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"""Simple interface for GeoCalib model."""
from pathlib import Path
from typing import Dict, Optional
import torch
import torch.nn as nn
from torch.nn.functional import interpolate
from siclib.geometry.base_camera import BaseCamera
from siclib.models.networks.geocalib import GeoCalib as Model
from siclib.utils.image import ImagePreprocessor, load_image
class GeoCalib(nn.Module):
"""Simple interface for GeoCalib model."""
def __init__(self, weights: str = "pinhole"):
"""Initialize the model with optional config overrides.
Args:
weights (str, optional): Weights to load. Defaults to "pinhole".
"""
super().__init__()
if weights not in {"pinhole", "distorted"}:
raise ValueError(f"Unknown weights: {weights}")
url = f"https://github.com/cvg/GeoCalib/releases/download/v1.0/geocalib-{weights}.tar"
# load checkpoint
model_dir = f"{torch.hub.get_dir()}/geocalib"
state_dict = torch.hub.load_state_dict_from_url(
url, model_dir, map_location="cpu", file_name=f"{weights}.tar"
)
self.model = Model({})
self.model.flexible_load(state_dict["model"])
self.model.eval()
self.image_processor = ImagePreprocessor({"resize": 320, "edge_divisible_by": 32})
def load_image(self, path: Path) -> torch.Tensor:
"""Load image from path."""
return load_image(path)
def _post_process(
self, camera: BaseCamera, img_data: dict[str, torch.Tensor], out: dict[str, torch.Tensor]
) -> tuple[BaseCamera, dict[str, torch.Tensor]]:
"""Post-process model output by undoing scaling and cropping."""
camera = camera.undo_scale_crop(img_data)
w, h = camera.size.unbind(-1)
h = h[0].round().int().item()
w = w[0].round().int().item()
for k in ["latitude_field", "up_field"]:
out[k] = interpolate(out[k], size=(h, w), mode="bilinear")
for k in ["up_confidence", "latitude_confidence"]:
out[k] = interpolate(out[k][:, None], size=(h, w), mode="bilinear")[:, 0]
inverse_scales = 1.0 / img_data["scales"]
zero = camera.new_zeros(camera.f.shape[0])
out["focal_uncertainty"] = out.get("focal_uncertainty", zero) * inverse_scales[1]
return camera, out
@torch.no_grad()
def calibrate(
self,
img: torch.Tensor,
camera_model: str = "pinhole",
priors: Optional[Dict[str, torch.Tensor]] = None,
shared_intrinsics: bool = False,
) -> Dict[str, torch.Tensor]:
"""Perform calibration with online resizing.
Assumes input image is in range [0, 1] and in RGB format.
Args:
img (torch.Tensor): Input image, shape (C, H, W) or (1, C, H, W)
camera_model (str, optional): Camera model. Defaults to "pinhole".
priors (Dict[str, torch.Tensor], optional): Prior parameters. Defaults to {}.
shared_intrinsics (bool, optional): Whether to share intrinsics. Defaults to False.
Returns:
Dict[str, torch.Tensor]: camera and gravity vectors and uncertainties.
"""
if len(img.shape) == 3:
img = img[None] # add batch dim
if not shared_intrinsics:
assert len(img.shape) == 4 and img.shape[0] == 1
img_data = self.image_processor(img)
if priors is None:
priors = {}
prior_values = {}
if prior_focal := priors.get("focal"):
prior_focal = prior_focal[None] if len(prior_focal.shape) == 0 else prior_focal
prior_values["prior_focal"] = prior_focal * img_data["scales"][1]
if "gravity" in priors:
prior_gravity = priors["gravity"]
prior_gravity = prior_gravity[None] if len(prior_gravity.shape) == 0 else prior_gravity
prior_values["prior_gravity"] = prior_gravity
self.model.optimizer.set_camera_model(camera_model)
self.model.optimizer.shared_intrinsics = shared_intrinsics
out = self.model(img_data | prior_values)
camera, gravity = out["camera"], out["gravity"]
camera, out = self._post_process(camera, img_data, out)
return {
"camera": camera,
"gravity": gravity,
"covariance": out["covariance"],
**{k: out[k] for k in out.keys() if "field" in k},
**{k: out[k] for k in out.keys() if "confidence" in k},
**{k: out[k] for k in out.keys() if "uncertainty" in k},
}
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