Masking Pipeline

CHX Olog NoteBook

CHX Olog (https://logbook.nsls2.bnl.gov/11-ID/)



In [21]:

    
from chxanalys.chx_libs import (np, roi, time, datetime, os,  getpass, db, get_images,LogNorm, plt,ManualMask)
from chxanalys.chx_libs import cmap_albula, cmap_vge, random
from chxanalys.chx_generic_functions import (get_detector, get_meta_data,create_user_folder,
                                             get_fields,  get_sid_filenames,load_data,
                                             RemoveHot, show_img,get_avg_img, 
                                             reverse_updown,create_cross_mask,mask_badpixels )
from skimage.draw import line_aa, line, polygon, circle
 
%matplotlib notebook

Path for Saving Results



In [7]:

    
CYCLE= '2017_2'  #change clycle here



In [8]:

    
path = '/XF11ID/analysis/%s/masks/'%CYCLE
print ("The analysis results will be saved in : %s"%path)









    



The analysis results will be saved in : /XF11ID/analysis/2017_2/masks/



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Get the image series and metadata from the uid



In [9]:

    
uid = '8bd04b' # (scan num: 22932) (Measurement: single frame 0.3 deg for mask )
uid = 'acac04' # (scan num: 23612) (Measurement: CoralPor for mask )
uid = 'c2afee' # (scan num: 23614) (Measurement: CoralPor for mask_new )
uid = 'e75486' # ] (scan num: 23675) (Measurement: 5 CoralPor images - make mask )

uid = 'ebb2b9' #ct : 1 ['ebb2b9'] (scan num: 23708) (Measurement: Single frame for masking )



In [10]:

    
#%run /XF11ID/analysis/Analysis_Pipelines/Develop/chxanalys/chxanalys/chx_generic_functions.py



In [11]:

    
md = get_meta_data( uid )
detector = get_detector( db[uid ] )
print ('Detector is:  %s'%detector  )
sud = get_sid_filenames(db[uid])

print ('scan_id, full-uid, data path are:  %s--%s--%s'%(sud[0], sud[1], sud[2][0] ))









    



Detector is:  eiger4m_single_image
scan_id, full-uid, data path are:  23708--ebb2b975-6ddf-4105-a30b-9f48730fb4f8--/XF11ID/data/2017/06/16/0b6306ac-4341-4993-ab83_1111



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print(md['beam_center_y'], md['beam_center_x'])









    



1468.0 1361.0



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imgs = load_data( uid, detector, reverse= False  )
#imgs = load_data( uid, detector, reverse= True  )
md.update( imgs.md );Nimg = len(imgs);
#if 'number of images'  not in list(md.keys()):
md['number of images']  = Nimg
pixel_mask =  1- np.int_( np.array( imgs.md['pixel_mask'], dtype= bool)  )
print( 'The data are: %s' %imgs )









    



The data are: <Frames>
Length: 1 frames
Frame Shape: 2167 x 2070
Pixel Datatype: uint32



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pixel_mask =  1- np.int_( np.array( md['pixel_mask'], dtype= bool)  )



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img_choice_N = 1  #can change this number to select more frames for average
img_samp_index = random.sample( range(len(imgs)), img_choice_N) 
avg_img =  get_avg_img( imgs, img_samp_index, plot_ = False, uid = uid)

show image and the pixel mask

show image



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show_img(  avg_img*pixel_mask , vmin=.001, vmax=1e3, logs=True, 
         image_name ='uid=%s'%uid, aspect=1, cmap= cmap_albula  )

pixel mask

Update Pixel Mask due to bad pixel of Eiger Detector



In [22]:

    
pixel_mask = mask_badpixels( pixel_mask, md['detector'])



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show_img( pixel_mask, vmin=0, vmax=1, image_name ='pixel_mask--uid=%s'%uid ,aspect=1 )

Remove hotspots in the image



In [24]:

    
#avg_img =  get_avg_img( imgs, sampling = 1000, plot_ = False, uid =uid)



In [25]:

    
mask_rh  = RemoveHot( avg_img, 2**16-1, plot_=True)



In [26]:

    
show_img(avg_img*pixel_mask,vmin=0.1,vmax=1e3, logs=True, 
         image_name= 'uid= %s with pixel mask'%uid , aspect=1, cmap= cmap_albula  )

Create a polygon mask

check beam center



In [27]:

    
#md['beam_center_x']= 1422
#md['beam_center_y']= 1439
#md['beam_center_x'],2167-md['beam_center_y']



In [28]:

    
md['beam_center_x'], md['beam_center_y']









    Out[28]:





(1361.0, 1468.0)



In [29]:

    
#md['beam_center_x'] = 1080
#md['beam_center_y'] = 1127



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To create multi-rectangle masks, for each sub-mask



In [30]:

    
#creat the right part mask
partial_mask = create_cross_mask(  avg_img, center=[1290,1469],
                            wy_left= 0, wy_right= 30, 
                              wx_up= 0, wx_down= 0,center_radius= 0 )



In [31]:

    
show_img(  partial_mask   )



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In [32]:

    
avg_img.shape









    Out[32]:





(2167, 2070)



In [33]:

    
#creat the left/right/up/down part mask
partial_mask *=  create_cross_mask(  avg_img, center=[ 1422,1439],
                            wy_left= 0, wy_right= 0, 
                              wx_up= 0, wx_down=0,center_radius= 0 )
#partial_mask2[1285:1350,1430:1440,] = False



In [34]:

    
show_img(  partial_mask   )



In [39]:

    
#creat the left/right/up/down part mask
partial_mask *=  create_cross_mask(  avg_img, center=[ 982, 2100],
                            wy_left= 0, wy_right= 0, 
                              wx_up= 0, wx_down= 0,center_radius= 0 )



In [40]:

    
#create a circle mask for windows
if False: #make it True to make window mask
    window_shadow  = ~create_cross_mask(  avg_img, center=[ 911,997],
                           wy_left= 0, wy_right= 0, 
                             wx_up= 0, wx_down= 0,center_circle=True, center_radius= 680)
else:
    window_shadow = 1



In [41]:

    
full_mask = partial_mask *window_shadow



In [42]:

    
show_img( full_mask, aspect = 1 )



In [50]:

    
#mask = np.array ( full_mask * pixel_mask*mask_rh  , dtype = bool )
mask = np.array ( full_mask * pixel_mask , dtype = bool )



In [51]:

    
fig, ax = plt.subplots()
#new_mask = 
im=ax.imshow(   (~mask) * avg_img,origin='lower' , 
      norm= LogNorm( vmin=0.001, vmax= 1e2 ), cmap= cmap_albula)
#im = ax.imshow(avg_img,  cmap='viridis',origin='lower', norm= LogNorm( vmin=0.001, vmax=100 ) )
plt.show()



In [52]:

    
fig, ax = plt.subplots()
im = ax.imshow((mask)*avg_img,  cmap= cmap_albula,origin='lower',norm= LogNorm( vmin=.1, vmax=1e5 ),
               interpolation='none')
plt.show()

Combine the hand-drawn/polygon mask and the pixel mask and hot pixel mask



In [53]:

    
#mask = np.array ( ~new_mask* ~plgon_mask * md['pixel_mask']*mask_rh, dtype = bool )

fig, ax = plt.subplots()
im=ax.imshow(mask, origin='lower' ,vmin=0, vmax=1,cmap='viridis')
fig.colorbar(im)
plt.show()

Save the combined mask to use in further data analysis



In [54]:

    
np.save(  path +   uid +"_mask", mask)



In [55]:

    
path +   uid +"_mask"









    Out[55]:





'/XF11ID/analysis/2017_2/masks/ebb2b9_mask'



In [56]:

    
meaningful_name = 'Jun16_SAXS_10m'



In [60]:

    
#np.save(  path +   meaningful_name, mask)



In [58]:

    
path +   meaningful_name









    Out[58]:





'/XF11ID/analysis/2017_2/masks/Jun16_SAXS_10m'



In [59]:

    
uid









    Out[59]:





'ebb2b9'



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