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_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(image).
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='')[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='')[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
Returns:

The image.

Return type:

numpy.array

Examples

>>> 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

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(image). 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')