value_scale(im, val=0.9, clip=[], is_random=False)[源代码]¶

Scales each value in the pixels of the image.

Parameters:	im : numpy array for one image. val : float. 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.

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)

正规化¶

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

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 : 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 : set each sample mean to 0. samplewise_std_normalization : divide each input by its std. epsilon : small position value for dividing standard deviation.

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.

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

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

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 : value for subtraction. std : value for division. epsilon : small position value for dividing standard deviation.

通道位移¶

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

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, default False If True, randomly shift. channel_index : int Index of channel, default 2.

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

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 : see channel_shift.

噪声¶

tensorlayer.prepro.drop(x, keep=0.5)[源代码]¶

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 (0, 1) The keeping probability, the lower more values will be set to zero.

矩阵圆心转换到图中央¶

tensorlayer.prepro.transform_matrix_offset_center(matrix, x, y)[源代码]¶

Return transform matrix offset center.

Parameters:	matrix : numpy array Transform matrix x, y : int Size of image.

Examples

• See rotation, shear, zoom.

基于矩阵的仿射变换¶

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

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 : string Method to fill missing pixel, default ‘nearest’, more options ‘constant’, ‘reflect’ or ‘wrap’ scipy ndimage affine_transform cval : scalar, optional Value used for points outside the boundaries of the input if mode='constant'. Default is 0.0 order : int, optional 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 scipy ndimage affine_transform

Examples

• See rotation, shift, shear, zoom.

基于坐标点的的投影变换¶

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

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 : dict, optional Keyword arguments passed to inverse_map. output_shape : tuple (rows, cols), optional 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, optional 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 : {‘constant’, ‘edge’, ‘symmetric’, ‘reflect’, ‘wrap’}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of numpy.pad. cval : float, optional Used in conjunction with mode ‘constant’, the value outside the image boundaries. clip : bool, optional 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 : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of img_as_float.

References

• scikit-image : geometric transformations
• scikit-image : examples

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 = projective_transform_by_points(X, src, dst)

Numpy 与 PIL¶

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

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

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

References

• PIL Image.fromarray

找轮廓¶

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

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, {‘low’, ‘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 : 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.

一列点到图¶

tensorlayer.prepro.pt2map(list_points=[], size=(100, 100), val=1)[源代码]¶

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

Parameters:	list_points : list of [x, y]. size : tuple of (w, h) for output size. val : float or int for the contour value.

二值膨胀¶

tensorlayer.prepro.binary_dilation(x, radius=3)[源代码]¶

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

Parameters:	x : 2D array image. radius : int for the radius of mask.

灰度膨胀¶

tensorlayer.prepro.dilation(x, radius=3)[源代码]¶

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

Parameters:	x : 2D array image. radius : int for the radius of mask.

二值腐蚀¶

tensorlayer.prepro.binary_erosion(x, radius=3)[源代码]¶

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

Parameters:	x : 2D array image. radius : int for the radius of mask.

灰度腐蚀¶

tensorlayer.prepro.erosion(x, radius=3)[源代码]¶

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

Parameters:	x : 2D array image. radius : int for the radius of mask.

目标检测¶

教程-图像增强¶

您好，这是基于VOC数据集的一个图像增强例子，请阅读这篇 知乎文章 。

import tensorlayer as tl

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

## 图片标记预处理为列表形式
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])

# 读取一张图片，并保存
idx = 2  # 可自行选择图片
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')

# 左右翻转
im_flip, coords = tl.prepro.obj_box_left_right_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')

# 调整图片大小
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')

# 裁剪
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')

# 位移
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')

# 高宽缩放
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')

实际中，你可能希望如下使用多线程方式来处理一个batch的数据。

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
    ## 随机改变图片亮度、对比度和饱和度
    im = tl.prepro.illumination(im, gamma=(0.5, 1.5),
             contrast=(0.5, 1.5), saturation=(0.5, 1.5), is_random=True)
    ## 随机左右翻转
    im, coords = tl.prepro.obj_box_left_right_flip(im, coords,
             is_rescale=True, is_center=True, is_random=True)
    ## 随机调整大小并裁剪出指定大小的图片，这同时达到了随机缩放的效果
    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)
    ## 把数值范围从 [0, 255] 转到 [-1, 1] (可选)
    im = im / 127.5 - 1
    return im, [clas, coords]

# 随机读取一个batch的图片及其标记
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]

# 多线程处理
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]

# 保存每一组图片以供体会
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)

坐标-像素单位到比例单位¶

tensorlayer.prepro.obj_box_coord_rescale(coord=[], shape=[100, 200])[源代码]¶

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 list for coordinates [[x, y, w, h], [x, y, w, h], ...] shape : list of 2 integers for [height, width] of the image.

Examples

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

坐标-像素单位到比例单位 （多个坐标）¶

tensorlayer.prepro.obj_box_coords_rescale(coords=[], shape=[100, 200])[源代码]¶

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 for coordinates [[x, y, w, h], [x, y, w, h], ...] shape : list of 2 integers for [height, width] of the image.

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]]

坐标-比例单位到像素单位¶

tensorlayer.prepro.obj_box_coord_scale_to_pixelunit(coord, shape=(100, 100, 3))[源代码]¶

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 float, [x, y, w (or x2), h (or y2)] in ratio format, i.e value range [0~1]. shape : tuple of (height, width, channel (optional))

Examples

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

坐标-[x_center, x_center, w, h]到左上-右下单位¶

tensorlayer.prepro.obj_box_coord_centroid_to_upleft_butright(coord, to_int=False)[源代码]¶

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

Examples

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

坐标-左上-右下单位到[x_center, x_center, w, h]¶

tensorlayer.prepro.obj_box_coord_upleft_butright_to_centroid(coord)[源代码]¶

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.

坐标-[x_center, x_center, w, h]到左上-高宽单位¶

tensorlayer.prepro.obj_box_coord_centroid_to_upleft(coord)[源代码]¶

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.

坐标-左上-高宽单位到[x_center, x_center, w, h]¶

tensorlayer.prepro.obj_box_coord_upleft_to_centroid(coord)[源代码]¶

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.

Darknet格式-字符转列表¶

tensorlayer.prepro.parse_darknet_ann_str_to_list(annotation)[源代码]¶

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

Darknet格式-分开列表的类别和坐标¶

tensorlayer.prepro.parse_darknet_ann_list_to_cls_box(annotation)[源代码]¶

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

图像-翻转¶

tensorlayer.prepro.obj_box_left_right_flip(im, coords=[], is_rescale=False, is_center=False, is_random=False)[源代码]¶

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 for coordinates [[x, y, w, h], [x, y, w, h], ...] is_rescale : boolean, default False Set to True, if the input coordinates are rescaled to [0, 1]. is_center : boolean, default False Set to True, if the x and y of coordinates are the centroid. (i.e. darknet format) is_random : boolean, default False If True, randomly flip.

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]]

图像-调整大小¶

tensorlayer.prepro.obj_box_imresize(im, coords=[], size=[100, 100], interp='bicubic', mode=None, is_rescale=False)[源代码]¶

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 for coordinates [[x, y, w, h], [x, y, w, h], ...] size, interp, mode : see tl.prepro.imresize for details. is_rescale : boolean, default False Set to True, if the input coordinates are rescaled to [0, 1], then return the original coordinates.

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)

图像-裁剪¶

tensorlayer.prepro.obj_box_crop(im, classes=[], coords=[], wrg=100, hrg=100, is_rescale=False, is_center=False, is_random=False, thresh_wh=0.02, thresh_wh2=12.0)[源代码]¶

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 class ID (int). coords : list of list for coordinates [[x, y, w, h], [x, y, w, h], ...] wrg, hrg, is_random : see tl.prepro.crop for details. is_rescale : boolean, default False Set to True, if the input coordinates are rescaled to [0, 1]. is_center : boolean, default False Set to True, if the x and y of coordinates are the centroid. (i.e. darknet format) 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.

图像-位移¶

tensorlayer.prepro.obj_box_shift(im, classes=[], coords=[], 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)[源代码]¶

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 class ID (int). coords : list of list for coordinates [[x, y, w, h], [x, y, w, h], ...] wrg, hrg, row_index, col_index, channel_index, is_random, fill_mode, cval, order : see tl.prepro.shift. is_rescale : boolean, default False Set to True, if the input coordinates are rescaled to [0, 1]. is_center : boolean, default False Set to True, if the x and y of coordinates are the centroid. (i.e. darknet format) 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.

图像-缩放¶

tensorlayer.prepro.obj_box_zoom(im, classes=[], coords=[], 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)[源代码]¶

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 class ID (int). coords : list of list for coordinates [[x, y, w, h], [x, y, w, h], ...] zoom_range, row_index, col_index, channel_index, is_random, fill_mode, cval, order : see tl.prepro.zoom. is_rescale : boolean, default False Set to True, if the input coordinates are rescaled to [0, 1]. is_center : boolean, default False Set to True, if the x and y of coordinates are the centroid. (i.e. darknet format) 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.

序列¶

更多相关函数，请见 tensorlayer.nlp 。

Padding¶

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

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 lists where each element is a sequence maxlen : int, maximum length dtype : type to cast the resulting sequence. padding : 'pre' or 'post', pad either before or after each sequence. truncating : '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 : numpy array with dimensions (number_of_sequences, maxlen)

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)[源代码]¶

Remove padding.

Parameters:	sequences : list of list. pad_id : 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)[源代码]¶

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 : numpy array or list of list with token IDs. e.g. [[4,3,5,3,2,2,2,2], [5,3,9,4,9,2,2,3]] 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, default True. Shorten the sequences. remain_end_id : boolean, default False. Keep an end_id in the end.

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)[源代码]¶

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

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)[源代码]¶

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

Parameters:	sequences : list of list. end_id : 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)[源代码]¶

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

Parameters:	sequences : list of list. end_id : int. pad_id : 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)[源代码]¶

Return mask for sequences.

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]]

Tensor Opt¶

注解

这几个函数将被弃用， 关于如何使用 Tensor Operator 请参考 tutorial_cifar10_tfrecord.py 。

艾伯特(http://www.aibbt.com/)国内第一家人工智能门户