Source code for tensorlayer.visualize

# -*- coding: utf-8 -*-

import os
import numpy as np
import scipy.misc  # save/read image(s)
from . import _logging as logging
from . import prepro

# Uncomment the following line if you got: _tkinter.TclError: no display name and no $DISPLAY environment variable
# import matplotlib
# matplotlib.use('Agg')

__all__ = [
    'read_image',
    'read_images',
    'save_image',
    'save_images',
    'draw_boxes_and_labels_to_image',
    'frame',
    'CNN2d',
    'images2d',
    'tsne_embedding',
    'draw_weights',
    'W',
]


[docs]def read_image(image, path='_.png'): """Read one image. Parameters ----------- image : str The image file name. path : str The image folder path. Returns ------- numpy.array The image. """
return scipy.misc.imread(os.path.join(path, image))
[docs]def read_images(img_list, path='_.png', n_threads=10, printable=True): """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 ------- list of numpy.array The images. """ imgs = [] for idx in range(0, len(img_list), n_threads): b_imgs_list = img_list[idx:idx + n_threads] b_imgs = prepro.threading_data(b_imgs_list, fn=read_image, path=path) # logging.info(b_imgs.shape) imgs.extend(b_imgs) if printable: logging.info('read %d from %s' % (len(imgs), path))
return imgs
[docs]def save_image(image, image_path='_temp.png'): """Save a image. Parameters ----------- image : numpy array [w, h, c] image_path : str path """ try: # RGB scipy.misc.imsave(image_path, image) except Exception: # Greyscale
scipy.misc.imsave(image_path, image[:, :, 0])
[docs]def save_images(images, size, image_path='_temp.png'): """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 ------- numpy.array The image. Examples --------- >>> images = np.random.rand(64, 100, 100, 3) >>> tl.visualize.save_images(images, [8, 8], 'temp.png') """ if len(images.shape) == 3: # Greyscale [batch, h, w] --> [batch, h, w, 1] images = images[:, :, :, np.newaxis] def merge(images, size): h, w = images.shape[1], images.shape[2] img = np.zeros((h * size[0], w * size[1], 3)) for idx, image in enumerate(images): i = idx % size[1] j = idx // size[1] img[j * h:j * h + h, i * w:i * w + w, :] = image return img def imsave(images, size, path): return scipy.misc.imsave(path, merge(images, size)) assert len(images) <= size[0] * size[1], "number of images should be equal or less than size[0] * size[1] {}".format(len(images))
return imsave(images, size, image_path)
[docs]def draw_boxes_and_labels_to_image(image, classes, coords, scores, classes_list, is_center=True, is_rescale=True, save_name=None): """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 ------- numpy.array The saved image. References ----------- - OpenCV rectangle and putText. - `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`__. """ assert len(coords) == len(classes), "number of coordinates and classes are equal" if len(scores) > 0: assert len(scores) == len(classes), "number of scores and classes are equal" import cv2 # don't change the original image, and avoid error https://stackoverflow.com/questions/30249053/python-opencv-drawing-errors-after-manipulating-array-with-numpy image = image.copy() imh, imw = image.shape[0:2] thick = int((imh + imw) // 430) for i, _v in enumerate(coords): if is_center: x, y, x2, y2 = prepro.obj_box_coord_centroid_to_upleft_butright(coords[i]) else: x, y, x2, y2 = coords[i] if is_rescale: # scale back to pixel unit if the coords are the portion of width and high x, y, x2, y2 = prepro.obj_box_coord_scale_to_pixelunit([x, y, x2, y2], (imh, imw)) cv2.rectangle( image, (int(x), int(y)), (int(x2), int(y2)), # up-left and botton-right [0, 255, 0], thick) cv2.putText( image, classes_list[classes[i]] + ((" %.2f" % (scores[i])) if (len(scores) != 0) else " "), (int(x), int(y)), # button left 0, 1.5e-3 * imh, # bigger = larger font [0, 0, 256], # self.meta['colors'][max_indx], int(thick / 2) + 1) # bold if save_name is not None: # cv2.imwrite('_my.png', image) save_image(image, save_name) # if len(coords) == 0: # logging.info("draw_boxes_and_labels_to_image: no bboxes exist, cannot draw !")
return image
[docs]def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836): """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) """ import matplotlib.pyplot as plt if saveable is False: plt.ion() plt.figure(fig_idx) # show all feature images if len(I.shape) and I.shape[-1] == 1: # (10,10,1) --> (10,10) I = I[:, :, 0] plt.imshow(I, cmap) plt.title(name) # plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick # plt.gca().yaxis.set_major_locator(plt.NullLocator()) if saveable: plt.savefig(name + '.pdf', format='pdf') else: plt.draw()
plt.pause(second)
[docs]def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362): """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) """ import matplotlib.pyplot as plt # logging.info(CNN.shape) # (5, 5, 3, 64) # exit() n_mask = CNN.shape[3] n_row = CNN.shape[0] n_col = CNN.shape[1] n_color = CNN.shape[2] row = int(np.sqrt(n_mask)) col = int(np.ceil(n_mask / row)) plt.ion() # active mode fig = plt.figure(fig_idx) count = 1 for _ir in range(1, row + 1): for _ic in range(1, col + 1): if count > n_mask: break fig.add_subplot(col, row, count) # logging.info(CNN[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5 # exit() # plt.imshow( # np.reshape(CNN[count-1,:,:,:], (n_row, n_col)), # cmap='gray', interpolation="nearest") # theano if n_color == 1: plt.imshow(np.reshape(CNN[:, :, :, count - 1], (n_row, n_col)), cmap='gray', interpolation="nearest") elif n_color == 3: plt.imshow(np.reshape(CNN[:, :, :, count - 1], (n_row, n_col, n_color)), cmap='gray', interpolation="nearest") else: raise Exception("Unknown n_color") plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick plt.gca().yaxis.set_major_locator(plt.NullLocator()) count = count + 1 if saveable: plt.savefig(name + '.pdf', format='pdf') else: plt.draw()
plt.pause(second)
[docs]def images2d(images=None, second=10, saveable=True, name='images', dtype=None, fig_idx=3119362): """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) """ import matplotlib.pyplot as plt # logging.info(images.shape) # (50000, 32, 32, 3) # exit() if dtype: images = np.asarray(images, dtype=dtype) n_mask = images.shape[0] n_row = images.shape[1] n_col = images.shape[2] n_color = images.shape[3] row = int(np.sqrt(n_mask)) col = int(np.ceil(n_mask / row)) plt.ion() # active mode fig = plt.figure(fig_idx) count = 1 for _ir in range(1, row + 1): for _ic in range(1, col + 1): if count > n_mask: break fig.add_subplot(col, row, count) # logging.info(images[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5 # plt.imshow( # np.reshape(images[count-1,:,:,:], (n_row, n_col)), # cmap='gray', interpolation="nearest") # theano if n_color == 1: plt.imshow(np.reshape(images[count - 1, :, :], (n_row, n_col)), cmap='gray', interpolation="nearest") # plt.title(name) elif n_color == 3: plt.imshow(images[count - 1, :, :], cmap='gray', interpolation="nearest") # plt.title(name) else: raise Exception("Unknown n_color") plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick plt.gca().yaxis.set_major_locator(plt.NullLocator()) count = count + 1 if saveable: plt.savefig(name + '.pdf', format='pdf') else: plt.draw()
plt.pause(second)
[docs]def tsne_embedding(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862): """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') """ import matplotlib.pyplot as plt def plot_with_labels(low_dim_embs, labels, figsize=(18, 18), second=5, saveable=True, name='tsne', fig_idx=9862): assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings" if saveable is False: plt.ion() plt.figure(fig_idx) plt.figure(figsize=figsize) #in inches for i, label in enumerate(labels): x, y = low_dim_embs[i, :] plt.scatter(x, y) plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom') if saveable: plt.savefig(name + '.pdf', format='pdf') else: plt.draw() plt.pause(second) try: from sklearn.manifold import TSNE from six.moves import xrange tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000) # plot_only = 500 low_dim_embs = tsne.fit_transform(embeddings[:plot_only, :]) labels = [reverse_dictionary[i] for i in xrange(plot_only)] plot_with_labels(low_dim_embs, labels, second=second, saveable=saveable, \ name=name, fig_idx=fig_idx) except ImportError:
logging.info("Please install sklearn and matplotlib to visualize embeddings.")
[docs]def draw_weights(W=None, second=10, saveable=True, shape=None, name='mnist', fig_idx=2396512): """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) """ if shape is None: shape = [28, 28] import matplotlib.pyplot as plt if saveable is False: plt.ion() fig = plt.figure(fig_idx) # show all feature images n_units = W.shape[1] num_r = int(np.sqrt(n_units)) # 每行显示的个数 若25个hidden unit -> 每行显示5个 num_c = int(np.ceil(n_units / num_r)) count = int(1) for _row in range(1, num_r + 1): for _col in range(1, num_c + 1): if count > n_units: break fig.add_subplot(num_r, num_c, count) # ------------------------------------------------------------ # plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray') # ------------------------------------------------------------ feature = W[:, count - 1] / np.sqrt((W[:, count - 1]**2).sum()) # feature[feature<0.0001] = 0 # value threshold # if count == 1 or count == 2: # print(np.mean(feature)) # if np.std(feature) < 0.03: # condition threshold # feature = np.zeros_like(feature) # if np.mean(feature) < -0.015: # condition threshold # feature = np.zeros_like(feature) plt.imshow(np.reshape(feature, (shape[0], shape[1])), cmap='gray', interpolation="nearest") #, vmin=np.min(feature), vmax=np.max(feature)) # plt.title(name) # ------------------------------------------------------------ # plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest") plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick plt.gca().yaxis.set_major_locator(plt.NullLocator()) count = count + 1 if saveable: plt.savefig(name + '.pdf', format='pdf') else: plt.draw()
plt.pause(second) W = draw_weights