API - Visualization

TensorFlow provides TensorBoard to visualize the model, activations etc. Here we provide more functions for data visualization.

read_image(image[, path])

Read one image.

read_images(img_list[, path, n_threads, …])

Returns all images in list by given path and name of each image file.

save_image(image[, image_path])

Save a image.

save_images(images, size[, image_path])

Save multiple images into one single image.

draw_boxes_and_labels_to_image(image, …[, …])

Draw bboxes and class labels on image.

draw_mpii_pose_to_image(image, poses[, …])

Draw people(s) into image using MPII dataset format as input, return or save the result image.

draw_weights([W, second, saveable, shape, …])

Visualize every columns of the weight matrix to a group of Greyscale img.

CNN2d([CNN, second, saveable, name, fig_idx])

Display a group of RGB or Greyscale CNN masks.

frame([I, second, saveable, name, cmap, fig_idx])

Display a frame.

images2d([images, second, saveable, name, …])

Display a group of RGB or Greyscale images.

tsne_embedding(embeddings, reverse_dictionary)

Visualize the embeddings by using t-SNE.

Save and read images

Read one image

tensorlayer.visualize.read_image(image, path='')[source]

Read one image.

Parameters

  • image (str) – The image file name.

  • path (str) – The image folder path.

Returns

The image.

Return type

numpy.array

Read multiple images

tensorlayer.visualize.read_images(img_list, path='', n_threads=10, printable=True)[source]

Returns all images in list by given path and name of each image file.

Parameters

  • img_list (list of str) – The image file names.

  • path (str) – The image folder path.

  • n_threads (int) – The number of threads to read image.

  • printable (boolean) – Whether to print information when reading images.

Returns

The images.

Return type

list of numpy.array

Save one image

tensorlayer.visualize.save_image(image, image_path='_temp.png')[source]

Save a image.

Parameters

  • image (numpy array) – [w, h, c]

  • image_path (str) – path

Save multiple images

tensorlayer.visualize.save_images(images, size, image_path='_temp.png')[source]

Save multiple images into one single image.

Parameters

  • images (numpy array) – (batch, w, h, c)

  • size (list of 2 ints) – row and column number. number of images should be equal or less than size[0] * size[1]

  • image_path (str) – save path

Examples

>>> import numpy as np
>>> import tensorlayer as tl
>>> images = np.random.rand(64, 100, 100, 3)
>>> tl.visualize.save_images(images, [8, 8], 'temp.png')

Save image for object detection

tensorlayer.visualize.draw_boxes_and_labels_to_image(image, classes, coords, scores, classes_list, is_center=True, is_rescale=True, save_name=None)[source]

Draw bboxes and class labels on image. Return or save the image with bboxes, example in the docs of tl.prepro.

Parameters

  • image (numpy.array) – The RGB image [height, width, channel].

  • classes (list of int) – A list of class ID (int).

  • coords (list of int) –

    A list of list for coordinates.

    • Should be [x, y, x2, y2] (up-left and botton-right format)

    • If [x_center, y_center, w, h] (set is_center to True).

  • scores (list of float) – A list of score (float). (Optional)

  • classes_list (list of str) – for converting ID to string on image.

  • is_center (boolean) –

    Whether the coordinates is [x_center, y_center, w, h]

    • If coordinates are [x_center, y_center, w, h], set it to True for converting it to [x, y, x2, y2] (up-left and botton-right) internally.

    • If coordinates are [x1, x2, y1, y2], set it to False.

  • is_rescale (boolean) –

    Whether to rescale the coordinates from pixel-unit format to ratio format.

    • If True, the input coordinates are the portion of width and high, this API will scale the coordinates to pixel unit internally.

    • If False, feed the coordinates with pixel unit format.

  • save_name (None or str) – The name of image file (i.e. image.png), if None, not to save image.

Returns

The saved image.

Return type

numpy.array

References

Save image for pose estimation (MPII)

tensorlayer.visualize.draw_mpii_pose_to_image(image, poses, save_name='image.png')[source]

Draw people(s) into image using MPII dataset format as input, return or save the result image.

This is an experimental API, can be changed in the future.

Parameters

  • image (numpy.array) – The RGB image [height, width, channel].

  • poses (list of dict) – The people(s) annotation in MPII format, see tl.files.load_mpii_pose_dataset.

  • save_name (None or str) – The name of image file (i.e. image.png), if None, not to save image.

Returns

The saved image.

Return type

numpy.array

Examples

>>> import pprint
>>> import tensorlayer as tl
>>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
>>> image = tl.vis.read_image(img_train_list[0])
>>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png')
>>> pprint.pprint(ann_train_list[0])

References

Visualize model parameters

Visualize CNN 2d filter

tensorlayer.visualize.CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362)[source]

Display a group of RGB or Greyscale CNN masks.

Parameters

  • CNN (numpy.array) – The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).

  • second (int) – The display second(s) for the image(s), if saveable is False.

  • saveable (boolean) – Save or plot the figure.

  • name (str) – A name to save the image, if saveable is True.

  • fig_idx (int) – The matplotlib figure index.

Examples

>>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)

Visualize weights

tensorlayer.visualize.draw_weights(W=None, second=10, saveable=True, shape=None, name='mnist', fig_idx=2396512)[source]

Visualize every columns of the weight matrix to a group of Greyscale img.

Parameters

  • W (numpy.array) – The weight matrix

  • second (int) – The display second(s) for the image(s), if saveable is False.

  • saveable (boolean) – Save or plot the figure.

  • shape (a list with 2 int or None) – The shape of feature image, MNIST is [28, 80].

  • name (a string) – A name to save the image, if saveable is True.

  • fig_idx (int) – matplotlib figure index.

Examples

>>> tl.visualize.draw_weights(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)

Visualize images

Image by matplotlib

tensorlayer.visualize.frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836)[source]

Display a frame. Make sure OpenAI Gym render() is disable before using it.

Parameters

  • I (numpy.array) – The image.

  • second (int) – The display second(s) for the image(s), if saveable is False.

  • saveable (boolean) – Save or plot the figure.

  • name (str) – A name to save the image, if saveable is True.

  • cmap (None or str) – ‘gray’ for greyscale, None for default, etc.

  • fig_idx (int) – matplotlib figure index.

Examples

>>> env = gym.make("Pong-v0")
>>> observation = env.reset()
>>> tl.visualize.frame(observation)

Images by matplotlib

tensorlayer.visualize.images2d(images=None, second=10, saveable=True, name='images', dtype=None, fig_idx=3119362)[source]

Display a group of RGB or Greyscale images.

Parameters

  • images (numpy.array) – The images.

  • second (int) – The display second(s) for the image(s), if saveable is False.

  • saveable (boolean) – Save or plot the figure.

  • name (str) – A name to save the image, if saveable is True.

  • dtype (None or numpy data type) – The data type for displaying the images.

  • fig_idx (int) – matplotlib figure index.

Examples

>>> X_train, y_train, X_test, y_test = tl.files.load_cifar10_dataset(shape=(-1, 32, 32, 3), plotable=False)
>>> tl.visualize.images2d(X_train[0:100,:,:,:], second=10, saveable=False, name='cifar10', dtype=np.uint8, fig_idx=20212)

Visualize embeddings

tensorlayer.visualize.tsne_embedding(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862)[source]

Visualize the embeddings by using t-SNE.

Parameters

  • embeddings (numpy.array) – The embedding matrix.

  • reverse_dictionary (dictionary) – id_to_word, mapping id to unique word.

  • plot_only (int) – The number of examples to plot, choice the most common words.

  • second (int) – The display second(s) for the image(s), if saveable is False.

  • saveable (boolean) – Save or plot the figure.

  • name (str) – A name to save the image, if saveable is True.

  • fig_idx (int) – matplotlib figure index.

Examples

>>> see 'tutorial_word2vec_basic.py'
>>> final_embeddings = normalized_embeddings.eval()
>>> tl.visualize.tsne_embedding(final_embeddings, labels, reverse_dictionary,
...                   plot_only=500, second=5, saveable=False, name='tsne')