API - Visualization¶
TensorFlow provides TensorBoard to visualize the model, activations etc. Here we provide more functions for data visualization.
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Read one image. |
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Returns all images in list by given path and name of each image file. |
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Save a image. |
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Save multiple images into one single image. |
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Draw bboxes and class labels on image. |
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Draw people(s) into image using MPII dataset format as input, return or save the result image. |
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Visualize every columns of the weight matrix to a group of Greyscale img. |
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Display a group of RGB or Greyscale CNN masks. |
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Display a frame. |
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Display a group of RGB or Greyscale images. |
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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='_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¶
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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.
Save image for pose estimation (MPII)¶
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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¶
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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. 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')