def stereo_calibrate_detections(detections, board, cameras, i, j, **kwargs):
matching = [
intersect_detections(board, d1, d2)
for d1, d2 in zip(detections[i], detections[j])
]
matching_frames = transpose_structs(filter_none(matching))
return stereo_calibrate((cameras[i], cameras[j]), matching_frames,
**kwargs)
def board_correspondences(board, detections):
non_empty = [d for d in detections if board.has_min_detections(d)]
if len(non_empty) == 0:
return struct(corners = [], object_points=[], ids=[])
detections = transpose_structs(non_empty)
return detections._extend(
object_points=[board.points[ids].astype(np.float32) for ids in detections.ids],
corners=[corners.astype(np.float32) for corners in detections.corners]
)
def matching_points(points, board, cam1, cam2):
points1, points2 = points._index[cam1], points._index[cam2]
matching = []
for i, j in zip(points1._sequence(0), points2._sequence(0)):
row1, row2, ids = common_entries(i, j)
matching.append(
struct(points1=row1.points,
points2=row2.points,
object_points=board.points[ids],
ids=ids))
return transpose_structs(matching)