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tensorlayer3/tensorlayer/visualize.py

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#! /usr/bin/python
# -*- coding: utf-8 -*-
import os
import imageio
import numpy as np
import tensorlayer as tl
from tensorlayer.lazy_imports import LazyImport
import colorsys, random
cv2 = LazyImport("cv2")
# 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',
'draw_mpii_people_to_image', 'frame', 'CNN2d', 'images2d', 'tsne_embedding', 'draw_weights', 'W',
'draw_boxes_and_labels_to_image_with_json'
]
def read_image(image, path=''):
"""Read one image.
Parameters
-----------
image : str
The image file name.
path : str
The image folder path.
Returns
-------
numpy.array
The image.
"""
return imageio.imread(os.path.join(path, image))
def read_images(img_list, path='', 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 = tl.prepro.threading_data(b_imgs_list, fn=read_image, path=path)
# tl.logging.info(b_imgs.shape)
imgs.extend(b_imgs)
if printable:
tl.logging.info('read %d from %s' % (len(imgs), path))
return imgs
def save_image(image, image_path='_temp.png'):
"""Save a image.
Parameters
-----------
image : numpy array
[w, h, c]
image_path : str
path
"""
try: # RGB
imageio.imwrite(image_path, image)
except Exception: # Greyscale
imageio.imwrite(image_path, image[:, :, 0])
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
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')
"""
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), dtype=images.dtype)
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):
if np.max(images) <= 1 and (-1 <= np.min(images) < 0):
images = ((images + 1) * 127.5).astype(np.uint8)
elif np.max(images) <= 1 and np.min(images) >= 0:
images = (images * 255).astype(np.uint8)
return imageio.imwrite(path, merge(images, size))
if len(images) > size[0] * size[1]:
raise AssertionError("number of images should be equal or less than size[0] * size[1] {}".format(len(images)))
return imsave(images, size, image_path)
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>`__.
"""
if len(coords) != len(classes):
raise AssertionError("number of coordinates and classes are equal")
if len(scores) > 0 and len(scores) != len(classes):
raise AssertionError("number of scores and classes are equal")
# 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 = tl.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 = tl.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:
# tl.logging.info("draw_boxes_and_labels_to_image: no bboxes exist, cannot draw !")
return image
def draw_mpii_pose_to_image(image, poses, save_name='image.png'):
"""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
--------
numpy.array
The saved image.
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
-----------
- `MPII Keyponts and ID <http://human-pose.mpi-inf.mpg.de/#download>`__
"""
# import skimage
# 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)
# radius = int(image.shape[1] / 500) + 1
radius = int(thick * 1.5)
if image.max() < 1:
image = image * 255
for people in poses:
# Pose Keyponts
joint_pos = people['joint_pos']
# draw sketch
# joint id (0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee,
# 5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck,
# 9 - head top, 10 - r wrist, 11 - r elbow, 12 - r shoulder,
# 13 - l shoulder, 14 - l elbow, 15 - l wrist)
#
# 9
# 8
# 12 ** 7 ** 13
# * * *
# 11 * 14
# * * *
# 10 2 * 6 * 3 15
# * *
# 1 4
# * *
# 0 5
lines = [
[(0, 1), [100, 255, 100]],
[(1, 2), [50, 255, 50]],
[(2, 6), [0, 255, 0]], # right leg
[(3, 4), [100, 100, 255]],
[(4, 5), [50, 50, 255]],
[(6, 3), [0, 0, 255]], # left leg
[(6, 7), [255, 255, 100]],
[(7, 8), [255, 150, 50]], # body
[(8, 9), [255, 200, 100]], # head
[(10, 11), [255, 100, 255]],
[(11, 12), [255, 50, 255]],
[(12, 8), [255, 0, 255]], # right hand
[(8, 13), [0, 255, 255]],
[(13, 14), [100, 255, 255]],
[(14, 15), [200, 255, 255]] # left hand
]
for line in lines:
start, end = line[0]
if (start in joint_pos) and (end in joint_pos):
cv2.line(
image,
(int(joint_pos[start][0]), int(joint_pos[start][1])),
(int(joint_pos[end][0]), int(joint_pos[end][1])), # up-left and botton-right
line[1],
thick
)
# rr, cc, val = skimage.draw.line_aa(int(joint_pos[start][1]), int(joint_pos[start][0]), int(joint_pos[end][1]), int(joint_pos[end][0]))
# image[rr, cc] = line[1]
# draw circles
for pos in joint_pos.items():
_, pos_loc = pos # pos_id, pos_loc
pos_loc = (int(pos_loc[0]), int(pos_loc[1]))
cv2.circle(image, center=pos_loc, radius=radius, color=(200, 200, 200), thickness=-1)
# rr, cc = skimage.draw.circle(int(pos_loc[1]), int(pos_loc[0]), radius)
# image[rr, cc] = [0, 255, 0]
# Head
head_rect = people['head_rect']
if head_rect: # if head exists
cv2.rectangle(
image,
(int(head_rect[0]), int(head_rect[1])),
(int(head_rect[2]), int(head_rect[3])), # up-left and botton-right
[0, 180, 0],
thick
)
if save_name is not None:
# cv2.imwrite(save_name, image)
save_image(image, save_name)
return image
draw_mpii_people_to_image = draw_mpii_pose_to_image
def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
"""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)
"""
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)
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
# tl.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)
# tl.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)
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
# tl.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)
# tl.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)
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):
if low_dim_embs.shape[0] < len(labels):
raise AssertionError("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:
_err = "Please install sklearn and matplotlib to visualize embeddings."
tl.logging.error(_err)
raise ImportError(_err)
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
def draw_boxes_and_labels_to_image_with_json(image, json_result, class_list, save_name=None):
"""Draw bboxes and class labels on image. Return the image with bboxes.
Parameters
-----------
image : numpy.array
The RGB image [height, width, channel].
json_result : list of dict
The object detection result with json format.
classes_list : list of str
For converting ID to string on image.
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>`__.
"""
image_h, image_w, _ = image.shape
num_classes = len(class_list)
hsv_tuples = [(1.0 * x / num_classes, 1., 1.) for x in range(num_classes)]
colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
colors = list(map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)), colors))
random.seed(0)
random.shuffle(colors)
random.seed(None)
bbox_thick = int(0.6 * (image_h + image_w) / 600)
fontScale = 0.5
for bbox_info in json_result:
image_name = bbox_info['image']
category_id = bbox_info['category_id']
if category_id < 0 or category_id > num_classes: continue
bbox = bbox_info['bbox'] # the order of coordinates is [x1, y2, x2, y2]
score = bbox_info['score']
bbox_color = colors[category_id]
c1, c2 = (int(bbox[0]), int(bbox[1])), (int(bbox[2]), int(bbox[3]))
cv2.rectangle(image, c1, c2, bbox_color, bbox_thick)
bbox_mess = '%s: %.2f' % (class_list[category_id], score)
t_size = cv2.getTextSize(bbox_mess, 0, fontScale, thickness=bbox_thick // 2)[0]
c3 = (c1[0] + t_size[0], c1[1] - t_size[1] - 3)
cv2.rectangle(image, c1, (np.float32(c3[0]), np.float32(c3[1])), bbox_color, -1)
cv2.putText(
image, bbox_mess, (c1[0], np.float32(c1[1] - 2)), cv2.FONT_HERSHEY_SIMPLEX, fontScale, (0, 0, 0),
bbox_thick // 2, lineType=cv2.LINE_AA
)
if save_name is not None:
save_image(image, save_name)
return image
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