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¶
Read multiple images¶
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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¶
Save multiple images¶
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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¶
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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
- OpenCV rectangle and putText.
- scikit-image.
Visualize model parameters¶
Visualize CNN 2d filter¶
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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¶
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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¶
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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¶
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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¶
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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')