def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label)
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper right')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
def _distplot(dists, color, label, distkey, plot_type=plot_type):
data = sorted(dists)
ax = df2.gca()
min_ = distkey2_min[distkey]
max_ = distkey2_max[distkey]
if plot_type == 'plot':
df2.plot(data, color=color, label=label)
#xticks = np.linspace(np.min(data), np.max(data), 3)
#yticks = np.linspace(0, len(data), 5)
#ax.set_xticks(xticks)
#ax.set_yticks(yticks)
ax.set_ylim(min_, max_)
ax.set_xlim(0, len(dists))
ax.set_ylabel('distance')
ax.set_xlabel('matches indexes (sorted by distance)')
df2.legend(loc='lower right')
if plot_type == 'pdf':
df2.plot_pdf(data, color=color, label=label)
ax.set_ylabel('pr')
ax.set_xlabel('distance')
ax.set_xlim(min_, max_)
df2.legend(loc='upper right')
df2.dark_background(ax)
df2.small_xticks(ax)
df2.small_yticks(ax)
def plot_rank_histogram(allres, orgres_type):
print('[viz] plotting %r rank histogram' % orgres_type)
ranks = allres.__dict__[orgres_type].ranks
label = 'P(rank | ' + orgres_type + ' match)'
title = orgres_type + ' match rankings histogram\n' + allres.title_suffix
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_histpdf(ranks, label=label) # FIXME
df2.set_xlabel('ground truth ranks')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.hs, 'rankviz')
def plot_score_pdf(allres, orgres_type, colorx=0.0, variation_truncate=False):
print('[viz] plotting ' + orgres_type + ' score pdf')
title = orgres_type + ' match score frequencies\n' + allres.title_suffix
scores = allres.__dict__[orgres_type].scores
print('[viz] len(scores) = %r ' % (len(scores), ))
label = 'P(score | %r)' % orgres_type
df2.figure(fnum=FIGNUM, doclf=True, title=title)
df2.draw_pdf(scores, label=label, colorx=colorx)
if variation_truncate:
df2.variation_trunctate(scores)
#df2.variation_trunctate(false.scores)
df2.set_xlabel('score')
df2.set_ylabel('frequency')
df2.legend()
__dump_or_browse(allres.hs, 'scoreviz')
def in_depth_ellipse2x2(rchip, kp):
#-----------------------
# SETUP
#-----------------------
from hotspotter import draw_func2 as df2
np.set_printoptions(precision=8)
tau = 2 * np.pi
df2.reset()
df2.figure(9003, docla=True, doclf=True)
ax = df2.gca()
ax.invert_yaxis()
def _plotpts(data, px, color=df2.BLUE, label=''):
#df2.figure(9003, docla=True, pnum=(1, 1, px))
df2.plot2(data.T[0], data.T[1], '.', '', color=color, label=label)
df2.update()
def _plotarrow(x, y, dx, dy, color=df2.BLUE, label=''):
ax = df2.gca()
arrowargs = dict(head_width=.5, length_includes_head=True, label=label)
arrow = df2.FancyArrow(x, y, dx, dy, **arrowargs)
arrow.set_edgecolor(color)
arrow.set_facecolor(color)
ax.add_patch(arrow)
df2.update()
def _2x2_eig(M2x2):
(evals, evecs) = np.linalg.eig(M2x2)
l1, l2 = evals
v1, v2 = evecs
return l1, l2, v1, v2
#-----------------------
# INPUT
#-----------------------
# We will call perdoch's invA = invV
print('--------------------------------')
print('Let V = Perdoch.A')
print('Let Z = Perdoch.E')
print('--------------------------------')
print('Input from Perdoch\'s detector: ')
# We are given the keypoint in invA format
(x, y, ia11, ia21, ia22), ia12 = kp, 0
invV = np.array([[ia11, ia12], [ia21, ia22]])
V = np.linalg.inv(invV)
# <HACK>
#invV = V / np.linalg.det(V)
#V = np.linalg.inv(V)
# </HACK>
Z = (V.T).dot(V)
print('invV is a transform from points on a unit-circle to the ellipse')
helpers.horiz_print('invV = ', invV)
print('--------------------------------')
print('V is a transformation from points on the ellipse to a unit circle')
helpers.horiz_print('V = ', V)
print('--------------------------------')
print('Points on a matrix satisfy (x).T.dot(Z).dot(x) = 1')
print('where Z = (V.T).dot(V)')
helpers.horiz_print('Z = ', Z)
# Define points on a unit circle
theta_list = np.linspace(0, tau, 50)
cicrle_pts = np.array([(np.cos(t), np.sin(t)) for t in theta_list])
# Transform those points to the ellipse using invV
ellipse_pts1 = invV.dot(cicrle_pts.T).T
# Transform those points to the ellipse using V
ellipse_pts2 = V.dot(cicrle_pts.T).T
#Lets check our assertion: (x_).T.dot(Z).dot(x_) = 1
checks1 = [x_.T.dot(Z).dot(x_) for x_ in ellipse_pts1]
checks2 = [x_.T.dot(Z).dot(x_) for x_ in ellipse_pts2]
assert all([abs(1 - check) < 1E-11 for check in checks1])
#assert all([abs(1 - check) < 1E-11 for check in checks2])
print('... all of our plotted points satisfy this')
#=======================
# THE CONIC SECTION
#=======================
# All of this was from the Perdoch paper, now lets move into conic sections
# We will use the notation from wikipedia
# http://en.wikipedia.org/wiki/Conic_section
# http://en.wikipedia.org/wiki/Matrix_representation_of_conic_sections
#-----------------------
# MATRIX REPRESENTATION
#-----------------------
# The matrix representation of a conic is:
(A, B2, B2_, C) = Z.flatten()
(D, E, F) = (0, 0, 1)
B = B2 * 2
assert B2 == B2_, 'matrix should by symmetric'
print('--------------------------------')
print('Now, using wikipedia\' matrix representation of a conic.')
con = np.array(((' A', 'B / 2', 'D / 2'), ('B / 2', ' C', 'E / 2'),
('D / 2', 'E / 2', ' F')))
helpers.horiz_print('A matrix A_Q = ', con)
# A_Q is our conic section (aka ellipse matrix)
A_Q = np.array(((A, B / 2, D / 2), (B / 2, C, E / 2), (D / 2, E / 2, F)))
helpers.horiz_print('A_Q = ', A_Q)
#-----------------------
# DEGENERATE CONICS
#-----------------------
print('----------------------------------')
print('As long as det(A_Q) != it is not degenerate.')
print('If the conic is not degenerate, we can use the 2x2 minor: A_33')
print('det(A_Q) = %s' % str(np.linalg.det(A_Q)))
assert np.linalg.det(A_Q) != 0, 'degenerate conic'
A_33 = np.array(((A, B / 2), (B / 2, C)))
helpers.horiz_print('A_33 = ', A_33)
#-----------------------
# CONIC CLASSIFICATION
#-----------------------
print('----------------------------------')
print('The determinant of the minor classifies the type of conic it is')
print('(det == 0): parabola, (det < 0): hyperbola, (det > 0): ellipse')
print('det(A_33) = %s' % str(np.linalg.det(A_33)))
assert np.linalg.det(A_33) > 0, 'conic is not an ellipse'
print('... this is indeed an ellipse')
#-----------------------
# CONIC CENTER
#-----------------------
print('----------------------------------')
print('the centers of the ellipse are obtained by: ')
print('x_center = (B * E - (2 * C * D)) / (4 * A * C - B ** 2)')
print('y_center = (D * B - (2 * A * E)) / (4 * A * C - B ** 2)')
# Centers are obtained by solving for where the gradient of the quadratic
# becomes 0. Without going through the derivation the calculation is...
# These should be 0, 0 if we are at the origin, or our original x, y
# coordinate specified by the keypoints. I'm doing the calculation just for
# shits and giggles
x_center = (B * E - (2 * C * D)) / (4 * A * C - B**2)
y_center = (D * B - (2 * A * E)) / (4 * A * C - B**2)
helpers.horiz_print('x_center = ', x_center)
helpers.horiz_print('y_center = ', y_center)
#-----------------------
# MAJOR AND MINOR AXES
#-----------------------
# Now we are going to determine the major and minor axis
# of this beast. It just the center augmented by the eigenvecs
print('----------------------------------')
# The angle between the major axis and our x axis is:
l1, l2, v1, v2 = _2x2_eig(A_33)
x_axis = np.array([1, 0])
theta = np.arccos(x_axis.dot(v1))
# The eccentricity is determined by:
nu = 1
numer = 2 * np.sqrt((A - C)**2 + B**2)
denom = nu * (A + C) + np.sqrt((A - C)**2 + B**2)
eccentricity = np.sqrt(numer / denom)
from scipy.special import ellipeinc
#-----------------------
# DRAWING
#-----------------------
# Lets start off by drawing the ellipse that we are goign to work with
# Create unit circle sample
# Draw the keypoint using the tried and true df2
# Other things should subsiquently align
df2.draw_kpts2(np.array([(0, 0, ia11, ia21, ia22)]),
ell_linewidth=4,
ell_color=df2.DEEP_PINK,
ell_alpha=1,
arrow=True,
rect=True)
# Plot ellipse points
_plotpts(ellipse_pts1, 0, df2.YELLOW, label='invV.dot(cicrle_pts.T).T')
# Plot ellipse axis
# !HELP! I DO NOT KNOW WHY I HAVE TO DIVIDE, SQUARE ROOT, AND NEGATE!!!
l1, l2, v1, v2 = _2x2_eig(A_33)
dx1, dy1 = (v1 / np.sqrt(l1))
dx2, dy2 = (v2 / np.sqrt(l2))
_plotarrow(0, 0, dx1, -dy1, color=df2.ORANGE, label='ellipse axis')
_plotarrow(0, 0, dx2, -dy2, color=df2.ORANGE)
# Plot ellipse orientation
orient_axis = invV.dot(np.eye(2))
dx1, dx2, dy1, dy2 = orient_axis.flatten()
_plotarrow(0, 0, dx1, dy1, color=df2.BLUE, label='ellipse rotation')
_plotarrow(0, 0, dx2, dy2, color=df2.BLUE)
df2.legend()
df2.dark_background()
df2.gca().invert_yaxis()
return locals()
def in_depth_ellipse2x2(rchip, kp):
#-----------------------
# SETUP
#-----------------------
from hotspotter import draw_func2 as df2
np.set_printoptions(precision=8)
tau = 2 * np.pi
df2.reset()
df2.figure(9003, docla=True, doclf=True)
ax = df2.gca()
ax.invert_yaxis()
def _plotpts(data, px, color=df2.BLUE, label=''):
#df2.figure(9003, docla=True, pnum=(1, 1, px))
df2.plot2(data.T[0], data.T[1], '.', '', color=color, label=label)
df2.update()
def _plotarrow(x, y, dx, dy, color=df2.BLUE, label=''):
ax = df2.gca()
arrowargs = dict(head_width=.5, length_includes_head=True, label=label)
arrow = df2.FancyArrow(x, y, dx, dy, **arrowargs)
arrow.set_edgecolor(color)
arrow.set_facecolor(color)
ax.add_patch(arrow)
df2.update()
def _2x2_eig(M2x2):
(evals, evecs) = np.linalg.eig(M2x2)
l1, l2 = evals
v1, v2 = evecs
return l1, l2, v1, v2
#-----------------------
# INPUT
#-----------------------
# We will call perdoch's invA = invV
print('--------------------------------')
print('Let V = Perdoch.A')
print('Let Z = Perdoch.E')
print('--------------------------------')
print('Input from Perdoch\'s detector: ')
# We are given the keypoint in invA format
(x, y, ia11, ia21, ia22), ia12 = kp, 0
invV = np.array([[ia11, ia12],
[ia21, ia22]])
V = np.linalg.inv(invV)
# <HACK>
#invV = V / np.linalg.det(V)
#V = np.linalg.inv(V)
# </HACK>
Z = (V.T).dot(V)
print('invV is a transform from points on a unit-circle to the ellipse')
helpers.horiz_print('invV = ', invV)
print('--------------------------------')
print('V is a transformation from points on the ellipse to a unit circle')
helpers.horiz_print('V = ', V)
print('--------------------------------')
print('Points on a matrix satisfy (x).T.dot(Z).dot(x) = 1')
print('where Z = (V.T).dot(V)')
helpers.horiz_print('Z = ', Z)
# Define points on a unit circle
theta_list = np.linspace(0, tau, 50)
cicrle_pts = np.array([(np.cos(t), np.sin(t)) for t in theta_list])
# Transform those points to the ellipse using invV
ellipse_pts1 = invV.dot(cicrle_pts.T).T
# Transform those points to the ellipse using V
ellipse_pts2 = V.dot(cicrle_pts.T).T
#Lets check our assertion: (x_).T.dot(Z).dot(x_) = 1
checks1 = [x_.T.dot(Z).dot(x_) for x_ in ellipse_pts1]
checks2 = [x_.T.dot(Z).dot(x_) for x_ in ellipse_pts2]
assert all([abs(1 - check) < 1E-11 for check in checks1])
#assert all([abs(1 - check) < 1E-11 for check in checks2])
print('... all of our plotted points satisfy this')
#=======================
# THE CONIC SECTION
#=======================
# All of this was from the Perdoch paper, now lets move into conic sections
# We will use the notation from wikipedia
# http://en.wikipedia.org/wiki/Conic_section
# http://en.wikipedia.org/wiki/Matrix_representation_of_conic_sections
#-----------------------
# MATRIX REPRESENTATION
#-----------------------
# The matrix representation of a conic is:
(A, B2, B2_, C) = Z.flatten()
(D, E, F) = (0, 0, 1)
B = B2 * 2
assert B2 == B2_, 'matrix should by symmetric'
print('--------------------------------')
print('Now, using wikipedia\' matrix representation of a conic.')
con = np.array(((' A', 'B / 2', 'D / 2'),
('B / 2', ' C', 'E / 2'),
('D / 2', 'E / 2', ' F')))
helpers.horiz_print('A matrix A_Q = ', con)
# A_Q is our conic section (aka ellipse matrix)
A_Q = np.array((( A, B / 2, D / 2),
(B / 2, C, E / 2),
(D / 2, E / 2, F)))
helpers.horiz_print('A_Q = ', A_Q)
#-----------------------
# DEGENERATE CONICS
#-----------------------
print('----------------------------------')
print('As long as det(A_Q) != it is not degenerate.')
print('If the conic is not degenerate, we can use the 2x2 minor: A_33')
print('det(A_Q) = %s' % str(np.linalg.det(A_Q)))
assert np.linalg.det(A_Q) != 0, 'degenerate conic'
A_33 = np.array((( A, B / 2),
(B / 2, C)))
helpers.horiz_print('A_33 = ', A_33)
#-----------------------
# CONIC CLASSIFICATION
#-----------------------
print('----------------------------------')
print('The determinant of the minor classifies the type of conic it is')
print('(det == 0): parabola, (det < 0): hyperbola, (det > 0): ellipse')
print('det(A_33) = %s' % str(np.linalg.det(A_33)))
assert np.linalg.det(A_33) > 0, 'conic is not an ellipse'
print('... this is indeed an ellipse')
#-----------------------
# CONIC CENTER
#-----------------------
print('----------------------------------')
print('the centers of the ellipse are obtained by: ')
print('x_center = (B * E - (2 * C * D)) / (4 * A * C - B ** 2)')
print('y_center = (D * B - (2 * A * E)) / (4 * A * C - B ** 2)')
# Centers are obtained by solving for where the gradient of the quadratic
# becomes 0. Without going through the derivation the calculation is...
# These should be 0, 0 if we are at the origin, or our original x, y
# coordinate specified by the keypoints. I'm doing the calculation just for
# shits and giggles
x_center = (B * E - (2 * C * D)) / (4 * A * C - B ** 2)
y_center = (D * B - (2 * A * E)) / (4 * A * C - B ** 2)
helpers.horiz_print('x_center = ', x_center)
helpers.horiz_print('y_center = ', y_center)
#-----------------------
# MAJOR AND MINOR AXES
#-----------------------
# Now we are going to determine the major and minor axis
# of this beast. It just the center augmented by the eigenvecs
print('----------------------------------')
# The angle between the major axis and our x axis is:
l1, l2, v1, v2 = _2x2_eig(A_33)
x_axis = np.array([1, 0])
theta = np.arccos(x_axis.dot(v1))
# The eccentricity is determined by:
nu = 1
numer = 2 * np.sqrt((A - C) ** 2 + B ** 2)
denom = nu * (A + C) + np.sqrt((A - C) ** 2 + B ** 2)
eccentricity = np.sqrt(numer / denom)
from scipy.special import ellipeinc
#-----------------------
# DRAWING
#-----------------------
# Lets start off by drawing the ellipse that we are goign to work with
# Create unit circle sample
# Draw the keypoint using the tried and true df2
# Other things should subsiquently align
df2.draw_kpts2(np.array([(0, 0, ia11, ia21, ia22)]), ell_linewidth=4,
ell_color=df2.DEEP_PINK, ell_alpha=1, arrow=True, rect=True)
# Plot ellipse points
_plotpts(ellipse_pts1, 0, df2.YELLOW, label='invV.dot(cicrle_pts.T).T')
# Plot ellipse axis
# !HELP! I DO NOT KNOW WHY I HAVE TO DIVIDE, SQUARE ROOT, AND NEGATE!!!
l1, l2, v1, v2 = _2x2_eig(A_33)
dx1, dy1 = (v1 / np.sqrt(l1))
dx2, dy2 = (v2 / np.sqrt(l2))
_plotarrow(0, 0, dx1, -dy1, color=df2.ORANGE, label='ellipse axis')
_plotarrow(0, 0, dx2, -dy2, color=df2.ORANGE)
# Plot ellipse orientation
orient_axis = invV.dot(np.eye(2))
dx1, dx2, dy1, dy2 = orient_axis.flatten()
_plotarrow(0, 0, dx1, dy1, color=df2.BLUE, label='ellipse rotation')
_plotarrow(0, 0, dx2, dy2, color=df2.BLUE)
df2.legend()
df2.dark_background()
df2.gca().invert_yaxis()
return locals()