def thresholdCenterlines(nms, tLow=0.012, tHigh=0.12, bimodal=True):
""" Uses a continuity-preserving hysteresis thresholding to classify
centerlines.
Inputs:
nms -- Non-maxima suppressed singularity index response
Keyword Arguments:
bimodal -- true if the areas of rivers in the image are sufficiently
large that the distribution of ψ is bimodal
tLow -- lower threshold (automatically set if bimodal=True)
tHigh -- higher threshold (automatically set if bimodal=True)
Returns:
centerlines -- a binary matrix that indicates centerline locations
"""
if bimodal:
#Otsu's algorithm
nms = preprocess.double2im(nms, 'uint8')
tHigh,_ = cv2.threshold(nms, nms.min(), nms.max(), cv2.THRESH_OTSU)
tLow = tHigh * 0.1
strongCenterline = nms >= tHigh
centerlineCandidate = nms >= tLow
# Find connected components that has at least one strong centerline pixel
strel = np.ones((3, 3), dtype=bool)
cclabels, numcc = ndlabel(centerlineCandidate, strel)
sumstrong = ndsum(strongCenterline, cclabels, list(range(1, numcc+1)))
centerlines = np.hstack((0, sumstrong > 0)).astype('bool')
centerlines = centerlines[cclabels]
return centerlines
def thresholdCenterlines(nms, tLow=0.012, tHigh=0.12, bimodal=True):
""" Uses a continuity-preserving hysteresis thresholding to classify
centerlines.
Inputs:
nms -- Non-maxima suppressed singularity index response
Keyword Arguments:
bimodal -- true if the areas of rivers in the image are sufficiently
large that the distribution of ψ is bimodal
tLow -- lower threshold (automatically set if bimodal=True)
tHigh -- higher threshold (automatically set if bimodal=True)
Returns:
centerlines -- a binary matrix that indicates centerline locations
"""
if bimodal:
#Otsu's algorithm
nms = preprocess.double2im(nms, 'uint8')
tHigh,_ = cv2.threshold(nms, nms.min(), nms.max(), cv2.THRESH_OTSU)
tLow = tHigh * 0.1
strongCenterline = nms >= tHigh
centerlineCandidate = nms >= tLow
# Find connected components that has at least one strong centerline pixel
strel = np.ones((3, 3), dtype=bool)
cclabels, numcc = ndlabel(centerlineCandidate, strel)
sumstrong = ndsum(strongCenterline, cclabels, range(1, numcc+1))
centerlines = np.hstack((0, sumstrong > 0)).astype('bool')
centerlines = centerlines[cclabels]
return centerlines
def test_double2im():
'''
Unit test for double2im function
'''
# create dummy image
I = np.array([10. / 255, 50. / 255, 100. / 255])
# apply function
result = preprocess.double2im(I, 'uint8')
# make assertion
assert np.all(result) == np.all(np.array([10, 50, 100], dtype='uint8'))
centerlines = delineate.thresholdCenterlines(nms)
# Generate a raster map of the extracted channels
raster = visualization.generateRasterMap(centerlines, orient, widthMap)
# Generate a vector map of the extracted channels
visualization.generateVectorMap(centerlines,
orient,
widthMap,
saveDest="vector.pdf")
# Generate a quiver plot
visualization.quiverPlot(psi, orient, saveDest="quiver.pdf")
# Save the images that are created at the intermediate steps
cv2.imwrite("mndwi.TIF", cv2.normalize(I1, None, 0, 255, cv2.NORM_MINMAX))
cv2.imwrite("psi.TIF", cv2.normalize(psi, None, 0, 255, cv2.NORM_MINMAX))
cv2.imwrite("nms.TIF", cv2.normalize(nms, None, 0, 255, cv2.NORM_MINMAX))
cv2.imwrite("centerlines.TIF", centerlines.astype(int) * 255)
cv2.imwrite("rasterMap.TIF",
cv2.normalize(raster, None, 0, 255, cv2.NORM_MINMAX))
# An example of exporting a geotiff file
gm = georef.loadGeoMetadata("LC81380452015067LGN00_B3.TIF")
psi = preprocess.contrastStretch(psi)
psi = preprocess.double2im(psi, 'uint16')
georef.saveAsGeoTiff(gm, psi, "psi_geotagged.TIF")
# Export the (coordinate, width) pairs to a comma separated text file
georef.exportCSVfile(centerlines, widthMap, gm, "results.csv")
B6 = cv2.imread(B6s[i], cv2.IMREAD_UNCHANGED)
I1 = preprocess.mndwi(B3, B6)
psi, widthMap, orient = singularity_index.applyMMSI(I1,
filters,
togglePolarity=False)
nms = delineate.extractCenterlines(orient, psi)
centerlines = delineate.thresholdCenterlines(nms)
print("creating files")
gm = georef.loadGeoMetadata(B3s[i])
base = os.path.basename(B3s[i])
georef.exportCSVfile(centerlines, widthMap, gm,
str(base) + "_geo_points.csv")
psi = preprocess.contrastStretch(psi)
psi = preprocess.double2im(psi, 'uint16')
georef.saveAsGeoTiff(gm, psi, str(base) + "_geo_psi.TIF")
mndwi = preprocess.contrastStretch(I1)
mndwi = preprocess.double2im(mndwi, 'uint16')
georef.saveAsGeoTiff(gm, mndwi, str(base) + "_geo_mndwi.TIF")
#optional other file format
#cv2.imwrite("MNDWI.TIF", cv2.normalize(I1, None, 0, 255, cv2.NORM_MINMAX))
centerlines = preprocess.contrastStretch(centerlines)
centerlines = preprocess.double2im(centerlines, 'uint16')
georef.saveAsGeoTiff(gm, centerlines, str(base) + "_geo_centerlines.TIF")
#optional other file format
#cv2.imwrite("centerlines.TIF", centerlines.astype(int)*255)
print('completed')
psi, widthMap, orient = singularity_index.applyMMSI(I1, filters)
# Extract channel centerlines
nms = delineate.extractCenterlines(orient, psi)
centerlines = delineate.thresholdCenterlines(nms)
# Generate a raster map of the extracted channels
raster = visualization.generateRasterMap(centerlines, orient, widthMap)
# Generate a vector map of the extracted channels
visualization.generateVectorMap(centerlines, orient, widthMap, saveDest = "vector.pdf")
# Generate a quiver plot
visualization.quiverPlot(psi, orient, saveDest = "quiver.pdf")
# Save the images that are created at the intermediate steps
cv2.imwrite("mndwi.TIF", cv2.normalize(I1, None, 0, 255, cv2.NORM_MINMAX))
cv2.imwrite("psi.TIF", cv2.normalize(psi, None, 0, 255, cv2.NORM_MINMAX))
cv2.imwrite("nms.TIF", cv2.normalize(nms, None, 0, 255, cv2.NORM_MINMAX))
cv2.imwrite("centerlines.TIF", centerlines.astype(int)*255)
cv2.imwrite("rasterMap.TIF", cv2.normalize(raster, None, 0, 255, cv2.NORM_MINMAX))
# An example of exporting a geotiff file
gm = georef.loadGeoMetadata("LC81380452015067LGN00_B3.TIF")
psi = preprocess.contrastStretch(psi)
psi = preprocess.double2im(psi, 'uint16')
georef.saveAsGeoTiff(gm, psi, "psi_geotagged.TIF")
# Export the (coordinate, width) pairs to a comma separated text file
georef.exportCSVfile(centerlines, widthMap, gm, "results.csv")
