def test_show_matplotlib(self):
set_showlib("matplotlib")
video = fromarrays((vid, vid))
with self.assertRaises(ValueError):
show_fft(video, mode="wrong")
video = show_video(video)
video = show_fft(video, mode="real")
video = show_fft(video, clip=256)
video = show_diff(video)
video = play(video, fps=100)
video = load(video, 128)
import matplotlib.pyplot as plt
# test dual camera video (regular spaced)
video = simple_brownian_video(range(NFRAMES),
range(NFRAMES),
shape=SHAPE,
background=BACKGROUND)
#: apply dust particles
dust1 = plt.imread('dust1.png')[..., 0] #float normalized to (0,1)
dust2 = plt.imread('dust2.png')[..., 0]
dust = ((dust1, dust2), ) * NFRAMES
video = multiply(video, dust)
video = show_video(video)
video = show_diff(video)
video = show_fft(video, mode="real")
#: set fps to your required FPS. Video will be updated only if visualization
#: is fast enough not to interfere with the acquisition.
#: here video is again a valid video iterator, no visualization has yet took place
video = play(video, fps=100)
#: you should use either cv2 or pyqtgraph, matplotlib is too slow
#set_showlib("cv2")
set_showlib("pyqtgraph")
# now go through frames and show videos
for frames in video:
pass
from cddm import conf
import numpy as np
#video iterator
from simple_brownian_video import get_dual_video
#trigger parameters
from simple_brownian_video import t1, t2, PERIOD, SHAPE
#setting this to 2 shows progress bar
conf.set_verbose(2)
#obtain frames iterator
dual_video = get_dual_video()
#apply blackman window
#dual_video = apply_window(dual_video, blackman(SHAPE))
dual_video = show_video(dual_video)
fdual_video = rfft2(dual_video, kisize=64, kjsize=64)
fdual_video = show_fft(fdual_video)
fdual_video = play(fdual_video, fps=100)
data, bg = iccorr_multi(fdual_video,
t1,
t2,
period=PERIOD,
level=5,
chunk_size=256,
show=True,
auto_background=False,
binning=True,
return_background=True)