def fitness_e(particle):
if not args.nerror:
return -float('inf')
globals()['Ftimes'] += args.nerror
for i in range(len(args.cameras)):
x, h, d, beta = particle[4 * i: 4 * (i + 1)]
if d < lut[i].bounds[0] or d > lut[i].bounds[1]:
return -float('inf')
modify_camera(ex.model, args.cameras[i][0], lut[i],
x, h, d, beta)
original_pose = {}
for obj in [ex.tasks[args.task].mount.name] + [c[0] for c in args.cameras]:
original_pose[obj] = ex.model[obj].pose
perf = []
for i in range(args.nerror):
for obj in original_pose:
if obj in ex.model.cameras:
ex.model[obj].set_absolute_pose(gaussian_yz_pose_error(original_pose[obj], *args.cerror))
else:
ex.model[obj].set_absolute_pose(gaussian_pose_error(original_pose[obj], *args.terror))
coverage = ex.model.range_coverage(ex.tasks[args.task], transport)
perf.append(ex.model.performance(ex.tasks[args.task], coverage=coverage))
for obj in original_pose:
ex.model[obj].set_absolute_pose(original_pose[obj])
return sum(perf) / float(args.nerror)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-C', '--cerror', dest='cerror', type=float,
nargs=2, default=(0.0, 0.0))
parser.add_argument('-T', '--terror', dest='terror', type=float,
nargs=2, default=(0.0, 0.0))
parser.add_argument('-n', '--number', dest='number', type=int, default=1)
parser.add_argument('datafile')
args = parser.parse_args()
result = pickle.load(open(args.datafile, 'r'))
ex, lut = load_model(result['args'].modelfile, result['args'].cameras, result['args'].fnumber)
for c, camera in enumerate(result['args'].cameras):
modify_camera(ex.model, camera[0], lut[c], *(result['best'][4 * c: 4 * (c + 1)]))
original_pose = {}
for obj in [ex.tasks[result['args'].task].mount.name] + [c[0] for c in result['args'].cameras]:
original_pose[obj] = ex.model[obj].pose
perf = []
for i in range(args.number):
for obj in original_pose:
if obj in ex.model.cameras:
ex.model[obj].set_absolute_pose(gaussian_yz_pose_error(original_pose[obj], *args.cerror))
else:
ex.model[obj].set_absolute_pose(gaussian_pose_error(original_pose[obj], *args.terror))
coverage = ex.model.range_coverage(ex.tasks[result['args'].task], RangeModel.LinearTargetTransport)
perf.append(ex.model.performance(ex.tasks[result['args'].task], coverage=coverage))
print('Nominal Performance: %g' % result['F'][-1])
print('Average Performance: %g' % (sum(perf) / float(len(perf))))
