def plot_ModuleLoads(MappingFile, CMSSW_Silicon, CMSSW_Scintillator):
#Load external data
data = loadDataFile(MappingFile) #dataframe
data_tcs_passing, data_tcs_passing_scin = getTCsPassing(
CMSSW_Silicon, CMSSW_Scintillator) #from CMSSW
lpgbt_loads_tcs, lpgbt_loads_words, lpgbt_layers = getHexModuleLoadInfo(
data, data_tcs_passing, data_tcs_passing_scin)
plot(lpgbt_loads_tcs,
"loads_tcs.png",
binwidth=0.1,
xtitle='Number of TCs on a single lpGBT')
plot(lpgbt_loads_words,
"loads_words.png",
binwidth=0.1,
xtitle='Number of words on a single lpGBT')
plot2D(lpgbt_loads_tcs,
lpgbt_layers,
"tcs_vs_layer.png",
xtitle='Number of TCs on a single lpGBT')
plot2D(lpgbt_loads_words,
lpgbt_layers,
"words_vs_layer.png",
xtitle='Number of words on a single lpGBT')
# if(constants.URL.__contains__('sonar')) :
# best_fitness = output(best_fitness)
# print("BEST FITNESS ::",output(best_fitness))
print("BEST FITNESS ::", (best_fitness))
print("labelled :: \n", labelled)
##############################
#### FMEASURE
if (constants.URL.__contains__('ionosphere')
or constants.URL.__contains__('iris')
or constants.URL.__contains__('wine')
or constants.URL.__contains__('parkinsons')
or constants.URL.__contains__('sonar')
or constants.URL.__contains__('segmentation')
or constants.URL.__contains__('glass')):
# classdata = pd.read_csv(constants.cURL)
classdata = np.array(y_dataset_glob)
#print(classdata)
fmeasure(labelled, classdata)
##############################
if constants.URL.__contains__('random2'):
plot2D(got_back[best_index], pop_distri)
if constants.URL.__contains__('random3'):
plot3D(got_back[best_index], pop_distri)
def synchroniseAtApex(pts_1, pts_2):
syncd1 = []
syncd2 = []
shorter = []
longer = []
short_flag = 0
if len(pts_1) < len(pts_2):
shorter = pts_1
longer = pts_2
short_flag = 1
else:
shorter = pts_2
longer = pts_1
short_flag = 2
diff = len(longer) - len(shorter)
# find the highest y value in each point set
apex1 = max(float(p[1]) for p in shorter)
apex2 = max(float(p[1]) for p in longer)
apex1_i = [i for i, y in enumerate(shorter) if y[1] == str(apex1)]
apex2_i = [i for i, y in enumerate(longer) if y[1] == str(apex2)]
if debug:
print "\n------Apexes------"
print "> Short:", apex1, apex1_i, "of", len(shorter)
print "> Long:", apex2, apex2_i, "of", len(longer)
shift = apex2_i[0] - apex1_i[0]
# remove the front end dangle
if debug:
print "\nShift by:", shift
if shift > 0:
longer = longer[shift:]
if debug:
print "Longer front trimmed, new length:", len(longer)
else:
shorter = shorter[abs(shift):]
if debug:
print "Shorter front trimmed, new length:", len(shorter)
remainder = diff - shift
# remove the rear end dangle
if remainder >= 0:
if debug:
print "\nTrim longer by remainder:", remainder
index = len(longer) - remainder
longer = longer[:index]
if remainder < 0:
index = len(shorter) - abs(remainder)
if debug:
print "\nShift > diff in lengths, trim the shorter end to:", index
shorter = shorter[:index]
if debug:
print "New length of shorter:", len(shorter)
print "New length of longer:", len(longer)
# find the highest y value in each point set
apex1 = max(float(p[1]) for p in shorter)
apex2 = max(float(p[1]) for p in longer)
apex1_i = [i for i, y in enumerate(shorter) if y[1] == str(apex1)]
apex2_i = [i for i, y in enumerate(longer) if y[1] == str(apex2)]
if debug:
print "\nNew apex positions:"
print apex1, apex1_i
print apex2, apex2_i
if short_flag == 1:
syncd1 = shorter
syncd2 = longer
else:
syncd1 = longer
syncd2 = shorter
if view and debug:
plot.plot2D(syncd1, name='First Synced Trajectory')
plot.plot2D(syncd2, name='Second Synced Trajectory')
return syncd1, syncd2
labelled = fitness.get_pop_distri_label(file, centroid, constants.K)
print("labelled :: \n", labelled)
##############################
#### FMEASURE
if (constants.URL.__contains__('ionosphere')
or constants.URL.__contains__('iris')
or constants.URL.__contains__('wine')
or constants.URL.__contains__('parkinsons')
or constants.URL.__contains__('sonar')
or constants.URL.__contains__('segmentation')
or constants.URL.__contains__('glass')):
# classdata = pd.read_csv(constants.cURL)
classdata = np.array(y_dataset)
#print(classdata)
fmeasure(labelled, classdata)
##############################
if constants.URL.__contains__('random2'):
plot2D(centroid, pop_distri)
if constants.URL.__contains__('random3'):
plot3D(centroid, pop_distri)
#data = np.array(data)
#print(dist(centroids[0],data[0]),dist(centroids[1],data[0]),dist(centroids[2],data[0]))
#print(dist(centroids[0],data[55]),dist(centroids[1],data[55]),dist(centroids[2],data[55]))
#print(dist(centroids[0],data[127]),dist(centroids[1],data[127]),dist(centroids[2],data[127]))
def run():
global pts3
global pts4
if simulation and view:
plot.plot3D(data3D, 'Original 3D Data')
if view:
plot.plot2D(pts1_raw, name='First Statics (Noise not shown)')
plot.plot2D(pts2_raw, name='Second Statics (Noise not shown)')
# FUNDAMENTAL MATRIX
F = getFundamentalMatrix(pts1, pts2)
# ESSENTIAL MATRIX (HZ 9.12)
E, w, u, vt = getEssentialMatrix(F, K1, K2)
# PROJECTION/CAMERA MATRICES from E (HZ 9.6.2)
P1, P2 = getNormalisedPMatrices(u, vt)
P1_mat = np.mat(P1)
P2_mat = np.mat(P2)
# FULL PROJECTION MATRICES (with K) P = K[Rt]
KP1 = K1 * P1_mat
KP2 = K2 * P2_mat
print "\n> KP1:\n", KP1
print "\n> KP2:\n", KP2
# SYNCHRONISATION + CORRECTION
if rec_data and simulation is False:
print "---Synchronisation---"
pts3, pts4 = synchroniseGeometric(pts3, pts4, F)
pts3 = pts3.reshape((1, -1, 2))
pts4 = pts4.reshape((1, -1, 2))
newPoints3, newPoints4 = cv2.correctMatches(F, pts3, pts4)
pts3 = newPoints3.reshape((-1, 2))
pts4 = newPoints4.reshape((-1, 2))
elif simulation:
print "> Simulation: Use whole point set for reconstruction"
pts3 = pts1
pts4 = pts2
# Triangulate the trajectory
p3d = triangulateCV(KP1, KP2, pts3, pts4)
# Triangulate goalposts
if simulation is False:
goalPosts = triangulateCV(KP1, KP2, postPts1, postPts2)
# SCALING AND PLOTTING
if simulation:
if view:
plot.plot3D(p3d, 'Simulation Reconstruction')
reprojectionError(K1, P1_mat, K2, P2_mat, pts3, pts4, p3d)
p3d = simScale(p3d)
if view:
plot.plot3D(p3d, 'Scaled Simulation Reconstruction')
else:
# add the post point data into the reconstruction for context
if len(postPts1) == 4:
print "> Concatenate goal posts to trajectory"
pts3_gp = np.concatenate((postPts1, pts3), axis=0)
pts4_gp = np.concatenate((postPts2, pts4), axis=0)
p3d_gp = np.concatenate((goalPosts, p3d), axis=0)
scale = getScale(goalPosts)
scaled_gp_only = [[a * scale for a in inner] for inner in goalPosts]
scaled_gp = [[a * scale for a in inner] for inner in p3d_gp]
scaled = [[a * scale for a in inner] for inner in p3d]
if view:
plot.plot3D(scaled_gp, 'Scaled 3D Reconstruction')
reprojectionError(K1, P1_mat, K2, P2_mat, pts3_gp, pts4_gp, p3d_gp)
getMetrics(scaled, scaled_gp_only)
scaled_gp = transform(scaled_gp)
if view:
plot.plot3D(scaled_gp, 'Final (Reorientated) 3D Reconstruction')
if ground_truth_provided:
reconstructionError(data3D, scaled_gp)
# write X Y Z to file
outfile = open('sessions/' + clip + '/3d_out.txt', 'w')
for p in scaled_gp:
p0 = round(p[0], 2)
p1 = round(p[1], 2)
p2 = round(p[2], 2)
string = str(p0) + ' ' + str(p1) + ' ' + str(p2)
outfile.write(string + '\n')
outfile.close()
def reprojectionError(K1, P1_mat, K2, P2_mat, pts_3, pts_4, points3d):
# Nx4 array for filling with homogeneous points
new = np.zeros((len(points3d), 4))
for i, point in enumerate(points3d):
new[i][0] = point[0]
new[i][1] = point[1]
new[i][2] = point[2]
new[i][3] = 1
errors1 = []
errors2 = []
reprojected1 = []
reprojected2 = []
# for each 3d point
for i, X in enumerate(new):
# x_2d = K * P * X_3d
xp1 = K1 * P1_mat * np.mat(X).T
xp2 = K2 * P2_mat * np.mat(X).T
# normalise the projected (homogenous) coordinates
# (x,y,1) = (xz,yz,z) / z
xp1 = xp1 / xp1[2]
xp2 = xp2 / xp2[2]
reprojected1.append(xp1)
reprojected2.append(xp2)
# and get the orginally measured points
x1 = pts_3[i]
x2 = pts_4[i]
# difference between them is:
dist1 = math.hypot(xp1[0] - x1[0], xp1[1] - x1[1])
dist2 = math.hypot(xp2[0] - x2[0], xp2[1] - x2[1])
errors1.append(dist1)
errors2.append(dist2)
avg1 = sum(errors1) / len(errors1)
avg2 = sum(errors2) / len(errors2)
std1 = np.std(errors1)
std2 = np.std(errors2)
errors = errors1 + errors2
avg = np.mean(errors)
std = np.std(errors)
print "\n> average reprojection error in image 1:", avg1
print "\n> average reprojection error in image 2:", avg2
if view and debug:
plot.plotOrderedBar(errors1, 'Reprojection Error Image 1', 'Index',
'px')
plot.plotOrderedBar(errors2, 'Reprojection Error Image 2', 'Index',
'px')
plot.plot2D(reprojected1, pts_3,
'Reprojection of Reconstruction onto Image 1')
plot.plot2D(reprojected2, pts_4,
'Reprojection of Reconstruction onto Image 2')
outfile = open('sessions/' + clip + statdir + 'reprojection.txt', 'w')
string = 'avg:' + str(avg) + '\nstd:' + str(std)
outfile.write(string)
outfile.close()
statfile.write(str(avg) + ' ')
def synchroniseAtApex(pts_1, pts_2):
syncd1 = []
syncd2 = []
shorter = []
longer = []
short_flag = 0
if len(pts_1) < len(pts_2):
shorter = pts_1
longer = pts_2
short_flag = 1
else:
shorter = pts_2
longer = pts_1
short_flag = 2
diff = len(longer) - len(shorter)
# find the highest y value in each point set
apex1 = max(float(p[1]) for p in shorter)
apex2 = max(float(p[1]) for p in longer)
apex1_i = [i for i, y in enumerate(shorter) if y[1] == apex1]
apex2_i = [i for i, y in enumerate(longer) if y[1] == apex2]
if debug:
print "\n------Apexes------"
print "> Short:", apex1, apex1_i, "of", len(shorter)
print "> Long:", apex2, apex2_i, "of", len(longer)
shift = apex2_i[0] - apex1_i[0]
# remove the front end dangle
if debug:
print "\nShift by:", shift
if shift > 0:
longer = longer[shift:]
if debug:
print "Longer front trimmed, new length:", len(longer)
else:
shorter = shorter[abs(shift):]
if debug:
print "Shorter front trimmed, new length:", len(shorter)
remainder = diff - shift
# remove the rear end dangle
if remainder >= 0:
if debug:
print "\nTrim longer by remainder:", remainder
index = len(longer) - remainder
longer = longer[:index]
if remainder < 0:
index = len(shorter) - abs(remainder)
if debug:
print "\nShift > diff in lengths, trim the shorter end to:", index
shorter = shorter[:index]
if debug:
print "New length of shorter:", len(shorter)
print "New length of longer:", len(longer)
# find the highest y value in each point set
apex1 = max(float(p[1]) for p in shorter)
apex2 = max(float(p[1]) for p in longer)
apex1_i = [i for i, y in enumerate(shorter) if y[1] == apex1]
apex2_i = [i for i, y in enumerate(longer) if y[1] == apex2]
if debug:
print "\nNew apex positions:"
print apex1, apex1_i
print apex2, apex2_i
if short_flag == 1:
syncd1 = shorter
syncd2 = longer
else:
syncd1 = longer
syncd2 = shorter
if view and debug:
plot.plot2D(syncd1, name='First Synced Trajectory')
plot.plot2D(syncd2, name='Second Synced Trajectory')
return syncd1, syncd2
def synchroniseGeometric(pts_1, pts_2, F):
print "> GEOMETRIC SYNCHRONISATION:"
syncd1 = []
syncd2 = []
shorter = []
longer = []
short_flag = 0
# Work out which of two trajectories is shorter
if len(pts_1) < len(pts_2):
shorter = pts_1
longer = pts_2
short_flag = 1
else:
shorter = pts_2
longer = pts_1
short_flag = 2
diff = len(longer) - len(shorter)
if debug:
print "Longer:", len(longer)
print "Shorter:", len(shorter)
print "Diff:", diff
# Convert to homogeneous
shorter_hom = cv2.convertPointsToHomogeneous(shorter)
longer_hom = cv2.convertPointsToHomogeneous(longer)
averages = []
# Shift the shorter through the longer
for offset in xrange(0, diff + 1):
err = 0
avg = 0
for i in xrange(0, len(shorter)):
# current matching of pts a-b from trajectories A-B
a = shorter_hom[i]
b = longer_hom[i + offset]
# Test x'Fx = 0
this_err = abs(np.mat(a) * F * np.mat(b).T)
err += this_err
avg = err / len(shorter)
avg_off = (avg, offset)
averages.append(avg_off)
m = min(float(a[0]) for a in averages)
ret = [item for item in averages if item[0] == m]
# trim the beginning of the longer list
offset = ret[0][1]
longer = longer[offset:]
# trim its end
tail = len(longer) - len(shorter)
if tail != 0:
longer = longer[:-tail]
if short_flag == 1:
syncd1 = shorter
syncd2 = longer
else:
syncd1 = longer
syncd2 = shorter
if debug:
print "Synced Trajectory Length:", len(longer), len(shorter)
if view and debug:
plot.plot2D(syncd1, name='First Synced Trajectory')
plot.plot2D(syncd2, name='Second Synced Trajectory')
return syncd1, syncd2
def reprojectionError(K1, P1_mat, K2, P2_mat, pts_3, pts_4, points3d):
# Nx4 array for filling with homogeneous points
new = np.zeros((len(points3d), 4))
for i, point in enumerate(points3d):
new[i][0] = point[0]
new[i][1] = point[1]
new[i][2] = point[2]
new[i][3] = 1
errors1 = []
errors2 = []
reprojected1 = []
reprojected2 = []
# for each 3d point
for i, X in enumerate(new):
# x_2d = K * P * X_3d
xp1 = K1 * P1_mat * np.mat(X).T
xp2 = K2 * P2_mat * np.mat(X).T
# normalise the projected (homogenous) coordinates
# (x,y,1) = (xz,yz,z) / z
xp1 = xp1 / xp1[2]
xp2 = xp2 / xp2[2]
reprojected1.append(xp1)
reprojected2.append(xp2)
# and get the orginally measured points
x1 = pts_3[i]
x2 = pts_4[i]
# difference between them is:
dist1 = math.hypot(xp1[0] - x1[0], xp1[1] - x1[1])
dist2 = math.hypot(xp2[0] - x2[0], xp2[1] - x2[1])
errors1.append(dist1)
errors2.append(dist2)
avg1 = sum(errors1) / len(errors1)
avg2 = sum(errors2) / len(errors2)
std1 = np.std(errors1)
std2 = np.std(errors2)
errors = errors1 + errors2
avg = np.mean(errors)
std = np.std(errors)
print "\n> average reprojection error in image 1:", avg1
print "\n> average reprojection error in image 2:", avg2
if view and debug:
plot.plotOrderedBar(
errors1, 'Reprojection Error Image 1', 'Index', 'px')
plot.plotOrderedBar(
errors2, 'Reprojection Error Image 2', 'Index', 'px')
plot.plot2D(reprojected1, pts_3,
'Reprojection of Reconstruction onto Image 1')
plot.plot2D(reprojected2, pts_4,
'Reprojection of Reconstruction onto Image 2')
outfile = open('sessions/' + clip + statdir + 'reprojection.txt', 'w')
string = 'avg:' + str(avg) + '\nstd:' + str(std)
outfile.write(string)
outfile.close()
statfile.write(str(avg) + ' ')
def run():
global pts3
global pts4
if simulation and view:
plot.plot3D(data3D, 'Original 3D Data')
if view:
plot.plot2D(pts1_raw, name='First Statics (Noise not shown)')
plot.plot2D(pts2_raw, name='Second Statics (Noise not shown)')
# FUNDAMENTAL MATRIX
F = getFundamentalMatrix(pts1, pts2)
# ESSENTIAL MATRIX (HZ 9.12)
E, w, u, vt = getEssentialMatrix(F, K1, K2)
# PROJECTION/CAMERA MATRICES from E (HZ 9.6.2)
P1, P2 = getNormalisedPMatrices(u, vt)
P1_mat = np.mat(P1)
P2_mat = np.mat(P2)
# FULL PROJECTION MATRICES (with K) P = K[Rt]
KP1 = K1 * P1_mat
KP2 = K2 * P2_mat
print "\n> KP1:\n", KP1
print "\n> KP2:\n", KP2
# SYNCHRONISATION + CORRECTION
if rec_data and simulation is False:
print "---Synchronisation---"
pts3, pts4 = synchroniseGeometric(pts3, pts4, F)
pts3 = pts3.reshape((1, -1, 2))
pts4 = pts4.reshape((1, -1, 2))
newPoints3, newPoints4 = cv2.correctMatches(F, pts3, pts4)
pts3 = newPoints3.reshape((-1, 2))
pts4 = newPoints4.reshape((-1, 2))
elif simulation:
print "> Simulation: Use whole point set for reconstruction"
pts3 = pts1
pts4 = pts2
# Triangulate the trajectory
p3d = triangulateCV(KP1, KP2, pts3, pts4)
# Triangulate goalposts
if simulation is False:
goalPosts = triangulateCV(KP1, KP2, postPts1, postPts2)
# SCALING AND PLOTTING
if simulation:
if view:
plot.plot3D(p3d, 'Simulation Reconstruction')
reprojectionError(K1, P1_mat, K2, P2_mat, pts3, pts4, p3d)
p3d = simScale(p3d)
if view:
plot.plot3D(p3d, 'Scaled Simulation Reconstruction')
else:
# add the post point data into the reconstruction for context
if len(postPts1) == 4:
print "> Concatenate goal posts to trajectory"
pts3_gp = np.concatenate((postPts1, pts3), axis=0)
pts4_gp = np.concatenate((postPts2, pts4), axis=0)
p3d_gp = np.concatenate((goalPosts, p3d), axis=0)
scale = getScale(goalPosts)
scaled_gp_only = [[a * scale for a in inner] for inner in goalPosts]
scaled_gp = [[a * scale for a in inner] for inner in p3d_gp]
scaled = [[a * scale for a in inner] for inner in p3d]
if view:
plot.plot3D(scaled_gp, 'Scaled 3D Reconstruction')
reprojectionError(K1, P1_mat, K2, P2_mat, pts3_gp, pts4_gp, p3d_gp)
getMetrics(scaled, scaled_gp_only)
scaled_gp = transform(scaled_gp)
if view:
plot.plot3D(scaled_gp, 'Final (Reorientated) 3D Reconstruction')
if ground_truth_provided:
reconstructionError(data3D, scaled_gp)
# write X Y Z to file
outfile = open('sessions/' + clip + '/3d_out.txt', 'w')
for p in scaled_gp:
p0 = round(p[0], 2)
p1 = round(p[1], 2)
p2 = round(p[2], 2)
string = str(p0) + ' ' + str(p1) + ' ' + str(p2)
outfile.write(string + '\n')
outfile.close()
def synchroniseGeometric(pts_1, pts_2, F):
print "> GEOMETRIC SYNCHRONISATION:"
syncd1 = []
syncd2 = []
shorter = []
longer = []
short_flag = 0
# Work out which of two trajectories is shorter
if len(pts_1) < len(pts_2):
shorter = pts_1
longer = pts_2
short_flag = 1
else:
shorter = pts_2
longer = pts_1
short_flag = 2
diff = len(longer) - len(shorter)
if debug:
print "Longer:", len(longer)
print "Shorter:", len(shorter)
print "Diff:", diff
# Convert to homogeneous
shorter_hom = cv2.convertPointsToHomogeneous(shorter)
longer_hom = cv2.convertPointsToHomogeneous(longer)
averages = []
# Shift the shorter through the longer
for offset in xrange(0, diff + 1):
err = 0
avg = 0
for i in xrange(0, len(shorter)):
# current matching of pts a-b from trajectories A-B
a = shorter_hom[i]
b = longer_hom[i + offset]
# Test x'Fx = 0
this_err = abs(np.mat(a) * F * np.mat(b).T)
err += this_err
avg = err / len(shorter)
avg_off = (avg, offset)
averages.append(avg_off)
m = min(float(a[0]) for a in averages)
ret = [item for item in averages if item[0] == m]
# trim the beginning of the longer list
offset = ret[0][1]
longer = longer[offset:]
# trim its end
tail = len(longer) - len(shorter)
if tail != 0:
longer = longer[:-tail]
if short_flag == 1:
syncd1 = shorter
syncd2 = longer
else:
syncd1 = longer
syncd2 = shorter
if debug:
print "Synced Trajectory Length:", len(longer), len(shorter)
if view and debug:
plot.plot2D(syncd1, name='First Synced Trajectory')
plot.plot2D(syncd2, name='Second Synced Trajectory')
return syncd1, syncd2
