def creatTrainigSam_3D(dataPath, iRnum = 6,iSnum = 12, gRnum = 4,gSnum = 12):
""" Creat training samples(3D) and compute features for each training sample
Parameters
----------
dataPath: str
The directory where training image ROIs are stores.
opt: str
Options for compute features.
iRnum: integer
The number of rings to be divided in computing intensity features
iSnum: integer
The number of sectors to be divided in computing intensity features
gRnum: integer
The number of rings to be divided in computing gradient features
gSnum: integer
The number of sectors to be divided in computing gradient features
"""
file_list = os.listdir(dataPath)
LightPatchList = []
bagid = 0
instanceid = 0
for fil in file_list:
im = ImageIO.imReader(dataPath, fil,'tif',3)
for i in range(im.size_2):
# Calculating intensity features
patch = TPatch.TPatch()
patch.initialize(im.data[i])
int_feats = patch.getIntenfeats(iRnum,iSnum)
# Calculating segment features
seg_feats = patch.getSegmentFeats()
# Calculating gradient features
im.downSample(rate = 2)
eqimg = histEqualization.histEqualization(im.sampled_data[i], 16)
smoothimg = filters.gaussian_filter(eqimg, sigma = 2, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0)
patch = TPatch.TPatch()
patch.initialize(smoothimg)
gr_feats = patch.getGradfeats(gRnum,gSnum)
feats = np.hstack((int_feats, gr_feats, seg_feats))
lightPatch = TPatch.TLightPatch()
lightPatch.pdata = im.data[i]
lightPatch.feats = feats
lightPatch.patch_center = (im.data[i].shape[0]/2, im.data[i].shape[1]/2)
lightPatch.bagID = bagid
lightPatch.instanceID = instanceid
LightPatchList.append(lightPatch)
instanceid = instanceid + 1
bagid = bagid + 1
return LightPatchList
def test_func():
"""Please specify the image tiff stack directory, file name and output
path. Extracted masses list will be save in a workspace file for further use.
Also please specify how many cores will be allocated for the parallel process.
"""
dataPath = '/home/yanbin/Tomosynthesis/data/tiffs_3d/5016/'
outputPath = '/home/yanbin/Tomosynthesis/script_test/'
fileName = '5016EMML08.tif'
## load image data
im = ImageIO.imReader(dataPath,fileName, 'tif')
## allocate cpu source
pool = Pool(processes=6)
params =[(i,im.data[i]) for i in range(im.size_2)]
## run in parallel
sliceList = []
sliceList = pool.map(mass3d.parallelWrapper,params)
## save the workspace
output = open(outputPath + 'suspicious.pkl', 'wb')
pickle.dump(sliceList, output)
output.close()
def test_mass():
dataPath = 'C:/Tomosynthesis/localtest/'
fileName = '5131R-recon08_45-1.tif'
outputPath = 'C:/Tomosynthesis/localtest/res/'
im = ImageIO.imReader(dataPath,fileName, 'tif',2)
# padding borarders
'''
paddingrd = 10
bordares = ((paddingrd,paddingrd),(paddingrd,paddingrd))
paddingv = 10000
bordarevs = ((paddingv,paddingv),(paddingv,paddingv))
im = np.lib.pad(im.data[0], bordares, 'constant',constant_values = bordarevs)
'''
eqimg = histEqualization.histEqualization(im.data[0], 16)
denoised = AT_denoising.DenoisingAW(eqimg)
denoised = AT_denoising.DenoisingAW(denoised)
denoised = AT_denoising.DenoisingAW(denoised)
img = AT_denoising.DenoisingAW(denoised)
tiffLib.imsave(outputPath + 'denoised.tif',img)
# g(I)
gI = morphsnakes.gborders(img, alpha=1, sigma=8)
tiffLib.imsave(outputPath + 'gI.tif',np.float32(gI))
# Morphological GAC. Initialization of the level-set.
mgac = morphsnakes.MorphGAC(gI, smoothing=2, threshold=0.035, balloon=-1)
mgac.levelset = circle_levelset(img.shape, (img.shape[0]/2, img.shape[1]/2), 140, scalerow=0.75)
# Visual evolution.
ppl.figure()
ls = morphsnakes.evolve_visual(mgac, num_iters=110, background=img)
tiffLib.imsave(outputPath + 'ls.tif',np.float32(ls))
def test_func():
"""You can conver a single smv file to 3D tiff by specifying
data_path, output_path, fileName and set the SigleConvert flag to 1.
You can conver a batch of smv files to 3D tiffs by specifying
root data_path, output_path and set the BatchConvert flag to 1.
"""
## Please specify paths ##
data_path = '/home/yanbin/Tomosynthesis/data/SAP_test_datasets/Screening_30_cases/6002/'
output_path = '/home/yanbin/Tomosynthesis/script_test/'
fileName = '6002L06.smv'
## Please specify Parameters ##
BatchConvert = 0
SigleConvert = 0
dim = 3
## data_path check
if not os.path.isdir(data_path):
print "Data directory:\n"+ data_path +"\ndoes not exist"
sys.exit()
## Format convert batch
if BatchConvert == 1:
print 'here'
dir_list = os.listdir(data_path)
print dir_list
for dirc in dir_list:
print dirc
if os.path.isdir(data_path + dirc):
# make directory for output files
opath = output_path + dirc + '/'
print opath
if not os.path.isdir(opath):
os.makedirs(opath)
file_list = os.listdir(data_path + dirc)
for fil in file_list:
im = ImageIO.imReader(data_path + dirc + '/', fil, 'smv')
ImageIO.imWriter(opath, fil.strip('smv') + 'tif',im,dim)
## Format convert single
if SigleConvert == 1:
im = ImageIO.imReader(data_path,fileName, 'smv')
ImageIO.imWriter(output_path, fileName.strip('smv') + 'tif',im, dim)
def test_func():
"""Please specify the data directory and file name.This function runs
the detection for a 3D stack.
"""
dataPath = 'C:/Tomosynthesis/localtest/'
outputPath = 'C:/Tomosynthesis/test_script/'
fileName = '5092-1.tif'
# Loading data
im = ImageIO.imReader(dataPath,fileName, 'tif',3)
# run detection in parallel
mc_Lists = []
pool = Pool(processes=1)
params =[(i,im.data[i]) for i in range(im.size_2)]
mc_Lists = pool.map(mc.parallelWrapper,params)
global_id = mc.MC_connect_3d(mc_Lists)
gloabal_list = mc.MCs_constuct_3d(mc_Lists,global_id)
MC_List_3D = mc.MCs_constrain(gloabal_list)
for item in MC_List_3D:
print(item.center, item.intensity, item.volume)
import ImageIO import TImage import tiffLib from scipy import ndimage as nd import scipy from skimage.filter import gabor_kernel import numpy as np import time from numpy.fft import fft, ifft, fft2, ifft2, fftshift dataPath = 'C:/Tomosynthesis/localtest/' fileName = 'test-crop.tif' outputPath = 'C:/Tomosynthesis/localtest/res/' im = ImageIO.imReader(dataPath,fileName, 'tif',2) kernel = np.real(gabor_kernel(0.0185, 0, 20, 20/float(0.9))) print kernel.shape start = time.clock() temp_response = nd.convolve(im.data[0], kernel, mode='nearest') elapsed = (time.clock() - start) print elapsed start = time.clock() data = np.lib.pad(im.data[0], ((0,kernel.shape[0]),(0,kernel.shape[1])),'edge') temp_response_2 = np.fft.irfft2(np.fft.rfft2(data) * np.fft.rfft2(kernel,data.shape)) temp_response_2 = temp_response_2[kernel.shape[0]/2:data.shape[0] - kernel.shape[0]/2,kernel.shape[1]/2:data.shape[1] - kernel.shape[1]/2] elapsed = (time.clock() - start) print elapsed
def creatTrainigSam(dataPath, opt = 'all', iRnum = 6,iSnum = 12, gRnum = 4,gSnum = 12):
""" Creat training samples(2D) and compute features for each training sample
Parameters
----------
dataPath: str
The directory where training image ROIs are stores.
opt: str
Options for compute features.
iRnum: integer
The number of rings to be divided in computing intensity features
iSnum: integer
The number of sectors to be divided in computing intensity features
gRnum: integer
The number of rings to be divided in computing gradient features
gSnum: integer
The number of sectors to be divided in computing gradient features
"""
file_list = os.listdir(dataPath)
int_feats = np.zeros((len(file_list),4),np.double)
gr_feats = np.zeros((len(file_list),4),np.double)
seg_feats = np.zeros((len(file_list),7),np.double)
all_feats = np.zeros((len(file_list),15),np.double)
LightPatchList = []
counter = 0
for fil in file_list:
im = ImageIO.imReader(dataPath, fil,'tif',2)
# Calculating intensity features
if opt == 'Int' or opt == 'all':
patch = TPatch.TPatch()
patch.initialize(im.data[0])
int_feats[counter,:] = patch.getIntenfeats(iRnum,iSnum)
# Calculating gradient features
if opt == 'Grad' or opt == 'all':
# preprocess
im.downSample(rate = 2)
eqimg = histEqualization.histEqualization(im.sampled_data[0], 16)
smoothimg = filters.gaussian_filter(eqimg, sigma = 2, order=0, output=None, mode='reflect', cval=0.0, truncate=4.0)
patch = TPatch.TPatch()
patch.initialize(smoothimg)
gr_feats[counter,:] = patch.getGradfeats(gRnum,gSnum)
# Calculating segment features
if opt == 'seg' or opt == 'all':
patch = TPatch.TPatch()
patch.initialize(im.data[0])
seg_feats[counter,:] = patch.getSegmentFeats()
if opt == 'all':
all_feats[counter,:] = np.hstack((int_feats[counter,:], gr_feats[counter,:], seg_feats[counter,:]))
lightPatch = TPatch.TLightPatch()
lightPatch.pdata = im.data[0]
lightPatch.feats = all_feats[counter,:]
lightPatch.patch_center = (im.data[0].shape[0]/2, im.data[0].shape[1]/2)
LightPatchList.append(lightPatch)
counter = counter + 1
if opt == 'all':
return LightPatchList
if opt == 'Int':
return int_feats
if opt == 'Grad':
return gr_feats
if opt == 'seg':
return seg_feats
def graphrepresentation(myarray,im):
'''creating a graph and add images in tiff image as nodes of graphs '''
G=nx.Graph()
Size=myarray.shape
a=Size[0]
for i in range(0,a-1):
G.add_node(i,point=myarray[i])
#G.add_nodes_from(im)
#G.add_nodes_from(myarray)
'''after create graph we should add edges and weight to edges by mutual information '''
numberofedges=myarray.shape[0]
for i in range (myarray.shape[0]):
sum=0
path='/Users/Shared/TomosynthesisData/processed/5039/'
fileName='5039.tif'
fileName = fileName.split('.')
fileName = fileName[0] + str(i) + '.tif'
im1 = ImageIO.imReader(path,fileName,'tif',2)
for j in range(myarray.shape[0]):
G.add_edge(i,j)
#im1=im[i]
fileName='5039.tif'
fileName = fileName.split('.')
fileName = fileName[0] + str(j) + '.tif'
im2 = ImageIO.imReader(path,fileName,'tif',2)
'''finding mutual information of different features then add them for finding weight'''
feature1=waveletcLBP.concatinationhist(im1.data[0])
feature2=waveletcLBP.concatinationhist(im2.data[0])
weight=weightinginfo(feature1,feature2,im1,im2)
sum=sum+weight
feature1=RBST.shortrunfacility(im1.data[0],im1.data[0])
feature2=RBST.shortrunfacility(im2.data[0],im2.data[0])
weight=weightinginfo(feature1,feature2,im1,im2)
print len(feature1), len(feature2)
feature1=RBST.shortrunfacility(im1.data[0],im1.data[0])
feature2=RBST.shortrunfacility(im2.data[0],im2.data[0])
weight=weightinginfo(feature1,feature2,im1,im2)
sum=sum+weight
G.add_weighted_edges_from([i,j,sum])
listpath=longest_path(G)
"after finding longest we can combine features with the weighting list and order in list"
lengthlist=len(listpath)
for i in range(lengthlist-1):
a=listpath(i)
b=listpath(i+1)
weightonthelist=G.adjacency_ite(a,b)
weightlist=list.append(weightonthelist)
return weightlist, listpath
warped_im1,d1, d2 = registration.registration(im_r.data[0], im_l.data[0], 15,'c')
tiffLib.imsave(outputPath + 'warped_im1.tif',np.float32(warped_im1))
tiffLib.imsave(outputPath + 'd1.tif',np.float32(d1))
tiffLib.imsave(outputPath + 'd2.tif',np.float32(d2))
'''
############################# comparision test #####################################
dataPath = 'C:/Tomosynthesis/localtest/reg/'
outputPath = 'C:/Tomosynthesis/localtest/reg/'
fileName_r = 'op.tif'
fileName_l = '6044_l.tif'
im_r = ImageIO.imReader(dataPath,fileName_r, 'tif',2)
im_l = ImageIO.imReader(dataPath,fileName_l, 'tif',2)
params = []
params.append(('1d', 'cv_comp', cv.CV_COMP_CORREL))
params.append(('1d', 'scipy_comp', 'Euclidean'))
params.append(('1d', 'scipy_comp', 'Manhattan'))
params.append(('1d', 'kl_div', 'None'))
params.append(('2d', cv.CV_TM_SQDIFF_NORMED, 'None'))
params.append(('2d', cv.CV_TM_CCORR_NORMED, 'None'))
params.append(('2d', cv.CV_TM_CCOEFF_NORMED, 'None'))
params.append(('decomp', 'eigen', 'None'))
params.append(('decomp', 'NMF', 'None'))
data_projected = Dimreduction.dim_Reduction(data, label, opt, n_components=2, visualize = True)
classifier = classification.classifier(data_projected,label)
classifier.train(opt ='SVM')
classifier.classify()
'''
############################# Mass 3D extraction ########################################
dataPath = '/home/yanbin/Tomosynthesis/data/tiffs_3d/5016/'
paraPath = '/home/yanbin/localtest/'
outputPath = '/home/yanbin/Tomosynthesis/results/5016/'
im_name = '5016EMML08.tif'
# loading
im = ImageIO.imReader(dataPath,im_name, 'tif',3)
print (im.size_0,im.size_1,im.size_2)
control_name = 'feats_control_1.txt'
cancer_name = 'feats_cancer.txt'
control = np.loadtxt(paraPath + control_name)
cancer = np.loadtxt(paraPath + cancer_name)
# training
data = np.vstack((control,cancer))
label = np.zeros((control.shape[0] + cancer.shape[0],),np.int)
label[0:control.shape[0]] = 1
data_projected = Dimreduction.dim_Reduction(data, label, opt='randtree', n_components=5, visualize = False)
classifier = classification.classifier(data_projected,label)
classifier.train(opt ='SVM')
def main():
## Please specify paths ##
data_path = '/home/yanbin/Tomosynthesis/data/SAP_test_datasets/Screening_30_cases/'
output_path = '/home/yanbin/Tomosynthesis/data/tiffs_3d/'
exe_path= '/home/yanbin/Tomosynthesis/code/'
## Please specify Run Flags ##
FormatConvert = 1
AWDenoising = 0
ContrastEnhancement = 0
## Please specify parameters ##
dim = 3 # For format convert: save as 2d slices / 3d stack
opt = 'asymptotic' # For AWdenoising inverse transform options
block_m=5
block_n=5 # For AWdenoising Wiener filter window size block_m = block_n
###################### Avalability Check #######################
# data_path check
if not os.path.isdir(data_path):
print "Data directory:\n"+ data_path +"\ndoes not exist"
sys.exit()
# exe_path check
if not os.path.isdir(exe_path):
print "Executable directory:\n"+ exe_path +"\ndoes not exist"
sys.exit()
###################### Format Convert #######################
if FormatConvert == 1:
dir_list = os.listdir(data_path)
print dir_list
for dirc in dir_list:
print dirc
if os.path.isdir(data_path + dirc):
# make directory for output files
opath = output_path + dirc + '/'
print opath
if not os.path.isdir(opath):
os.makedirs(opath)
file_list = os.listdir(data_path + dirc)
for fil in file_list:
im = ImageIO.imReader(data_path + dirc + '/', fil, 'smv')
ImageIO.imWriter(opath, fil.strip('smv') + 'tif',im,dim)
############################# Denoising ##########################
if AWDenoising == 1:
dir_list = os.listdir(data_path)
print dir_list
for dirc in dir_list:
print dirc
if os.path.isdir(data_path + dirc):
# make directory for output files
opath = output_path + dirc + '/'
print opath
if not os.path.isdir(opath):
os.makedirs(opath)
file_list = os.listdir(data_path + dirc)
for fil in file_list:
im = ImageIO.imReader(data_path + dirc + '/', fil, 'tif',2)
denoised = AT_denoising.DenoisingAW(im.data[0], opt = 'asymptotic', block_m=5,block_n=5)
tiffLib.imsave(opath + 'denoised_' + fil,denoised)
###################### Contrast enhancement #######################
if ContrastEnhancement == 1:
dir_list = os.listdir(data_path)
print dir_list
for dirc in dir_list:
print dirc
if os.path.isdir(data_path + dirc):
# make directory for output files
opath = output_path + dirc + '/'
print opath
if not os.path.isdir(opath):
os.makedirs(opath)
file_list = os.listdir(data_path + dirc)
for fil in file_list:
im = ImageIO.imReader(data_path + dirc + '/', fil, 'tif',2)
enhanced = histEqualization.histEqualization(im.data[0], 16)
tiffLib.imsave(opath + 'enhanced_' + fil,enhanced)
