def test_poly_to_from_dict(order):
t = Transform('PolynomialModel', order=order)
t.parameters += np.random.randn(*t.parameters.shape) * 0.1
jt = t.to_dict()
t2 = Transform(json=jt)
assert t2.order == t.order
assert np.allclose(t2.parameters, t.parameters)
tp = PolynomialModel(json=jt)
assert tp.order == t.order
assert np.allclose(tp.parameters, t.parameters)
def test_chunked_ranges():
order = 1
axis = 2
t = Transform('ChunkedModel', order=order, axis=axis, nchunks=10)
src = np.random.randn(1000, 3)
t.estimate(src, src)
t2 = Transform('ChunkedModel', order=order, axis=axis, ranges=t.ranges)
assert t.nchunks == t2.nchunks
assert np.allclose(t.ranges, t2.ranges)
t2 = Transform('ChunkedModel', order=order, axis=axis, nchunks=t.nchunks)
t2.set_ranges(ranges=t.ranges)
assert t.nchunks == t2.nchunks
assert np.allclose(t.ranges, t2.ranges)
def test_polynomial_solution(order):
shape = (100, 3)
src = np.random.randn(*shape) * 1000
t = Transform(name="PolynomialModel", order=1)
t.parameters = np.array([[100, -234, 456], [1.01, 0.01, -0.01],
[-0.002, 0.97, 0.05], [-0.03, 0.001, 1.02]])
noise = np.random.randn(*shape)
dst = t.tform(src) + noise
tfit = Transform(name="PolynomialModel", order=order)
tfit.estimate(src, dst)
tdst = tfit.tform(src)
residuals = tdst - dst
rmag = np.linalg.norm(residuals, axis=1)
assert np.all(rmag < np.linalg.norm(noise, axis=1).max() * 2)
def test_chunked_values():
order = 1
axis = 2
nchunks = 10
src = np.random.rand(5000, 3)
t1 = PolynomialModel(order=order, parameters=random_affine_parameters())
t2 = PolynomialModel(order=order, parameters=random_affine_parameters())
ranges = np.linspace(src[:, axis].min(), src[:, axis].max(), nchunks)[1:-1]
inds_for_t2 = [3, 6, 7]
dst = np.zeros_like(src)
which = np.searchsorted(ranges, src[:, axis])
for i in range(nchunks):
inds = which == i
tf = t1
if i in inds_for_t2:
tf = t2
dst[inds, :] = tf.tform(src[inds, :])
t = Transform('ChunkedModel', order=order, axis=axis, ranges=ranges)
t.estimate(src, dst)
tsrc = t.tform(src)
res = np.linalg.norm(tsrc - dst, axis=1)
assert res.mean() < np.linalg.norm(dst, axis=1).mean() * 0.001
tc = ChunkedModel(order=order, axis=axis, ranges=ranges)
tc.estimate(src, dst)
tsrc = tc.tform(src)
res = np.linalg.norm(tsrc - dst, axis=1)
assert res.mean() < np.linalg.norm(dst, axis=1).mean() * 0.001
# to and from dict
jt = t.to_dict()
td = Transform(json=jt)
tsrc = td.tform(src)
res = np.linalg.norm(tsrc - dst, axis=1)
assert res.mean() < np.linalg.norm(dst, axis=1).mean() * 0.001
def test_transform_list():
tp = Transform(name="PolynomialModel", order=2)
tp.parameters[0, :] = np.random.randn(3)
tp.parameters[1:4, :] += np.random.randn(3, 3) * 0.02
tp.parameters[4:10, :] += np.random.randn(6, 3) * 0.0002
src = np.random.randn(3000, 3)
noise = np.random.randn(*src.shape) * 0.001
dst = tp.tform(src) + noise
tflist_args = [{
'name': "PolynomialModel",
'order': 1
}, {
'name': 'ChunkedModel',
'order': 1,
'axis': 2,
'nchunks': 4
}, {
'name': 'SplineModel',
'ncntrl': [3, 3, 3]
}, {
'name': 'SplineModel',
'src_is_cntrl': True
}]
tflist = TransformList(transforms=tflist_args)
tflist.estimate(src, dst)
tsrc = tflist.tform(src)
rmag = np.linalg.norm(dst - tsrc, axis=1)
assert rmag.mean() < 0.001
# to from dict
tflist2 = TransformList(json=tflist.to_dict())
tsrc2 = tflist2.tform(src)
rmag2 = np.linalg.norm(dst - tsrc2, axis=1)
assert rmag2.mean() < 0.001
assert np.allclose(tsrc, tsrc2)
# as Transform
tf = Transform(json=tflist.to_dict())
tsrc3 = tf.tform(src)
rmag3 = np.linalg.norm(dst - tsrc3, axis=1)
assert rmag3.mean() < 0.001
assert np.allclose(tsrc, tsrc3)
def test_chunked_identity(order, axis):
t = Transform('ChunkedModel', order=order, axis=axis, nchunks=10)
src = np.random.rand(10000, 3)
t.estimate(src, src)
dst = t.tform(src)
assert np.allclose(src, dst)
identity = Transform("PolynomialModel", order=order)
for tf in t.transforms:
if order == 3:
assert np.allclose(identity.parameters,
tf.parameters,
rtol=0,
atol=1e-3)
else:
assert np.allclose(identity.parameters, tf.parameters)
args_path = "./data/staged_transform_args.json"
output_path = "./data/staged_transform_solution.json"
s = Solve3D(args=["--input_json", args_path, "--output_json", output_path])
s.run()
print('worst points')
for r in s.sorted_labeled_residuals[0:10]:
print("%10s %10.6f" % (r[0], r[1]))
# look at residuals at every step of transform
with open(output_path, 'r') as f:
jout = json.load(f)
ntransforms = len(jout['transforms'])
for i in range(1, ntransforms + 1):
jtemp = copy.deepcopy(jout)
jtemp['transforms'] = jtemp['transforms'][0:i]
tf = Transform(json=jtemp)
residuals = tf.tform(s.data['src']) - s.data['dst']
rmag = np.linalg.norm(residuals, axis=1).mean()
lastone = tf.transforms[-1].__class__.__name__
infostr = "{} residual {:0.6f}".format(lastone, rmag)
if lastone == "SplineModel":
src = tf.transforms[-1].control_pts
dst = tf.transforms[-1].tform(src)
mov = np.linalg.norm(dst - src, axis=1).mean()
infostr += " {} cntrls moved {:0.6f}".format(src.shape[0], mov)
print(infostr)
alldata = s.predict_all_data()
# re-invert y for output back to annotators
alldata.data['src'][:, 1] = invert_y(alldata.data['src'][:, 1])
def test_polynomial_identity(order):
t = Transform(name="PolynomialModel", order=order)
src = np.random.randn(100, 3)
assert np.allclose(t.tform(src), src)
def test_polynomial_order(order):
with pytest.raises(ValueError):
Transform(name="PolynomialModel", order=order)
def test_spline_complicated_solution():
tp = Transform(name="PolynomialModel", order=2)
tp.parameters[0, :] = np.random.randn(3)
tp.parameters[1:4, :] += np.random.randn(3, 3) * 0.02
tp.parameters[4:10, :] += np.random.randn(6, 3) * 0.0002
src = np.random.randn(3000, 3)
noise = np.random.randn(*src.shape) * 0.001
dst = tp.tform(src) + noise
ts = Transform(name="SplineModel", ncntrl=[10, 10, 10])
ts.estimate(src, dst)
res = ts.tform(src) - dst
rmag = np.linalg.norm(res, axis=1)
assert rmag.mean() < np.linalg.norm(noise, axis=1).mean() * 10
# to from dict
td = Transform(json=ts.to_dict())
res = td.tform(src) - dst
rmag = np.linalg.norm(res, axis=1)
assert rmag.mean() < np.linalg.norm(noise, axis=1).mean() * 10
# set via init
tinit = Transform(name="SplineModel",
parameters=td.parameters.tolist(),
control_pts=td.control_pts.tolist())
res = tinit.tform(src) - dst
rmag = np.linalg.norm(res, axis=1)
assert rmag.mean() < np.linalg.norm(noise, axis=1).mean() * 10
# exact src points
ts = Transform(name="SplineModel", src_is_cntrl=True)
ts.estimate(src, dst)
res = ts.tform(src) - dst
rmag = np.linalg.norm(res, axis=1)
assert rmag.mean() < 1e-3
def test_spline_solution():
t = Transform(name="SplineModel")
src = np.random.randn(100, 3)
t.estimate(src, src)
assert np.allclose(t.tform(src), src)
def test_spline_identity():
t = Transform(name="SplineModel")
src = np.random.randn(100, 3)
t.set_control_pts_from_src(src)
assert np.allclose(t.tform(src), src)