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Author Topic: Reprojection error of sparse point cloud  (Read 6562 times)

b3059651

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Reprojection error of sparse point cloud
« on: April 30, 2015, 10:20:33 PM »
Hi all,

It is great the support we get from this forum to solve issues. It seems that Agisoft provides fantastic results. :)

I would like to export the reprojection error of a sparse point cloud in order to perform accuracy assessment of my results.

This follows the same question from here: http://www.agisoft.com/forum/index.php?topic=3534.msg19362#msg19362

Please any feedback would be great!!!

Regards,
Maria

Alexey Pasumansky

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Re: Reprojection error of sparse point cloud
« Reply #1 on: May 25, 2015, 06:22:15 PM »
Hello Maria,

Sorry for the late reply.

Please try the following script:

Code: [Select]
# Compatibility - Agisoft PhotoScan Professional 1.1.6
# saves reprojection error for the tie points in the cloud

# export format:
# point_index X-coord Y-coord Z-coord reproj_error

import PhotoScan
import math, time

doc = PhotoScan.app.document
chunk = doc.chunk
M = chunk.transform.matrix
crs = chunk.crs
point_cloud = chunk.point_cloud
projections = point_cloud.projections
points = point_cloud.points
npoints = len(points)
tracks = point_cloud.tracks

path = PhotoScan.app.getSaveFileName("Specify export path and filename:")
file = open(path, "wt")
print("Script started")

t0 = time.time()

points_coords = {}
points_errors = {}

for photo in chunk.cameras:

if not photo.transform:
continue

T = photo.transform.inv()
calib = photo.sensor.calibration

point_index = 0
for proj in projections[photo]:
track_id = proj.track_id
while point_index < npoints and points[point_index].track_id < track_id:
point_index += 1
if point_index < npoints and points[point_index].track_id == track_id:
if not points[point_index].valid:
continue

dist = calib.error(T.mulp(points[point_index].coord), proj.coord).norm() ** 2
v = M.mulp(points[point_index].coord)

if point_index in points_errors.keys():
point_index = int(point_index)
points_errors[point_index].x += dist
points_errors[point_index].y += 1
else:
points_errors[point_index] = PhotoScan.Vector([dist, 1])

for point_index in range(npoints):

if not points[point_index].valid:
continue

if chunk.crs:
X, Y, Z = chunk.crs.project(M.mulp(points[point_index].coord))
else:
X, Y, Z = M.mulp(points[point_index].coord)

error = math.sqrt(points_errors[point_index].x / points_errors[point_index].y)

file.write("{}\t{:.6f}\t{:.6f}\t{:.6f}\t{:.6f}\n".format(point_index, X, Y, Z, error))

t1 = time.time()

file.flush()
file.close()
print("Script finished in " + str(int(t1-t0)) + " seconds.")
Best regards,
Alexey Pasumansky,
Agisoft LLC

n@sk

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Re: Reprojection error of sparse point cloud
« Reply #2 on: May 26, 2015, 05:38:57 PM »
Hello

Maria, thanks for the request
 
Alexey, many thanks for your effort, I have been looking forward to this

I was wondering if this is what Gradual Selection->Reprojection Error is based on

If this is the case, there's a slight discrepancy, due to rounding errors perhaps?
...not a big deal , but it might affect people with OCD; I suppose I am one of them   ???

for example
I used the GUI to delete points with Gradual Selection->Reprojection Error  > 1px
and the script to export them:

Most of the times there was one single point with error slightly larger than 1px left behind i.e. 1.004907 or 1.031976

any ideas?

regards

n@sk

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« Last Edit: August 30, 2016, 01:46:14 AM by n@sk »