From 461aac50937eda0e02c1a80f8419b21de39a59c4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Iago=20Su=C3=A1rez?= Date: Sun, 15 Oct 2023 19:19:06 +0200 Subject: [PATCH] Adding integration tests Create python-tests.yml Adding ceres and eigen dependencies Adding coverage Adding parameterized Fixing GPU torch problems Avoiding explicit transfer of the model to CUDA Adding tests for eval utils and standardizing the batched/non-batched behaviour (only tested with homographies) Testing failures in pytest Fixing dummy test Removing MishaKav/pytest-coverage-comment Fixing problems in MegaDepth evaluation Fixing division by 0 and converting function to torch Fixing lint errors Fixing isort errors --- .github/workflows/python-tests.yml | 36 ++++++ docs/boat1.png | Bin 0 -> 531519 bytes docs/boat2.png | Bin 0 -> 593846 bytes gluefactory/eval/hpatches.py | 6 +- gluefactory/eval/utils.py | 45 +++++-- gluefactory/geometry/epipolar.py | 37 +++--- gluefactory/geometry/homography.py | 5 +- gluefactory/geometry/utils.py | 1 + gluefactory/models/matchers/gluestick.py | 2 +- .../models/matchers/lightglue_pretrained.py | 2 +- .../homography/homography_est.py | 14 +- .../robust_estimators/homography/poselib.py | 4 +- gluefactory/utils/tensor.py | 6 + pyproject.toml | 2 +- tests/__init__.py | 0 tests/test_eval_utils.py | 88 +++++++++++++ tests/test_integration.py | 120 ++++++++++++++++++ 17 files changed, 321 insertions(+), 47 deletions(-) create mode 100644 .github/workflows/python-tests.yml create mode 100644 docs/boat1.png create mode 100644 docs/boat2.png create mode 100644 tests/__init__.py create mode 100644 tests/test_eval_utils.py create mode 100644 tests/test_integration.py diff --git a/.github/workflows/python-tests.yml b/.github/workflows/python-tests.yml new file mode 100644 index 00000000..82a3b73c --- /dev/null +++ b/.github/workflows/python-tests.yml @@ -0,0 +1,36 @@ +name: Python Tests +on: + push: + branches: + - main + pull_request: + types: [ assigned, opened, synchronize, reopened ] +jobs: + build: + name: Run Python Tests + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v3 + - uses: actions/setup-python@v4 + with: + python-version: '3.10' + cache: 'pip' + - name: Install dependencies + run: | + sudo apt-get remove libunwind-14-dev || true + sudo apt-get install -y libceres-dev libeigen3-dev + python -m pip install --upgrade pip + python -m pip install pytest pytest-cov + python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu + python -m pip install -e .[dev] + python -m pip install -e .[extra] + - name: Test with pytest + run: | + set -o pipefail + pytest --junitxml=pytest.xml --cov=gluefactory tests/ +# pytest --junitxml=pytest.xml --cov-report=term-missing:skip-covered --cov=gluefactory tests/ | tee pytest-coverage.txt +# - name: Pytest coverage comment +# uses: MishaKav/pytest-coverage-comment@main +# with: +# pytest-coverage-path: ./pytest-coverage.txt +# junitxml-path: ./pytest.xml 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z2`}eH2u~z=Vn|c^8E~yg=99c*R5yO4#|VU^$C*e(;2euh=eB1FL_n*T(;PViz&TX9 zm&-jOk9yE?5oZf_p9O?s9$~f23jZ?Nup%C8)OOw0jvLH zdlX!?{{EBpt_9_^znI1Y$9Q#Z4=u_%%#G0tm)3ib dict: check_keys_recursive(data, ["view0", "view1", "T_0to1"]) check_keys_recursive( @@ -58,23 +69,25 @@ def eval_matches_epipolar(data: dict, pred: dict) -> dict: return results -def eval_matches_homography(data: dict, pred: dict, conf) -> dict: +def eval_matches_homography(data: dict, pred: dict) -> dict: check_keys_recursive(data, ["H_0to1"]) check_keys_recursive( pred, ["keypoints0", "keypoints1", "matches0", "matching_scores0"] ) H_gt = data["H_0to1"] + if H_gt.ndim > 2: + return eval_per_batch_item(data, pred, eval_matches_homography) + kp0, kp1 = pred["keypoints0"], pred["keypoints1"] m0, scores0 = pred["matches0"], pred["matching_scores0"] pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0) - err = sym_homography_error(pts0, pts1, H_gt[0]) + err = sym_homography_error(pts0, pts1, H_gt) results = {} results["prec@1px"] = (err < 1).float().mean().nan_to_num().item() results["prec@3px"] = (err < 3).float().mean().nan_to_num().item() results["num_matches"] = pts0.shape[0] results["num_keypoints"] = (kp0.shape[0] + kp1.shape[0]) / 2.0 - return results @@ -84,7 +97,7 @@ def eval_relative_pose_robust(data, pred, conf): pred, ["keypoints0", "keypoints1", "matches0", "matching_scores0"] ) - T_gt = data["T_0to1"][0] + T_gt = data["T_0to1"] kp0, kp1 = pred["keypoints0"], pred["keypoints1"] m0, scores0 = pred["matches0"], pred["matching_scores0"] pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0) @@ -107,9 +120,8 @@ def eval_relative_pose_robust(data, pred, conf): else: # R, t, inl = ret M = est["M_0to1"] - R, t = M.numpy() inl = est["inliers"].numpy() - r_error, t_error = relative_pose_error(T_gt, R, t) + t_error, r_error = relative_pose_error(T_gt, M.R, M.t) results["rel_pose_error"] = max(r_error, t_error) results["ransac_inl"] = np.sum(inl) results["ransac_inl%"] = np.mean(inl) @@ -119,6 +131,9 @@ def eval_relative_pose_robust(data, pred, conf): def eval_homography_robust(data, pred, conf): H_gt = data["H_0to1"] + if H_gt.ndim > 2: + return eval_per_batch_item(data, pred, eval_relative_pose_robust, conf) + estimator = load_estimator("homography", conf["estimator"])(conf) data_ = {} @@ -158,24 +173,26 @@ def eval_homography_robust(data, pred, conf): return results -def eval_homography_dlt(data, pred, *args): +def eval_homography_dlt(data, pred): H_gt = data["H_0to1"] H_inf = torch.ones_like(H_gt) * float("inf") kp0, kp1 = pred["keypoints0"], pred["keypoints1"] m0, scores0 = pred["matches0"], pred["matching_scores0"] pts0, pts1, scores = get_matches_scores(kp0, kp1, m0, scores0) + scores = scores.to(pts0) results = {} try: - Hdlt = kornia.geometry.homography.find_homography_dlt( - pts0[None], pts1[None], scores[None].to(pts0) - )[0] + if H_gt.ndim == 2: + pts0, pts1, scores = pts0[None], pts1[None], scores[None] + h_dlt = find_homography_dlt(pts0, pts1, scores) + if H_gt.ndim == 2: + h_dlt = h_dlt[0] except AssertionError: - Hdlt = H_inf + h_dlt = H_inf - error_dlt = homography_corner_error(Hdlt, H_gt, data["view0"]["image_size"]) + error_dlt = homography_corner_error(h_dlt, H_gt, data["view0"]["image_size"]) results["H_error_dlt"] = error_dlt.item() - return results diff --git a/gluefactory/geometry/epipolar.py b/gluefactory/geometry/epipolar.py index 7e1507c0..420c99fd 100644 --- a/gluefactory/geometry/epipolar.py +++ b/gluefactory/geometry/epipolar.py @@ -1,4 +1,3 @@ -import numpy as np import torch from .utils import skew_symmetric, to_homogeneous @@ -124,39 +123,39 @@ def decompose_essential_matrix(E): # pose errors -# TODO: port to torch and batch +# TODO: test for batched data def angle_error_mat(R1, R2): - cos = (np.trace(np.dot(R1.T, R2)) - 1) / 2 - cos = np.clip(cos, -1.0, 1.0) # numercial errors can make it out of bounds - return np.rad2deg(np.abs(np.arccos(cos))) + cos = (torch.trace(torch.einsum("...ij, ...jk -> ...ik", R1.T, R2)) - 1) / 2 + cos = torch.clip(cos, -1.0, 1.0) # numerical errors can make it out of bounds + return torch.rad2deg(torch.abs(torch.arccos(cos))) -def angle_error_vec(v1, v2): - n = np.linalg.norm(v1) * np.linalg.norm(v2) - return np.rad2deg(np.arccos(np.clip(np.dot(v1, v2) / n, -1.0, 1.0))) +def angle_error_vec(v1, v2, eps=1e-10): + n = torch.clip(v1.norm(dim=-1) * v2.norm(dim=-1), min=eps) + v1v2 = (v1 * v2).sum(dim=-1) # dot product in the last dimension + return torch.rad2deg(torch.arccos(torch.clip(v1v2 / n, -1.0, 1.0))) def compute_pose_error(T_0to1, R, t): R_gt = T_0to1[:3, :3] t_gt = T_0to1[:3, 3] error_t = angle_error_vec(t, t_gt) - error_t = np.minimum(error_t, 180 - error_t) # ambiguity of E estimation + error_t = torch.minimum(error_t, 180 - error_t) # ambiguity of E estimation error_R = angle_error_mat(R, R_gt) return error_t, error_R -def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0): +def relative_pose_error(T_0to1, R, t, ignore_gt_t_thr=0.0, eps=1e-10): + R_gt, t_gt = T_0to1.R, T_0to1.t + R_gt, t_gt = torch.squeeze(R_gt), torch.squeeze(t_gt) + # angle error between 2 vectors - R_gt, t_gt = T_0to1.numpy() - n = np.linalg.norm(t) * np.linalg.norm(t_gt) - t_err = np.rad2deg(np.arccos(np.clip(np.dot(t, t_gt) / n, -1.0, 1.0))) - t_err = np.minimum(t_err, 180 - t_err) # handle E ambiguity - if np.linalg.norm(t_gt) < ignore_gt_t_thr: # pure rotation is challenging + t_err = angle_error_vec(t, t_gt, eps) + t_err = torch.minimum(t_err, 180 - t_err) # handle E ambiguity + if t_gt.norm() < ignore_gt_t_thr: # pure rotation is challenging t_err = 0 # angle error between 2 rotation matrices - cos = (np.trace(np.dot(R.T, R_gt)) - 1) / 2 - cos = np.clip(cos, -1.0, 1.0) # handle numercial errors - R_err = np.rad2deg(np.abs(np.arccos(cos))) + r_err = angle_error_mat(R, R_gt) - return t_err, R_err + return t_err, r_err diff --git a/gluefactory/geometry/homography.py b/gluefactory/geometry/homography.py index 3acb9307..f87b9f90 100644 --- a/gluefactory/geometry/homography.py +++ b/gluefactory/geometry/homography.py @@ -164,7 +164,8 @@ def warp_points_torch(points, H, inverse=True): The inverse is used to be coherent with tf.contrib.image.transform Arguments: points: batched list of N points, shape (B, N, 2). - homography: batched or not (shapes (B, 3, 3) and (3, 3) respectively). + H: batched or not (shapes (B, 3, 3) and (3, 3) respectively). + inverse: Whether to multiply the points by H or the inverse of H Returns: a Tensor of shape (B, N, 2) containing the new coordinates of the warps. """ @@ -333,7 +334,7 @@ def sym_homography_error_all(kpts0, kpts1, H): def homography_corner_error(T, T_gt, image_size): - W, H = image_size[:, 0], image_size[:, 1] + W, H = image_size[..., 0], image_size[..., 1] corners0 = torch.Tensor([[0, 0], [W, 0], [W, H], [0, H]]).float().to(T) corners1_gt = from_homogeneous(to_homogeneous(corners0) @ T_gt.transpose(-1, -2)) corners1 = from_homogeneous(to_homogeneous(corners0) @ T.transpose(-1, -2)) diff --git a/gluefactory/geometry/utils.py b/gluefactory/geometry/utils.py index eec330a9..4734e341 100644 --- a/gluefactory/geometry/utils.py +++ b/gluefactory/geometry/utils.py @@ -23,6 +23,7 @@ def from_homogeneous(points, eps=0.0): """Remove the homogeneous dimension of N-dimensional points. Args: points: torch.Tensor or numpy.ndarray with size (..., N+1). + eps: Epsilon value to prevent zero division. Returns: A torch.Tensor or numpy ndarray with size (..., N). """ diff --git a/gluefactory/models/matchers/gluestick.py b/gluefactory/models/matchers/gluestick.py index 0187e0c3..e16a8a52 100644 --- a/gluefactory/models/matchers/gluestick.py +++ b/gluefactory/models/matchers/gluestick.py @@ -119,7 +119,7 @@ def _init(self, conf): "Loading GlueStick model from " f'"{self.url.format(conf.version)}"' ) state_dict = torch.hub.load_state_dict_from_url( - self.url.format(conf.version), file_name=fname + self.url.format(conf.version), file_name=fname, map_location="cpu" ) if "model" in state_dict: diff --git a/gluefactory/models/matchers/lightglue_pretrained.py b/gluefactory/models/matchers/lightglue_pretrained.py index b23976db..985a0ce1 100644 --- a/gluefactory/models/matchers/lightglue_pretrained.py +++ b/gluefactory/models/matchers/lightglue_pretrained.py @@ -17,7 +17,7 @@ class LightGlue(BaseModel): def _init(self, conf): dconf = OmegaConf.to_container(conf) - self.net = LightGlue_(dconf.pop("features"), **dconf).cuda() + self.net = LightGlue_(dconf.pop("features"), **dconf) self.set_initialized() def _forward(self, data): diff --git a/gluefactory/robust_estimators/homography/homography_est.py b/gluefactory/robust_estimators/homography/homography_est.py index 510650c4..780011ee 100644 --- a/gluefactory/robust_estimators/homography/homography_est.py +++ b/gluefactory/robust_estimators/homography/homography_est.py @@ -7,6 +7,7 @@ ransac_point_line_homography, ) +from ...utils.tensor import batch_to_numpy from ..base_estimator import BaseEstimator @@ -50,19 +51,20 @@ def _init(self, conf): pass def _forward(self, data): + feat = data["m_kpts0"] if "m_kpts0" in data else data["m_lines0"] + data = batch_to_numpy(data) m_features = { - "kpts0": data["m_kpts1"].numpy() if "m_kpts1" in data else None, - "kpts1": data["m_kpts0"].numpy() if "m_kpts0" in data else None, - "lines0": data["m_lines1"].numpy() if "m_lines1" in data else None, - "lines1": data["m_lines0"].numpy() if "m_lines0" in data else None, + "kpts0": data["m_kpts1"] if "m_kpts1" in data else None, + "kpts1": data["m_kpts0"] if "m_kpts0" in data else None, + "lines0": data["m_lines1"] if "m_lines1" in data else None, + "lines1": data["m_lines0"] if "m_lines0" in data else None, } - feat = data["m_kpts0"] if "m_kpts0" in data else data["m_lines0"] M = H_estimation_hybrid(**m_features, tol_px=self.conf.ransac_th) success = M is not None if not success: M = torch.eye(3, device=feat.device, dtype=feat.dtype) else: - M = torch.tensor(M).to(feat) + M = torch.from_numpy(M).to(feat) estimation = { "success": success, diff --git a/gluefactory/robust_estimators/homography/poselib.py b/gluefactory/robust_estimators/homography/poselib.py index e99e9493..6aa71496 100644 --- a/gluefactory/robust_estimators/homography/poselib.py +++ b/gluefactory/robust_estimators/homography/poselib.py @@ -16,8 +16,8 @@ def _init(self, conf): def _forward(self, data): pts0, pts1 = data["m_kpts0"], data["m_kpts1"] M, info = poselib.estimate_homography( - pts0.numpy(), - pts1.numpy(), + pts0.detach().cpu().numpy(), + pts1.detach().cpu().numpy(), { "max_reproj_error": self.conf.ransac_th, **OmegaConf.to_container(self.conf.options), diff --git a/gluefactory/utils/tensor.py b/gluefactory/utils/tensor.py index f31bb580..d0a8ca50 100644 --- a/gluefactory/utils/tensor.py +++ b/gluefactory/utils/tensor.py @@ -40,3 +40,9 @@ def rbd(data: dict) -> dict: k: v[0] if isinstance(v, (torch.Tensor, np.ndarray, list)) else v for k, v in data.items() } + + +def index_batch(tensor_dict): + batch_size = len(next(iter(tensor_dict.values()))) + for i in range(batch_size): + yield map_tensor(tensor_dict, lambda t: t[i]) diff --git a/pyproject.toml b/pyproject.toml index 5185a753..16288141 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -43,7 +43,7 @@ extra = [ "deeplsd @ git+https://github.com/cvg/DeepLSD.git", "homography_est @ git+https://github.com/rpautrat/homography_est.git", ] -dev = ["black", "flake8", "isort"] +dev = ["black", "flake8", "isort", "parameterized"] [tool.setuptools.packages.find] include = ["gluefactory*"] diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/test_eval_utils.py b/tests/test_eval_utils.py new file mode 100644 index 00000000..fead8964 --- /dev/null +++ b/tests/test_eval_utils.py @@ -0,0 +1,88 @@ +import unittest + +import torch + +from gluefactory.eval.utils import eval_matches_homography +from gluefactory.geometry.homography import warp_points_torch + + +class TestEvalUtils(unittest.TestCase): + @staticmethod + def default_pts(): + return torch.tensor( + [ + [10.0, 10.0], + [10.0, 20.0], + [20.0, 20.0], + [20.0, 10.0], + ] + ) + + @staticmethod + def default_pred(kps0, kps1): + return { + "keypoints0": kps0, + "keypoints1": kps1, + "matches0": torch.arange(len(kps0)), + "matching_scores0": torch.ones(len(kps1)), + } + + def test_eval_matches_homography_trivial(self): + data = {"H_0to1": torch.eye(3)} + kps = self.default_pts() + pred = self.default_pred(kps, kps) + + results = eval_matches_homography(data, pred) + + self.assertEqual(results["prec@1px"], 1) + self.assertEqual(results["prec@3px"], 1) + self.assertEqual(results["num_matches"], 4) + self.assertEqual(results["num_keypoints"], 4) + + def test_eval_matches_homography_real(self): + data = {"H_0to1": torch.tensor([[1.5, 0.2, 21], [-0.3, 1.6, 33], [0, 0, 1.0]])} + kps0 = self.default_pts() + kps1 = warp_points_torch(kps0, data["H_0to1"], inverse=False) + pred = self.default_pred(kps0, kps1) + + results = eval_matches_homography(data, pred) + + self.assertEqual(results["prec@1px"], 1) + self.assertEqual(results["prec@3px"], 1) + + def test_eval_matches_homography_real_outliers(self): + data = {"H_0to1": torch.tensor([[1.5, 0.2, 21], [-0.3, 1.6, 33], [0, 0, 1.0]])} + kps0 = self.default_pts() + kps0 = torch.cat([kps0, torch.tensor([[5.0, 5.0]])]) + kps1 = warp_points_torch(kps0, data["H_0to1"], inverse=False) + # Move one keypoint 1.5 pixels away in x and y + kps1[-1] += 1.5 + pred = self.default_pred(kps0, kps1) + + results = eval_matches_homography(data, pred) + self.assertAlmostEqual(results["prec@1px"], 0.8) + self.assertAlmostEqual(results["prec@3px"], 1.0) + + def test_eval_matches_homography_batched(self): + H0 = torch.tensor([[1.5, 0.2, 21], [-0.3, 1.6, 33], [0, 0, 1.0]]) + H1 = torch.tensor([[0.7, 0.1, -5], [-0.1, 0.65, 13], [0, 0, 1.0]]) + data = {"H_0to1": torch.stack([H0, H1])} + kps0 = torch.stack([self.default_pts(), self.default_pts().flip(0)]) + kps1 = warp_points_torch(kps0, data["H_0to1"], inverse=False) + # In the first element of the batch there is one outlier + kps1[0, -1] += 5 + matches0 = torch.stack([torch.arange(4), torch.arange(4)]) + # In the second element of the batch there is only 2 matches + matches0[1, :2] = -1 + pred = { + "keypoints0": kps0, + "keypoints1": kps1, + "matches0": matches0, + "matching_scores0": torch.ones_like(matches0), + } + + results = eval_matches_homography(data, pred) + self.assertAlmostEqual(results["prec@1px"][0], 0.75) + self.assertAlmostEqual(results["prec@1px"][1], 1.0) + self.assertAlmostEqual(results["num_matches"][0], 4) + self.assertAlmostEqual(results["num_matches"][1], 2) diff --git a/tests/test_integration.py b/tests/test_integration.py new file mode 100644 index 00000000..9835f566 --- /dev/null +++ b/tests/test_integration.py @@ -0,0 +1,120 @@ +import unittest +from collections import namedtuple + +import cv2 +import torch.cuda +from kornia import image_to_tensor +from omegaconf import OmegaConf +from parameterized import parameterized +from torch import Tensor + +from gluefactory.eval.utils import ( + eval_homography_dlt, + eval_homography_robust, + eval_matches_homography, +) +from gluefactory.models.two_view_pipeline import TwoViewPipeline +from gluefactory.settings import root +from gluefactory.utils.image import ImagePreprocessor +from gluefactory.utils.tensor import map_tensor +from gluefactory.utils.tools import set_seed + + +def create_input_data(cv_img0, cv_img1, device): + img0 = image_to_tensor(cv_img0).float() / 255 + img1 = image_to_tensor(cv_img1).float() / 255 + ip = ImagePreprocessor({}) + data = {"view0": ip(img0), "view1": ip(img1)} + data = map_tensor( + data, + lambda t: t[None].to(device) + if isinstance(t, Tensor) + else torch.from_numpy(t)[None].to(device), + ) + return data + + +def squeeze_if_tensor(t): + return torch.squeeze(t, dim=0) if isinstance(t, Tensor) else t + + +ExpectedResults = namedtuple("ExpectedResults", ("num_matches", "prec3px", "h_error")) + + +class TestIntegration(unittest.TestCase): + methods_to_test = [ + ("superpoint+NN.yaml", "poselib", ExpectedResults(1300, 0.8, 1.0)), + ("superpoint-open+NN.yaml", "poselib", ExpectedResults(1300, 0.8, 1.0)), + ( + "superpoint+lsd+gluestick.yaml", + "homography_est", + ExpectedResults(1300, 0.8, 1.0), + ), + ( + "superpoint+lightglue-official.yaml", + "poselib", + ExpectedResults(1300, 0.8, 1.0), + ), + ] + + @parameterized.expand(methods_to_test) + def test_real_homography(self, conf_file, estimator, exp_results): + set_seed(0) + model_path = root / "gluefactory" / "configs" / conf_file + img_path0 = root / "docs" / "boat1.png" + img_path1 = root / "docs" / "boat2.png" + h_gt = torch.tensor( + [ + [0.85799, 0.21669, 9.4839], + [-0.21177, 0.85855, 130.48], + [1.5015e-06, 9.2033e-07, 1], + ] + ) + + device = "cuda" if torch.cuda.is_available() else "cpu" + gs = TwoViewPipeline(OmegaConf.load(model_path).model).to(device).eval() + + cv_img0, cv_img1 = cv2.imread(str(img_path0)), cv2.imread(str(img_path1)) + data = create_input_data(cv_img0, cv_img1, device) + pred = gs(data) + pred = map_tensor(pred, squeeze_if_tensor) + data["H_0to1"] = h_gt.to(device) + data["H_1to0"] = torch.linalg.inv(h_gt).to(device) + + results = eval_matches_homography(data, pred) + results = {**results, **eval_homography_dlt(data, pred)} + + results = { + **results, + **eval_homography_robust( + data, + pred, + {"estimator": estimator}, + ), + } + + print(results) + self.assertGreater(results["num_matches"], exp_results.num_matches) + self.assertGreater(results["prec@3px"], exp_results.prec3px) + self.assertLess(results["H_error_ransac"], exp_results.h_error) + + # pred = batch_to_numpy(pred) + # # pred = map_tensor(pred, lambda t: t[0].detach().cpu().numpy()) + # kp0, kp1 = pred['keypoints0'], pred['keypoints1'] + # m0 = pred['matches0'] + # valid0 = m0 != -1 + # kpm0, kpm1 = kp0[valid0], kp1[m0[valid0]] + # + # plot_images([cv_img0, cv_img1]) + # plot_matches(kpm0, kpm1, a=0.0) + # plt.savefig(f'point_matches.png') + # + # lines0, lines1 = pred['lines0'], pred['lines1'] + # lm0 = pred['line_matches0'] + # lvalid0 = lm0 != -1 + # linem0, linem1 = lines0[lvalid0], lines1[lm0[lvalid0]] + # + # plot_images([cv_img0, cv_img1]) + # plot_color_line_matches([linem0, linem1]) + # plt.savefig(f'line_matches.svg') + # plt.show()