• Created by Unknown User (davidsch), last modified on Sep 07, 2016

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Data

From Siqi:

https://www.dropbox.com/s/8wrfbkpaosn23vq/labeledimg1.mat?dl=0
 https://www.dropbox.com/s/uw9nx8mp8pqe94e/labeledimg2.mat?dl=0

In the data structure there is vccimg and yagimg which refer to the images on VCC and YAG screens. There is vccbox and yagbox which refer to the box coordinate where it finds the beam, [ystart yend xstart xend]. If it's empty it means it detects no signal. I corrected the mislabeled ones using the fitting method so they should all be labeled correctly now.

From David

I have downloaded the files, they are at (on the psana nodes) ~davidsch/mlearn/accyag/labeledimg*.mat

Access from Python

scipy can load mat files, here is some code that investigates a little:

In [3]: import scipy.io as sio
In [4]: labeledimg1 = sio.loadmat('labeledimg1.mat')
In [8]: vccImg = labeledimg1['vccImg']
In [18]: vccBox = labeledimg1['vccbox']
# you'll see vccImg and vccBox show up as 1 x 110 arrays of 'object', they are the images and labels for 110 samples
 
# like Siqi says, a box entry is empty if no beam is present, here we get a count of the non empty boxes, or samples with beam
In [23]: len([bx for bx in vccBox[0,:] if len(bx)>0])
Out[23]: 80

The first entry with a box is 4, so you can plot like
In [24] %pylab
In [26]: imshow(vccImg[0,4])
In [27]: bx = vccBox[0,4]
In [31]: ymin,ymax,xmin,xmax=bx[0,:]
In [32]: plot([xmin,xmin,xmax,xmax,xmin],[ymin,ymax,ymax,ymin,ymin], 'w')

In which case I see

Data

  • Between the two files, there are 142 samples.
  • Each sample has a yag, vcc, and box for each
  • If there is a non empty box for yag, there is a non empty box for vcc, and vice versa.
  • vcc values are in [0,255], and the boxed beam can get quite brite
  • yag values go over 1000, I think, but the boxed value is always dim, like up to 14

First Pass

We have to fit the 480 x 640 vcc images, and 1040 x 1392 yag images into 224 x 224 x 3 RBG images.

I thresholed yag at 255, then made grayscale images for each, using a scipy imresize option.

I generated codewords for the yag and vcc. The yag, which has bright beam, shows alot of structure:

These are plotted with a very large aspect ratio, the bottom is the 'nobeam' images.

However with the yag images, there is very little difference between nobeam and beam:

There a

I suspect we will not be able to do much with these codewords without more preprocessing of the yag images - I think they are too faint for what vgg16 expects - it was trained on the imagenet color images.

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