def quantify(folder, params): ''' Quantify a stack ''' if not os.path.exists(params.inputfolder + '../statistics/' + folder[:-1] + '.csv'): #list files in the folder files = filelib.list_image_files(params.inputfolder + folder) files.sort() #create a folder for statistics filelib.make_folders([params.inputfolder + '../statistics/' + filelib.return_path(folder[:-1] + '.csv')]) #extract voxel size xsize, zsize = extract_zoom(folder) #compute volume of the kidney kidney_volume = 0 for i in range(len(files)): ind = np.load(params.inputfolder + '../segmented/masks/' + folder + files[i][:-4] + '.npy') kidney_volume = kidney_volume + len(ind[0]) stat = pd.DataFrame() stat['Kidney_volume'] = [kidney_volume*xsize**2*zsize] stat['Image_name'] = folder[:-1] stat.to_csv(params.inputfolder + '../statistics/' + folder[:-1] + '.csv', sep = '\t')
def list_subfolders(inputfolder, subfolders=[]):
'''
list folders, each containing layers of one stack
'''
files = filelib.list_subfolders(inputfolder, subfolders=subfolders)
files.sort()
folders = []
for f in files:
folders.append(filelib.return_path(f))
folders = np.unique(folders)
return folders
def quantify(folder, params):
'''
Quantify a stack
'''
start = time.time()
#list files in the folder
files = filelib.list_image_files(params.inputfolder + folder)
files.sort()
#create a folder for statistics
filelib.make_folders([
params.inputfolder + '../statistics/' +
filelib.return_path(folder[:-1] + '.csv')
])
#extract voxel size
xsize, zsize = extract_zoom(folder)
#compute volume of the kidney
kidney_volume = 0
for i in range(len(files)):
ind = np.load(params.inputfolder + '../segmented/masks/kidney/' +
folder + files[i][:-4] + '.npy')
kidney_volume = kidney_volume + len(ind[0])
#compute glomeruli characteristics
maxvol = 4. / 3 * np.pi * float(params.maxrad)**3 #maximal allowed volume
minvol = 4. / 3 * np.pi * float(params.minrad)**3 #minimal allowed volume
print 'load data', len(files)
ind0 = []
ind1 = []
ind2 = []
label = []
for i in range(len(files)):
ind = np.load(params.inputfolder + '../segmented/labels/glomeruli/' +
folder + files[i][:-4] + '.npy')
label.append(ind[0])
ind1.append(ind[1])
ind2.append(ind[2])
ind0.append(np.ones_like(ind[1]) * i)
ind0 = np.hstack(ind0)
ind1 = np.hstack(ind1)
ind2 = np.hstack(ind2)
label = np.hstack(label)
llist = np.unique(label)
volumes = ndimage.sum((label > 0) * 1., label, llist) * xsize**2 * zsize
ind = np.where((volumes < maxvol) & (volumes > minvol))
volumes = volumes[ind]
llist = llist[ind]
sizes = 2 * pow(3. / 4 * volumes / np.pi, 1. / 3)
stat = pd.DataFrame({'Label': llist, 'Diameter': sizes, 'Volume': volumes})
stat['Image_name'] = folder[:-1]
stat['Kidney_volume'] = kidney_volume * xsize**2 * zsize
stat.to_csv(params.inputfolder + '../statistics/' + folder[:-1] + '.csv',
sep='\t')
t = pd.Series({'Quantification': time.time() - start})
t.to_csv(params.inputfolder + '../log/' + folder + 'Quantification.csv',
sep='\t')
def quantify(folder, params):
'''
Quantify a stack
'''
start = time.time()
#list files in the folder
files = filelib.list_image_files(params.inputfolder + folder)
files.sort()
#create a folder for statistics
filelib.make_folders([
params.inputfolder + '../statistics/' +
filelib.return_path(folder[:-1] + '.csv')
])
#extract voxel size
xsize, zsize = extract_zoom(folder)
print("xsize: " + str(xsize) + ", zsize: " + str(zsize))
#compute volume of the kidney
kidney_volume = 0
for i in range(len(files)):
ind = np.load(params.inputfolder + '../segmented/masks/kidney/' +
folder + files[i][:-4] + '.npy')
kidney_volume = kidney_volume + len(ind[0])
#compute glomeruli characteristics
maxvol = np.pi * float(params.maxrad)**2 #maximal allowed area
minvol = np.pi * float(params.minrad)**2 #minimal allowed area
print 'load data', len(files)
ind0 = []
ind1 = []
ind2 = []
label = []
for i in range(len(files)):
ind = np.load(params.inputfolder + '../segmented/labels/glomeruli/' +
folder + files[i][:-4] + '.npy')
label.append(ind[0])
ind1.append(ind[1])
ind2.append(ind[2])
ind0.append(np.ones_like(ind[1]) * i)
ind0 = np.hstack(ind0)
ind1 = np.hstack(ind1)
ind2 = np.hstack(ind2)
label = np.hstack(label)
llist = np.unique(label)
volumes = ndimage.sum(
(label > 0) * 1., label,
llist) * xsize * xsize # Modification: Calculate areas
ind = np.where((volumes < maxvol) & (volumes > minvol))
volumes = volumes[ind]
llist = llist[ind]
sizes = 2 * np.sqrt(volumes / np.pi)
stat = pd.DataFrame({'Label': llist, 'Diameter': sizes, 'Volume': volumes})
stat['Image_name'] = folder[:-1]
stat['Kidney_volume'] = kidney_volume * xsize**2
stat.to_csv(params.inputfolder + '../statistics/' + folder[:-1] + '.csv',
sep='\t')
t = pd.Series({'Quantification': time.time() - start})
t.to_csv(params.inputfolder + '../log/' + folder + 'Quantification.csv',
sep='\t')
shape = tifffile.imread(
params.inputfolder + folder + files[0][:-4] +
'.tif').shape # get image dimensions from the first image
for i in range(len(files)):
ind = np.load(params.inputfolder + '../segmented/labels/glomeruli/' +
folder + files[i][:-4] + '.npy')
lbl = ind[0]
mask = np.ones(len(lbl), np.bool)
mask[not lbl in llist] = 0
lbl[mask] = 0
lbl = np.reshape(lbl, shape)
skimage.io.imsave(
params.inputfolder + '../segmented/labels/glomeruli/' +
params.folder + files[i][:-4] + '.tif', lbl.astype(np.int))