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ShapeStatistics.py
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ShapeStatistics.py
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#Purpose: To implement a suite of 3D shape statistics and to use them for point
#cloud classification
#TODO: Fill in all of this code for group assignment 2
import sys
sys.path.append("S3DGLPy")
from Primitives3D import *
from PolyMesh import *
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist, squareform
POINTCLOUD_CLASSES = ['biplane', 'desk_chair', 'dining_chair', 'fighter_jet', 'fish', 'flying_bird', 'guitar', 'handgun', 'head', 'helicopter', 'human', 'human_arms_out', 'potted_plant', 'race_car', 'sedan', 'shelves', 'ship', 'sword', 'table', 'vase']
NUM_PER_CLASS = 10
#########################################################
## UTILITY FUNCTIONS ##
#########################################################
#Purpose: Export a sampled point cloud into the JS interactive point cloud viewer
#Inputs: Ps (3 x N array of points), Ns (3 x N array of estimated normals),
#filename: Output filename
def exportPointCloud(Ps, Ns, filename):
N = Ps.shape[1]
fout = open(filename, "w")
fmtstr = "%g" + " %g"*5 + "\n"
for i in range(N):
fields = np.zeros(6)
fields[0:3] = Ps[:, i]
fields[3:] = Ns[:, i]
fout.write(fmtstr%tuple(fields.flatten().tolist()))
fout.close()
#Purpose: To sample a point cloud, center it on its centroid, and
#then scale all of the points so that the RMS distance to the origin is 1
def samplePointCloud(mesh, N):
(Ps, Ns) = mesh.randomlySamplePoints(N)
##TODO: Center the point cloud on its centroid and normalize
#by its root mean square distance to the origin. Note that this
#does not change the normals at all, only the points, since it's a
#uniform scale
# Center the randomly distributed point cloud on its centroid.
c = np.asmatrix([list(np.mean(Ps, axis=1))]).T
Ps = Ps - c
# Calculate scale
squares = list(np.einsum("ji,ji->i", Ps, Ps))
sums = np.sum(squares)
scale = math.sqrt(sums/len(squares))
# Apply Scale
Ps = Ps / scale
return (Ps, Ns)
#Purpose: To sample the unit sphere as evenly as possible. The higher
#res is, the more samples are taken on the sphere (in an exponential
#relationship with res). By default, samples 66 points
def getSphereSamples(res = 2):
m = getSphereMesh(1, res)
return m.VPos.T
#Purpose: To compute PCA on a point cloud
#Inputs: X (3 x N array representing a point cloud)
def doPCA(X):
##TODO: Fill this in for a useful helper function
# Compute covariance matrix A = X.T * X
A = X.dot(X.T)
# Compute eigenvalues/eigenvectors of A, sorted in decreasing order
eigenValues, eigenVectors = np.linalg.eig(A)
idx = eigenValues.argsort()[::-1]
eigenValues = eigenValues[idx]
eigenVectors = eigenVectors[:,idx]
return (eigenValues, eigenVectors)
#########################################################
## SHAPE DESCRIPTORS ##
#########################################################
#Purpose: To compute a shape histogram, counting points
#distributed in concentric spherical shells centered at the origin
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals) (not needed here
#but passed along for consistency)
#NShells (number of shells), RMax (maximum radius)
#Returns: hist (histogram of length NShells)
def getShapeHistogram(Ps, Ns, NShells, RMax):
hist = np.zeros(NShells) #[0,0,0,0..NShells]
bins = np.linspace(0, RMax, num = NShells+1) # np.linspace(2.0, 3.0, num=5) // array([ 2. , 2.25, 2.5 , 2.75, 3. ])
# Magnitudes / Euclidean Distance
mags = np.sqrt(np.einsum("ji,ji->i", Ps, Ps))
# Put em in the buckets / Return
return np.histogram(mags, bins=bins, density=True)[0]
#Purpose: To create shape histogram with concentric spherical shells and
#sectors within each shell, sorted in decreasing order of number of points
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals) (not needed here
#but passed along for consistency), NShells (number of shells),
#RMax (maximum radius), SPoints: A 3 x S array of points sampled evenly on
#the unit sphere (get these with the function "getSphereSamples")
def getShapeShellHistogram(Ps, Ns, NShells, RMax, SPoints):
NSectors = SPoints.shape[1] #A number of sectors equal to the number of
#points sampled on the sphere
#Create a 2D histogram that is NShells x NSectors
hist = np.zeros((NShells, NSectors))
binsShells = np.linspace(0, RMax, num = NShells+1) # np.linspace(2.0, 3.0, num=5) // array([ 2. , 2.25, 2.5 , 2.75, 3. ])
binsSectors = np.linspace(0, NSectors, num = NSectors+1)
##TODO: Finish this; fill in hist, then sort sectors in descending order
# Find which ring its in
for i in xrange(NShells):
# All points in a particular bin
p = Ps.T # N x 3
ptsInShell = p[(np.linalg.norm(p, axis=1) >= binsShells[i]) & (binsShells[i+1] > np.linalg.norm(p, axis=1)) ].T #Nx3 - #3xN
# Find each point's sector
dots = ptsInShell.T.dot(SPoints) # Nx66
# Indirectly sorts smallest to largest and gives a column vector containing the index of the ith point's sector
a = np.argsort(dots,axis=1)[:,0]
shellHist = np.sort(np.histogram(a, bins=binsSectors)[0])
hist[i,: ] = shellHist
hist = hist.flatten()
dot = hist.dot(hist.T)
return hist / np.sqrt(dot) #Flatten the 2D histogram to a 1D array
#Purpose: To create shape histogram with concentric spherical shells and to
#compute the PCA eigenvalues in each shell
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals) (not needed here
#but passed along for consistency), NShells (number of shells),
#RMax (maximum radius), sphereRes: An integer specifying points on the sphere
#to be used to cluster shells
def getShapeHistogramPCA(Ps, Ns, NShells, RMax):
#Create a 2D histogram, with 3 eigenvalues for each shell
hist = np.zeros((NShells, 3))
##TODO: Finish this; fill in hist
bins = np.linspace(0, RMax, num = NShells+1) # np.linspace(2.0, 3.0, num=5) // array([ 2. , 2.25, 2.5 , 2.75, 3. ])
# Magnitudes / Euclidean Distance
mags = np.sqrt(np.einsum("ji,ji->i", Ps, Ps))
for i in xrange(NShells):
p = Ps.T
ptsInShell = p[(np.linalg.norm(p, axis=1) >= bins[i]) & (bins[i+1] > np.linalg.norm(p, axis=1)) & (np.linalg.norm(p, axis=1) <= RMax) ].T
eigVal,eigVec = doPCA(ptsInShell)
hist[i,:] = eigVal
# Put em in the buckets / Return
return np.asarray(hist.flatten())
#return hist #Flatten the 2D histogram to a 1D array
#Purpose: To create shape histogram of the pairwise Euclidean distances between
#randomly sampled points in the point cloud
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals) (not needed here
#but passed along for consistency), DMax (Maximum distance to consider),
#NBins (number of histogram bins), NSamples (number of pairs of points sample
#to compute distances)
def getD2Histogram(Ps, Ns, DMax, NBins, NSamples):
##TODO: Finish this; fill in hist
bins = np.linspace(0, DMax, num = NBins+1) # np.linspace(2.0, 3.0, num=5) // array([ 2. , 2.25, 2.5 , 2.75, 3. ])
rand = np.random.random_integers(Ps.shape[1]-1, size=(NSamples,2))
r0 = rand[:,0]
r1 = rand[:,1]
Ps0 = Ps[:,r0] #np.take(Ps.T,r0)
Ps1 = Ps[:,r1] #np.take(Ps.T,r1)
vecs = Ps0 - Ps1
mags = np.sqrt(np.einsum("ji,ji->i", vecs, vecs))
hist = np.histogram(mags, bins=bins, density=True)[0] #np.histogram(mags, bins=bins)[0]
return hist
#Purpose: To create shape histogram of the angles between randomly sampled
#triples of points
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals) (not needed here
#but passed along for consistency), NBins (number of histogram bins),
#NSamples (number of triples of points sample to compute angles)
def getA3Histogram(Ps, Ns, NBins, NSamples):
hist = np.zeros(NBins)
##TODO: Finish this; fill in hist
bins = np.linspace(0, 3.141596, num = NBins+1)
rand = np.random.random_integers(Ps.shape[1]-1, size=(NSamples,3))
r0 = rand[:,0]
r1 = rand[:,1]
r2 = rand[:,2]
Ps0 = Ps[:,r0] #np.take(Ps.T,r0)
Ps1 = Ps[:,r1] #np.take(Ps.T,r1)
Ps2 = Ps[:,r2] #np.take(Ps.T,r1)
#Get appropriate vectos Ps0 -> Ps1 and Ps1 -> Ps2
vec01 = Ps1 - Ps0
vec12 = Ps2 - Ps1
# Normalized Vectors
norms01 = np.linalg.norm(vec01,axis=0)
norms12 = np.linalg.norm(vec12, axis=0)
vec01 = vec01.astype('float') / norms01
vec12 = vec12.astype('float') / norms12
### theta = a dot b / ||a||||b|| ###
# Dot product
num = np.einsum("ji,ji->i", vec01, vec12) # 1xN array
# Magnitudes of vectors
mag01 = np.sqrt(np.einsum("ji,ji->i", vec01, vec01))
mag12 = np.sqrt(np.einsum("ji,ji->i", vec12, vec12))
den = mag01 * mag12 #1xN
CosThetas = num / den
ArcCosT = np.arccos(CosThetas)
return np.histogram(ArcCosT, bins=bins, density=True)[0]
#Purpose: To create the Extended Gaussian Image by binning normals to
#sphere directions after rotating the point cloud to align with its principal axes
#Inputs: Ps (3 x N point cloud) (use to compute PCA), Ns (3 x N array of normals),
#SPoints: A 3 x S array of points sampled evenly on the unit sphere used to
#bin the normals
def getEGIHistogram(Ps, Ns, SPoints):
S = SPoints.shape[1]
hist = np.zeros(S)
# TODO: Finish this; fill in hist
# Project all points on PCA Axis
eigVal, eigVec = doPCA(Ps)
axis = eigVec[:,-1]
projPS = ((Ps.T.dot(axis)) / axis.T.dot(axis)) * axis
return hist
#Purpose: To create an image which stores the amalgamation of rotating
#a bunch of planes around the largest principal axis of a point cloud and
#projecting the points on the minor axes onto the image.
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals, not needed here),
#NAngles: The number of angles between 0 and 2*pi through which to rotate
#the plane, Extent: The extent of each axis, Dim: The number of pixels along
#each minor axis
def getSpinImage(Ps, Ns, NAngles, Extent, Dim):
#Create an image
hist = np.zeros((Dim, Dim))
# TODO: Finish this
# Project all points on PCA Axis
bins = np.linspace(0, Extent, num = Dim+1)
eigVal, eigVec = doPCA(Ps)
pAxis = eigVec[:,0]
mAxis1 = eigVec[:,1]
mAxis2 = eigVec[:,2]
projPs = np.asarray((Ps.T.dot(pAxis)) / pAxis.T.dot(pAxis))[:,0]
angle = 2 * math.pi/ NAngles
for i in xrange(NAngles):
ang = angle * i
vec = mAxis1 * math.cos(ang) + mAxis2 * math.sin(ang)
# plane is now defined by pAxis and vec
projVec = np.asarray((Ps.T.dot(vec)) / vec.T.dot(vec))[:,0]
tmpHist = np.histogram2d(projVec,projPs,bins=(bins,bins),normed=True)[0]
hist += tmpHist / NAngles
return hist.flatten()
def getSpinImageFast(Ps, Ns, NAngles, Extent, Dim):
#Create an image
hist = np.zeros((Dim, Dim))
# TODO: Finish this
# Project all points on PCA Axis
print Ps.shape
bins = np.linspace(0, Extent, num = Dim+1)
eigVal, eigVec = doPCA(Ps)
pAxis = eigVec[:,0]
projPs = np.asarray((Ps.T.dot(pAxis)) / pAxis.T.dot(pAxis))[:,0]
perpProj = Ps - pAxis.dot(pAxis.T).dot(Ps)
mags = np.sqrt(np.einsum("ji,ji->i", perpProj, perpProj))
heatmap, xedges, yedges = np.histogram2d(mags,projPs,bins=(bins,bins),normed=True)
extent = [xedges[0], xedges[-1], yedges[0], yedges[-1]]
print heatmap
plt.clf()
plt.imshow(heatmap, extent=extent)
plt.show()
f = open( 'file.py', 'w' )
f.write( 'dict = ' + repr(heatmap) + '\n' )
f.close()
return np.histogram2d(mags,projPs,bins=(bins,bins),normed=True)[0].flatten()
#Purpose: To create a histogram of spherical harmonic magnitudes in concentric
#spheres after rasterizing the point cloud to a voxel grid
#Inputs: Ps (3 x N point cloud), Ns (3 x N array of normals, not used here),
#VoxelRes: The number of voxels along each axis (for instance, if 30, then rasterize
#to 30x30x30 voxels), Extent: The number of units along each axis (if 2, then
#rasterize in the box [-1, 1] x [-1, 1] x [-1, 1]), NHarmonics: The number of spherical
#harmonics, NSpheres, the number of concentric spheres to take
def getSphericalHarmonicMagnitudes(Ps, Ns, VoxelRes, Extent, NHarmonics, NSpheres):
hist = np.zeros((NSpheres, NHarmonics))
#TODO: Finish this
return hist.flatten()
#Purpose: Utility function for wrapping around the statistics functions.
#Inputs: PointClouds (a python list of N point clouds), Normals (a python
#list of the N corresponding normals), histFunction (a function
#handle for one of the above functions), *args (addditional arguments
#that the descriptor function needs)
#Returns: AllHists (A KxN matrix of all descriptors, where K is the length
#of each descriptor)
def makeAllHistograms(PointClouds, Normals, histFunction, *args):
N = len(PointClouds)
#Call on first mesh to figure out the dimensions of the histogram
h0 = histFunction(PointClouds[0], Normals[0], *args)
K = h0.size
AllHists = np.zeros((K, N))
AllHists[:, 0] = h0
for i in range(1, N):
print "Computing histogram %i of %i..."%(i+1, N)
AllHists[:, i] = histFunction(PointClouds[i], Normals[i], *args)
return AllHists
#########################################################
## HISTOGRAM COMPARISONS ##
#########################################################
#Purpose: To compute the euclidean distance between a set
#of histograms
#Inputs: AllHists (K x N matrix of histograms, where K is the length
#of each histogram and N is the number of point clouds)
#Returns: D (An N x N matrix, where the ij entry is the Euclidean
#distance between the histogram for point cloud i and point cloud j)
def compareHistsEuclidean(AllHists):
N = AllHists.shape[1]
D = np.zeros((N, N))
#TODO: Finish this, fill in D
dotX = np.sum(AllHists**2, 0)[:, None]
dotY = np.sum(AllHists**2, 0)[None, :]
D = dotX + dotY - 2*AllHists.T.dot(AllHists)
D[D < 0] = 0
return np.sqrt(D)
#Purpose: To compute the cosine distance between a set
#of histograms
#Inputs: AllHists (K x N matrix of histograms, where K is the length
#of each histogram and N is the number of point clouds)
#Returns: D (An N x N matrix, where the ij entry is the cosine
#distance between the histogram for point cloud i and point cloud j)
def compareHistsCosine(AllHists):
N = AllHists.shape[1]
D = np.zeros((N, N))
#TODO: Finish this, fill in D
num = AllHists.T.dot(AllHists)
mag = np.asmatrix([list(np.sqrt(np.einsum("ji,ji->i", AllHists, AllHists)))])
den = mag.T.dot(mag)
return np.arccos(num/den)
#Purpose: To compute the chi squared distance between a set
#of histograms
#Inputs: AllHists (K x N matrix of histograms, where K is the length
#of each histogram and N is the number of point clouds)
#Returns: D (An N x N matrix, where the ij entry is the chi squared
#distance between the histogram for point cloud i and point cloud j)
def compareHistsChiSquared(AllHists):
shape = (AllHists.shape[1], AllHists.shape[1])
def chiSquaredDist(a,b):
h1 = AllHists[:,a]
h2 = AllHists[:,b]
f = np.vectorize(indvChiSquared)
return np.sum(f(h1.flatten(), h2.flatten()), dtype=float)
def indvChiSquared(a, b):
n = 2 * np.square(a - b)
d = a + b
if n ==0:
return 0
return (n / float(d))
f = np.vectorize(chiSquaredDist)
x = np.fromfunction(lambda i, j: f(i, j), shape, dtype=int)
return x
#Purpose: To compute the 1D Earth mover's distance between a set
#of histograms (note that this only makes sense for 1D histograms)
#Inputs: AllHists (K x N matrix of histograms, where K is the length
#of each histogram and N is the number of point clouds)
#Returns: D (An N x N matrix, where the ij entry is the earth mover's
#distance between the histogram for point cloud i and point cloud j)
def compareHistsEMD1D(AllHists):
N = AllHists.shape[1]
D = np.zeros((N, N))
#TODO: Finish this, fill in D
cdfs = np.sum(AllHists, axis=0)
return D
#########################################################
## CLASSIFICATION CONTEST ##
#########################################################
#Purpose: To implement your own custom distance matrix between all point
#clouds for the point cloud clasification contest
#Inputs: PointClouds, an array of point cloud matrices, Normals: an array
#of normal matrices
#Returns: D: A N x N matrix of distances between point clouds based
#on your metric, where Dij is the distnace between point cloud i and point cloud j
def getMyShapeDistances(PointClouds, Normals):
#TODO: Finish this
#This is just an example, but you should experiment to find which features
#work the best, and possibly come up with a weighted combination of
#different features
HistsD2 = makeAllHistograms(PointClouds, Normals, getD2Histogram, 3.0, 30, 100000)
DEuc = compareHistsEuclidean(HistsD2)
return DEuc
#########################################################
## EVALUATION ##
#########################################################
#Purpose: To return an average precision recall graph for a collection of
#shapes given the similarity scores of all pairs of histograms.
#Inputs: D (An N x N matrix, where the ij entry is the earth mover's distance
#between the histogram for point cloud i and point cloud j). It is assumed
#that the point clouds are presented in contiguous chunks of classes, and that
#there are "NPerClass" point clouds per each class (for the dataset provided
#there are 10 per class so that's the default argument). So the program should
#return a precision recall graph that has 9 elements
#Returns PR, an (NPerClass-1) length array of average precision values for all
#recalls
def getPrecisionRecall(D, NPerClass = 10):
PR = np.zeros(NPerClass-1) # [0,0,0...]
#TODO: Finish this, compute average precision recall graph using all point clouds as queries
# This is the average precision recall for every shape not just 1 shape
# Sort rows of D
s = np.argsort(D,axis=1)
# Walk through
for i in xrange(s.shape[1]): # On every row we calculate the precision and recall
iClass = i/NPerClass # the row indicates the shape
numP = 0
denP = 0
numR = 0
for j in xrange(s.shape[1]):
jClass = s[i, j]/NPerClass
if i != s[i, j]:
if iClass == jClass:
numR = numR + 1
numP = numP + 1
denP = denP + 1
PR[numR-1] += (1.0*numP/denP) * (1.0/D.shape[1])
if numR == NPerClass - 1:
break
else:
denP += 1
return PR
#########################################################
## MAIN TESTS ##
#########################################################
if __name__ == '__main__':
NRandSamples = 10000 #You can tweak this number
np.random.seed(100) #For repeatable results randomly sampling
#Load in and sample all meshes
PointClouds = []
Normals = []
'''for i in range(len(POINTCLOUD_CLASSES)):
print "LOADING CLASS %i of %i..."%(i, len(POINTCLOUD_CLASSES))
PCClass = []
for j in range(NUM_PER_CLASS):
m = PolyMesh()
filename = "models_off/%s%i.off"%(POINTCLOUD_CLASSES[i], j)
print "Loading ", filename
m.loadOffFileExternal(filename)
(Ps, Ns) = samplePointCloud(m, NRandSamples)
PointClouds.append(Ps)
Normals.append(Ps)'''
Ps = np.load("../data_0.npy")
print Ps.shape
Ps = Ps[:,:100]
print Ps.shape
Ps = Ps[1:4,:]
print Ps.shape
'''m = PolyMesh()
filename = "models_off/biplane0.off"
print "Loading ", filename
m.loadOffFileExternal(filename)
(Ps, Ns) = samplePointCloud(m, 10000)
# Center the randomly distributed point cloud on its centroid'''
#Ps = Ps.T
c = np.asmatrix([list(np.mean(Ps, axis=1))]).T
print c
Ps = Ps - c
# Calculate scale
squares = list(np.einsum("ji,ji->i", Ps, Ps))
sums = np.sum(squares)
scale = math.sqrt(sums/len(squares))
# Apply Scale
Ps = Ps / scale
PointClouds.append(Ps)
Normals.append(Ps)
#TODO: Finish this, run experiments. Also in the above code, you might
#just want to load one point cloud and test your histograms on that first
#so you don't have to wait for all point clouds to load when making
#minor tweaks
recalls = np.linspace(1.0/9.0, 1.0, 9)
HistsSpin = makeAllHistograms(PointClouds, Normals, getSpinImageFast,100, 2, 40)
#print "Make historgrams"
# Make All Histograms { Shell, Shell/Sector, Shell/PCA, D2, A3, Spin
'''HistsShape1 = makeAllHistograms(PointClouds, Normals, getShapeHistogram, 1, 5)
HistsShape10 = makeAllHistograms(PointClouds, Normals, getShapeHistogram, 10, 5)
HistsShape20 = makeAllHistograms(PointClouds, Normals, getShapeHistogram, 20, 5)
HistsShape30 = makeAllHistograms(PointClouds, Normals, getShapeHistogram, 30, 5)
HistsShapePCA = makeAllHistograms(PointClouds, Normals, getShapeHistogramPCA, 30, 5)
HistsShapeShell = makeAllHistograms(PointClouds, Normals, getShapeShellHistogram, 30, 5, getSphereSamples(2))
HistsShapePCA = makeAllHistograms(PointClouds, Normals, getShapeHistogramPCA, 30, 5)
HistsD2100 = makeAllHistograms(PointClouds, Normals, getD2Histogram, 3.0, 30, 100)
HistsD21000 = makeAllHistograms(PointClouds, Normals, getD2Histogram, 3.0, 30, 1000)
HistsD210000 = makeAllHistograms(PointClouds, Normals, getD2Histogram, 3.0, 30, 10000)
HistsD2100000 = makeAllHistograms(PointClouds, Normals, getD2Histogram, 3.0, 30, 100000)
HistsD21000000 = makeAllHistograms(PointClouds, Normals, getD2Histogram, 3.0, 30, 1000000)
HistsA3 = makeAllHistograms(PointClouds, Normals, getA3Histogram, 30, 100000)
HistsSpin = makeAllHistograms(PointClouds, Normals, getSpinImage,100, 2, 40)
HistsSpinFast = makeAllHistograms(PointClouds, Normals, getSpinImageFast,100, 2, 40)
#### Compare Histograms ####
# Shapes
DShape_E1 = compareHistsEuclidean(HistsShape1)
DShape_E10 = compareHistsEuclidean(HistsShape10)
DShape_E20 = compareHistsEuclidean(HistsShape20)
DShape_E30 = compareHistsEuclidean(HistsShape30)
DShape_C = compareHistsCosine(HistsShape30)
DShape_CS = compareHistsChiSquared(HistsShape30)
# Shape / Sector
DShapeShell_E = compareHistsEuclidean(HistsShapeShell)
DShapeShell_C = compareHistsCosine(HistsShapeShell)
DShapeShell_CS = compareHistsCosine(HistsShapeShell)
# Shape / PCA
DShapePCA_E = compareHistsEuclidean(HistsShapePCA)
DShapePCA_C = compareHistsCosine(HistsShapePCA)
DShapePCA_CS = compareHistsCosine(HistsShapePCA)
# D2
DD2_E100 = compareHistsEuclidean(HistsD2100)
DD2_E1000 = compareHistsEuclidean(HistsD21000)
DD2_E10000 = compareHistsEuclidean(HistsD210000)
DD2_E100000 = compareHistsEuclidean(HistsD2100000)
DD2_E1000000 = compareHistsEuclidean(HistsD21000000)
DD2_C = compareHistsCosine(HistsD21000000)
DD2_CS = compareHistsChiSquared(HistsD21000000)
# A3
DA3_E = compareHistsEuclidean(HistsA3)
DA3_C = compareHistsCosine(HistsA3)
DA3_CS = compareHistsChiSquared(HistsA3)
# Spin
DSpin_E = compareHistsEuclidean(HistsSpin)
DSpin_C = compareHistsCosine(HistsSpin)
DSpin_CS = compareHistsChiSquared(HistsSpin)
DSpinF_E = compareHistsEuclidean(HistsSpinFast)
DSpinF_C = compareHistsCosine(HistsSpinFast)
DSpinF_CS = compareHistsChiSquared(HistsSpinFast)
### Precision Recall Graphs
# Shapes
PRShape_E1 = getPrecisionRecall(DShape_E1)
PRShape_E10 = getPrecisionRecall(DShape_E10)
PRShape_E20 = getPrecisionRecall(DShape_E20)
PRShape_E30 = getPrecisionRecall(DShape_E30)
PRShape_C = getPrecisionRecall(DShape_C)
PRShape_CS = getPrecisionRecall(DShape_CS)
# Shape / Sector
PRShapeShell_E = getPrecisionRecall(DShapeShell_E)
PRShapeShell_C = getPrecisionRecall(DShapeShell_C)
PRShapeShell_CS = getPrecisionRecall(DShapeShell_CS)
# Shape / PCA
PRShapePCA_E = getPrecisionRecall(DShapePCA_E)
PRShapePCA_C = getPrecisionRecall(DShapePCA_C)
PRShapePCA_CS = getPrecisionRecall(DShapePCA_CS)
# D2
PRD2_E100 = getPrecisionRecall(DD2_E100)
PRD2_E1000 = getPrecisionRecall(DD2_E1000)
PRD2_E10000 = getPrecisionRecall(DD2_E10000)
PRD2_E100000 = getPrecisionRecall(DD2_E100000)
PRD2_E1000000 = getPrecisionRecall(DD2_E1000000)
PRD2_C = getPrecisionRecall(DD2_C)
PRD2_CS = getPrecisionRecall(DD2_CS)
# A3
PRA3_E = getPrecisionRecall(DA3_E)
PRA3_C = getPrecisionRecall(DA3_C)
PRA3_CS = getPrecisionRecall(DA3_CS)
# Spin
PRSpin_E = getPrecisionRecall(DSpin_E)
PRSpin_C = getPrecisionRecall(DSpin_C)
PRSpin_CS = getPrecisionRecall(DSpin_CS)
PRSpinF_E = getPrecisionRecall(DSpinF_E)
PRSpinF_C = getPrecisionRecall(DSpinF_C)
PRSpinF_CS = getPrecisionRecall(DSpinF_CS)
print "Graphing!"
recalls = np.linspace(1.0/9.0, 1.0, 9)
plt.figure(0)
plt.hold(True)
plt.title('Descriptor Comparison by Cosine Distance')
plt.plot(recalls, PRShape_C, 'b',label='Shell')
plt.plot(recalls, PRShapeShell_C, 'g',label='Shell Sector')
plt.plot(recalls, PRShapePCA_C, 'r',label='PCA')
plt.plot(recalls, PRD2_C, 'c',label='D2')
plt.plot(recalls, PRA3_C, 'm',label='A3')
plt.plot(recalls, PRSpin_C, 'y',label='Spin')
plt.plot(recalls, PRSpinF_C, 'k',label='SpinF')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()
plt.figure(1)
plt.hold(True)
plt.title('Descriptor Comparison by Euclidean Distance')
plt.plot(recalls, PRShape_E30, 'k',label='Shell')
plt.plot(recalls, PRShapeShell_E, 'b',label='Shell Sector')
plt.plot(recalls, PRShapePCA_E, 'g',label='PCA')
plt.plot(recalls, PRD2_E1000000, 'm',label='D2')
plt.plot(recalls, PRA3_E, 'y',label='A3')
plt.plot(recalls, PRSpin_E, 'b',label='Spin')
plt.plot(recalls, PRSpinF_E, 'r',label='SpinF')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()
plt.figure(2)
plt.hold(True)
plt.title('Descriptor Comparison by ChiSquared Distance')
plt.plot(recalls, PRShape_CS, 'k',label='Shell')
plt.plot(recalls, PRShapeShell_CS, 'g',label='Shell Sector ')
plt.plot(recalls, PRShapePCA_CS, 'r',label='Shell PCA')
plt.plot(recalls, PRD2_CS, 'c',label='D2')
plt.plot(recalls, PRA3_CS, 'm',label='A3')
plt.plot(recalls, PRSpinF_CS, 'b',label='SpinF CS')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()
plt.figure(3)
plt.hold(True)
plt.title('Shell Histogram Comparison by Euclidean Dist. w/diff. # of bins')
plt.plot(recalls, PRShape_E1, 'r',label='Shell -1')
plt.plot(recalls, PRShape_E10, 'k',label='Shell -10')
plt.plot(recalls, PRShape_E20, 'y',label='Shell -20')
plt.plot(recalls, PRShape_E30, 'b',label='Shell -30')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()
plt.figure(4)
plt.hold(True)
plt.title('D2 Histogram Comparison by Euclidean Dist. w/diff. # of Samples')
plt.plot(recalls, PRD2_E100, 'r',label='D2 -100')
plt.plot(recalls, PRD2_E1000, 'g',label='D2 -1000')
plt.plot(recalls, PRD2_E10000, 'b',label='D2 -10000')
plt.plot(recalls, PRD2_E100000, 'y',label='D2 -100000')
plt.plot(recalls, PRD2_E1000000, 'k',label='D2 -1000000')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()
plt.figure(5)
plt.hold(True)
plt.title('D2 Descriptor w/diff. comparisons')
plt.plot(recalls, PRD2_E1000000,'r',label='D2 Euc')
plt.plot(recalls, PRD2_C, 'b',label='D2 Cos')
plt.plot(recalls, PRD2_CS, 'g',label='D2 ChiSq')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()
plt.figure(6)
plt.hold(True)
plt.title('Spin and SpinF Descriptors w/diff. comparisons')
plt.plot(recalls, PRSpin_E, 'r',label='Spin E')
plt.plot(recalls, PRSpin_C, 'm',label='Spin C')
plt.plot(recalls, PRSpin_CS, 'y',label='Spin CS')
plt.plot(recalls, PRSpinF_E, 'g',label='SpinF E')
plt.plot(recalls, PRSpinF_C, 'b',label='SpinF C')
plt.plot(recalls, PRSpinF_CS, 'k',label='SpinF CS')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.legend()
plt.show()'''