def makeContours(xcoordinates,ycoordinates,width,height,binsize):
getLngLat = utilities.makeGetLngLat(metadata)
getMeters = utilities.makeGetMeters(metadata)
# make the 2d histogram
clusterdata, xedges, yedges = numpy.histogram2d(xcoordinates, ycoordinates, bins=(int(width/binsize), int(height/binsize)), range=((0, width), (0, height)))
if len(xcoordinates) == 0:
clusterdata = numpy.zeros((int(width/binsize), int(height/binsize)), dtype=numpy.dtype(float))
# make contours for the three levels; the contour polygons are expressed in pixel-index coordinates (not lng/lat or meters)
contoursMin = utilities.contours(clusterdata, xedges, yedges, 0.5, interpolate=True, smooth=True)
cutLevel50 = utilities.cutLevel(clusterdata, 50.0)
contours50 = utilities.contours(clusterdata, xedges, yedges, cutLevel50, interpolate=True, smooth=True)
cutLevel95 = utilities.cutLevel(clusterdata, 95.0)
contours95 = utilities.contours(clusterdata, xedges, yedges, cutLevel95, interpolate=True, smooth=True)
# construct output data that includes the polygons in lng,lat coordinates, circumferences in meters, and areas in meters^2
clusterData = {"contoursMin": [], "contours50": [], "contours95": [],
"numberOfLabeledPixels": len(xcoordinates), "cutLevel50": cutLevel50, "cutLevel95": cutLevel95}
for polygon in contoursMin:
lnglatPolygon = utilities.convert(polygon, getLngLat)
metersPolygon = utilities.convert(lnglatPolygon, getMeters)
data = {"rowcolpolygon": polygon, "lnglatpolygon": lnglatPolygon, "areaInPixels": numpy.abs(utilities.area(polygon)), "circumferenceInMeters": numpy.abs(utilities.circumference(metersPolygon)), "areaInMeters": numpy.abs(utilities.area(metersPolygon)), "centroidInLngLat": utilities.centroid(lnglatPolygon)}
clusterData["contoursMin"].append(data)
for polygon in contours50:
lnglatPolygon = utilities.convert(polygon, getLngLat)
metersPolygon = utilities.convert(lnglatPolygon, getMeters)
data = {"rowcolpolygon": polygon, "lnglatpolygon": lnglatPolygon, "areaInPixels": numpy.abs(utilities.area(polygon)), "circumferenceInMeters": numpy.abs(utilities.circumference(metersPolygon)), "areaInMeters": numpy.abs(utilities.area(metersPolygon)), "centroidInLngLat": utilities.centroid(lnglatPolygon)}
clusterData["contours50"].append(data)
for polygon in contours95:
lnglatPolygon = utilities.convert(polygon, getLngLat)
metersPolygon = utilities.convert(lnglatPolygon, getMeters)
data = {"rowcolpolygon": polygon, "lnglatpolygon": lnglatPolygon, "areaInPixels": numpy.abs(utilities.area(polygon)), "circumferenceInMeters": numpy.abs(utilities.circumference(metersPolygon)), "areaInMeters": numpy.abs(utilities.area(metersPolygon)), "centroidInLngLat": utilities.centroid(lnglatPolygon)}
clusterData["contours95"].append(data)
return clusterData
alphadata = numpy.reshape(alphadata, rgbDouble.shape[1] * rgbDouble.shape[2])
arrayToPng = ArrayToPng()
arrayToPng.putdata(rgbDouble.shape[1], rgbDouble.shape[2], reddata, greendata, bluedata, alphadata, flipy=False, onePixelBeyondBorder=False)
attrib = {"version": "1.1", "width": "100%", "height": "100%", "preserveAspectRatio": "xMidYMin meet"}
attrib["viewBox"] = "0 0 %d %d" % (rgbDouble.shape[2], rgbDouble.shape[1])
attrib["style"] = "fill: none; stroke: black; stroke-linejoin: miter; stroke-width: 2; text-anchor: middle;"
attrib["font-family"] = "DejaVu Sans Condensed, DejaVu Sans, Lucida Sans Unicode, Lucida Sans, Helvetica, Sans, sans-serif"
attrib["font-weight"] = "normal"
imagePlot = SVG.svg(SVG.g(), **attrib)
imageGroup = imagePlot[0]
imageGroup.append(SVG.image(**{defs.XLINK_HREF: "data:image/png;base64," + arrayToPng.b64encode(), "x": "0", "y": "0", "width": repr(rgbDouble.shape[1]), "height": repr(rgbDouble.shape[2])}))
getLngLat = utilities.makeGetLngLat(imageValue["metadata"])
wavelengths = imageValue["metadata"]["bandWavelength"]
bandNames = imageValue["metadata"]["bandNames"]
tableRows = []
for clusterKey in sorted(imageValue):
if clusterKey != "metadata":
cluster = imageValue[clusterKey]["contours95"][0]
clump = cluster["other"]
selectedObjects.append(cluster)
if makingImage:
thePath = ["L{},{}".format(x, y) for x, y in cluster["rowcolpolygon"]]
thePath[0] = "M" + thePath[0][1:]
thePath.append("Z")
imageGroup.append(SVG.path(d=" ".join(thePath), style="stroke: #ff0000; stroke-width: 1.5; line-join: round;"))
def main(metadata,mask,imgArray):
imgMean = numpy.mean(imgArray,axis=0) # find the mean of each band
imgCov = numpy.cov(imgArray.T - imgMean.reshape(-1,1)) # Find covariance
imgPval, imgPvec = numpy.linalg.eig(imgCov) # The eigenvalues and eigenvectors of the covariance matrix
indexList = numpy.argsort(-imgPval) # Sort principal components
imgPval = imgPval[indexList]
imgPvec = imgPvec[:,indexList]
imgProj = numpy.dot(imgPvec.T,imgArray.T - imgMean.reshape(-1,1)) # Projection of original data on PCs
imgMdist = numpy.sum(numpy.square(imgProj.T) / imgPval,axis=1) # Mahalanobis distance
# sort distances and select top k1 std devs above
# take k1 = 6
# Assume distances have log-normal distribution,
# so find k1-sigma extreme point in log distribution, and transform back
k1 = 6
lmean = numpy.mean(numpy.log(imgMdist))
lsd = numpy.std(numpy.log(imgMdist))
npix = numpy.size(imgMdist)
dtol = numpy.exp(lmean + k1*lsd)
imgFlag = (imgMdist > dtol)
nsubpix = numpy.sum(imgFlag)
raredict = {}
if nsubpix > 2:
# Similarity matrix (cosine)
imgNorm = numpy.sqrt(numpy.sum(imgProj[:,imgFlag]**2,axis=0))
imgDot = numpy.dot(imgProj[:,imgFlag].T,imgProj[:,imgFlag])
simmat = imgDot / numpy.outer(imgNorm,imgNorm)
# Proximity matrix (taxi-cab metric)
ny,nx = numpy.mgrid[0:mask.shape[0],0:mask.shape[1]-2]
ny = numpy.reshape(ny,(-1,),'F')
nx = numpy.reshape(nx,(-1,),'F')
imgRna = ny[imgFlag]
imgCna = nx[imgFlag]
proxmat = numpy.abs(imgRna - imgRna.reshape(-1,1)) + numpy.abs(imgCna - imgCna.reshape(-1,1))
# Thresholds
k2 = .95
k3 = 3
k4 = 5
spfmat = numpy.logical_and(numpy.abs(simmat) > k2, proxmat < k3)
imgRct = numpy.sum(spfmat,axis=0)
imgCct = numpy.sum(spfmat,axis=1)
imgRall = imgRna[imgRct >= k4]
imgCall = imgCna[imgCct >= k4]
if (imgCall.size > 3):
img3Find = mask.shape[0]*imgCall + imgRall
imgMdistRpc = imgMdist[img3Find]
# Get the original spectra of the rare pixels
imageRPC = imgArray[img3Find]
proxmat2 = proxmat[imgRct>=k4,:]
proxmat2 = proxmat2[:,imgCct>=k4]
proxmat2[proxmat2 != 1] = 0
sprox = numpy.sum(proxmat2==1)
if (sprox > 0):
clout = clump(proxmat2)
plmap = numpy.hstack((numpy.reshape(numpy.array(imgRall),(-1,1)),numpy.reshape(numpy.array(imgCall),(-1,1)),imgMdistRpc.reshape(-1,1),numpy.array(clout[1].reshape(-1,1),dtype=int),imageRPC))
plmap = plmap[numpy.argsort(plmap[:,3]),:]
plmap2 = numpy.hstack((plmap,numpy.zeros((plmap.shape[0],2))))
for x in numpy.unique(plmap[:,3]):
plmean = numpy.mean(plmap[plmap[:,3]==x,2])
plsd = numpy.var(plmap[plmap[:,3]==x,2],ddof=1)
plrpcmean = numpy.mean(plmap[plmap[:,3]==x,4:],axis=0) # average radiance
# for a color
plmap2[plmap[:,3]==x,-2] = plmean
plmap2[plmap[:,3]==x,-1] = plsd
plmap2[plmap[:,3]==x,4:plmap2.shape[1]-2] = plrpcmean
plmap2 = plmap2[numpy.lexsort((plmap2[:,0],plmap2[:,1],-plmap2[:,-2])),:]
getLngLat = utilities.makeGetLngLat(metadata)
lnglatPolygon = utilities.convert(plmap2[:,:2].tolist(), getLngLat)
for x in numpy.unique(plmap2[:,3]):
raredict['%d' % x] = {"color": x,
"row,column": plmap2[plmap2[:,3]==x,:2].tolist(),
"longitude,latitude": [[lnglatPolygon[ll][0],lnglatPolygon[ll][1]] for ll in numpy.arange(plmap2.shape[0])[plmap2[:,3]==x]],
"mdist": plmap2[plmap2[:,3]==x,2].tolist(),
"mdistmean": plmap2[plmap2[:,3]==x,-2][0], # these values are identical, so just take one of them
"mdistvar": plmap2[plmap2[:,3]==x,-1][0],
"colorSpectrum": plmap2[plmap2[:,3]==x,4:plmap2.shape[1]-2].tolist()[0]
}
return raredict
def main(filename, metadata, mask, bands, start, end, iscan, trueRowsStart, nTrueRows, trueColsStart, nTrueCols, FCUT = .08, PCUTBLOB = .001, PCUTCHI = .01, MINCATALOGUEDSIZE = 0):
START = start; END = end
tstart = datetime.datetime.now()
imageBands = sorted(bands.keys())
windowRadius = 1
truePositive = 0
falsePositive = 0
taggedPixels = 0
imageArray = numpy.zeros((bands[imageBands[0]].shape + (len(bands),) ))
maxScore = len(imageBands)/PCUTBLOB
# Attempt to remove some problematic bands, in particular, ones with 'stripes'.
bandToRemove = []
sys.stderr.write('Number of Bands: %i\n'%len(imageBands))
sys.stderr.write('Evaluate Whether Columns are reasonable\n')
for i, band in enumerate(imageBands):
imageArray[:,:,i] = bands[band]
if config.blobspectra.VALIDATECOLUMNS:
ok = True
bandrange = numpy.ma.max(bands[band].data) - numpy.ma.min(bands[band].data)
sys.stderr.write('For band %i bandrange is %f\n'%(i,bandrange))
sys.stderr.write('At %s\n'%(repr(numpy.unravel_index(numpy.ma.argmax(bands[band].data), bands[band].data.shape))))
maxpix = numpy.unravel_index(numpy.ma.argmax(bands[band].data), bands[band].data.shape)
sys.stderr.write('Value at max %d\n'%bands[band][maxpix])
for j in range(1, bands[band].shape[1] - 1):
if (numpy.ma.max(bands[band].data[:, j]) - numpy.ma.min(bands[band].data[:, j])) / bandrange < .01:
if True:
sys.stderr.write('%i %f %f\n'%(j, numpy.ma.max(bands[band].data[:,j]),numpy.ma.min(bands[band].data[:,j])))
ok = False
if not ok:
sys.stderr.write('Remove band %i because of un-natural radiance distribution in Cols\n' % i)
bandToRemove.append(i)
if config.blobspectra.VALIDATEROWS:
sys.stderr.write('Evaluate Whether Rows are reasonable\n')
for i, band in enumerate(imageBands):
ok = True
bandrange = numpy.ma.max(bands[band].data) - numpy.ma.min(bands[band].data)
for j in range(1, bands[band].shape[0] - 1 ):
if (numpy.ma.max(bands[band].data[j,:]) - numpy.ma.min(bands[band].data[j,:])) / bandrange < .01:
ok = False
if not ok:
sys.stderr.write('Remove band %i because of un-natural radiance distribution in Rows\n' % i)
bandToRemove.append(i)
imageArray = numpy.delete(imageArray, bandToRemove, 2)
sys.stderr.write('Shape %s\n'%repr(imageArray.shape))
try:
sys.stderr.write('Sample %s\n'%repr(imageArray[400,400,:]))
except:
pass
nPixels = imageArray.shape[0] * imageArray.shape[1]
sys.stderr.write("this is the size of the image: " + str(imageArray.shape) + '\n')
NimageRows = imageArray.shape[0]
sys.stderr.write("This is the number of pixels per band in the image: " + str(imageArray.shape[0] * imageArray.shape[1]) + '\n')
sys.stderr.write("This is the total number of pixels in the image: " + str(imageArray.size) + '\n')
# This is the number of pixels that are non-zero. Here, a pixel is a 3-index object: x,y,wavelength. Dividing
# this number by the number of bands ought to be pretty close to the number of unmasked geospatial pixels in this portion.
sys.stderr.write("This is the total number of non-zero pixels: " + str((imageArray.reshape(-1, ) > 0).sum()) + '\n')
# numpy.prod(imageArray,2) multiplies the radiances across all wavelengths together. This construct will add up
# number of geospatial pixels for which at least one band is zero.
sys.stderr.write("This is the total number of zero pixels: " + str((numpy.prod(imageArray, 2) == 0).sum()) + '\n')
imageIndices = numpy.arange(imageArray.shape[0] * imageArray.shape[1]).reshape((imageArray.shape[0], imageArray.shape[1]))
nbands = imageArray.shape[2]
# (ij)th element of ny yields i. (ij)th element of nx yields j
ny, nx = numpy.mgrid[0 : mask.shape[0], 0 : mask.shape[1]]
globalMean = numpy.mean(imageArray.reshape(-1, ))
sys.stderr.write('global mean: %f\n'%globalMean)
sys.stderr.write('global var : %f\n'%numpy.var(imageArray.reshape(-1,)))
if globalMean <= 5 :
sys.stderr.write('=====> Insufficient radiance in image. Returning!\n')
return
if numpy.var(imageArray.reshape(-1,)) < 200**2 :
sys.stderr.write('=====> Insufficient variability in image. Returning!\n')
return
globalMean = globalMean * mask.sum() / (mask.shape[0] * mask.shape[1]) #This line is because the original mean
#over all pixels, not just the non-zeros in the mask
#################################
#####STANDARD DEVIATION WINDOW###
#################################
#Calculate the mean and standard deviation of 3-by-3 pixel windows
boxStandardDev = numpy.zeros(imageArray.shape)
boxMean = numpy.zeros(imageArray.shape)
nBoxPixels = (1 + 2 * windowRadius) ** 2
for i in xrange(nbands):
box = rolling_window(imageArray[:, :, i], 1)
boxsq = rolling_window(imageArray[:, :, i] ** 2, 1)
boxStandardDev[:, :, i] = numpy.sqrt(boxsq / nBoxPixels - (box / nBoxPixels) ** 2)
boxMean[:, :, i] = box / nBoxPixels
boxStdZero = numpy.where(boxStandardDev[:,:,i] == 0)
#################################
####CREATE STANDARD DEV MASK#####
#################################
#Create a boolean array (mask) with TRUE entries where standard deviation is below FCUT * max standard dev of each band
zfilter = numpy.zeros(imageArray.shape)
for i in xrange(nbands):
#sys.stderr.write('%s\n'%repr(numpy.histogram((numpy.ma.masked_array(boxStandardDev[:,:,i], mask=mask)).compressed(), 100)))
zfilter[:, :, i] = boxStandardDev[:, :, i] < FCUT * numpy.max(boxStandardDev[:, :, i])
zfilterAll = numpy.ma.all(zfilter, 2)
zfilterAll = numpy.ma.masked_array(zfilterAll, mask=mask)
sys.stderr.write('Unmasked pixels %i\n'%zfilterAll.count())
sys.stderr.write('Pixels in chromatically homogenous region %s\n'%repr(zfilterAll.sum()))
#Combine this mask with the original image shape mask
#sdMask = numpy.ma.masked_array(zfilterAll.astype('uint8'), mask | ~zfilterAll)
sdMask = zfilterAll
######################################
#####CONNECTED COMPONENTS LABELING####
######################################
#This step uses a connected components labeling algorithm.
#It is applied to the binary mask generated in the previous step,
#and labels each connected group of pixels with a separate integer.
#Note that the background pixels are also labeled. If, as is most often the case,
#the background is connected, it takes on the value 0.
connected_regions, num_features = find_regions(numpy.ma.masked_array(sdMask, mask=mask))
sys.stderr.write('Using find_regions \n')
#connected_regions, num_features = ndimage.label(sdMask)
#sys.stderr.write('Using scipy %i\n'%num_features)
connected_regions[numpy.prod(imageArray, 2) == 0] = sorted(numpy.unique(connected_regions))[-1] + 1
regions_labels = sorted(numpy.unique(connected_regions))
#
# background (i.e. region 0) will be green, masked out (radiance=0) areas will be red per
# the rgb convention used here.
#connectedPlot.save("connectedPlot.png", "PNG", options="optimize")
######################################
#####BLOB STATISTICS##################
######################################
regions = {}
regions["bandNames"] = imageBands
if num_features == 0:
sys.stderr.write('=====> No features identified in this image. Returning!\n')
return
#Calcuate the mean and standard deviation of each connected, across each band.
#The arrays are placed in a dictionary which contains the row-column coordinates,
#along with the mean and standard deviation for each band.
#In most cases, the background of the image will be simply connected. In rare cases, a feature from the initial sdMask
#may cut the image, in which case there will be more than one background. Here, the x-y coordinates of each region are
#rigorously checked against the background (which is False in the sdMask) x-y coordinates.
nyx_background = set(map(tuple,numpy.hstack((ny[sdMask == False].reshape(-1, 1),nx[sdMask == False].reshape(-1, 1)))))
sys.stderr.write('Number of background pixels %i\n'%len(nyx_background))
regions = defaultdict(dict)
background_regions = defaultdict(dict)
#The bad pixel (i.e., zero radiance because information was missing) has the last label, which gets avoided here
for i,label in enumerate(regions_labels[:-1]):
#First assign pixels which are "background", i.e., did not pass the FCUT, to a separate dictionary
regionindices = connected_regions == label
regionrows = ny[regionindices].reshape(-1,1)
regioncols = nx[regionindices].reshape(-1,1)
regionpixels = set(map(tuple, numpy.hstack((regionrows, regioncols))))
if regionpixels <= nyx_background:
background_regions[label]["yxCoordinates"] = numpy.hstack((regionrows , regioncols))
background_regions[label]["mean"] = numpy.mean(imageArray[regionindices , :] , axis=0)
background_regions[label]["standard_deviation"] = numpy.std(imageArray[regionindices , :] , axis=0)
background_regions[label]["mySubBlobs"] = [[label , regionindices.sum()]] #a record is kept of the background region
background_regions[label]["radiances"] = imageArray[regionindices , :]
else:
regions[label]["yxCoordinates"] = numpy.hstack((regionrows , regioncols))
regions[label]["mean"] = numpy.mean(imageArray[regionindices , :] , axis = 0)
regions[label]["background"] = []
try:
regions[label]["mean"] = regions[label]["mean"] * globalMean / numpy.mean(regions[label]["mean"])
except:
sys.stderr.write('Fail to normalize this mean for region %i\n'%label)
sys.stderr.write(repr(regions[label]["mean"]))
regions[label]["standard_deviation"] = numpy.std(imageArray[regionindices , :] , axis=0)
regions[label]["mySubBlobs"] = [[label , regionindices.sum()]] #a record is kept of all blobs and their respective sizes
if regions[label]["mySubBlobs"][0][1] > 1:
regions[label]["standard_deviation"] = numpy.mean(boxStandardDev[regionindices,:], axis=0)
else:
regions[label]["standard_deviation"] = boxStandardDev[regionindices,:].reshape(-1,)
regionlabels = regions.keys()
sys.stderr.write('non background region labels %i\n'%len(regionlabels))
singletonRad = 0
for key in background_regions.keys():
if singletonRad == 0:
singletonRad = background_regions[key]["radiances"]
singletonYX = background_regions[key]["yxCoordinates"]
else:
singletonRad = numpy.vstack((singletonRad,background_regions[key]["radiances"]))
singletonYX = numpy.vstack((singletonYX,background_regions[key]["yxCoordinates"]))
#This is where the merging of blobs (hierarchical clustering) takes place.
#First test whether regions which made the initial cut (where sdMask=True)
#can be grouped together.
#Start out by using the mean and standard dev of blobs to construct a t-test statistic.
#If this statistic is above a threshold PCUT, the blobs will be merged. The second blob
#is always appended to the first.
#For regions below 3 pixels, use a chi-squared test in place of t-test to determined
#whether pixels should be merged to a blob.
regionsBlobbed = defaultdict(dict) #regions will be appended to this dictionary as they are merged with other regions
regionsUnblobbed = copy.deepcopy(regions) #regions will be removed from this dictioanry when they have been merged to another region
merge_keys = copy.copy(regions.keys())
unmerged_regions = sorted([x for x in merge_keys if regions[x]["yxCoordinates"].shape[0] > MINCATALOGUEDSIZE])
sys.stderr.write('Merging %i valid among a total of %i multi-cell blobs first\n'%(len(unmerged_regions), len(merge_keys)))
merged_regions = []
merge_count = 0
for i, band1 in enumerate(sorted([x for x in merge_keys if regions[x]["yxCoordinates"].shape[0] > MINCATALOGUEDSIZE])):
if band1 in unmerged_regions:
unmerged_regions.remove(band1)
regionsBlobbed[band1] = copy.deepcopy(regions[band1])
del regionsUnblobbed[band1]
n1 = sum([subblob[1] for subblob in regions[band1]['mySubBlobs']])
for j, band2 in enumerate(unmerged_regions):
n2 = sum([subblob[1] for subblob in regions[band2]['mySubBlobs']])
blobbed_test_stat, test_stat, n1n2 = calc_tdist(regions[band1]["mean"], regions[band2]["mean"], regions[band1]["standard_deviation"],regions[band2]["standard_deviation"], n1, n2)
blobbed_test_stat = numpy.min(test_stat)
if blobbed_test_stat > PCUTBLOB / len(regions[band1]["mean"]):
#sys.stderr.write('%s, %s, %s \n'%(repr(blobbed_test_stat), repr(test_stat), repr(n1n2)))
merged_regions.append(band2)
merge_count += 1
regionsBlobbed[band1]["yxCoordinates"] = numpy.vstack((regionsBlobbed[band1]["yxCoordinates"], regions[band2]["yxCoordinates"]))
regionsBlobbed[band1]["mySubBlobs"].append([band2,regions[band2]["yxCoordinates"].shape[0]])
for merged in merged_regions:
unmerged_regions.remove(merged)
merge_count -= 1
del regionsUnblobbed[merged]
merged_regions = []
sys.stderr.write('Number of Blobs after blob-to-blob merging %i Number of SubBlobs %i\n'%(len(regionsBlobbed), numpy.sum([len(regionsBlobbed[b]["mySubBlobs"]) for b in regionsBlobbed.keys()])))
sys.stderr.write('Spread in pixels associated with blobs:\n')
blobsizes = []
for k in regionsBlobbed.keys():
npixels = regionsBlobbed[k]["yxCoordinates"].size/2
if npixels != 1:
sys.stderr.write('largish blob %i %s\n'%(k, repr(regionsBlobbed[k]["mySubBlobs"])))
sys.stderr.write('%d\n'%(regionsBlobbed[k]["yxCoordinates"].size/2.0))
blobsizes.append(regionsBlobbed[k]["yxCoordinates"].size/2)
blobdist = numpy.histogram(blobsizes)
sys.stderr.write('%s\n'%repr(blobdist))
sys.stderr.write('Number of Pixels in Merged blobs: %i\n'%numpy.array(blobsizes).sum())
regions = []
merged_blobs = numpy.zeros(sdMask.shape)
for band in regionsBlobbed.keys():
rows = regionsBlobbed[band]["yxCoordinates"][:,0]
cols = regionsBlobbed[band]["yxCoordinates"][:,1]
merged_blobs[rows, cols] = band
############################
#Re-group blob statistics###
############################
blobMeans = []
blobStandardDevs = []
blobbedRegionKeys = sorted(regionsBlobbed.keys())
for blob in blobbedRegionKeys:
blobMeans.append(regionsBlobbed[blob]["mean"])
blobStandardDevs.append(regionsBlobbed[blob]["standard_deviation"])
try:
if blobStandardDevs[-1][-1] == 0.0:
row = regionsBlobbed[blob]['yxCoordinates'][0][0]
col = regionsBlobbed[blob]['yxCoordinates'][0][1]
except:
sys.stderr.write('############\n')
sys.stderr.write('Shape of blobStanardDevs %s\n'%repr((numpy.array(blobStandardDevs)).shape))
sys.stderr.write('%s\n'%repr(regionsBlobbed[blob]))
sys.stderr.write('%s\n'%repr(blobStandardDevs[-1][-1]))
blobMeans = numpy.array(blobMeans)
try:
blobMeansMean = numpy.mean(blobMeans,axis=1)
except:
sys.stderr.write('blobmeans: %s\n'%repr(blobMeans))
if len(blobMeans)==0:
return
blobStandardDevs = numpy.array(blobStandardDevs)
sys.stderr.write('Done blob-blob merging\n')
###################################################################
######MERGE UNBLOBBED, PASSED FCUT REGIONS-Chi Squared Test#######
###################################################################
if len(regionsUnblobbed.keys()) > 0:
#Merge any blobs (pixels that passed initial FCUT) which are smaller than MINCATALOGUEDSIZE
#based on result of a chi-squared test
unBlobbedMeans = []
unBlobbedRegionKeys = sorted(regionsUnblobbed.keys()) #Avoid first key, which are the pixels that do not pass FCUT
for unblobbed in unBlobbedRegionKeys:
unBlobbedMeans.append(regionsUnblobbed[unblobbed]["mean"]) #Table the mean of the unblobbed regions
unBlobbedMeans = numpy.array(unBlobbedMeans)
unBlobbedChiSq = numpy.zeros((unBlobbedMeans.shape[0],blobMeans.shape[0]))
for i in numpy.arange(blobMeans.shape[0]):
#This is the vectorized argument to be used in the chi-squared test
try:
a=(unBlobbedMeans.T/numpy.mean(unBlobbedMeans, axis=1)).T
b=blobMeans[i,:]/blobMeansMean[i]
unBlobbedChiSq[:,i] = numpy.sum(numpy.square( ((a-b)*globalMean)/blobStandardDevs[i,:]), axis=1)
except:
sys.stderr.write('Unable to perform this chi-squared test ' + repr(unBlobbedMeans) + '\n')
unblobbed_test_stat = calc_chisq(unBlobbedChiSq, unBlobbedMeans.shape[1])
unblobbed_test_stat_max = numpy.max(unblobbed_test_stat, axis=1) #Take max
#Table the label of the unblobbed region with the label of the blobbed region where the unblobbed-blobbed chi-squared test has its max value
unblobbed_to_merge_with = numpy.hstack((numpy.array(unBlobbedRegionKeys).reshape(-1,1),numpy.array(blobbedRegionKeys)[numpy.argmax(unblobbed_test_stat,axis=1)].reshape(-1,1)))
#Find out which unblobbed regions pass the chi-squared test
unblobbed_to_merge_with = unblobbed_to_merge_with[unblobbed_test_stat_max > PCUTCHI,:]
#Do the merging of the unblobbed regions that passed chi-squared test with appropriate blobbed region
for unblobbed in unblobbed_to_merge_with:
regionsBlobbed[unblobbed[1]]["yxCoordinates"] = numpy.vstack((regionsBlobbed[unblobbed[1]]["yxCoordinates"],regionsUnblobbed[unblobbed[0]]["yxCoordinates"]))
regionsBlobbed[unblobbed[1]]["mySubBlobs"].append([unblobbed[0],regionsUnblobbed[unblobbed[0]]["yxCoordinates"].shape[0]])
del regionsUnblobbed[unblobbed[0]]
#################################################################
## MERGE UNBLOBBED, DID NOT PASS FCUT ##
#Do the chi-squared test again, but this time with the pixels ##
#which did not pass the initial FCUT ##
#################################################################
sys.stderr.write('Merge Single Pixels with high local SD to known blobs\n')
sdNotMask = sdMask==False
#
#sdPlot = Image.fromarray(numpy.array(255*sdNotMask,dtype=numpy.uint8))
#sdPlot.save("sdNotMask.png","PNG",options="optimize")
#
unBlobbedMeans = []
for key in background_regions.keys():
unBlobbedMeans = background_regions[key]["radiances"]
unBlobbedChiSq = numpy.zeros((unBlobbedMeans.shape[0], blobMeans.shape[0]))
# this construct normalizes each bg pixel by its mean over all bands.
a = (unBlobbedMeans.T/numpy.mean(unBlobbedMeans, axis=1)).T
b = blobMeans / blobMeansMean[:, None]
unBlobbedChiSq = numpy.apply_along_axis(chisq, 1, a, b, blobStandardDevs, globalMean)
best_chisquared = numpy.min(unBlobbedChiSq, axis = 1)
unblobbed_test_stat = calc_chisq(best_chisquared, unBlobbedMeans.shape[1])
#unblobbed_test_stat_max = numpy.max(unblobbed_test_stat, axis = 1)
bgcoord = background_regions[key]["yxCoordinates"]
background_coords = imageIndices[bgcoord[:, 0], bgcoord[:, 1]]
# this variable has two rows, the first is a flattened index into the image raster,
# the second is the best chi-squared match to a blob.
unblobbed_to_merge_with = numpy.hstack((background_coords.reshape(-1, 1), \
numpy.array(blobbedRegionKeys)[numpy.argmin(unBlobbedChiSq, axis = 1)].reshape(-1, 1)))
# Background pixels will be merged if they have a good enough match.
unblobbed_to_merge_with = unblobbed_to_merge_with[unblobbed_test_stat > PCUTCHI, :]
# this construct yields an N x 2 array where N is the number of pixels in the image.
# the array is a list of the row, column index of the ith pixel (counting across and then down).
singletonCoords = numpy.hstack((ny.reshape(-1, 1), nx.reshape(-1, 1)))
blobsToUpdate = numpy.unique(unblobbed_to_merge_with[:, 1])
testregionsBlobbed = {}
for blob in blobsToUpdate:
testregionsBlobbed[blob] = {}
pixelsToAdd = unblobbed_to_merge_with[unblobbed_to_merge_with[:, 1] == blob][:,0]
try:
regionsBlobbed[blob]["yxCoordinates"] = numpy.vstack((regionsBlobbed[blob]["yxCoordinates"], singletonCoords[pixelsToAdd, : ]))
#testregionsBlobbed[blob]["yxCoordinates"] = numpy.vstack((regionsBlobbed[blob]["yxCoordinates"], singletonCoords[pixelsToAdd, : ]))
except:
sys.stderr.write('%s\n'%repr(blob))
sys.stderr.write('pixelstoadd %s\n'%repr(pixelsToAdd))
sys.stderr.write('shapes: %s %s \n'%(regionsBlobbed[blob]["yxCoordinates"].shape, singletonCoords[pixelsToAdd,:].shape))
sys.stderr.write('singleton coordinates %s\n'%repr(singletonCoords[pixelsToAdd,:]))
for unblobbed in unblobbed_to_merge_with:
regionsBlobbed[unblobbed[1]]["mySubBlobs"].append(["background pixel " + str(unblobbed[0]),1])
regionsBlobbed[unblobbed[1]]["background"].append(1)
background_regions[key]["yxCoordinates"] = numpy.delete(background_regions[key]["yxCoordinates"], numpy.arange(unblobbed_test_stat.size)[unblobbed_test_stat > PCUTCHI],0)
background_regions[key]["radiances"] = numpy.delete(background_regions[key]["radiances"], numpy.arange(unblobbed_test_stat.size)[unblobbed_test_stat > PCUTCHI],0)
background_regions[key]["mySubBlobs"][0][1] -= (unblobbed_test_stat > PCUTCHI).sum()
sys.stderr.write('Done Merging Single High SD Pixels\n')
##################################
# UNMATCHED BACKGROUND PIXELS #
##################################
singletonRad = 0
for key in background_regions.keys():
if singletonRad == 0:
singletonRad = background_regions[key]["radiances"]
singletonYX = background_regions[key]["yxCoordinates"]
else:
singletonRad = numpy.vstack((singletonRad,background_regions[key]["radiances"]))
singletonYX = numpy.vstack((singletonYX,background_regions[key]["yxCoordinates"]))
removeZeroSingletons = numpy.where( numpy.prod(singletonRad, 1) == 0 )
if len(removeZeroSingletons[0]) > 0:
singletonRad = numpy.delete(singletonRad, removeZeroSingletons, 0)
singletonyx = numpy.delete(singletonYX, removeZeroSingletons, 0)
singletonRadMeans = numpy.mean(singletonRad, axis = 0)
singletonRad = singletonRad * globalMean / singletonRadMeans
##################################
# TRY TO FIND BINARY BLOB MIXES ##
##################################
#for key in background_regions.keys():
# singleton
sys.stderr.write('Cluster locally hight SD pixels that differ from blobs with one another\n')
##################################
# CLUSTER UNMERGED PIXELS #
##################################
#binlabelStart = int(sorted(regionsBlobbed.keys())[-1])
binlabelStart = num_features + 1
if len(singletonRad) > 0:
start = 0
chunk = 10000
mergeSingletons = {}
total = 0
while total < len(singletonRad):
end = min(len(singletonRad), start + chunk)
mergeSingletons.update(mergevecs(singletonRad[start:end,:]))
total += (end - start)
start = start + chunk
for i, tlabel in mergeSingletons.items():
binlabel = binlabelStart + i
#tlabel = mergeSingletons[i]
regionsBlobbed[binlabel]["radiances"] = singletonRad[tlabel,:]
regionsBlobbed[binlabel]["yxCoordinates"] = singletonYX[tlabel,:]
regionsBlobbed[binlabel]["mySubBlobs"] = [["background pixels " + str(binlabel), len(tlabel)]]
try:
regionsBlobbed[binlabel]["background"].append(len(tlabel))
except:
regionsBlobbed[binlabel]["background"] = [len(tlabel)]
regionsBlobbed[binlabel]["mean"] = numpy.mean(singletonRad[tlabel,:], axis=0)
regionsBlobbed[binlabel]["standard_deviation"] = numpy.std(singletonRad[tlabel,:], axis=0)
merged_blobs = numpy.zeros(sdMask.shape)
for band in regionsBlobbed.keys():
for i in xrange(regionsBlobbed[band]["yxCoordinates"].shape[0]):
coord = regionsBlobbed[band]["yxCoordinates"]
merged_blobs[regionsBlobbed[band]["yxCoordinates"][i,0],regionsBlobbed[band]["yxCoordinates"][i,1]] = band
sys.stderr.write('Done Categorizing Pixels. Fill out catalog\n')
imageArray[:, :, 4] = numpy.array(255. * imageArray[:, :, 4] / imageArray[:, :, 4].max(), dtype=numpy.uint8)
catalog = {}
candregions = []
# no need to distinguish between the two categories at this point.
regionsBlobbed.update(regionsUnblobbed)
finalregions = regionsBlobbed.keys()
finalregions.sort()
ncand = 0
select = selectioncriteria.selectioncriteria[config.blobspectra.SELECTIONCRITERIA]
paramval = selectioncriteria.scanvals[iscan]
select = tuple([x.replace('SCAN', str(paramval)) for x in select])
if len(select) == 1:
select = (select[0], "True")
nLevel0 = 0
sys.stderr.write('Evaluating %i Regions\n'%len(finalregions))
nEvaluated = 0
for iregion in range(len(finalregions)):
nEvaluated += 1
select = selectioncriteria.selectioncriteria[config.blobspectra.SELECTIONCRITERIA]
select = tuple([x.replace('SCAN', str(paramval)) for x in select])
if len(select) == 1:
select = (select[0], "True")
region = finalregions[iregion]
spectraldistances = []
regionsBlobbed[region]["mean"] = regionsBlobbed[region]["mean"] * globalMean / numpy.mean(regionsBlobbed[region]["mean"])
catalog[region] = {'color' : region, 'nsubBlobs' : len(regionsBlobbed[region]["mySubBlobs"])}
blobsizes = [sblob[1] for sblob in regionsBlobbed[region]["mySubBlobs"]]
nsingleton = 0
nblobs = 0
nsingleton = str(regionsBlobbed[region]["mySubBlobs"]).count('background')
if 'background' in regionsBlobbed[region].keys():
singletonpixels = sum(regionsBlobbed[region]['background'])
nbgadded = len(regionsBlobbed[region]['background'])
else:
singletonpixels = 0
nbgadded = 0
nblobs = len(regionsBlobbed[region]["mySubBlobs"]) + singletonpixels - nbgadded
catalog[region]['nsubBlobs'] = nblobs
try:
if catalog[region]['nsubBlobs']==nsingleton:
catalog[region]['meanNonSingletonSize'] = 0.0
else:
catalog[region]['meanNonSingletonSize'] = (numpy.sum(blobsizes) - nsingleton)/ float(catalog[region]['nsubBlobs'] - nsingleton)
except:
catalog[region]['meanNonSingletonSize'] = 0.0
catalog[region]['singletons'] = nsingleton
catalog[region]['meanBlobSize'] = numpy.mean(blobsizes)
catalog[region]['sdBlobSize' ] = numpy.std(blobsizes)
catalog[region]['nNonSingletonBlobs'] = catalog[region]['nsubBlobs'] - catalog[region]['singletons']
saveSelect0 = select[0]
#sys.stderr.write('Evaluating Region %i %d %d %d\n'%(nEvaluated, catalog[region]['meanNonSingletonSize'], catalog[region]['meanBlobSize'], catalog[region]['nNonSingletonBlobs']))
if eval(select[0]):
nLevel0 += 1
n1 = max(2, sum(blobsizes))
for j in finalregions:
if 'mean' in regionsBlobbed[j].keys():
regionsBlobbed[j]["mean"] = regionsBlobbed[j]["mean"] * globalMean / numpy.mean(regionsBlobbed[j]["mean"])
n2 = max(2, sum([subblob[1] for subblob in regionsBlobbed[j]['mySubBlobs']]))
blobbed_test_stat, test_stat, n1n2 = calc_tdist(regionsBlobbed[region]["mean"],regionsBlobbed[j]["mean"],regionsBlobbed[region]["standard_deviation"],regionsBlobbed[j]["standard_deviation"], n1, n2)
blobbed_test_stat = numpy.min(test_stat)
if numpy.isnan(blobbed_test_stat):
blobbed_test_stat = -numpy.inf
elif 'radiances' in regionsBlobbed[j].keys():
if (regionsBlobbed[j]['radiances']==0).all():
blobbed_test_stat = -numpy.inf
else:
regionindices = connected_regions == j
jmean = numpy.mean(imageArray[regionindices , :] , axis=0)
regionsBlobbed[j]["mean"] = jmean * globalMean / numpy.mean(jmean)
regionsBlobbed[j]["standard_deviation"] = numpy.std(imageArray[regionindices , :] , axis=0)
n2 = sum([subblob[1] for subblob in regionsBlobbed[j]['mySubBlobs']])
blobbed_test_stat, test_stat, n1n2 = calc_tdist(regionsBlobbed[region]["mean"],regionsBlobbed[j]["mean"],regionsBlobbed[region]["standard_deviation"],regionsBlobbed[j]["standard_deviation"], n1, n2)
blobbed_test_stat = numpy.min(test_stat)
else:
blobbed_test_stat = -numpy.inf
if j==region:
spectraldistances.append( -numpy.inf)
else:
spectraldistances.append(blobbed_test_stat)
spectralneighborind = numpy.argmax(spectraldistances)
spectralneighbor = finalregions[spectralneighborind]
catalog[region]['ClosestSpectralAlternate'] = (spectralneighbor, spectraldistances[spectralneighborind] * nbands / PCUTBLOB)
else:
# Failing the Level 0 cut forces Level 1 to fail
select = ("True", "False")
catalog[region]['ClosestSpectralAlternate'] = (0, numpy.inf)
if eval(select[1]):
candregions.append(region)
nyx = regionsBlobbed[region]["yxCoordinates"]
overlaprows = numpy.in1d(nyx[:,0], range(trueRowsStart, trueRowsStart + nTrueRows))
overlapcols = numpy.in1d(nyx[:,1], range(trueColsStart, trueColsStart + nTrueCols))
Noverlap = sum(overlaprows & overlapcols)
truePositive += Noverlap
falsePositive += (numpy.sum(blobsizes) - Noverlap)
taggedPixels += numpy.sum(blobsizes)
key = filename + '-' + str(region)
if not config.blobspectra.KNOWNTRUTH:
key = 'KEY'
score = '%5.6f'%((maxScore - catalog[region]['ClosestSpectralAlternate'][1])/maxScore)
sys.stderr.write('%d %s\n'%(maxScore, repr(catalog[region]['ClosestSpectralAlternate'])))
idcand = '%s'%repr(catalog[region]['color'])
image = '%s'%os.path.basename(os.environ["map_input_file"])
#sys.stderr.write('%s\n'%os.path.basename(os.environ["map_input_file"]))
#for key in catalog[region].keys():
# sys.stderr.write('%s %s\n'%(repr(key), repr(catalog[region][key])))
conv = utilities.makeGetLngLat(metadata)
for pixel in nyx:
latit, longit = conv(pixel[1], START + pixel[0])
sys.stderr.write('%s %d %d %d %d %s\n'%(mask[pixel[0], pixel[1]], START + pixel[0], pixel[1], conv(0, 0)[0], conv(0, 0)[1], score))
#sys.stderr.write('%6.3f %6.3f\n'%(latit, longit))
#sys.stderr.write('%d %i %d %d\n'%(catalog[region]['meanNonSingletonSize'], catalog[region]['singletons'], catalog[region]['sdBlobSize'], catalog[region]['nNonSingletonBlobs']))
outrec = ','.join((image, idcand, '%6.3f'%longit, '%6.3f'%latit, '%i'%(START + pixel[0]), '%i'%(pixel[1]), score, '%10f'%float(catalog[region]['meanBlobSize'])))
binaryhadoop.emit(sys.stdout, key, outrec, encoding = binaryhadoop.TYPEDBYTES_JSON)
if sum(overlaprows & overlapcols) > 0:
sys.stderr.write('%s\n'%repr(saveSelect0))
sys.stderr.write('%s\n'%repr(eval(saveSelect0)))
sys.stderr.write('%s\n'%repr(catalog[region]))
sys.stderr.write('tested with %i %i %i %i\n'%(trueRowsStart, nTrueRows, trueColsStart, nTrueCols))
sys.stderr.write('%s\n'%repr(nyx[:,1]))
sys.stderr.write('%s\n'%repr(range(trueRowsStart, trueRowsStart + nTrueRows)))
else:
catalog.pop(region)
merged_blobs = numpy.zeros(sdMask.shape)
for band in regionsBlobbed.keys():
rows = regionsBlobbed[band]["yxCoordinates"][:,0]
cols = regionsBlobbed[band]["yxCoordinates"][:,1]
if band in candregions:
merged_blobs[rows, cols] = band
else:
merged_blobs[rows, cols] = 0
trueNegative = (nTrueRows * nTrueCols) - truePositive
falseNegative = nPixels- taggedPixels - trueNegative
key = str(paramval)
if taggedPixels > 0:
fp = float(falsePositive)/taggedPixels
else:
fp = 0.0
if (truePositive + trueNegative) > 0:
eff = float(truePositive)/(truePositive + trueNegative)
else:
eff = 0.0
sys.stderr.write('Confusion Matrix - TP: %d FP: %d TN: %d FN: %d Eff: %5.3f FPfrac:%5.3f \n'%(truePositive, falsePositive, trueNegative, falseNegative, eff, fp))
val = '%d, %d, %d, %d, %5.3f, %5.3f'%(truePositive, falsePositive, trueNegative, falseNegative, eff, fp)
if config.blobspectra.KNOWNTRUTH:
outrec = int(truePositive), int(falsePositive), int(trueNegative), int(falseNegative), float(eff), float(fp)
binaryhadoop.emit(sys.stdout, key, outrec, encoding = binaryhadoop.TYPEDBYTES_JSON)
def doEverything(metadata, mask, originalBlock, numSetColors, numBands, bandToIndexLookup):
globalStart = time.time()
if numSetColors < numBands:
heartbeat("Image should have {} bands, but only {} were set\n".format(numBands, numSetColors))
return
if cameraName != "ALI":
heartbeat("Reducing its dimensionality to 26\n")
windowsOfBandsToTake = range(0, 27) + range(43, 46) + range(58, 67) + range(77, 92) + range(115, 139)
reducedBlock = originalBlock[windowsOfBandsToTake,:,:]
bandsToTake = []
reducedBlock2 = numpy.zeros((reducedBlock.shape[0]/3, reducedBlock.shape[1], reducedBlock.shape[2]), dtype=numpy.double)
for i in xrange(reducedBlock2.shape[0]):
bandsToTake.append(windowsOfBandsToTake[3*i + 1])
reducedBlock2[i] = reducedBlock[3*i:3*(i+1),:,:].mean(axis=0)
del reducedBlock
else:
bandsToTake = numpy.argsort(metadata["bandNames"])
reducedBlock2 = originalBlock
heartbeat("Improving the mask\n")
betterMask = mask > 0
for i in xrange(reducedBlock2.shape[0]):
numpy.logical_and(betterMask, reducedBlock2[i,:,:] > 0.0, betterMask)
del mask
shrinkmask = betterMask > 0
for roll in -2, -1, 1, 2:
for axis in 0, 1:
numpy.logical_and(shrinkmask, numpy.roll(betterMask, roll, axis=axis) > 0, shrinkmask)
heartbeat("Taking logarithm\n")
oldsettings = numpy.seterr(divide="ignore", invalid="ignore")
block = numpy.log(reducedBlock2)
numpy.seterr(**oldsettings)
heartbeat("Reducing image to a bag of pixels\n")
bag = block.view()
bag.shape = (block.shape[0], block.shape[1] * block.shape[2])
del block
bagMask = betterMask.view()
bagMask.shape = (originalBlock.shape[1] * originalBlock.shape[2])
bag = bag[:, bagMask]
del bagMask
projectionMatrix = numpy.matrix([[1 if j == i else -1 if j == i + 1 else 0 for j in xrange(reducedBlock2.shape[0])] for i in xrange(reducedBlock2.shape[0] - 1)])
projectionInverse = projectionMatrix.I
heartbeat("Projecting the bag onto the color-only basis\n")
projected = numpy.array(numpy.dot(projectionMatrix, bag))
del bag
heartbeat("Casting the projected bag onto the image shape\n")
projectedBlock = numpy.empty((reducedBlock2.shape[0] - 1, reducedBlock2.shape[1], reducedBlock2.shape[2]), dtype=numpy.double)
for i in xrange(reducedBlock2.shape[0] - 1):
projectedBlock[i,betterMask] = projected[i,:]
heartbeat("Detecting edges\n")
# 5x5 Kroon without integer-rounding: http://www.k-zone.nl/Kroon_DerivativePaper.pdf
Gx = numpy.array([[ 0.0007, 0.0052, 0.0370, 0.0052, 0.0007],
[ 0.0037, 0.1187, 0.2589, 0.1187, 0.0037],
[ 0.0, 0.0, 0.0, 0.0, 0.0],
[-0.0037, -0.1187, -0.2589, -0.1187, -0.0037],
[-0.0007, -0.0052, -0.0370, -0.0052, -0.0007]])
Gy = Gx.T
startTime = time.time()
convBlock = numpy.zeros((projectedBlock.shape[1], projectedBlock.shape[2]), numpy.double)
for index in xrange(projectedBlock.shape[0]):
heartbeat(" {} {} {}\n".format(index, time.time() - startTime, convBlock.max()))
convGx2 = numpy.power(convolve(projectedBlock[index,:,:], Gx)[2:projectedBlock.shape[1]+2, 2:projectedBlock.shape[2]+2], 2)
convGy2 = numpy.power(convolve(projectedBlock[index,:,:], Gy)[2:projectedBlock.shape[1]+2, 2:projectedBlock.shape[2]+2], 2)
convBlock = convBlock + convGx2
convBlock = convBlock + convGy2
convBlock = numpy.sqrt(convBlock)
heartbeat("Edges took {} seconds to detect\n".format(time.time() - startTime))
heartbeat("Optimizing GMM\n")
numGMMcomponents = 20
startTime = time.time()
if projected.shape[1] < 10 * numGMMcomponents or projected.shape[1] < 10 * projected.shape[0]:
heartbeat("There are only {} points; skipping (number of GMM components is {} and number of dimensions in the space is {})\n".format(projected.shape[1], numGMMcomponents, projected.shape[0]))
return
attempts = 0
done = False
while not done:
try:
if 10000 < projected.shape[1]:
randomSelection = projected[:,random.sample(xrange(projected.shape[1]), 10000)]
model = MoG(randomSelection, numGMMcomponents)
model.em(10)
heartbeat(" time for first pass: {} seconds\n".format(time.time() - startTime))
randomSelection = projected[:,random.sample(xrange(projected.shape[1]), 10000)]
model = MoG(randomSelection, numGMMcomponents, means=model.means, covs=model.covs, mixprops=model.mixprops)
model.em(10)
heartbeat(" time for second pass: {} seconds\n".format(time.time() - startTime))
randomSelection = projected[:,random.sample(xrange(projected.shape[1]), 10000)]
model = MoG(randomSelection, numGMMcomponents, means=model.means, covs=model.covs, mixprops=model.mixprops)
model.em(10)
heartbeat(" time for third pass: {} seconds\n".format(time.time() - startTime))
model = MoG(projected, numGMMcomponents, means=model.means, covs=model.covs, mixprops=model.mixprops)
model.em(5)
done = True
else:
heartbeat(" skipping three-pass subfit because the dataset is small\n")
model = MoG(projected, numGMMcomponents)
model.em(5)
done = True
except numpy.linalg.linalg.LinAlgError:
attempts += 1
if attempts > 4:
heartbeat(" could not fit in 4 attempts; giving up\n")
return
heartbeat("GMM took {} seconds to optimize\n".format(time.time() - startTime))
heartbeat("Scoring all pixels with GMM\n")
startTime = time.time()
scores = logsumexp(model.compute_posteriors(projected, reinit=True, normalize=False, logscale=True), 0)
scoresBlock = numpy.zeros((reducedBlock2.shape[1], reducedBlock2.shape[2]), dtype=numpy.double)
scoresBlock[betterMask] = scores
del scores
themin, themax = numpy.percentile(convBlock[shrinkmask], [0.5, 99.5])
convNorm = (convBlock[shrinkmask] - themin)/(themax - themin)
convBlockNorm = (convBlock - themin)/(themax - themin)
themin, themax = numpy.percentile(scoresBlock[shrinkmask], [0.5, 99.5])
scoresNorm = 1.0 - (scoresBlock[shrinkmask] - themin)/(themax - themin)
scoresBlockNorm = 1.0 - (scoresBlock - themin)/(themax - themin)
rawscoresmin, rawscoresmax = themin, themax
del scoresBlock
del scoresNorm
selection = numpy.logical_and(scoresBlockNorm > 1.0, shrinkmask)
indexes = zip(*numpy.nonzero(selection))
scoredBag = numpy.argmax(model.compute_posteriors(projected, reinit=True), axis=0)
heartbeat("GMM took {} seconds to score all pixels (a few times in different ways)\n".format(time.time() - startTime))
heartbeat("Blurring scores for bucket-fill to spread better\n")
startTime = time.time()
spot = numpy.array([[math.exp(-((i - 2)**2 + (j - 2)**2)/2.0/1.0**2) for i in xrange(5)] for j in xrange(5)])
spot = spot / spot.sum()
blurScores = convolve(scoresBlockNorm[:,:], spot)[2:scoresBlockNorm.shape[0]+2, 2:scoresBlockNorm.shape[1]+2]
heartbeat("Time to blur image: {} seconds\n".format(time.time() - startTime))
heartbeat("Building the KD-tree\n")
startTime = time.time()
kdtree = KDTree(projected)
dynamicRange = (lambda x: x[1] - x[0])(numpy.percentile(projected, [1, 99]))
heartbeat("KD-tree took {} seconds to build\n".format(time.time() - startTime))
heartbeat("Performing bucket-fill searches\n")
startTime = time.time()
clumps = set()
assigned = numpy.empty((projectedBlock.shape[1], projectedBlock.shape[2]), dtype=numpy.dtype(object))
considered = numpy.zeros((projectedBlock.shape[1], projectedBlock.shape[2]), dtype=numpy.dtype(bool))
Clump.assigned = assigned
Clump.projectedBlock = projectedBlock
Clump.projectionInverse = projectionInverse
Clump.metadata = metadata
Clump.kdtree = kdtree
Clump.bandsToTake = bandsToTake
Clump.projected = projected
Clump.convBlockNorm = convBlockNorm
Clump.model = model
Clump.rawscoresmin = rawscoresmin
Clump.rawscoresmax = rawscoresmax
Clump.dynamicRange = dynamicRange
Clump.blurBlock = None
Clump.scoresBlockNorm = scoresBlockNorm
Clump.blurScores = blurScores
for index, (x, y) in enumerate(indexes):
if index % 100 == 0:
heartbeat(" {} {}\n".format(float(index)/len(indexes), time.time() - startTime))
queue = [(x, y)]
clump = Clump((x, y))
while len(queue) > 0:
i, j = queue.pop()
if i >= 0 and j >= 0 and i < shrinkmask.shape[0] and j < shrinkmask.shape[1] and shrinkmask[i, j]:
if not considered[i, j]:
if blurScores[i, j] > 1.0:
clump.add((i, j))
assigned[i, j] = clump
if clump.size() > Clump.maxSize:
heartbeat(" clump is too big!\n")
break
newQueue = []
for ii in -1, 0, 1:
for jj in -1, 0, 1:
if ii != jj:
newQueue.append((i + ii, j + jj))
queue = newQueue + queue
elif assigned[i, j] is not None and assigned[i, j] != clump:
heartbeat(" merging clumps\n")
clump = assigned[i, j].mergeIn(clump)
if clump.size() > Clump.maxSize:
heartbeat(" ... into one that was too big!\n")
break
considered[i, j] = True
if not clump.isEmpty() and clump.size() <= Clump.maxSize:
heartbeat(" new clump with size {}\n".format(clump.size()))
clumps.add(clump)
heartbeat("bucket-fill search took {} seconds\n".format(time.time() - startTime))
heartbeat("Calculating attributes of the image and clumps\n")
startTime = time.time()
def gmmSpectrum(index):
unnormalized = numpy.exp(numpy.array(numpy.dot(projectionInverse, model.means[:,index].T))[0])
return dict(zip(list(numpy.array(metadata["bandNames"])[bandsToTake]), unnormalized / unnormalized.sum()))
def fullSpectrumAt(indexes):
unnormalized = numpy.zeros(originalBlock.shape[0], dtype=numpy.double)
for i, j in indexes:
unnormalized += originalBlock[:,i,j]
return dict(zip(metadata["bandNames"], unnormalized / len(indexes)))
output = {"metadata": metadata}
getLngLat = utilities.makeGetLngLat(metadata)
getMeters = utilities.makeGetMeters(metadata)
wavelengths = [metadata["bandWavelength"][x] for x in numpy.array(metadata["bandNames"])[bandsToTake]]
clusterNumber = 0
for clump in clumps:
heartbeat(" do clump {}\n".format(clump.indexes))
border1 = clump.border()
borderPoint1 = numpy.zeros(projectedBlock.shape[0], dtype=numpy.double)
for i, j in clump.indexes:
borderPoint1 = borderPoint1 + projectedBlock[:, i, j]
borderPoint1 = borderPoint1 / len(border1)
border2 = clump.border(list(clump.indexes) + list(border1))
borderPoint2 = numpy.zeros(projectedBlock.shape[0], dtype=numpy.double)
for i, j in clump.indexes:
borderPoint2 = borderPoint2 + projectedBlock[:, i, j]
borderPoint2 = borderPoint2 / len(border2)
numSeeds = len(clump.seeds)
numPixels = clump.size()
seeds = sorted((int(i), int(j)) for i, j in clump.seeds)
indexes = sorted((int(i), int(j)) for i, j in clump.indexes)
border1 = sorted((int(i), int(j)) for i, j in border1)
border2 = sorted((int(i), int(j)) for i, j in border2)
mean = list(clump.mean())
meanSeeds = list(clump.meanSeeds())
stdev = clump.stdev()
specMean = clump.spectrumOf(clump.mean())
specMeanSeeds = clump.spectrumOf(clump.meanSeeds())
borderSpec1 = clump.spectrumOf(borderPoint1)
borderSpec2 = clump.spectrumOf(borderPoint2)
edgeScore1 = clump.edginess(border1)
edgeScore2 = clump.edginess(border2)
gmmScoreMean = clump.gmmscoreOf(clump.mean())
gmmScoreMeanSeeds = clump.gmmscoreOf(clump.meanSeeds())
fullSpectrum = fullSpectrumAt(clump.indexes)
fullSpectrumSeeds = fullSpectrumAt(clump.seeds)
fullSpectrumBorder1 = fullSpectrumAt(border1)
fullSpectrumBorder2 = fullSpectrumAt(border2)
r200 = float(clump.density(clump.meanSeeds(), 0.200))
r500 = float(clump.density(clump.meanSeeds(), 0.500))
if r200 > 0.0 and r500 > 0.0 and gmmScoreMeanSeeds > 1.42 and gmmScoreMeanSeeds - gmmScoreMean > 0.12 and math.log10(r200) < -2.78 and math.log10(r500) < -1.30 and stdev > 0.067 and edgeScore2 < 0.61:
clusterName = "cluster_{}".format(clusterNumber)
output[clusterName] = {}
clusterNumber += 1
output[clusterName]["contours95"] = [{}]
c95 = output[clusterName]["contours95"][0]
x0 = numpy.mean([x for x, y in border2])
y0 = numpy.mean([y for x, y in border2])
order = numpy.argsort([math.atan2(y - y0, x - x0) for x, y in border2])
c95["rowcolpolygon"] = [(int(x), int(y)) for x, y in numpy.array(border2)[order]]
c95["lnglatpolygon"] = [getLngLat(x, y) for x, y in numpy.array(border2)[order]]
c95["centroidInLngLat"] = getLngLat(x0, y0)
c95["areaInPixels"] = numPixels
pixelLength = abs(getMeters(*getLngLat(x0 + 0.5, y0))[0] - getMeters(*getLngLat(x0 - 0.5, y0))[0])
pixelHeight = abs(getMeters(*getLngLat(x0, y0 + 0.5))[1] - getMeters(*getLngLat(x0, y0 - 0.5))[1])
c95["areaInMeters"] = numPixels * pixelLength * pixelHeight
c95["circumferenceInMeters"] = 0.5*(pixelLength + pixelHeight) * len(border1)
rawGMMscore = float(logsumexp(model.compute_posteriors(numpy.array([clump.meanSeeds()]).T, reinit=True, normalize=False, logscale=True), 0)[0])
rawKNNscore = float(r500)
c95["score"] = rawGMMscore, rawKNNscore
c95["other"] = {
"numSeeds": numSeeds,
"numPixels": numPixels,
"seeds": seeds,
"indexes": indexes,
"border1": border1,
"border2": border2,
"mean": mean,
"meanSeeds": meanSeeds,
"stdev": stdev,
"specMean": specMean,
"specMeanSeeds": specMeanSeeds,
"borderSpec1": borderSpec1,
"borderSpec2": borderSpec2,
"edgeScore1": edgeScore1,
"edgeScore2": edgeScore2,
"gmmScoreMean": gmmScoreMean,
"gmmScoreMeanSeeds": gmmScoreMeanSeeds,
"fullSpectrum": fullSpectrum,
"fullSpectrumSeeds": fullSpectrumSeeds,
"fullSpectrumBorder1": fullSpectrumBorder1,
"fullSpectrumBorder2": fullSpectrumBorder2,
"r200": r200,
"r500": r500}
binaryhadoop.emit(sys.stdout, metadata["originalDirName"], output, encoding=binaryhadoop.TYPEDBYTES_JSON)
heartbeat("Calculating attributes took {} seconds\n".format(time.time() - startTime))
totalTime = time.time() - globalStart
heartbeat("Time to do everything: {} sec, which is {} min\n".format(totalTime, totalTime/60.0))
