def fft_save(frames_iter, path_out, size, count=None, kmax=None):
if not kmax:
# Ignore features of size smaller than 32 pixels
# (My avi file has JPEG artifact of size 8 pixels)
kmax = size // 32
# These codes are copied from the examples in cddm package
#: create window for multiplication...
# window = blackman(SHAPE)
#: we must create a video of windows for multiplication
# window_video = ((window,),)*NFRAMES
#: perform the actual multiplication
# video = multiply(video_simulator.video, window_video)
video = frames_iter
#: if the intesity of light source flickers you can normalize each frame to the intensity of the frame
# video = normalize_video(video)
#: perform rfft2 and crop results, to take only first kimax and first kjmax wavenumbers.
fft = rfft2(video, kimax=kmax, kjmax=kmax)
#: you can also normalize each frame with respect to the [0,0] component of the fft
#: this it therefore equivalent to normalize_video
# fft = normalize_fft(fft)
#: load in numpy array
# fft_array, = asarrays(fft, count)
# np.save(fft_array, path_out)
# save into disk
asmemmaps(path_out, fft, count=count)
return {
'kmax': kmax
}
#obtain frames iterator
video = fromarrays((vid, ))
##apply blackman window
window = blackman(SHAPE)
video = apply_window(video, (window, ))
#perform rfft2 and crop data
video = rfft2(video, kisize=64, kjsize=64)
#load all frames into numpy array
#video, = asmemmaps("brownian_single_camera_fft", video, nframes)
#compute and create numpy array
video, = asarrays(video, nframes)
np.save("simple_brownian_ddm_fft.npy", video)
v1 = np.load("simple_brownian_cddm_video_0.npy")
v2 = np.load("simple_brownian_cddm_video_1.npy")
nframes = len(v1)
#obtain frames iterator
video = fromarrays((v1, v2))
##apply blackman window
window = blackman(SHAPE)
video = apply_window(video, (window, window))
#perform rfft2 and crop data
video = rfft2(video, kisize=64, kjsize=64)
#load all frames into numpy array
#video, = asmemmaps("brownian_single_camera_fft", video, nframes)
#compute and create numpy array
asmemmaps("simple_brownian_cddm_fft", video, nframes)
#return ((frame1+frame2,frame1+frame2) for frame1,frame2 in zip(vid1,vid2))
if __name__ == "__main__":
print("Computing ddm video...")
vid = get_video(seed=0)
vid, = video.asarrays(vid, NFRAMES_SINGLE)
print("Writing to HD ...")
np.save("simple_brownian_ddm_video.npy", vid)
print("Computing cddm video...")
vid = get_dual_video(seed=0)
#we can write directly to HD by creating and writing to numpy memmap
v1, v2 = video.asmemmaps("simple_brownian_cddm_video", vid, NFRAMES_DUAL)
np.save("simple_brownian_cddm_t1.npy", t1)
np.save("simple_brownian_cddm_t2.npy", t2)
#print("Writing to HD ...")
#np.save("brownian_dual_camera_0.npy",v1)
#np.save("brownian_dual_camera_1.npy",v2)
#v1 = viewer.VideoViewer(get_dual_video(), n = 1024)
#v1.show()
v = viewer.VideoViewer(v1)
v.show()
v = viewer.VideoViewer(v2)
v.show()
def test_memmaps(self):
video = fromarrays((vid, ))
with self.assertRaises(ValueError):
video = asmemmaps("deleteme", video)
video = asmemmaps("deleteme", video, 128)