Here's short sample code for PhotoScan 1.1.0 that estimates average reprojection error for all images and also returns dictionary instance that contain camera label, average reprojection error for each image and number of tie-points on each photo.
import math
import PhotoScan
def calc_reprojection(chunk):
point_cloud = chunk.point_cloud
points = point_cloud.points
npoints = len(points)
projections = chunk.point_cloud.projections
err_sum = 0
num = 0
photo_avg = {}
for camera in chunk.cameras:
if not camera.transform:
continue
T = camera.transform.inv()
calib = camera.sensor.calibration
point_index = 0
photo_num = 0
photo_err = 0
for proj in projections[camera]:
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
err_sum += dist
num += 1
photo_num += 1
photo_err += dist
photo_avg[camera.label] = (math.sqrt(photo_err / photo_num), photo_num)
sigma = math.sqrt(err_sum / num)
rep_avg = sigma
return (rep_avg, photo_avg)
doc = PhotoScan.app.document
chunk = doc.chunk
total_error, ind_error = calc_reprojection(chunk)