def segment(self, widget): # running converting and indexing bash script
#img = mahotas.imread('thyroid.png', as_grey=True)
#img = img.astype(np.uint8)
##segmented = mahotas.otsu(photo)
##f = segmented.astype(np.uint8)
### mahotas.imsave('segmented.png')
##mahotas.imsave('.png')
## savefig(segmented)
#T_otsu = mahotas.thresholding.otsu(img)
#seeds,_ = mahotas.label(img > T_otsu)
#labeled = mahotas.cwatershed(img.max() - img, seeds)
#pylab.imshow(labeled)
#pylab.savefig('seeds.png')
##imshow(photo > segmented)
#gray()
dna = mahotas.imread('thyroid.png')
dna = dna.astype(np.uint8)
dna = dna.squeeze()
dna = pymorph.to_gray(dna)
#print dna.shape
#print dna.dtype
#print dna.max()
#print dna.min()
#dnaf = mahotas.gaussian_filter(dna, 8)
#T = mahotas.thresholding.otsu(dnaf)
#labeled, nr_objects = ndimage.label(dnaf > T)
#print nr_objects
#pylab.imshow(labeled)
#pylab.jet()
#pylab.show()
T = mahotas.thresholding.otsu(dna)
pylab.imshow(dna > T)
gray()
pylab.savefig('Segmentation_Results.png')
pylab.show()
show()
def tentDetection_wt_mm(strInputFile, maxTentArea, strOutputFile, strShape='box', iThresh_coeff=0):
import pywt
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif(nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# decomposition until 1 level
wp = pywt.WaveletPacket2D(data=objnImg, wavelet='db4', mode='sym', maxlevel=1)
# iMaxLevel = wp.maxlevel()
# top-hat
wp['h'].data = pymm.openrecth(pymm.to_int32(wp['h'].data), objStructureElement, objStructureElement)
wp['v'].data = pymm.openrecth(pymm.to_int32(wp['v'].data), objStructureElement, objStructureElement)
wp['d'].data = pymm.openrecth(pymm.to_int32(wp['d'].data), objStructureElement, objStructureElement)
wp['a'].data = 0.5 * wp['a'].data
# reconstruction
wp.reconstruct(update=True)
# top-hat for reconstructed image
objtophat = pymm.openrecth(pymm.to_int32(wp.data), objStructureElement, objStructureElement)
# y = mean + k*std
(minValue, maxValue, meanValue, stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2, stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3, stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, stdValue, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
def tentDetection_MM(strInputFile, maxTentArea, strOutputFile, strShape='box', iThresh_coeff=0):
# five step to do this
# 1. opening-determine the square structure element (6-60 m2/resolution)
# 2. opening by reconstruction
# 3. top-hat by reconstruction
# 4. lower threshold
# 5. double threshold
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif(nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# opening
objOpen = pymm.open(objnImg, objStructureElement)
# opening by reconstruction
objOpenRec = pymm.openrec(objOpen, objStructureElement, objStructureElement)
objtophat = pymm.openrecth(objnImg, objStructureElement, objStructureElement)
# objtophat = pymm.subm(objnImg, objOpenRec)
# objTent = pymm.threshad(objtophat, 0.25 * objnImg, 0.40 * objnImg)
# y = mean + k*std
(minValue, maxValue, meanValue, stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2, stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3, stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, threshad, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
'''
def test_2d():
img = pylab.imread('pymorph/data/fabric.tif')
assert img.shape == pymorph.to_gray(img).shape
assert np.all(img == pymorph.to_gray(img))
def test_to_gray():
pieces = pylab.imread('pymorph/data/pieces_bw.tif')
assert np.all(pymorph.to_gray(pieces) == pieces[:,:,:3].max(2))
def tentDetection_wt_mm(strInputFile,
maxTentArea,
strOutputFile,
strShape='box',
iThresh_coeff=0):
import pywt
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif (nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# decomposition until 1 level
wp = pywt.WaveletPacket2D(data=objnImg,
wavelet='db4',
mode='sym',
maxlevel=1)
# iMaxLevel = wp.maxlevel()
# top-hat
wp['h'].data = pymm.openrecth(pymm.to_int32(wp['h'].data),
objStructureElement, objStructureElement)
wp['v'].data = pymm.openrecth(pymm.to_int32(wp['v'].data),
objStructureElement, objStructureElement)
wp['d'].data = pymm.openrecth(pymm.to_int32(wp['d'].data),
objStructureElement, objStructureElement)
wp['a'].data = 0.5 * wp['a'].data
# reconstruction
wp.reconstruct(update=True)
# top-hat for reconstructed image
objtophat = pymm.openrecth(pymm.to_int32(wp.data), objStructureElement,
objStructureElement)
# y = mean + k*std
(minValue, maxValue, meanValue,
stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2,
stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3,
stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, stdValue, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
def tentDetection_MM(strInputFile,
maxTentArea,
strOutputFile,
strShape='box',
iThresh_coeff=0):
# five step to do this
# 1. opening-determine the square structure element (6-60 m2/resolution)
# 2. opening by reconstruction
# 3. top-hat by reconstruction
# 4. lower threshold
# 5. double threshold
import pymorph as pymm
objImg = osgeo.gdal.Open(strInputFile, GA_ReadOnly)
nRasterCount = objImg.RasterCount
poDataset = objImg.ReadAsArray().astype(np.float)
geotransform = objImg.GetGeoTransform()
pixelWidth = np.fabs(geotransform[1])
pixelHeight = np.fabs(geotransform[5])
resolution = pixelWidth * pixelHeight
# NoDataValue = objImg.GetRasterBand(1).GetNoDataValue()
# gray scale image
if (nRasterCount == 1):
objnImg = pymm.to_int32(poDataset)
# RGB image
elif (nRasterCount == 3):
objnImg = pymm.to_gray(poDataset)
else:
print 'it only supports gray-scale or RGB image'
sys.exit(1)
# determine the structure element
iNum = int(np.sqrt(maxTentArea) / resolution) + 1
if (strShape == 'box'):
objStructureElement = pymm.sebox(iNum)
elif (strShape == 'cross'):
objStructureElement = pymm.secross(iNum)
else:
objStructureElement = pymm.sedisk(iNum)
# opening
objOpen = pymm.open(objnImg, objStructureElement)
# opening by reconstruction
objOpenRec = pymm.openrec(objOpen, objStructureElement,
objStructureElement)
objtophat = pymm.openrecth(objnImg, objStructureElement,
objStructureElement)
# objtophat = pymm.subm(objnImg, objOpenRec)
# objTent = pymm.threshad(objtophat, 0.25 * objnImg, 0.40 * objnImg)
# y = mean + k*std
(minValue, maxValue, meanValue,
stdValue) = objImg.GetRasterBand(1).GetStatistics(0, 1)
if (nRasterCount == 3):
(minValue2, maxValue2, meanValue2,
stdValue2) = objImg.GetRasterBand(2).GetStatistics(0, 1)
(minValue3, maxValue3, meanValue3,
stdValue3) = objImg.GetRasterBand(3).GetStatistics(0, 1)
meanValue = 0.2989 * meanValue + 0.5870 * meanValue2 + 0.1140 * meanValue3
maxValue = 0.2989 * maxValue + 0.5870 * maxValue2 + 0.1140 * maxValue3
# meanValue = 438
# maxValue = 2047
threshad = meanValue + iThresh_coeff * stdValue
objTent = pymm.threshad(objtophat, threshad, maxValue)
data_list = []
data_list.append(objTent)
WriteOutputImage(strOutputFile, 1, data_list, 0, 0, 0, strInputFile)
'''
