def register(self, src, trg, trg_mask=None, src_mask=None):
""" Implementation of pair-wise registration using thunder-registration
For more information on the model estimation, refer to https://github.com/thunder-project/thunder-registration
This function takes two 2D single channel images and estimates a 2D translation that best aligns the pair. The
estimation is done by maximising the correlation of the Fourier transforms of the images. Once, the translation
is estimated, it is applied to the (multi-channel) image to warp and, possibly, ot hte ground-truth. Different
interpolations schemes could be more suitable for images and ground-truth values (or masks).
:param src: 2D single channel source moving image
:param trg: 2D single channel target reference image
:param src_mask: Mask of source image. Not used in this method.
:param trg_mask: Mask of target image. Not used in this method.
:return: Estimated 2D transformation matrix of shape 2x3
"""
# Initialise instance of CrossCorr object
ccreg = registration.CrossCorr()
# padding_value = 0
# Compute translation between pair of images
model = ccreg.fit(src, reference=trg)
# Get translation as an array
translation = [-x for x in model.toarray().tolist()[0]]
# Fill in transformation matrix
warp_matrix = np.eye(2, 3)
warp_matrix[0, 2] = translation[1]
warp_matrix[1, 2] = translation[0]
# Return transformation matrix
return warp_matrix
def RegisterImages(path, outputpathfile, slice1=42, limit=30):
tiffiles, indx = osDB.getFileContString(path, 'tif')
#load first 30 tifffiles
img1 = tifffile.imread(os.path.join(path, tiffiles.sort_values().iloc[0]))
img2 = np.zeros(([30, img1.shape[0]-3, img1.shape[1], img1.shape[2]]), dtype = img1.dtype)
img2[0, :, :, :] = img1[:slice1, :, :]
#img10 = td.images.fromtif(os.path.join(outputdata['Raw_Folder'].loc[cindex], '')
for i, imgfile in enumerate(tiffiles.sort_values().iloc[1:limit]):
#print(os.path.join(outputdata['Raw_Folder'].loc[cindex], imgfile))
img2[i+1, : ,: ,:] = tifffile.imread(os.path.join(path, imgfile))[:slice1, :, :]
reg = registration.CrossCorr()
reference = img2.mean(axis=0)
reference = scipy.signal.medfilt(reference)
registrationModel = reg.fit(img2, reference = reference)
#displacements = registrationModel.toarray()
images = registrationModel.transform(img2)
#images2 = images.toarray()
meanImg = images.mean()
#images.cache()
saveNifti(meanImg, outputpathfile)
path_base, path_head = os.path.split(path)
path_base, flyID =os.path.split(path_base)
detect ='fromfile'
if not os.path.exists(path_base+os.path.sep+flyID+folderN+os.path.sep):
os.makedirs(path_base+os.path.sep+flyID+folderN+os.path.sep)
fname = path_base+os.path.sep+flyID+folderN+os.path.sep+flyID+'_stimtimes'+detect+'.csv'
times = [29, 59, 89, 119, 149, 178, 208, 238, 268, 298]
stimData = pd.DataFrame({'Stimulation_time': times} )
stimData.to_csv(fname)
# In[5]:
## registration -- correct for movement in image
fname = path_base+os.path.sep+flyID+folderN+os.path.sep+flyID+'_registration.json'
#create new registration
reg = registration.CrossCorr()
#reference = rawdata[100:110, : , :, :].mean().toarray()
reference = rawdata.mean().toarray()
#plt.imshow(amax(reference,0), cmap='gray', clim=(0,2000))
#reg.prepare(rawdata, startIdx=100, stopIdx=110);
registrationModel = reg.fit(rawdata, reference = reference)
#registrationModel.save(fname, overwrite=True)
#import json
#with open(fname, 'w') as outfile:
# json.dump(registrationModel, fname)
# In[6]:
displacements = registrationModel.toarray()