def test():
(hs, qcx, cx, fm, fs, rchip1, rchip2, kpts1, kpts2) = ld2.get_sv_test_data()
# READ IMAGE AND ROI
cx2_roi = hs.tables.cx2_roi
cx2_gx = hs.tables.cx2_gx
gx2_gname = hs.tables.gx2_gname
#---
roi = cx2_roi[cx]
gx = cx2_gx[cx]
img_fname = gx2_gname[gx]
img_path = os.path.join(hs.dirs.img_dir, img_fname)
#---
#print('Showing Input')
## Original Image
#df2.imshow(mask_input, fignum=1, plotnum=122)
#df2.present()
# Crop out chips
#chip = img[ry:(ry+rh), rx:(rx+rw)]
#chip_mask = mask[ry:(ry+rh), rx:(rx+rw)]
# IMAGE AND MASK
#df2.imshow(img, plotnum=121, fignum=2, doclf=True)
#df2.imshow(mask, plotnum=122, fignum=2)
# MASKED CHIP
#df2.figure(fignum=3, doclf=True)
#df2.imshow(chip, plotnum=131, fignum=3, doclf=True)
#df2.imshow(color_mask, plotnum=132, fignum=3)
#df2.imshow(seg_chip, plotnum=133, fignum=3)
seg_chip = segment(img_path, roi)
df2.show_img(hs, cx, fignum=1, plotnum=121, title='original', doclf=True)
df2.imshow(seg_chip, fignum=1, plotnum=122, title='segmented')
df2.present()
def test():
(hs, qcx, cx, fm, fs, rchip1, rchip2, kpts1,
kpts2) = ld2.get_sv_test_data()
# READ IMAGE AND ROI
cx2_roi = hs.tables.cx2_roi
cx2_gx = hs.tables.cx2_gx
gx2_gname = hs.tables.gx2_gname
#---
roi = cx2_roi[cx]
gx = cx2_gx[cx]
img_fname = gx2_gname[gx]
img_path = os.path.join(hs.dirs.img_dir, img_fname)
#---
#print('Showing Input')
## Original Image
#df2.imshow(mask_input, fignum=1, plotnum=122)
#df2.present()
# Crop out chips
#chip = img[ry:(ry+rh), rx:(rx+rw)]
#chip_mask = mask[ry:(ry+rh), rx:(rx+rw)]
# IMAGE AND MASK
#df2.imshow(img, plotnum=121, fignum=2, doclf=True)
#df2.imshow(mask, plotnum=122, fignum=2)
# MASKED CHIP
#df2.figure(fignum=3, doclf=True)
#df2.imshow(chip, plotnum=131, fignum=3, doclf=True)
#df2.imshow(color_mask, plotnum=132, fignum=3)
#df2.imshow(seg_chip, plotnum=133, fignum=3)
seg_chip = segment(img_path, roi)
df2.show_img(hs, cx, fignum=1, plotnum=121, title='original', doclf=True)
df2.imshow(seg_chip, fignum=1, plotnum=122, title='segmented')
df2.present()
#----
df2.imshow(rchip, plotnum=(2,3,3), title='2 identity')
draw_kpts3(kpts1.copy(), 2)
df2.update()
#----
df2.imshow(rchip, plotnum=(2,3,4), title='3 identity')
draw_kpts3(kpts1.copy(), 3)
df2.update()
#----
df2.imshow(rchip, plotnum=(2,3,5), title='inv sqrtm 4')
draw_kpts3(kpts1.copy(), 4)
df2.update()
#----
df2.imshow(rchip, plotnum=(2,3,6), title='inv sqrtm 5')
draw_kpts3(kpts1.copy(), 5)
df2.update()
# TEST
hprint = helpers.horiz_print
invA1 = inv(A1)
hprint('invE = ', invE)
hprint('A1 = ', A1)
hprint('invA1 = ', invA1)
exec(df2.present())
def test():
weird_A = np.array([(2.7, 4.2, 10),
(1.7, 3.1, 20),
(2.2, 2.4, 1.1)])
num_mat = 2000
frac_in = .5
num_inl = int(num_mat * frac_in)
kpts1_m = np.random.rand(5, num_mat)
kpts2_m = np.random.rand(5, num_mat)
kpts2_cor = kpts1_m[:, 0:num_inl]
x_cor = kpts2_cor[0]
y_cor = kpts2_cor[1]
z_cor = np.ones(num_inl)
a_cor = kpts2_cor[2]
zeros = np.zeros(num_inl)
c_cor = kpts2_cor[3]
d_cor = kpts2_cor[4]
kpts2_mats = np.array([(a_cor, zeros, x_cor),
(c_cor, d_cor, y_cor),
(zeros, zeros, z_cor)]).T
# Do some weird transform on some keypoints
import scipy.linalg
for x in xrange(num_inl):
mat = weird_A.dot(kpts2_mats[x].T)
mat /= mat[2,2]
kpts2_m[0,x] = mat[0,2]
kpts2_m[1,x] = mat[1,2]
kpts2_m[2,x] = mat[0,0]
kpts2_m[3,x] = mat[1,0]
kpts2_m[4,x] = mat[1,2]
# Find some inliers?
xy_thresh_sqrd = 7
H, inliers = H_homog_from_DELSAC(kpts1_m, kpts2_m, xy_thresh_sqrd)
#aff_inliers1 = sv2.__affine_inliers(x1_m, y1_m, x2_m, y2_m,
#acd1_m, acd2_m, xy_thresh_sqrd, scale_thresh)
H_prime = sv2.compute_homog(x1_mn, y1_mn, x2_mn, y2_mn)
H = linalg.solve(T2, H_prime).dot(T1) # Unnormalize
Hdet = linalg.det(H)
# Estimate final inliers
acd1_m = kpts1_m[2:5,:] # keypoint shape matrix [a 0; c d] matches
acd2_m = kpts2_m[2:5,:]
# Precompute the determinant of lower triangular matrix (a*d - b*c); b = 0
det1_m = acd1_m[0] * acd1_m[2]
det2_m = acd2_m[0] * acd2_m[2]
# Matrix Multiply xyacd matrix by H
# [[A, B, X],
# [C, D, Y],
# [E, F, Z]]
# dot
# [(a, 0, x),
# (c, d, y),
# (0, 0, 1)]
# =
# [(a*A + c*B + 0*E, 0*A + d*B + 0*X, x*A + y*B + 1*X),
# (a*C + c*D + 0*Y, 0*C + d*D + 0*Y, x*C + y*D + 1*Y),
# (a*E + c*F + 0*Z, 0*E + d*F + 0*Z, x*E + y*F + 1*Z)]
# =
# [(a*A + c*B, d*B, x*A + y*B + X),
# (a*C + c*D, d*D, x*C + y*D + Y),
# (a*E + c*F, d*F, x*E + y*F + Z)]
# # IF x=0 and y=0
# =
# [(a*A + c*B, d*B, 0*A + 0*B + X),
# (a*C + c*D, d*D, 0*C + 0*D + Y),
# (a*E + c*F, d*F, 0*E + 0*F + Z)]
# =
# [(a*A + c*B, d*B, X),
# (a*C + c*D, d*D, Y),
# (a*E + c*F, d*F, Z)]
# ---
# A11 = a*A + c*B
# A21 = a*C + c*D
# A31 = a*E + c*F
# A12 = d*B
# A22 = d*D
# A32 = d*F
# A31 = X
# A32 = Y
# A33 = Z
#
# det(A) = A11*(A22*A33 - A23*A32) - A12*(A21*A33 - A23*A31) + A13*(A21*A32 - A22*A31)
det1_mAt = det1_m * Hdet
# Check Error in position and scale
xy_sqrd_err = (x1_mAt - x2_m)**2 + (y1_mAt - y2_m)**2
scale_sqrd_err = det1_mAt / det2_m
# Check to see if outliers are within bounds
xy_inliers = xy_sqrd_err < xy_thresh_sqrd
s1_inliers = scale_sqrd_err > scale_thresh_low
s2_inliers = scale_sqrd_err < scale_thresh_high
_inliers, = np.where(np.logical_and(np.logical_and(xy_inliers, s1_inliers), s2_inliers))
xy1_mHt = transform_xy(H, xy1_m) # Transform Kpts1 to Kpts2-space
sqrd_dist_error = np.sum( (xy1_mHt - xy2_m)**2, axis=0) # Final Inlier Errors
inliers = sqrd_dist_error < xy_thresh_sqrd
df2.show_matches2(rchip1, rchip2, kpts1_m.T[best_inliers1], kpts2_m.T[aff_inliers1], title=title, fignum=2, vert=False)
df2.show_matches2(rchip1, rchip2, kpts1_m.T[best_inliers2], kpts2_m.T[aff_inliers2], title=title, fignum=3, vert=False)
df2.present(wh=(600,400))
# This new function is much faster .035 vs .007
# EXPLOITS LOWER TRIANGULAR MATRIXES
# Precompute the determinant of matrix 2 (a*d - b*c), but b = 0
# Need the inverse of acd2_m: 1/det * [(d, -b), (-c, a)]
# Precompute lower triangular affine tranforms inv2_m (dot) acd1_m
# [(a2*a1), (c2*a1+d2*c1), (d2*d1)]
# IN HOMOGRAPHY INLIERS
'''
y_data = []
x_data = []
for xy_thresh in np.linspace(.001, .005, 50):
xy_thresh_sqrd = img2_diaglen_sqrd * xy_thresh
aff_inliers = __affine_inliers(x1_m, y1_m, acd1_m,
x2_m, y2_m, acd2_m,
xy_thresh_sqrd,
scale_thresh_low,
scale_thresh_high)
y_data.append(len(aff_inliers))
x_data.append(xy_thresh)
plt.plot(x_data, y_data)
df2.show_matches2(rchip1, rchip2, kpts1, kpts2, fm=fm[aff_inliers,:],
fignum=3, vert=True)
'''
'''
def test_realdata2():
from helpers import printWARN, printINFO
import warnings
import numpy.linalg as linalg
import numpy as np
import scipy.sparse as sparse
import scipy.sparse.linalg as sparse_linalg
import load_data2
import params
import draw_func2 as df2
import helpers
import spatial_verification
#params.reload_module()
#load_data2.reload_module()
#df2.reload_module()
db_dir = load_data2.MOTHERS
hs = load_data2.HotSpotter(db_dir)
assign_matches = hs.matcher.assign_matches
qcx = 0
cx = hs.get_other_cxs(qcx)[0]
fm, fs, score = hs.get_assigned_matches_to(qcx, cx)
# Get chips
rchip1 = hs.get_chip(qcx)
rchip2 = hs.get_chip(cx)
# Get keypoints
kpts1 = hs.get_kpts(qcx)
kpts2 = hs.get_kpts(cx)
# Get feature matches
kpts1_m = kpts1[fm[:, 0], :].T
kpts2_m = kpts2[fm[:, 1], :].T
title='(qx%r v cx%r)\n #match=%r' % (qcx, cx, len(fm))
df2.show_matches2(rchip1, rchip2, kpts1, kpts2, fm, fs, title=title)
np.random.seed(6)
subst = helpers.random_indexes(len(fm),len(fm))
kpts1_m = kpts1[fm[subst, 0], :].T
kpts2_m = kpts2[fm[subst, 1], :].T
df2.reload_module()
df2.SHOW_LINES = True
df2.ELL_LINEWIDTH = 2
df2.LINE_ALPHA = .5
df2.ELL_ALPHA = 1
df2.reset()
df2.show_keypoints(rchip1, kpts1_m.T, fignum=0, plotnum=121)
df2.show_keypoints(rchip2, kpts2_m.T, fignum=0, plotnum=122)
df2.show_matches2(rchip1, rchip2, kpts1_m.T, kpts2_m.T, title=title,
fignum=1, vert=True)
spatial_verification.reload_module()
with helpers.Timer():
aff_inliers1 = spatial_verification.aff_inliers_from_ellshape2(kpts1_m, kpts2_m, xy_thresh_sqrd)
with helpers.Timer():
aff_inliers2 = spatial_verification.aff_inliers_from_ellshape(kpts1_m, kpts2_m, xy_thresh_sqrd)
# Homogonize+Normalize
xy1_m = kpts1_m[0:2,:]
xy2_m = kpts2_m[0:2,:]
(xyz_norm1, T1) = spatial_verification.homogo_normalize_pts(xy1_m[:,aff_inliers1])
(xyz_norm2, T2) = spatial_verification.homogo_normalize_pts(xy2_m[:,aff_inliers1])
H_prime = spatial_verification.compute_homog(xyz_norm1, xyz_norm2)
H = linalg.solve(T2, H_prime).dot(T1) # Unnormalize
Hdet = linalg.det(H)
# Estimate final inliers
acd1_m = kpts1_m[2:5,:] # keypoint shape matrix [a 0; c d] matches
acd2_m = kpts2_m[2:5,:]
# Precompute the determinant of lower triangular matrix (a*d - b*c); b = 0
det1_m = acd1_m[0] * acd1_m[2]
det2_m = acd2_m[0] * acd2_m[2]
# Matrix Multiply xyacd matrix by H
# [[A, B, X],
# [C, D, Y],
# [E, F, Z]]
# dot
# [(a, 0, x),
# (c, d, y),
# (0, 0, 1)]
# =
# [(a*A + c*B + 0*E, 0*A + d*B + 0*X, x*A + y*B + 1*X),
# (a*C + c*D + 0*Y, 0*C + d*D + 0*Y, x*C + y*D + 1*Y),
# (a*E + c*F + 0*Z, 0*E + d*F + 0*Z, x*E + y*F + 1*Z)]
# =
# [(a*A + c*B, d*B, x*A + y*B + X),
# (a*C + c*D, d*D, x*C + y*D + Y),
# (a*E + c*F, d*F, x*E + y*F + Z)]
# # IF x=0 and y=0
# =
# [(a*A + c*B, d*B, 0*A + 0*B + X),
# (a*C + c*D, d*D, 0*C + 0*D + Y),
# (a*E + c*F, d*F, 0*E + 0*F + Z)]
# =
# [(a*A + c*B, d*B, X),
# (a*C + c*D, d*D, Y),
# (a*E + c*F, d*F, Z)]
# ---
# A11 = a*A + c*B
# A21 = a*C + c*D
# A31 = a*E + c*F
# A12 = d*B
# A22 = d*D
# A32 = d*F
# A31 = X
# A32 = Y
# A33 = Z
#
# det(A) = A11*(A22*A33 - A23*A32) - A12*(A21*A33 - A23*A31) + A13*(A21*A32 - A22*A31)
det1_mAt = det1_m * Hdet
# Check Error in position and scale
xy_sqrd_err = (x1_mAt - x2_m)**2 + (y1_mAt - y2_m)**2
scale_sqrd_err = det1_mAt / det2_m
# Check to see if outliers are within bounds
xy_inliers = xy_sqrd_err < xy_thresh_sqrd
s1_inliers = scale_sqrd_err > scale_thresh_low
s2_inliers = scale_sqrd_err < scale_thresh_high
_inliers, = np.where(np.logical_and(np.logical_and(xy_inliers, s1_inliers), s2_inliers))
xy1_mHt = transform_xy(H, xy1_m) # Transform Kpts1 to Kpts2-space
sqrd_dist_error = np.sum( (xy1_mHt - xy2_m)**2, axis=0) # Final Inlier Errors
inliers = sqrd_dist_error < xy_thresh_sqrd
df2.show_matches2(rchip1, rchip2, kpts1_m.T[best_inliers1], kpts2_m.T[aff_inliers1], title=title, fignum=2, vert=False)
df2.show_matches2(rchip1, rchip2, kpts1_m.T[best_inliers2], kpts2_m.T[aff_inliers2], title=title, fignum=3, vert=False)
df2.present(wh=(600,400))
elif cmd in ['fig']:
state.fnum_offset += 1
elif cmd in ['smin', 'scale_min']:
state.scale_min = int(ans[1])
update_valid()
elif cmd in ['smax', 'scale_max']:
state.scale_max = int(ans[1])
update_valid()
else:
print('I dont understand the answer. I hope you know what you are doing');
print(raw)
exec raw in globals(), locals()
print('>>>')
except Exception as ex:
print(repr(ex))
if 'doraise' in vars():
raise
if __name__ == '__main__':
from multiprocessing import freeze_support
import draw_func2 as df2
freeze_support()
print('[interact] __main__ ')
main_locals = dev.dev_main()
exec(helpers.execstr_dict(main_locals, 'main_locals'))
fnum = 1
interact1(hs, qon_list, fnum)
#df2.update()
exec(df2.present()) #**df2.OooScreen2()
if __name__ == '__main__':
print("[patch] __name__ == 'extract_patch.py'")
if not 'hs' in vars():
if len(sys.argv) == 1:
db_dir = params.GZ
qcx = 111
low = 0
high=6
else:
db_dir = params.DEFAULT
qcx = helpers.get_arg_after('--qcx', type_=int)
low = helpers.get_arg_after('--low', type_=int)
high = helpers.get_arg_after('--high', type_=int)
if qcx is None:
raise Exception('fds')
qcx = 1
if low is None:
low = 0
if high is None:
high = 6
hs = ld2.HotSpotter()
hs.load_all(db_dir)
#test(hs, qcx)
#test3()
test2(hs, qcx, low=low, high=high)
df2.all_figures_tight_layout()
exec(df2.present(no_tile=True))
