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| import time
import visdom
import matplotlib
import torch as t
import numpy as np
matplotlib.use("Agg")
from matplotlib import pyplot as plt
def vis_image(img, ax=None):
"""Visualize a color image
img: RGB format with values between [0, 255], 3 x H x W, array
ax: matplotlib.axes.Axis, where to display the img
return: returns the Axes object with the plot for further tweaking
"""
if ax is None:
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
img = img.transpose((1, 2, 0))
ax.imshow(img.astype(np.uint8))
return ax
def vis_bbox(img, bbox, label=None, score=None, gt=False, ax=None):
"""Visualize bounding boxes inside image.
"""
if label is not None and not len(bbox) == len(label):
raise ValueError('The length of label must be same as that of bbox')
if score is not None and not len(bbox) == len(score):
raise ValueError('The length of score must be same as that of bbox')
ax = vis_image(img, ax=ax)
if len(bbox) == 0:
return ax
if gt:
n = int(len(bbox)/2)
else:
n = len(bbox)
for i, bb in enumerate(bbox):
if i<n:
color = 'blue'
else:
color = 'red'
xy = (bb[1], bb[0])
height = bb[2] - bb[0]
width = bb[3] - bb[1]
ax.add_patch(plot.Rectangle(
xy, width, height, fill=False, edgecolor=color, linewidth=2))
caption = list()
if label is not None:
lb = label[i]
caption.append(str(lb).zfill(3))
if score is not None:
sc = score[i]
caption.append('{:.2f}'.format(sc))
if len(caption) > 0:
ax.text(bb[1], bb[0],
': '.join(caption),
style='italic',
bbox={'facecolor': 'white', 'alpha': 0.5, 'pad': 0})
return ax
def fig2data(fig):
"""
brief Convert a Matplotlib figure to a 4D numpy array with RGBA
channels and return it
@param fig: a matplotlib figure
@return a numpy 3D array of RGBA values
"""
fig.canvas.draw()
w, h = fig.canvas.get_width_height()
buf = np.fromstring(fig.canvas.tostring_argb(), dtype=np.uint8)
buf.shape = (w, h, 4)
buf = np.roll(buf, 3, axis=2)
return buf.reshape(h, w, 4)
def fig4vis(fig):
"""
convert figure to ndarray
"""
ax = fig.get_figure()
img_data = fig2data(ax).astype(np.int32)
plot.close()
return img_data[:, :, :3].transpose((2, 0, 1)) / 255.
def visdom_bbox(*args, **kwargs):
fig = vis_bbox(*args, **kwargs)
data = fig4vis(fig)
return data
class Visualizer(object):
"""
wrapper for visdom
you can still access naive visdom function by
self.line, self.scater,self._send,etc.
due to the implementation of `__getattr__`
"""
def __init__(self, env='default', **kwargs):
self.vis = visdom.Visdom(env=env, use_incoming_socket=False, **kwargs)
self._vis_kw = kwargs
self.index = {}
self.log_text = ''
def reinit(self, env='default', **kwargs):
"""
change the config of visdom
"""
self.vis = visdom.Visdom(env=env, **kwargs)
return self
def plot(self, name, y, showlegend=False, **kwargs):
""" plot line
name: the name of variable
y: the current value of variable
showlenged: whether show variable's lengend
Example:
self.plot('loss',1.00)
"""
x = self.index.get(name, 0)
self.vis.line(Y=np.array([y]), X=np.array([x]),
win=name,
opts=dict(title=name, showlegend=showlegend),
update=None if x == 0 else 'append',
**kwargs
)
self.index[name] = x + 1
def plot_many(self, d):
"""
plot multi values
@params d: dict (name,value) i.e. ('loss',0.11)
"""
for k, v in d.items():
if v is not None:
self.plot(k, v)
def img(self, name, img_, **kwargs):
"""
self.img('input_img',t.Tensor(64,64))
self.img('input_imgs',t.Tensor(3,64,64))
self.img('input_imgs',t.Tensor(100,1,64,64)) 100张灰度图像
self.img('input_imgs',t.Tensor(100,3,64,64),nrows=10)
!!don't ~~self.img('input_imgs',t.Tensor(100,64,64),nrows=10)~~!!
"""
self.vis.images(t.Tensor(img_).cpu().numpy(),
win=name,
opts=dict(title=name),
**kwargs
)
def img_many(self, d):
for k, v in d.items():
self.img(k, v)
def log(self, info, win='log_text'):
"""
self.log({'loss':1,'lr':0.0001})
"""
self.log_text += ('[{time}] {info} <br>'.format(
time=time.strftime('%m%d_%H%M%S'), \
info=info))
self.vis.text(self.log_text, win)
def __getattr__(self, name):
return getattr(self.vis, name)
def state_dict(self):
return {
'index': self.index,
'vis_kw': self._vis_kw,
'log_text': self.log_text,
'env': self.vis.env
}
def image_bbox(self, name, image, bbox, label=None, score=None, gt=False, ax=None):
img_data = visdom_bbox(image, bbox, label=label, score=score, gt=gt)
self.img(name, img_data)
def load_state_dict(self, d):
self.vis = visdom.Visdom(env=d.get('env', self.vis.env), **(self.d.get('vis_kw')))
self.log_text = d.get('log_text', '')
self.index = d.get('index', dict())
return self
def img_heatmap(self, name, X):
"""heatmaps
X: H x W, array
"""
self.vis.heatmap(X, win=name)
if __name__ == '__main__':
vis = Visualizer()
import torch
img = torch.randn(35, 1, 40, 60)
print(torch.max(img[0]))
vis.img('test3', img)
vis.img('test2', img, nrow=10)
vis.log('test', win='test')
import numpy as np
img = np.random.rand(3,100, 120)
bbox = np.array([[20, 30, 30, 60], [30, 40, 70, 80]])
label = np.array([0, 1])
vis.image_bbox('image_bbox', img, bbox, label=label)
vis.img_heatmap('heatmap', np.random.rand(180, 240))
p0_1 = np.random.rand(10)
p0_2 = np.random.rand(10)
for i in range(10):
vis.plot('precision', [p0_1[i], p0_2[i]], showlegend=True)
|
