def runGabor(args):
infile = args.infile
if (not os.path.isfile(infile)):
print infile, ' is not a file!'
exit(0)
outfile = args.outfile
printlocation = os.path.dirname(os.path.abspath(outfile))
_utils.deleteExistingSubResults(printlocation)
M_transducerWindowSize = args.M
if ((M_transducerWindowSize % 2) == 0):
print 'Gaussian window size not odd, using next odd number'
M_transducerWindowSize += 1
k_clusters = args.k
k_gaborSize = args.gk
spatialWeight = args.spw
gammaSigmaPsi = []
gammaSigmaPsi.append(args.gamma)
gammaSigmaPsi.append(args.sigma)
gammaSigmaPsi.append(args.psi)
variance_Threshold = args.vt
howManyFeatureImages = args.fi
R_threshold = args.R
sigmaWeight = args.siw
greyOutput = args.c
printIntermediateResults = args.i
img = cv2.imread(infile, cv2.CV_LOAD_IMAGE_GRAYSCALE)
lambdas = getLambdaValues(img)
filters = build_filters(lambdas, k_gaborSize, gammaSigmaPsi)
print "Gabor kernels created, getting filtered images"
filteredImages = getFilterImages(filters, img)
filteredImages = filterSelection(filteredImages, R_threshold, img,
howManyFeatureImages)
if (printIntermediateResults):
_utils.printFeatureImages(filteredImages, "filter", printlocation)
print "Applying nonlinear transduction with Gaussian smoothing"
featureImages = nonLinearTransducer(img, filteredImages,
M_transducerWindowSize, sigmaWeight,
filters)
featureImages = removeFeatureImagesWithSmallVariance(
featureImages, variance_Threshold)
if (printIntermediateResults):
_utils.printFeatureImages(featureImages, "feature", printlocation)
featureVectors = _utils.constructFeatureVectors(featureImages, img)
featureVectors = _utils.normalizeData(featureVectors,
False,
spatialWeight=spatialWeight)
print "Clustering..."
labels = _utils.clusterFeatureVectors(featureVectors, k_clusters)
_utils.printClassifiedImage(labels, k_clusters, img, outfile, greyOutput)
def runMoments(args):
infile = args.infile
if (not os.path.isfile(infile)):
print infile, ' is not a file!'
exit(0)
outfile = args.outfile
printlocation = os.path.dirname(os.path.abspath(outfile))
_utils.deleteExistingSubResults(printlocation)
k_clusters = args.k
greyOutput = args.c
printIntermediateResults = args.i
W = args.W
if ((W % 2) == 0):
print 'size of moments window is not odd, using next odd number'
W += 1
L_transducerWindowSize = args.L
if ((L_transducerWindowSize % 2) == 0):
print 'size of moments window is not odd, using next odd number'
L_transducerWindowSize += 1
pqThreshold = args.pq
spatialWeight = args.spw
bruteForceMomentCalc = args.b
img = cv2.imread(infile, cv2.CV_LOAD_IMAGE_GRAYSCALE)
print "Applying moment masks, If brute force was selected please wait..."
momentImages = getAllMomentImages(img, W, pqThreshold,
bruteForceMomentCalc)
if (printIntermediateResults):
_utils.printFeatureImages(momentImages, "filter", printlocation)
print "Applying nonlinear transduction with averaging"
featureImages = nonLinearTransducer(img, momentImages,
L_transducerWindowSize)
if (printIntermediateResults):
_utils.printFeatureImages(featureImages, "feature", printlocation)
featureVectors = _utils.constructFeatureVectors(featureImages, img)
featureVectors = _utils.normalizeData(featureVectors,
True,
spatialWeight=spatialWeight)
print "Clustering..."
labels = _utils.clusterFeatureVectors(featureVectors, k_clusters)
_utils.printClassifiedImage(labels, k_clusters, img, outfile, greyOutput)
def runMoments(args):
infile = args.infile
if (not os.path.isfile(infile)):
print(infile, ' is not a file!')
exit(0)
outfile = args.outfile
printlocation = os.path.dirname(os.path.abspath(outfile))
_utils.deleteExistingSubResults(printlocation)
k_clusters = args.k
greyOutput = args.c
printIntermediateResults = args.i
W = args.W
if ((W % 2) == 0):
print('size of moments window is not odd, using next odd number')
W += 1
L_transducerWindowSize = args.L
if ((L_transducerWindowSize % 2) == 0):
print('size of moments window is not odd, using next odd number')
L_transducerWindowSize += 1
pqThreshold = args.pq
spatialWeight = args.spw
bruteForceMomentCalc = args.b
################################
# input may be an image, or a txt file
################################
if infile[-3:] != 'txt':
img = cv2.imread(infile, 0)
else:
test = np.loadtxt(infile)
img = (test / test.max() * 255).astype('uint8')
print("Applying moment masks, If brute force was selected please wait...")
momentImages = getAllMomentImages(img, W, pqThreshold,
bruteForceMomentCalc)
if (printIntermediateResults):
_utils.printFeatureImages(momentImages, "filter", printlocation)
print("Applying nonlinear transduction with averaging")
featureImages = nonLinearTransducer(img, momentImages,
L_transducerWindowSize)
if (printIntermediateResults):
_utils.printFeatureImages(featureImages, "feature", printlocation)
featureVectors = _utils.constructFeatureVectors(featureImages, img)
featureVectors = _utils.normalizeData(featureVectors,
True,
spatialWeight=spatialWeight)
print("Clustering...")
labels = _utils.clusterFeatureVectors(featureVectors, k_clusters)
labels = _utils.printClassifiedImage(labels, k_clusters, img, outfile,
greyOutput)
plt.subplot(121)
plt.imshow(img)
plt.xticks([])
plt.yticks([])
plt.subplot(122)
plt.imshow(labels.reshape(img.shape))
plt.xticks([])
plt.yticks([])
plt.tight_layout()
plt.savefig(outfile)
plt.close()
def runGabor(infile, outfile, k, gk, M, **args):
# infile = args.infile
if (not os.path.isfile(infile)):
print(infile, ' is not a file!')
exit(0)
# outfile = args.outfile
printlocation = os.path.dirname(outfile)
_utils.deleteExistingSubResults(printlocation)
M_transducerWindowSize = M
if ((M_transducerWindowSize % 2) == 0):
# print('Gaussian window size not odd, using next odd number')
M_transducerWindowSize += 1
# クラスタ数．何個に画像を分割するか
k_clusters = k
# ガボールフィルタのサイズ
k_gaborSize = gk
# 各種引数設定
spatialWeight = args['spw']
gammaSigmaPsi = []
gammaSigmaPsi.append(args['gamma'])
gammaSigmaPsi.append(args['sigma'])
gammaSigmaPsi.append(args['psi'])
variance_Threshold = args['vt']
howManyFeatureImages = args['fi']
R_threshold = args['R']
sigmaWeight = args['siw']
greyOutput = args['c']
printIntermediateResults = args['i']
# 画像の読み込み
img = cv2.imread(infile, cv2.IMREAD_GRAYSCALE)
# lambdaの取得.サンプリングレートを決める．
lambdas = getLambdaValues(img)
# ガボールフィルタの作成
filters = build_filters(lambdas, k_gaborSize, gammaSigmaPsi)
# print("Gabor kernels created, getting filtered images")
# ガボールフィルタを入力画像にかける．
filteredImages = getFilterImages(filters, img)
# どのフィルタを利用するか選ぶ
filteredImages = filterSelection(filteredImages, R_threshold, img,
howManyFeatureImages)
if (printIntermediateResults):
_utils.printFeatureImages(filteredImages, "filter", printlocation,
infile)
# print("Applying nonlinear transduction with Gaussian smoothing")
featureImages = nonLinearTransducer(img, filteredImages,
M_transducerWindowSize, sigmaWeight,
filters)
# 分散の小さいデータを除く．
featureImages = removeFeatureImagesWithSmallVariance(
featureImages, variance_Threshold)
if (printIntermediateResults):
_utils.printFeatureImages(featureImages, "feature", printlocation,
infile)
# 特徴ベクトルの作成．
featureVectors = _utils.constructFeatureVectors(featureImages, img)
# 特徴ベクトルの保存
featureVectors = _utils.normalizeData(featureVectors,
False,
spatialWeight=spatialWeight)
_utils.printFeatureVectors(printlocation, infile, featureVectors)
# print("Clustering...")
labels = _utils.clusterFeatureVectors(featureVectors, k_clusters)
_utils.printClassifiedImage(labels, k_clusters, img, outfile, greyOutput)
def runGabor(args):
infile = args.infile
if (not os.path.isfile(infile)):
print(infile, " is not a file!")
exit(0)
outfile = args.outfile
printlocation = os.path.dirname(os.path.abspath(outfile))
_utils.deleteExistingSubResults(printlocation)
M_transducerWindowSize = args.M
if M_transducerWindowSize == 0:
on_off_M = False
else:
on_off_M = True
if ((M_transducerWindowSize % 2) == 0):
print('Gaussian window size not odd, using next odd number')
M_transducerWindowSize += 1
k_clusters = args.k
k_gaborSize = args.gk
spatialWeight = args.spw
gammaSigmaPsi = []
gammaSigmaPsi.append(args.gamma)
gammaSigmaPsi.append(args.sigma)
gammaSigmaPsi.append(args.psi)
variance_Threshold = args.vt
howManyFeatureImages = args.fi
R_threshold = args.R
sigmaWeight = args.siw
greyOutput = args.c
printIntermediateResults = args.i
if int(cv2.__version__[0]) >= 3:
img = cv2.imread(infile, 0)
else:
img = cv2.imread(infile, cv2.CV_LOAD_IMAGE_GRAYSCALE)
img = cv2.resize(img, (1200, 1200), interpolation=cv2.INTER_NEAREST)
lambdas = getLambdaValues(img)
filters = build_filters(lambdas, k_gaborSize, gammaSigmaPsi)
print("Gabor kernels created, getting filtered images")
filteredImages = getFilterImages(filters, img)
filteredImages = filterSelection(filteredImages, R_threshold, img,
howManyFeatureImages)
if (printIntermediateResults):
_utils.printFeatureImages(filteredImages, "filter", printlocation)
print("Applying nonlinear transduction with Gaussian smoothing")
if on_off_M:
featureImages = nonLinearTransducer(img, filteredImages,
M_transducerWindowSize,
sigmaWeight, filters)
else:
featureImages = filteredImages
featureImages = removeFeatureImagesWithSmallVariance(
featureImages, variance_Threshold)
if (printIntermediateResults):
_utils.printFeatureImages(featureImages, "feature", printlocation)
featureVectors = _utils.constructFeatureVectors(featureImages, img)
featureVectors = _utils.normalizeData(featureVectors,
False,
spatialWeight=spatialWeight)
temp_otfs = os.path.split(outfile)
outfile_csv = os.path.join(temp_otfs[0],
temp_otfs[1].split('.')[0] + '.csv')
save_csv(featureVectors, outfile_csv)
print("Clustering...")
labels = _utils.clusterFeatureVectors(featureVectors, k_clusters)
_utils.printClassifiedImage(labels, k_clusters, img, outfile, greyOutput)
def runGabor(args):
infile = args.infile
if (not os.path.isfile(infile)):
print(infile, " is not a file!")
exit(0)
outfile = args.outfile
printlocation = os.path.dirname(os.path.abspath(outfile))
_utils.deleteExistingSubResults(printlocation)
M_transducerWindowSize = args.M
if ((M_transducerWindowSize % 2) == 0):
print('Gaussian window size not odd, using next odd number')
M_transducerWindowSize += 1
k_clusters = args.k
k_gaborSize = args.gk
spatialWeight = args.spw
gammaSigmaPsi = []
gammaSigmaPsi.append(args.gamma)
gammaSigmaPsi.append(args.sigma)
gammaSigmaPsi.append(args.psi)
variance_Threshold = args.vt
howManyFeatureImages = args.fi
R_threshold = args.R
sigmaWeight = args.siw
greyOutput = args.c
printIntermediateResults = args.i
if infile[-3:] != 'txt':
if int(cv2.__version__[0]) >= 3:
img = cv2.imread(infile, 0)
else:
img = cv2.imread(infile, cv2.CV_LOAD_IMAGE_GRAYSCALE)
else:
test = np.loadtxt(infile)
img = (test / test.max() * 255).astype('uint8')
lambdas = getLambdaValues(img)
filters = build_filters(lambdas, k_gaborSize, gammaSigmaPsi)
print("Gabor kernels created, getting filtered images")
filteredImages = getFilterImages(filters, img)
filteredImages = filterSelection(filteredImages, R_threshold, img,
howManyFeatureImages)
if (printIntermediateResults):
_utils.printFeatureImages(filteredImages, "filter", printlocation)
print("Applying nonlinear transduction with Gaussian smoothing")
featureImages = nonLinearTransducer(img, filteredImages,
M_transducerWindowSize, sigmaWeight,
filters)
featureImages = removeFeatureImagesWithSmallVariance(
featureImages, variance_Threshold)
if (printIntermediateResults):
_utils.printFeatureImages(featureImages, "feature", printlocation)
featureVectors = _utils.constructFeatureVectors(featureImages, img)
featureVectors = _utils.normalizeData(featureVectors,
False,
spatialWeight=spatialWeight)
print("Clustering...")
labels = _utils.clusterFeatureVectors(featureVectors, k_clusters)
labels = _utils.printClassifiedImage(labels, k_clusters, img, outfile,
greyOutput)
plt.subplot(121)
plt.imshow(img)
plt.xticks([])
plt.yticks([])
plt.subplot(122)
plt.imshow(labels.reshape(img.shape))
plt.xticks([])
plt.yticks([])
plt.tight_layout()
plt.savefig(outfile)
plt.close()
