Merge branch 'master' of https://github.com/borglab/gtsfm into avg-co…

…ord-nms

.github/workflows/benchmark-self-hosted.yml

@@ -29,7 +29,7 @@ jobs:
       PYTHON_VERSION: 3.8
 
     steps:
-      - uses: actions/checkout@v4.0.0
+      - uses: actions/checkout@v4.1.0
       - name: Cache frontend
         uses: actions/cache@v3
         env:

.github/workflows/benchmark.yml

@@ -36,7 +36,7 @@ jobs:
       PYTHON_VERSION: 3.8
 
     steps:
-      - uses: actions/checkout@v4.0.0
+      - uses: actions/checkout@v4.1.0
       - name: Cache frontend
         uses: actions/cache@v3
         env:

.github/workflows/build-wheels.yml

@@ -18,7 +18,7 @@ jobs:
         shell: bash -l {0}
 
     steps:
-      - uses: actions/checkout@v4.0.0
+      - uses: actions/checkout@v4.1.0
       - uses: conda-incubator/setup-miniconda@v2
         with:
           mamba-version: "*"

.github/workflows/test-python.yml

@@ -14,7 +14,7 @@ jobs:
       PYTHON_VERSION: 3.8
 
     steps:
-      - uses: actions/checkout@v4.0.0
+      - uses: actions/checkout@v4.1.0
       - uses: conda-incubator/setup-miniconda@v2
         with:
           mamba-version: "*"

.github/workflows/test-reproducibility.yml

@@ -14,7 +14,7 @@ jobs:
       PYTHON_VERSION: 3.8
 
     steps:
-      - uses: actions/checkout@v4.0.0
+      - uses: actions/checkout@v4.1.0
       - uses: conda-incubator/setup-miniconda@v2
         with:
           mamba-version: "*"

gtsfm/common/keypoints.py

@@ -34,9 +34,9 @@ def __init__(
         """Initializes the attributes.
 
         Args:
-            coordinates: the (x, y) coordinates of the features, of shape Nx2.
-            scales: optional scale of the detections, of shape N.
-            responses: optional confidences/responses for each detection, of shape N.
+            coordinates: The (x, y) coordinates of the features, of shape Nx2.
+            scales: Optional scale of the detections, of shape N.
+            responses: Optional confidences/responses for each detection, of shape N.
         """
         self.coordinates = coordinates
         self.scales = scales
@@ -61,10 +61,10 @@ def __eq__(self, other: object) -> bool:
         if not isinstance(other, Keypoints):
             return False
 
-        # equality check on coordinates
+        # Equality check on coordinates.
         coordinates_eq = np.array_equal(self.coordinates, other.coordinates)
 
-        # equality check on scales
+        # Equality check on scales.
         if self.scales is None and other.scales is None:
             scales_eq = True
         elif self.scales is not None and other.scales is not None:
@@ -93,10 +93,10 @@ def get_top_k(self, k: int) -> Tuple["Keypoints", np.ndarray]:
         If k keypoints are requested, and only n < k are available, then returning n keypoints is the expected behavior.
 
         Args:
-            k: max number of keypoints to return.
+            k: Maximum number of keypoints to return.
 
         Returns:
-            subset of current keypoints.
+            Subset of current keypoints.
         """
         if k >= len(self):
             return copy.deepcopy(self), np.arange(self.__len__())
@@ -114,7 +114,7 @@ def filter_by_mask(self, mask: np.ndarray) -> Tuple["Keypoints", np.ndarray]:
 
         Args:
             mask: (H, W) array of 0's and 1's corresponding to valid portions of the original image.
-            keypoints: detected keypoints with length M.
+            keypoints: Detected keypoints with length M.
             descriptors: (M, D) array of descriptors D is the dimension of each descriptor.
 
         Returns:
@@ -148,7 +148,7 @@ def cast_to_float(self) -> "Keypoints":
         """Cast all attributes which are numpy arrays to float.
 
         Returns:
-            keypoints with the type-casted attributes.
+            Keypoints with the type-casted attributes.
         """
         return Keypoints(
             coordinates=None if self.coordinates is None else self.coordinates.astype(np.float32),
@@ -166,7 +166,7 @@ def cast_to_opencv_keypoints(self) -> List[cv.KeyPoint]:
             List of OpenCV's keypoints with the same information as input keypoints.
         """
 
-        # cast input attributed to floating point numpy arrays.
+        # Cast input attributed to floating point numpy arrays.
         keypoints = self.cast_to_float()
 
         opencv_keypoints = []
@@ -216,14 +216,13 @@ def extract_indices(self, indices: np.ndarray) -> "Keypoints":
         """Form subset with the given indices.
 
         Args:
-            indices: indices to extract, as a 1-D vector.
+            indices: Indices to extract, as a 1-D vector.
 
         Returns:
             Subset of data at the given indices.
         """
-
         if indices.size == 0:
-            return Keypoints(coordinates=np.array([]))
+            return Keypoints(coordinates=np.zeros(shape=(0, 2)))
 
         return Keypoints(
             self.coordinates[indices],

gtsfm/data_association/cpp_dsf_tracks_estimator.py

@@ -49,9 +49,15 @@ def run(self, matches_dict: Dict[Tuple[int, int], np.ndarray], keypoints_list: L
         # Check to ensure dimensions of coordinates are correct.
         dims_valid = all([kps.coordinates.ndim == 2 for kps in keypoints_list])
         if not dims_valid:
-            raise Exception(
-                "Dimensions for Keypoint coordinates incorrect. Array needs to be 2D, but found "
-                f"{[kps.coordinates.shape for kps in keypoints_list]}"
+            error_str = " ".join(
+                [
+                    f"i={i}: shape={kps.coordinates.shape},"
+                    for i, kps in enumerate(keypoints_list)
+                    if kps.coordinates.ndim != 2
+                ]
+            )
+            raise ValueError(
+                f"Dimensions for Keypoint coordinates incorrect. Array needs to be 2D, but found {error_str}"
             )
 
         # For each image pair (i1,i2), we provide a (K,2) matrix of corresponding keypoint indices (k1,k2).
@@ -65,11 +71,7 @@ def run(self, matches_dict: Dict[Tuple[int, int], np.ndarray], keypoints_list: L
         for keypoint in keypoints_list:
             keypoints_vector.append(gtsam.gtsfm.Keypoints(keypoint.coordinates))
 
-        tracks = gtsam.gtsfm.tracksFromPairwiseMatches(
-            matches_map,
-            keypoints_vector,
-            verbose=True,
-        )
+        tracks = gtsam.gtsfm.tracksFromPairwiseMatches(matches_map, keypoints_vector, verbose=True)
 
         track_2d_list = []
         for track in tracks:

gtsfm/data_association/point3d_initializer.py

@@ -69,15 +69,15 @@ class TriangulationOptions(NamedTuple):
     See the following slides for a derivation of the #req'd samples: http://www.cse.psu.edu/~rtc12/CSE486/lecture15.pdf
 
     Args:
-        mode: triangulation mode, which dictates whether or not to use robust estimation.
-        reproj_error_threshold: the maximum reprojection error allowed.
-        min_triangulation_angle: threshold for the minimum angle (in degrees) subtended at the triangulated track from 2
+        mode: Triangulation mode, which dictates whether or not to use robust estimation.
+        reproj_error_threshold: The maximum reprojection error allowed.
+        min_triangulation_angle: Threshold for the minimum angle (in degrees) subtended at the triangulated track from 2
             cameras. Tracks for which the maximum angle at any two cameras is less then this threshold will be rejected.
-        min_inlier_ratio: a priori assumed minimum probability that a point is an inlier.
-        confidence: desired confidence that at least one hypothesis is outlier free.
+        min_inlier_ratio: A priori assumed minimum probability that a point is an inlier.
+        confidence: Desired confidence that at least one hypothesis is outlier free.
         dyn_num_hypotheses_multiplier: multiplication factor for dynamically computed hyptheses based on confidence.
-        min_num_hypotheses: minimum number of hypotheses.
-        max_num_hypotheses: maximum number of hypotheses.
+        min_num_hypotheses: Minimum number of hypotheses.
+        max_num_hypotheses: Maximum number of hypotheses.
     """
 
     mode: TriangulationSamplingMode
@@ -123,15 +123,18 @@ class Point3dInitializer:
 
     Args:
         track_cameras: Dict of cameras and their indices.
-        mode: triangulation mode, which dictates whether or not to use robust estimation.
-        reproj_error_thresh: threshold on reproj errors for inliers.
-        num_ransac_hypotheses (optional): desired number of RANSAC hypotheses.
+        mode: Triangulation mode, which dictates whether or not to use robust estimation.
+        reproj_error_thresh: Threshold on reproj errors for inliers.
+        num_ransac_hypotheses (optional): Desired number of RANSAC hypotheses.
     """
 
     def __init__(self, track_camera_dict: Dict[int, gtsfm_types.CAMERA_TYPE], options: TriangulationOptions) -> None:
         self.track_camera_dict = track_camera_dict
         self.options = options
 
+        if len(track_camera_dict) == 0:
+            raise ValueError("No camera positions were estimated, so triangulation is not feasible.")
+
         sample_camera = list(self.track_camera_dict.values())[0]
         self._camera_set_class = (
             CameraSetCal3Bundler if isinstance(sample_camera, PinholeCameraCal3Bundler) else CameraSetCal3Fisheye
@@ -142,23 +145,23 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
         RANSAC chooses one of 3 different sampling schemes to execute.
 
         Args:
-            track: feature track with N 2d measurements in separate images
+            track: Feature track with N 2d measurements in separate images
 
         Returns:
-            best_inliers: boolean array of length N. Indices of measurements
-               are set to true if they correspond to the best RANSAC hypothesis
+            best_inliers: Boolean array of length N. Indices of measurements
+               are set to true if they correspond to the best RANSAC hypothesis.
         """
 
-        # Generate all possible matches
+        # Generate all possible matches.
         measurement_pairs = generate_measurement_pairs(track_2d)
 
-        # limit the number of samples to the number of available pairs
+        # Limit the number of samples to the number of available pairs.
         num_hypotheses = min(self.options.num_ransac_hypotheses(), len(measurement_pairs))
 
-        # Sampling
+        # Sampling.
         samples = self.sample_ransac_hypotheses(track_2d, measurement_pairs, num_hypotheses)
 
-        # Initialize the best output containers
+        # Initialize the best output containers.
         best_num_votes = 0
         best_error = MAX_TRACK_REPROJ_ERROR
         best_inliers = np.zeros(len(track_2d.measurements), dtype=bool)
@@ -168,7 +171,7 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
             i1, uv1 = track_2d.measurements[k1]
             i2, uv2 = track_2d.measurements[k2]
 
-            # check for unestimated cameras
+            # Check for unestimated cameras.
             if self.track_camera_dict.get(i1) is None or self.track_camera_dict.get(i2) is None:
                 logger.warning("Unestimated cameras found at indices %d or %d. Skipping them.", i1, i2)
                 continue
@@ -181,7 +184,7 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
             img_measurements.append(uv1)
             img_measurements.append(uv2)
 
-            # triangulate point for track
+            # Triangulate point for track.
             try:
                 triangulated_pt = gtsam.triangulatePoint3(
                     camera_estimates,
@@ -204,11 +207,11 @@ def execute_ransac_variant(self, track_2d: SfmTrack2d) -> np.ndarray:
             # If the inlier number are the same, check the average error of inliers
             is_inlier = errors < self.options.reproj_error_threshold
 
-            # tally the number of votes
+            # Tally the number of votes.
             inlier_errors = errors[is_inlier]
 
             if inlier_errors.size > 0:
-                # only tally error over the inlier measurements
+                # Only tally error over the inlier measurements.
                 avg_error = inlier_errors.mean()
                 num_votes = is_inlier.astype(int).sum()
 
@@ -223,7 +226,7 @@ def triangulate(self, track_2d: SfmTrack2d) -> Tuple[Optional[SfmTrack], Optiona
         """Triangulates 3D point according to the configured triangulation mode.
 
         Args:
-            track: feature track from which measurements are to be extracted
+            track: Feature track from which measurements are to be extracted.
 
         Returns:
             track with inlier measurements and 3D landmark. None returned if triangulation fails or has high error.
@@ -300,11 +303,12 @@ def sample_ransac_hypotheses(
         """Sample a list of hypotheses (camera pairs) to use during triangulation.
 
         Args:
-            track: feature track from which measurements are to be extracted
-            measurement_pairs: all possible indices of pairs of measurements in a given track
-            num_hypotheses: desired number of samples
+            track: Feature track from which measurements are to be extracted.
+            measurement_pairs: All possible indices of pairs of measurements in a given track.
+            num_hypotheses: Desired number of samples.
+
         Returns:
-            Indices of selected match
+            Indices of selected match.
         """
         # Initialize scores as uniform distribution
         scores = np.ones(len(measurement_pairs), dtype=float)
@@ -320,10 +324,10 @@ def sample_ransac_hypotheses(
                 wTc1 = self.track_camera_dict[i1].pose()
                 wTc2 = self.track_camera_dict[i2].pose()
 
-                # rough approximation approximation of baseline between the 2 cameras
+                # Rough approximation approximation of baseline between the 2 cameras
                 scores[k] = np.linalg.norm(wTc1.inverse().compose(wTc2).translation())
 
-        # Check the validity of scores
+        # Check the validity of scores.
         if sum(scores) <= 0.0:
             raise Exception("Sum of scores cannot be zero (or smaller than zero)! It must a bug somewhere")
 
@@ -351,7 +355,7 @@ def extract_measurements(
         Returns None, None if less than 2 measurements were found with estimated camera poses after averaging.
 
         Args:
-            track: feature track from which measurements are to be extracted.
+            track: Feature track from which measurements are to be extracted.
 
         Returns:
             Vector of individual camera calibrations pertaining to track
@@ -377,14 +381,13 @@ def extract_measurements(
 
 
 def generate_measurement_pairs(track: SfmTrack2d) -> List[Tuple[int, ...]]:
-    """
-    Extract all possible measurement pairs in a track for triangulation.
+    """Extract all possible measurement pairs in a track for triangulation.
 
     Args:
-        track: feature track from which measurements are to be extracted
+        track: Feature track from which measurements are to be extracted.
 
     Returns:
-        measurement_idxs: all possible matching measurement indices in a given track
+        measurement_idxs: All possible matching measurement indices in a given track
     """
     num_track_measurements = track.number_measurements()
     all_measurement_idxs = range(num_track_measurements)

gtsfm/evaluation/compare_metrics.py

@@ -24,16 +24,17 @@ def compute_metrics_from_txt(
     """Calculate metrics from pipeline outputs parsed from COLMAP txt format.
 
     Args:
-        cameras: dictionary of COLMAP-formatted Cameras
-        images: dictionary of COLMAP-formatted Images
-        points3D: dictionary of COLMAP-formatted Point3Ds
+        cameras: Dictionary of COLMAP-formatted Cameras.
+        images: Dictionary of COLMAP-formatted Images.
+        points3D: Dictionary of COLMAP-formatted Point3Ds.
         reproj_error_threshold: Reprojection error threshold for filtering tracks.
 
     Returns:
         other_pipeline_metrics: A dictionary of metrics from another pipeline that are comparable with GTSfM
     """
-    image_files, images, cameras, sfmtracks = io_utils.colmap2gtsfm(cameras, images, points3d, load_sfmtracks=True)
-    num_cameras = len(cameras)
+    _, _, intrinsics_gtsfm, sfmtracks, _, _, _ = io_utils.colmap2gtsfm(cameras, images, points3d, load_sfmtracks=True)
+
+    num_cameras = len(intrinsics_gtsfm)
     unfiltered_track_lengths = []
     image_id_num_measurements = {}
     for track in sfmtracks:

gtsfm/evaluation/metrics.py

@@ -89,9 +89,9 @@ def __init__(
 
         self._name = name
         if data is not None:
-            # Cast to a numpy array
+            # Cast to a numpy array.
             if isinstance(data, list):
-                # Replace None with NaN
+                # Replace None with NaN.
                 data = [x if x is not None else np.NaN for x in data]
                 if all(isinstance(x, int) for x in data):
                     data = np.array(data, dtype=np.int32)
@@ -113,7 +113,7 @@ def __init__(
             else:
                 raise ValueError("Unsupported plot type for the data dimension")
 
-            # Create a summary if the data is a 1D distribution
+            # Create a summary if the data is a 1D distribution.
             if self._dim == 1:
                 self._summary = self._create_summary(data)
 
@@ -123,7 +123,7 @@ def __init__(
             else:
                 self._data = None
         else:
-            # Metrics created from summary alone are 1D distribution metrics
+            # Metrics created from summary alone are 1D distribution metrics.
             self._dim = 1
             self._summary = summary
             self._plot_type = self.PlotType.HISTOGRAM if "histogram" in summary else self.PlotType.BOX