1.,
1.,
]])
## CREATE A SET OF IMAGE POINTS FOR VALIDATION OF THE HOMOGRAPHY ESTIMATION
# This will create a grid of 16 points of size = (0.3,0.3) meters
validation_plane = Plane(origin=np.array([0, 0, 0]),
normal=np.array([0, 0, 1]),
size=(0.3, 0.3),
n=(4, 4))
validation_plane.uniform()
## we create the gradient for the point distribution
normalize = False
n = 0.00000004 #condition number norm 4 points
gradient = gd.create_gradient(metric='condition_number', n=n)
#define the plots
#one Figure for image and object points
fig11 = plt.figure('Image Plane Coordinates')
ax_image = fig11.add_subplot(111)
fig12 = plt.figure('Control Points plane Coordinates')
ax_object = fig12.add_subplot(111)
if calc_metrics:
#another figure for Homography error and condition numbers
fig2 = plt.figure('Effect of point configuration in homography estimation')
ax_cond = plt.subplot(211)
ax_homo_error = plt.subplot(212, sharex=ax_cond)
#another figure for Pose errors
#%% ## CREATE A SET OF IMAGE POINTS FOR VALIDATION OF THE HOMOGRAPHY ESTIMATION validation_plane = Plane(origin=np.array([0, 0, 0]), normal = np.array([0, 0, 1]), size=(0.3,0.3), n = (4,4)) validation_plane.uniform() ## we create the gradient for the point distribution normalize= False n = 0.00001 #condition number norm gradient = gd.create_gradient(metric='repro_error', n = n) #normalize= True #n = 0.0001 #condition number norm #gradient = gd.create_gradient(metric='condition_number', n = n) #gradient = gd.create_gradient(metric='pnorm_condition_number') #gradient = gd.create_gradient(metric='volker_metric') objectPoints_des = pl.get_points() # we now replace the first 4 points with the border positions #pl.uniform()
