def main():
print('\n')
print('##############################################')
print('##############################################')
print('#### ####')
print('#### DOWNSAMPLE SINOGRAM ####')
print('#### ####')
print('##############################################')
print('##############################################')
print('\n')
## Get input arguments
args = getArgs()
## Read input sinogram
pathin = args.pathin
if pathin[len(pathin)-1] != '/':
pathin += '/'
filename = args.sino
sino = io.readImage( pathin + filename )
nang , npix = sino.shape
print('\nInput path:\n', pathin)
print('\nInput sinogram:\n', filename)
print('Sinogram size: ', nang,' ang X ', npix,' pixels')
## Get input sinogram
factor = int( args.factor )
print('\nDownsampling factor: ', factor)
## Downsample in pixels
sino_down = sino[:,::factor]
if args.plot is True:
sino_list = [ sino , sino_down ]
title_list = [ 'Original sinogram' , 'Undersampled sinogram' ]
dis.plot_multi( sino_list , title_list )
## Write image
if args.pathout is None:
pathout = pathin
else:
pathout = args.pathout
fileout = filename[:len(filename)-4] + '_downsampl' + str( factor ) + '.DMP'
io.writeImage( pathout + fileout , sino_down )
print('\n')
def main():
print('\n')
print('##############################################')
print('##############################################')
print('#### ####')
print('#### DOWNSAMPLE SINOGRAM ####')
print('#### ####')
print('##############################################')
print('##############################################')
print('\n')
## Get input arguments
args = getArgs()
## Read input sinogram
pathin = args.pathin
if pathin[len(pathin)-1] != '/':
pathin += '/'
filename = args.sino
sino = io.readImage( pathin + filename )
nang , npix = sino.shape
print('\nInput path:\n', pathin)
print('\nInput sinogram:\n', filename)
print('Sinogram size: ', nang,' ang X ', npix,' pixels')
## Getting projection geometry
angles = create_projection_angles( args , nang )
## Downsample in angles
sino_down , angles_down = downsample_sinogram_angles( sino , angles , args )
## Display check plot
if args.plot is True:
sino_list = [ sino , sino_down ]
title_list = [ 'Original sinogram' , 'Undersampled sinogram' ]
dis.plot_multi( sino_list , title_list )
## Write image & angle list
write_output_files( sino_down , angles_down , args , pathin , filename )
print('\n')
def main():
print('\n')
print('#######################################')
print('#######################################')
print('### ###')
print('### STRUCTURAL SIMILARITY INDEX ###')
print('### ###')
print('#######################################')
print('#######################################')
print('\n')
## Get input arguments
args = getArgs()
## Get oracle image
currDir = os.getcwd()
image1 = io.readImage( args.image1 )
image1 = image1.astype( myfloat )
print('\nReading reference image:\n', args.image1)
print('Image shape: ', image1.shape)
image_list = []
results = []
## CASE OF SINGLE IMAGE TO ANALYZE
if args.image2 is not None:
if args.image2.find( ':' ) == -1:
image_list.append( args.image2 )
image2 = io.readImage( args.image2 ) # image2 --> image to analyze
image2 = image2.astype(myfloat)
num_img = 1
print('\nReading image to analyze:\n', args.image2)
print('Image shape: ', image2.shape)
## Get time in which the prgram starts to run
time1 = time.time()
## Scale image to analyze with respect to the reference one
if args.scaling is True:
print('\nPerforming linear regression ....')
image2 = proc.linear_regression( image1 , image2 )
## Register images
if args.register is True:
print('\nPerforming registration of the image to analize ....')
image2 = proc.image_registration( image2 , image1 , 'ssd' )
## Crop resolution circle of the images
if args.resol_circle is True:
print('\nSelecting the resolution circle')
image1 = proc.select_resol_square( image1 )
image2 = proc.select_resol_square( image2 )
## Crop images if enabled
if args.roi is not None:
roi = args.roi
if roi.find( ':' ) != -1:
roi = roi.split(',')
p0 = [int(roi[0].split(':')[1]),int(roi[0].split(':')[0])]
p1 = [int(roi[1].split(':')[1]),int(roi[1].split(':')[0])]
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
else:
print('\nUsing pixels specified in file:\n', roi)
pixels = np.loadtxt( roi )
pixels = pixels.astype( int )
p0 = np.array([pixels[0,0],pixels[0,1]])
p1 = np.array([pixels[len(pixels)-1,0],pixels[len(pixels)-1,1]])
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
## Compute the gradient of the images, if enabled
if args.gradient is True:
image1 = compute_gradient_image( image1 )
image2 = compute_gradient_image( image2 )
## Check whether the 2 images have the same shape
if image1.shape != image2.shape:
sys.error('\nERROR: The input images have different shapes!\n')
## Plot to check whether the images have the same orientation
if args.plot is True:
print('\nPlotting images to check orientation ....')
img_list = [ image1 , image2 ]
title_list = [ 'Oracle image' , 'Image to analyze' ]
dis.plot_multi( img_list , title_list , 'Check plot' )
## Get window size
window_size = args.window
print('\nSize of the computation window: ', window_size)
if window_size % 2 != 0:
window_size += 1
print('Window size is even: window size changed to ', window_size)
## Get sigma of the gaussian kernel
sigma = SIGMA
print('Sigma of the gaussian kernel: ', sigma)
## Calculate map of SSIM values
map_ssim , MSSIM = compute_map_ssim( image1 , image2 , window_size , sigma )
results.append( MSSIM )
if args.plot is True:
print('\nPlotting images + map of ssim ....')
img_list = [ image1 , image2 , map_ssim ]
title_list = [ 'Oracle image' , 'Image to analyze' , 'Map of SSIM' ]
dis.plot_multi( img_list , title_list , 'Images and map of SSIM' , colorbar=True )
## Save SSIM map
filename = args.image2[:len(args.image2)-4] + '_ssim_map.png'
io.writeImage( filename , map_ssim )
## CASE OF MULTIPLE SPECIFIC IMAGES
else:
image_list = args.image2.split(':')
img_list = [ ]
title_list = [ ]
num_img = len( image_list )
for im in range( num_img ):
img_file = image_list[im]
image1 = io.readImage( args.image1 )
image2 = io.readImage( img_file ) # image2 --> image to analyze
image2 = image2.astype(myfloat)
print('\nReading image to analyze:\n', args.image2)
print('Image shape: ', image2.shape)
## Get time in which the prgram starts to run
time1 = time.time()
## Scale image to analyze with respect to the reference one
if args.scaling is True:
print('\nPerforming linear regression ....')
image2 = proc.linear_regression( image1 , image2 )
## Register images
if args.register is True:
print('\nPerforming registration of the image to analize ....')
image2 = proc.image_registration( image2 , image1 , 'ssd' )
## Crop resolution circle of the images
if args.resol_circle is True:
print('\nSelecting the resolution circle')
image1 = proc.select_resol_square( image1 )
image2 = proc.select_resol_square( image2 )
## Crop images if enabled
if args.roi is not None:
roi = args.roi
if args.roi.find(',') != -1:
roi = roi.split(',')
p0 = [int(roi[0].split(':')[1]),int(roi[0].split(':')[0])]
p1 = [int(roi[1].split(':')[1]),int(roi[1].split(':')[0])]
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
else:
print('\nUsing pixels specified in file:\n', roi)
pixels = np.loadtxt( roi )
pixels = pixels.astype( int )
p0 = np.array([pixels[0,0],pixels[0,1]])
p1 = np.array([pixels[len(pixels)-1,0],pixels[len(pixels)-1,1]])
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
## Compute the gradient of the images, if enabled
if args.gradient is True:
image1 = compute_gradient_image( image1 )
image2 = compute_gradient_image( image2 )
## Check whether the 2 images have the same shape
if image1.shape != image2.shape:
sys.exit('\nERROR: The input images have different shapes!\n')
## Plot to check whether the images have the same orientation
if args.plot is True:
print('\nPlotting images to check orientation ....')
img_list2 = [ image1 , image2 ]
title_list2 = [ 'Oracle image' , 'Image to analyze' ]
dis.plot_multi( img_list2 , title_list2 , 'Check plot' )
## Get window size
window_size = args.window
print('\nSize of the computation window: ', window_size)
if window_size % 2 != 0:
window_size += 1
print('Window size is even: window size changed to ', window_size)
## Get sigma of the gaussian kernel
sigma = SIGMA
print('Sigma of the gaussian kernel: ', sigma)
## Calculate map of SSIM values
map_ssim , MSSIM = compute_map_ssim( image1 , image2 , window_size , sigma )
results.append( MSSIM )
map_ssim[ map_ssim < 0 ] = 0.0
if args.plot is True:
img_list.append( map_ssim )
title_list.append( 'SSIM map n.' + str( im + 1 ) )
## Save SSIM map
filename = img_file[:len(img_file)-4] + '_ssim_map.png'
io.writeImage( filename , map_ssim )
if args.plot is True:
print('\nPlotting images + map of ssim ....')
dis.plot( img_list[0] )
dis.plot( img_list[1] )
dis.plot_multi_colorbar( img_list , title_list , 'Maps of SSIM' )
## CASE OF BUNCH OF IMAGES TO ANALYZE
else:
os.chdir(args.path)
image_list = sorted( glob.glob('*') )
num_images = len( image_list )
img_list.append( image1 )
title_list.append('Oracle image')
## Get time in which the prgram starts to run
time1 = time.time()
## Loop on all the images to analyze
for i in range( num_img ):
image1 = io.readImage( args.image1 )
image2 = io.readImage( image_list[i] )
image2 = image2.astype(myfloat)
print('\n\n\nIMAGE TO ANALYZE NUMBER: ', i)
print('\nReading image to analyze:\n', fileIn[i])
print('Image shape: ', image2.shape)
## Scale image to analyze with respect to the reference one
if args.scaling is True:
print('\nPerforming linear regression ....')
image2 = proc.linear_regression( image1 , image2 )
## Register images
if args.register is True:
print('\nPerforming registration of the image to analize ....')
image2 = proc.image_registration( image2 , image1 , 'ssd' )
## Crop resolution circle of the images
if args.resol_circle is True:
print('\nSelecting the resolution circle')
image1 = proc.select_resol_square( image1 )
image2 = proc.select_resol_square( image2 )
## Crop images if enabled
if args.roi is not None:
roi = args.roi
if args.roi.find(',') != -1:
roi = roi.split(',')
p0 = [int(roi[0].split(':')[1]),int(roi[0].split(':')[0])]
p1 = [int(roi[1].split(':')[1]),int(roi[1].split(':')[0])]
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
else:
print('\nUsing pixels specified in file:\n', roi)
pixels = np.loadtxt( roi )
pixels = pixels.astype( int )
p0 = np.array([pixels[0,0],pixels[0,1]])
p1 = np.array([pixels[len(pixels)-1,0],pixels[len(pixels)-1,1]])
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
## Compute the gradient of the images, if enabled
if args.gradient is True:
image1 = compute_gradient_image( image1 )
image2 = compute_gradient_image( image2 )
## Check whether the 2 images have the same shape
if image1.shape != image2.shape and args.roi is None:
sys.error('\nERROR: The input images have different shapes!\n')
## Plot to check whether the images have the same orientation
if args.plot is True:
print('\nPlotting images to check orientation ....')
img_list = [ image1 , image2 ]
title_list = [ 'Oracle image' , 'Image to analyze' ]
dis.plot_multi( img_list , title_list , 'Check plot' )
## Get window size
window_size = args.window
print('\nSize of the computation window: ', window_size)
if window_size % 2 != 0:
window_size += 1
print('Window size is even: window size changed to ', window_size)
## Get sigma of the gaussian kernel
sigma = SIGMA
print('Sigma of the gaussian kernel: ', sigma)
## Calculate map of SSIM values
map_ssim,MSSIM = compute_map_ssim( image1 , image2 , window_size , sigma )
results.append( MSSIM )
## Diplay map of SSIM
if args.plot is True:
fig = plt.figure()
plt.title('Map of SSIM indeces')
plt.imshow(map_ssim,cmap = cm.Greys_r)
plt.colorbar()
plt.show()
## Save SSIM map
filename = image_list[i][:len(image_list[i])-4] + '_ssim_map.png'
io.writeImage( filename , map_ssim )
os.chdir(currDir)
## Summary print of the results
print('\n\nSUMMARY OF THE RESULTS:\n')
print('\nReference image:\n', args.image1)
for i in range( num_img ):
print('\nTest image number ', i,'\n', image_list[i])
print('SSIM = ' , results[i])
## Get time elapsed for the run of the program
time2 = time.time() - time1
print('\n\nTime elapsed for the calculation: ', time2)
## Write log file
write_log_file( args , image_list , results )
print('\n\n')
def compute_lir( args ):
## Get input images
inputs = args.inputs
file1 , file2 , file3 = inputs.split( ':' )
rec_ref = io.readImage( file1 )
rec_imp = io.readImage( file2 )
imp_pos = np.loadtxt( file3 ).astype( int )
npix = rec_ref.shape[0]
nimp = imp_pos.shape[0]
## Display input images
if args.plot is True:
image_imp = np.zeros( ( npix , npix ) )
image_list = [ rec_ref , rec_imp ]
title_list = [ 'Reference reconstruction' ,
'Impulsed reconstruction' ]
dis.plot_multi( image_list , title_list )
## Compute image difference
rec_diff = rec_imp - rec_ref
## Resize image
zoom_factor = args.zoom
rec_diff = zoom( rec_diff , zoom_factor )
if args.plot is True:
dis.plot( rec_diff , 'Difference image x' + str( zoom_factor ), ')')
## Compute FWHM for each impulse and get an average of them
fwhm = np.zeros( nimp , dtype=myfloat )
denom = zoom_factor
for i in range( nimp ):
x0 = int( zoom_factor * imp_pos[i,0] ); y0 = int( zoom_factor * imp_pos[i,1] )
square = rec_diff[x0-r:x0+r,y0-r:y0+r]
hh = np.max( square ) * 0.5
aux = np.argwhere( square >= hh )
fwhm[i] = np.sqrt( len( aux ) ) / denom
FWHM = np.sum( fwhm ) / myfloat( nimp )
print('\nFWHM values:\n')
pp.printArray( fwhm )
print('\nFWHM average: ', FWHM)
print('\n')
## Write log file of the results
namebase = file1
pathout = None
if namebase.find( '/' ) != -1:
namebase = namebase.split( '/' )
filename = namebase[len(namebase)-1]
filename = filename[:len(filename)-4]
for i in range( len(namebase)-1 ):
if i == 0:
pathout = '/' + namebase[i]
else:
pathout += namebase[i] + '/'
else:
filename = namebase
filename = filename[:len(filename)-4]
pathout = './'
print('\nWriting files ...')
fileout = pathout + filename + '_lir_diff.DMP'
io.writeImage( fileout , rec_diff )
print( fileout )
fileout = pathout + filename + '_lir_analysis.txt'
fp = open( fileout , 'w' )
fp.write('\n')
fp.write('\n###########################################')
fp.write('\n###########################################')
fp.write('\n### ###')
fp.write('\n### LOCAL IMPULSE RESPONSE ANALYSIS ###')
fp.write('\n### ###')
fp.write('\n###########################################')
fp.write('\n###########################################')
fp.write('\n')
today = datetime.datetime.now()
fp.write('\nLIR calculation performed on the ' + str( today ))
fp.write('\n\nNumbers of impulses: ' + str( nimp ))
fp.write('\n\nAverage FWHM: ' + str( FWHM ))
print( fileout )
print( '\n' )
def create_dataset( args ):
## Get input image
image_ref = io.readImage( args.inputs )
npix = image_ref.shape[0]
## Create impulsed image
nimp = int( np.floor( npix / nimp_factor ) )
c = npix * 0.5
ind_imp = np.zeros( ( nimp , 2 ) , dtype=int )
i = 0
print('\nSelecting impulse locations ...')
while i < nimp:
x = int( np.round( np.random.rand() * npix ) )
y = int( np.round( np.random.rand() * npix ) )
dist1 = np.ceil( np.sqrt( ( x - c )**2 + ( y - c )**2 ) )
if dist1 < c:
if i != 0:
for j in range( i + 1 ):
x0 = ind_imp[j,0]; y0 = ind_imp[j,1]
dist2 = np.ceil( np.sqrt( ( x - x0 )**2 + ( y - y0 )**2 ) )
if dist2 <= r:
break
elif dist2 > r and j < i:
continue
elif dist2 > r and j == i:
ind_imp[i,0] = x; ind_imp[i,1] = y
i += 1
else:
ind_imp[i,0] = x; ind_imp[i,1] = y
i += 1
print('... locations selected!')
print('\nImpulse locations:')
pp.printArray2D( ind_imp )
image_imp = image_ref.copy()
imp_factor = args.imp_factor
for i in range( len( ind_imp ) ):
x0 = ind_imp[i][0]; y0 = ind_imp[i][1]
image_imp[x0,y0] += image_imp[x0,y0] * imp_factor
## Create sinograms
nang = int( np.ceil( npix * np.pi * 0.5 ) )
angles = myfloat( np.arange( nang ) / myfloat( nang ) * 180.0 )
image_ref = myfloat( image_ref ); image_imp = myfloat( image_imp )
sino_ref = grid.forwproj( image_ref , angles )
sino_imp = grid.forwproj( image_imp , angles )
## Check plots
if args.plot is True:
image_list = [ image_ref , image_imp ]
title_list = [ 'Reference image' , 'Impulsed image' ]
dis.plot_multi( image_list , title_list )
image_list = [ sino_ref , sino_imp ]
title_list = [ 'Sinogram of reference image' , 'Sinogram of impulsed image' ]
dis.plot_multi( image_list , title_list )
## Write output files
namebase = args.inputs
if namebase.find( '/' ) != -1:
namebase = namebase.split( '/' )
filename = namebase[len(namebase)-1]
filename = filename[:len(filename)-4]
for i in range( len(namebase)-1 ):
if i == 0:
pathout = '/' + namebase[i]
else:
pathout += namebase[i] + '/'
else:
filename = namebase
filename = filename[:len(filename)-4]
pathout = './'
print('\nWriting output files ...')
fileout = pathout + filename + '_lir.DMP'
io.writeImage( fileout , image_imp )
print( fileout )
fileout = pathout + filename + '_sin.DMP'
io.writeImage( fileout , sino_ref )
print( fileout )
fileout = pathout + filename + '_lir_sin.DMP'
io.writeImage( fileout , sino_imp )
print( fileout )
fileout = pathout + filename + '_lir.txt'
np.savetxt( fileout , ind_imp , newline= '\n' )
print( fileout )
print('\n')
def main():
print('\n')
print('#######################################')
print('#######################################')
print('### ###')
print('### MEAN SQUARE ERROR ###')
print('### ###')
print('#######################################')
print('#######################################')
print('\n')
## Get input arguments
args = getArgs()
## Get oracle image
currDir = os.getcwd()
image1 = io.readImage( args.image1 )
image1 = image1.astype(myfloat)
print('\nReading reference image:\n', args.image1)
print('Image shape: ', image1.shape)
image_list = []
results = []
## CASE OF SINGLE IMAGE TO ANALYZE
if args.image2 is not None:
if args.image2.find( ':' ) == -1:
image_list.append( args.image2 )
image2 = io.readImage( args.image2 )
image2 = image2.astype(myfloat)
num_img = 1
print('\nReading image to analyze:\n', args.image2)
print('Image shape: ', image2.shape)
## Get time in which the prgram starts to run
time1 = time.time()
## Scale image to analyze with respect to the reference one
if args.scaling is True:
print('\nPerforming linear regression ....')
image2 = proc.linear_regression( image1 , image2 )
## Register images
if args.register is True:
print('\nPerforming registration of the image to analize ....')
image2 = proc.image_registration( image2 , image1 , 'ssd' )
## Crop resolution circle of the images
if args.resol_circle is True:
print('\nSelecting the resolution circle')
image1 = proc.select_resol_square( image1 )
image2 = proc.select_resol_square( image2 )
## Crop images if enabled
if args.roi is not None:
roi = args.roi
if args.roi.find(',') != -1:
roi = roi.split(',')
p0 = [int(roi[0].split(':')[1]),int(roi[0].split(':')[0])]
p1 = [int(roi[1].split(':')[1]),int(roi[1].split(':')[0])]
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
else:
print('\nUsing pixels specified in file:\n', roi)
file_roi = open( roi , 'r' )
pixels = np.loadtxt( file_roi )
image1 = image1[pixels[:,0],pixels[:,1]]
image2 = image2[pixels[:,0],pixels[:,1]]
num_pix = len( image1 )
fact = factors( num_pix )
image1 = image1.reshape( fact[0] , int( num_pix/fact[0] ) )
image2 = image2.reshape( fact[0] , int( num_pix/fact[0] ) )
## Compute the gradient of the images, if enabled
if args.gradient is True:
image1 = compute_gradient_image( image1 )
image2 = compute_gradient_image( image2 )
## Check whether the 2 images have the same shape
if image1.shape != image2.shape:
sys.error('\nERROR: The input images have different shapes!\n')
## Plot to check whether the images have the same orientation
if args.plot is True:
print('\nPlotting images to check orientation ....')
img_list = [ image1 , image2 ]
title_list = [ 'Oracle image' , 'Image to analyze' ]
dis.plot_multi( img_list , title_list , 'Check plot' )
## Compute figures of merit
SNR = calc_snr( image1 , image2 )
PSNR = calc_psnr( image1 , image2 )
RMSE = calc_rmse( image1 , image2 )
MAE = calc_rmse( image1 , image2 )
results.append( np.array( [ SNR , PSNR , RMSE , MAE ] ) )
## CASE OF MULTIPLE SPECIFIC IMAGES
else:
image_list = args.image2.split(':')
img_list = [ ]
title_list = [ ]
num_img = len( image_list )
for im in range( num_img ):
img_file = image_list[im]
image1 = io.readImage( args.image1 )
image2 = io.readImage( img_file ) # image2 --> image to analyze
image2 = image2.astype(myfloat)
print('\nReading image to analyze:\n', args.image2)
print('Image shape: ', image2.shape)
## Get time in which the prgram starts to run
time1 = time.time()
## Scale image to analyze with respect to the reference one
if args.scaling is True:
print('\nPerforming linear regression ....')
image2 = proc.linear_regression( image1 , image2 )
## Register images
if args.register is True:
print('\nPerforming registration of the image to analize ....')
image2 = proc.image_registration( image2 , image1 , 'ssd' )
## Crop resolution circle of the images
if args.resol_circle is True:
print('\nSelecting the resolution circle')
image1 = proc.select_resol_square( image1 )
image2 = proc.select_resol_square( image2 )
## Crop images if enabled
if args.roi is not None:
roi = args.roi
if args.roi.find(',') != -1:
roi = roi.split(',')
p0 = [int(roi[0].split(':')[1]),int(roi[0].split(':')[0])]
p1 = [int(roi[1].split(':')[1]),int(roi[1].split(':')[0])]
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
else:
print('\nUsing pixels specified in file:\n', roi)
file_roi = open( roi , 'r' )
pixels = np.loadtxt( file_roi )
pixels = pixels.astype( int )
image1 = image1[pixels[:,0],pixels[:,1]]
image2 = image2[pixels[:,0],pixels[:,1]]
num_pix = len( image1 )
fact = factors( num_pix )
image1 = image1.reshape( fact[0] , int( num_pix/fact[0] ) )
image2 = image2.reshape( fact[0] , int( num_pix/fact[0] ) )
## Compute the gradient of the images, if enabled
if args.gradient is True:
image1 = compute_gradient_image( image1 )
image2 = compute_gradient_image( image2 )
## Check whether the 2 images have the same shape
if image1.shape != image2.shape:
sys.exit('\nERROR: The input images have different shapes!\n')
## Plot to check whether the images have the same orientation
if args.plot is True:
print('\nPlotting images to check orientation ....')
img_list2 = [ image1 , image2 ]
title_list2 = [ 'Oracle image' , 'Image to analyze' ]
dis.plot_multi( img_list2 , title_list2 , 'Check plot' )
## Compute figures of merit
SNR = calc_snr( image1 , image2 )
PSNR = calc_psnr( image1 , image2 )
RMSE = calc_rmse( image1 , image2 )
MAE = calc_rmse( image1 , image2 )
results.append( np.array( [ SNR , PSNR , RMSE , MAE ] ) )
## CASE OF BUNCH OF IMAGES TO ANALYZE
else:
os.chdir(args.path)
image_list = sorted(glob.glob('*'))
num_img = len(fileIn)
## Get time in which the prgram starts to run
time1 = time.time()
## Loop on all the images to analyze
for i in range( num_img ):
image1 = io.readImage( args.image1 )
image2 = io.readImage( image_list[i] )
image2 = image2.astype(myfloat)
print('\n\n\nIMAGE TO ANALYZE NUMBER: ', i)
print('\nReading image to analyze:\n', image_list[i])
print('Image shape: ', image2.shape)
if args.fileout is not None:
fileout.write('\nReading image to analyze:\n' + fileIn[i])
## Scale image to analyze with respect to the reference one
if args.scaling is True:
print('\nPerforming linear regression ....')
image2 = proc.linear_regression( image1 , image2 )
## Register images
if args.register is True:
print('\nPerforming registration of the image to analize ....')
image2 = proc.image_registration( image2 , image1 , 'ssd' )
## Crop resolution circle of the images
if args.resol_circle is True:
print('\nSelecting the resolution circle')
image1 = proc.select_resol_square( image1 )
image2 = proc.select_resol_square( image2 )
## Crop images if enabled
if args.roi is not None:
roi = args.roi
if args.roi.find(',') != -1:
roi = roi.split(',')
p0 = [int(roi[0].split(':')[1]),int(roi[0].split(':')[0])]
p1 = [int(roi[1].split(':')[1]),int(roi[1].split(':')[0])]
print('Cropping rectangular ROI with vertices: ( ', \
p0[0],' , ', p0[1], ') ( ', p1[0],' , ',p1[1], ')')
image1 = proc.crop_image( image1 , p0 , p1 )
image2 = proc.crop_image( image2 , p0 , p1 )
else:
print('\nUsing pixels specified in file:\n', roi)
file_roi = open( roi , 'r' )
pixels = np.loadtxt( file_roi )
pixels = pixels.astype( int )
image1 = image1[pixels[:,0],pixels[:,1]]
image2 = image2[pixels[:,0],pixels[:,1]]
num_pix = len( image1 )
fact = factors( num_pix )
image1 = image1.reshape( fact[0] , int( num_pix/fact[0] ) )
image2 = image2.reshape( fact[0] , int( num_pix/fact[0] ) )
## Check whether the 2 images have the same shape
if image1.shape != image2.shape:
sys.exit('\nERROR: The input images have different shapes!\n')
## Compute the gradient of the images, if enabled
if args.gradient is True:
image1 = compute_gradient_image( image1 )
image2 = compute_gradient_image( image2 )
## Plot to check whether the images have the same orientation
if args.plot is True and args.roi.find(',') != -1:
print('\nPlotting images to check orientation ....')
img_list2 = [ image1 , image2 ]
title_list2 = [ 'Oracle image' , 'Image to analyze' ]
dis.plot_multi( img_list2 , title_list2 , 'Check plot' )
## Compute figures of merit
SNR = calc_snr( image1 , image2 )
PSNR = calc_psnr( image1 , image2 )
RMSE = calc_rmse( image1 , image2 )
MAE = calc_rmse( image1 , image2 )
results.append( np.array( [ SNR , PSNR , RMSE , MAE ] ) )
os.chdir(currDir)
## Summary print of the results
print('\n\nSUMMARY OF THE RESULTS:\n')
print('\nReference image:\n', args.image1)
for i in range( num_img ):
print('\nTest image number ', i,'\n', image_list[i])
print('SNR = ' , results[i][0])
print('PSNR = ' , results[i][1])
print('MRSE = ' , results[i][2])
print('MAE = ' , results[i][3])
## Get time elapsed for the run of the program
time2 = time.time() - time1
print('\n\nTime elapsed for the calculation: ', time2)
## Write log file
write_log_file( args , image_list , results )
print('\n\n')
