我正在尝试从MATLAB迁移到Python,我在Matlab开发过程中经常依赖的一件事就是能够通过循环遍历图层并调用drawow来快速可视化数据立方体的切片。
tst = randn(1000,1000,100);
for n = 1:size(tst, 3)
imagesc(tst(:,:,n));
drawnow;
end
当我在MATLAB中对此进行tic / toc时,它表明该数字正在以大约28fps更新。相反,当我尝试使用matplotlib的imshow()命令执行此操作时,相比之下,即使使用set_data(),它也会以蜗牛的速度运行。
import matplotlib as mp
import matplotlib.pyplot as plt
import numpy as np
tmp = np.random.random((1000,1000,100))
myfig = plt.imshow(tmp[:,:,i], aspect='auto')
for i in np.arange(0,tmp.shape[2]):
myfig.set_data(tmp[:,:,i])
mp.pyplot.title(str(i))
mp.pyplot.pause(0.001)
在我的计算机上,它以大约16fps的速度运行,默认(非常小)的比例,如果我将其调整为更大并且与matlab图形相同,它会减慢到大约5 fps。从一些较旧的线程我看到了使用glumpy的建议,我安装了所有相应的包和库(glfw等),并且包本身工作正常,但它不再支持suggested in a previous thread的简单图像可视化。
然后我下载了vispy,我可以使用this thread的代码作为模板用它来制作图像:
import sys
from vispy import scene
from vispy import app
import numpy as np
canvas = scene.SceneCanvas(keys='interactive')
canvas.size = 800, 600
canvas.show()
# Set up a viewbox to display the image with interactive pan/zoom
view = canvas.central_widget.add_view()
# Create the image
img_data = np.random.random((800,800, 3))
image = scene.visuals.Image(img_data, parent=view.scene)
view.camera.set_range()
# unsuccessfully tacked on the end to see if I can modify the figure.
# Does nothing.
img_data_new = np.zeros((800,800, 3))
image = scene.visuals.Image(img_data_new, parent=view.scene)
view.camera.set_range()
Vispy似乎非常快,看起来它会让我在那里,但你如何用新数据更新画布?谢谢,
请参阅ImageVisual.set_data方法
# Create the image
img_data = np.random.random((800,800, 3))
image = scene.visuals.Image(img_data, parent=view.scene)
view.camera.set_range()
# Generate new data :
img_data_new = np.zeros((800,800, 3))
img_data_new[400:, 400:, 0] = 1. # red square
image.set_data(img_data_new)