Add: mast3r

.gitignore

@@ -22,3 +22,8 @@ third_party/QuadTreeAttention
 desktop.ini
 *.egg-info
 output.pkl
+experiments*
+gen_example.py
+datasets/lines/terrace0.JPG
+datasets/lines/terrace1.JPG
+datasets/South-Building*

.gitmodules

@@ -64,3 +64,6 @@
 [submodule "third_party/DarkFeat"]
 	path = third_party/DarkFeat
 	url = https://github.com/agipro/DarkFeat.git
+[submodule "third_party/mast3r"]
+	path = third_party/mast3r
+	url = https://github.com/naver/mast3r

Dockerfile

@@ -20,7 +20,7 @@ ENV PATH /opt/conda/envs/imw/bin:$PATH
 # Make RUN commands use the new environment
 SHELL ["conda", "run", "-n", "imw", "/bin/bash", "-c"]
 RUN pip install --upgrade pip
-RUN pip install -r env-docker.txt
+RUN pip install -r requirements.txt
 RUN apt-get update && apt-get install ffmpeg libsm6 libxext6 -y
 
 # Export port

README.md

@@ -23,6 +23,7 @@ Here is a demo of the tool:
 https://github.com/Vincentqyw/image-matching-webui/assets/18531182/263534692-c3484d1b-cc00-4fdc-9b31-e5b7af07ecd9
 
 The tool currently supports various popular image matching algorithms, namely:
+- [x] [MASt3R](https://github.com/naver/mast3r), CVPR 2024
 - [x] [DUSt3R](https://github.com/naver/dust3r), CVPR 2024
 - [x] [OmniGlue](https://github.com/Vincentqyw/omniglue-onnx), CVPR 2024
 - [x] [XFeat](https://github.com/verlab/accelerated_features), CVPR 2024

hloc/__init__.py

@@ -3,7 +3,7 @@
 import torch
 from packaging import version
 
-__version__ = "1.3"
+__version__ = "1.5"
 
 formatter = logging.Formatter(
     fmt="[%(asctime)s %(name)s %(levelname)s] %(message)s",
@@ -23,14 +23,18 @@
 except ImportError:
     logger.warning("pycolmap is not installed, some features may not work.")
 else:
-    minimal_version = version.parse("0.3.0")
-    found_version = version.parse(getattr(pycolmap, "__version__"))
-    if found_version < minimal_version:
-        logger.warning(
-            "hloc now requires pycolmap>=%s but found pycolmap==%s, "
-            "please upgrade with `pip install --upgrade pycolmap`",
-            minimal_version,
-            found_version,
-        )
+    min_version = version.parse("0.6.0")
+    found_version = pycolmap.__version__
+    if found_version != "dev":
+        version = version.parse(found_version)
+        if version < min_version:
+            s = f"pycolmap>={min_version}"
+            logger.warning(
+                "hloc requires %s but found pycolmap==%s, "
+                'please upgrade with `pip install --upgrade "%s"`',
+                s,
+                found_version,
+                s,
+            )
 
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

hloc/colmap_from_nvm.py

@@ -0,0 +1,220 @@
+import argparse
+import sqlite3
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+from tqdm import tqdm
+
+from . import logger
+from .utils.read_write_model import (
+    CAMERA_MODEL_NAMES,
+    Camera,
+    Image,
+    Point3D,
+    write_model,
+)
+
+
+def recover_database_images_and_ids(database_path):
+    images = {}
+    cameras = {}
+    db = sqlite3.connect(str(database_path))
+    ret = db.execute("SELECT name, image_id, camera_id FROM images;")
+    for name, image_id, camera_id in ret:
+        images[name] = image_id
+        cameras[name] = camera_id
+    db.close()
+    logger.info(
+        f"Found {len(images)} images and {len(cameras)} cameras in database."
+    )
+    return images, cameras
+
+
+def quaternion_to_rotation_matrix(qvec):
+    qvec = qvec / np.linalg.norm(qvec)
+    w, x, y, z = qvec
+    R = np.array(
+        [
+            [
+                1 - 2 * y * y - 2 * z * z,
+                2 * x * y - 2 * z * w,
+                2 * x * z + 2 * y * w,
+            ],
+            [
+                2 * x * y + 2 * z * w,
+                1 - 2 * x * x - 2 * z * z,
+                2 * y * z - 2 * x * w,
+            ],
+            [
+                2 * x * z - 2 * y * w,
+                2 * y * z + 2 * x * w,
+                1 - 2 * x * x - 2 * y * y,
+            ],
+        ]
+    )
+    return R
+
+
+def camera_center_to_translation(c, qvec):
+    R = quaternion_to_rotation_matrix(qvec)
+    return (-1) * np.matmul(R, c)
+
+
+def read_nvm_model(
+    nvm_path, intrinsics_path, image_ids, camera_ids, skip_points=False
+):
+    with open(intrinsics_path, "r") as f:
+        raw_intrinsics = f.readlines()
+
+    logger.info(f"Reading {len(raw_intrinsics)} cameras...")
+    cameras = {}
+    for intrinsics in raw_intrinsics:
+        intrinsics = intrinsics.strip("\n").split(" ")
+        name, camera_model, width, height = intrinsics[:4]
+        params = [float(p) for p in intrinsics[4:]]
+        camera_model = CAMERA_MODEL_NAMES[camera_model]
+        assert len(params) == camera_model.num_params
+        camera_id = camera_ids[name]
+        camera = Camera(
+            id=camera_id,
+            model=camera_model.model_name,
+            width=int(width),
+            height=int(height),
+            params=params,
+        )
+        cameras[camera_id] = camera
+
+    nvm_f = open(nvm_path, "r")
+    line = nvm_f.readline()
+    while line == "\n" or line.startswith("NVM_V3"):
+        line = nvm_f.readline()
+    num_images = int(line)
+    assert num_images == len(cameras)
+
+    logger.info(f"Reading {num_images} images...")
+    image_idx_to_db_image_id = []
+    image_data = []
+    i = 0
+    while i < num_images:
+        line = nvm_f.readline()
+        if line == "\n":
+            continue
+        data = line.strip("\n").split(" ")
+        image_data.append(data)
+        image_idx_to_db_image_id.append(image_ids[data[0]])
+        i += 1
+
+    line = nvm_f.readline()
+    while line == "\n":
+        line = nvm_f.readline()
+    num_points = int(line)
+
+    if skip_points:
+        logger.info(f"Skipping {num_points} points.")
+        num_points = 0
+    else:
+        logger.info(f"Reading {num_points} points...")
+    points3D = {}
+    image_idx_to_keypoints = defaultdict(list)
+    i = 0
+    pbar = tqdm(total=num_points, unit="pts")
+    while i < num_points:
+        line = nvm_f.readline()
+        if line == "\n":
+            continue
+
+        data = line.strip("\n").split(" ")
+        x, y, z, r, g, b, num_observations = data[:7]
+        obs_image_ids, point2D_idxs = [], []
+        for j in range(int(num_observations)):
+            s = 7 + 4 * j
+            img_index, kp_index, kx, ky = data[s : s + 4]
+            image_idx_to_keypoints[int(img_index)].append(
+                (int(kp_index), float(kx), float(ky), i)
+            )
+            db_image_id = image_idx_to_db_image_id[int(img_index)]
+            obs_image_ids.append(db_image_id)
+            point2D_idxs.append(kp_index)
+
+        point = Point3D(
+            id=i,
+            xyz=np.array([x, y, z], float),
+            rgb=np.array([r, g, b], int),
+            error=1.0,  # fake
+            image_ids=np.array(obs_image_ids, int),
+            point2D_idxs=np.array(point2D_idxs, int),
+        )
+        points3D[i] = point
+
+        i += 1
+        pbar.update(1)
+    pbar.close()
+
+    logger.info("Parsing image data...")
+    images = {}
+    for i, data in enumerate(image_data):
+        # Skip the focal length. Skip the distortion and terminal 0.
+        name, _, qw, qx, qy, qz, cx, cy, cz, _, _ = data
+        qvec = np.array([qw, qx, qy, qz], float)
+        c = np.array([cx, cy, cz], float)
+        t = camera_center_to_translation(c, qvec)
+
+        if i in image_idx_to_keypoints:
+            # NVM only stores triangulated 2D keypoints: add dummy ones
+            keypoints = image_idx_to_keypoints[i]
+            point2D_idxs = np.array([d[0] for d in keypoints])
+            tri_xys = np.array([[x, y] for _, x, y, _ in keypoints])
+            tri_ids = np.array([i for _, _, _, i in keypoints])
+
+            num_2Dpoints = max(point2D_idxs) + 1
+            xys = np.zeros((num_2Dpoints, 2), float)
+            point3D_ids = np.full(num_2Dpoints, -1, int)
+            xys[point2D_idxs] = tri_xys
+            point3D_ids[point2D_idxs] = tri_ids
+        else:
+            xys = np.zeros((0, 2), float)
+            point3D_ids = np.full(0, -1, int)
+
+        image_id = image_ids[name]
+        image = Image(
+            id=image_id,
+            qvec=qvec,
+            tvec=t,
+            camera_id=camera_ids[name],
+            name=name,
+            xys=xys,
+            point3D_ids=point3D_ids,
+        )
+        images[image_id] = image
+
+    return cameras, images, points3D
+
+
+def main(nvm, intrinsics, database, output, skip_points=False):
+    assert nvm.exists(), nvm
+    assert intrinsics.exists(), intrinsics
+    assert database.exists(), database
+
+    image_ids, camera_ids = recover_database_images_and_ids(database)
+
+    logger.info("Reading the NVM model...")
+    model = read_nvm_model(
+        nvm, intrinsics, image_ids, camera_ids, skip_points=skip_points
+    )
+
+    logger.info("Writing the COLMAP model...")
+    output.mkdir(exist_ok=True, parents=True)
+    write_model(*model, path=str(output), ext=".bin")
+    logger.info("Done.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--nvm", required=True, type=Path)
+    parser.add_argument("--intrinsics", required=True, type=Path)
+    parser.add_argument("--database", required=True, type=Path)
+    parser.add_argument("--output", required=True, type=Path)
+    parser.add_argument("--skip_points", action="store_true")
+    args = parser.parse_args()
+    main(**args.__dict__)

hloc/extract_features.py

@@ -329,6 +329,11 @@
         "model": {"name": "cosplace"},
         "preprocessing": {"resize_max": 1024},
     },
+    "eigenplaces": {
+        "output": "global-feats-eigenplaces",
+        "model": {"name": "eigenplaces"},
+        "preprocessing": {"resize_max": 1024},
+    },
 }
 
 

hloc/extractors/eigenplaces.py

@@ -0,0 +1,57 @@
+"""
+Code for loading models trained with EigenPlaces (or CosPlace) as a global
+features extractor for geolocalization through image retrieval.
+Multiple models are available with different backbones. Below is a summary of
+models available (backbone : list of available output descriptors
+dimensionality). For example you can use a model based on a ResNet50 with
+descriptors dimensionality 1024.
+
+EigenPlaces trained models:
+    ResNet18:  [     256, 512]
+    ResNet50:  [128, 256, 512, 2048]
+    ResNet101: [128, 256, 512, 2048]
+    VGG16:     [     512]
+
+CosPlace trained models:
+    ResNet18:  [32, 64, 128, 256, 512]
+    ResNet50:  [32, 64, 128, 256, 512, 1024, 2048]
+    ResNet101: [32, 64, 128, 256, 512, 1024, 2048]
+    ResNet152: [32, 64, 128, 256, 512, 1024, 2048]
+    VGG16:     [    64, 128, 256, 512]
+
+EigenPlaces paper (ICCV 2023): https://arxiv.org/abs/2308.10832
+CosPlace paper (CVPR 2022): https://arxiv.org/abs/2204.02287
+"""
+
+import torch
+import torchvision.transforms as tvf
+
+from ..utils.base_model import BaseModel
+
+
+class EigenPlaces(BaseModel):
+    default_conf = {
+        "variant": "EigenPlaces",
+        "backbone": "ResNet101",
+        "fc_output_dim": 2048,
+    }
+    required_inputs = ["image"]
+
+    def _init(self, conf):
+        self.net = torch.hub.load(
+            "gmberton/" + conf["variant"],
+            "get_trained_model",
+            backbone=conf["backbone"],
+            fc_output_dim=conf["fc_output_dim"],
+        ).eval()
+
+        mean = [0.485, 0.456, 0.406]
+        std = [0.229, 0.224, 0.225]
+        self.norm_rgb = tvf.Normalize(mean=mean, std=std)
+
+    def _forward(self, data):
+        image = self.norm_rgb(data["image"])
+        desc = self.net(image)
+        return {
+            "global_descriptor": desc,
+        }