Adding support for multiple cameras in different subdirectories

src/deep_image_matching/extractors/extractor_base.py

@@ -205,7 +205,7 @@ def extract(self, img: Union[Image, Path, str]) -> np.ndarray:
         save_features_h5(
             feature_path,
             features,
-            im_path.name,
+            img.name,
             as_half=self.features_as_half,
         )
 
@@ -435,4 +435,4 @@ def viz_keypoints(
                 [int(cv2.IMWRITE_JPEG_QUALITY), jpg_quality],
             )
         else:
-            cv2.imwrite(out_path, out)
+            cv2.imwrite(out_path, out)

src/deep_image_matching/extractors/no_extractor.py

@@ -43,14 +43,14 @@ def extract(self, img: Union[Image, Path, str]) -> np.ndarray:
         output_dir = Path(self.config["general"]["output_dir"])
         feature_path = output_dir / "features.h5"
         output_dir.mkdir(parents=True, exist_ok=True)
-        im_name = im_path.name
 
         # Build fake features
         features = {}
         features["keypoints"] = np.array([])
         features["descriptors"] = np.array([])
         features["scores"] = np.array([])
         img_obj = Image(im_path)
+        im_name = img_obj.name
         # img_obj.read_exif()
         features["image_size"] = np.array(img_obj.size)
         features["tile_idx"] = np.array([])
@@ -99,4 +99,4 @@ def _frame2tensor(self, image: np.ndarray, device: str = "cuda"):
 
 
 if __name__ == "__main__":
-    pass
+    pass

src/deep_image_matching/image_matching.py

@@ -420,6 +420,7 @@ def rotate_upright_images(
         pairs = [(item[0].name, item[1].name) for item in self.pairs]
         path_to_upright_dir = self.output_dir / "upright_images"
         os.makedirs(path_to_upright_dir, exist_ok=False)
+        # I guess will break here, use recursive folder iterator
         images = os.listdir(self.image_dir)
 
         logger.info(f"Copying images to {path_to_upright_dir}")
@@ -745,4 +746,4 @@ def rotate_back_features(self, feature_path: Path) -> None:
                     if isinstance(v, np.ndarray):
                         grp.create_dataset(k, data=v)
 
-        logger.info("Features rotated back.")
+        logger.info("Features rotated back.")

src/deep_image_matching/io/h5_to_db.py

@@ -25,9 +25,11 @@
 import h5py
 import numpy as np
 import yaml
-from PIL import ExifTags, Image
+from PIL import ExifTags
+from PIL import Image as PIL_Image
 from tqdm import tqdm
 
+from ..utils.image import Image
 from ..utils.database import COLMAPDatabase, image_ids_to_pair_id
 
 logger = logging.getLogger("dim")
@@ -127,7 +129,7 @@ def get_focal(image_path: Path, err_on_default: bool = False) -> float:
         This function calculates the focal length based on the maximum size of the image and the EXIF data. If the focal length cannot be determined from the EXIF data, it uses a default prior value.
 
     """
-    image = Image.open(image_path)
+    image = PIL_Image.open(image_path)
     max_size = max(image.size)
 
     exif = image.getexif()
@@ -156,7 +158,7 @@ def get_focal(image_path: Path, err_on_default: bool = False) -> float:
 
 
 def create_camera(db: Path, image_path: Path, camera_model: str):
-    image = Image.open(image_path)
+    image = PIL_Image.open(image_path)
     width, height = image.size
 
     focal = get_focal(image_path)
@@ -237,7 +239,7 @@ def add_keypoints(db: Path, h5_path: Path, image_path: Path, camera_options: dic
 
     with h5py.File(str(h5_path), "r") as keypoint_f:
         fname_to_id = {}
-        k = 0
+        created_cameras = {}
         for filename in tqdm(list(keypoint_f.keys())):
             keypoints = keypoint_f[filename]["keypoints"].__array__()
 
@@ -247,19 +249,31 @@ def add_keypoints(db: Path, h5_path: Path, image_path: Path, camera_options: dic
 
             if filename not in list(grouped_images.keys()):
                 if camera_options["general"]["single_camera"] is False:
-                    camera_id = create_camera(db, path, camera_options["general"]["camera_model"])
+                    image = Image(path)
+                    if image.camera_id != None:
+                        if image.camera_id not in created_cameras:
+                            camera_id = create_camera(
+                                db, path, camera_options[f"cam{image.camera_id}"]["camera_model"]
+                            )
+                            created_cameras[image.camera_id] = camera_id
+                        else:
+                            camera_id = created_cameras[image.camera_id]
+                    else:
+                        camera_id = create_camera(
+                            db, path, camera_options["general"]["camera_model"]
+                        )
+                        created_cameras[camera_id] = camera_id
                 elif camera_options["general"]["single_camera"] is True:
-                    if k == 0:
-                        camera_id = create_camera(db, path, camera_options["general"]["camera_model"])
+                    if len(created_cameras) == 0:
+                        camera_id = create_camera(
+                            db, path, camera_options["general"]["camera_model"]
+                        )
                         single_camera_id = camera_id
-                        k += 1
-                    elif k > 0:
+                        created_cameras[camera_id] = camera_id
+                    else:
                         camera_id = single_camera_id
-            elif filename in list(grouped_images.keys()):
-                camera_id = grouped_images[filename]["camera_id"]
             else:
-                print('ERROR in h5_to_db.py')
-                quit()
+                camera_id = grouped_images[filename]["camera_id"]
             image_id = db.add_image(filename, camera_id)
             fname_to_id[filename] = image_id
 
@@ -402,4 +416,4 @@ def add_matches(db, h5_path, fname_to_id):
         fname_to_id,
     )
 
-    db.commit()
+    db.commit()

src/deep_image_matching/matchers/loftr.py

@@ -8,6 +8,7 @@
 
 from ..constants import TileSelection, Timer
 from ..utils.tiling import Tiler
+from ..utils.image import Image
 from .matcher_base import DetectorFreeMatcherBase, tile_selection
 
 logger = logging.getLogger("dim")
@@ -92,13 +93,15 @@ def _match_pairs(
         Raises:
             torch.cuda.OutOfMemoryError: If an out-of-memory error occurs while matching images.
         """
-
-        img0_name = img0_path.name
-        img1_name = img1_path.name
+        # Could just rename args but they might be used as keyword args elsewhere
+        img0 = img0_path
+        img1 = img1_path
+        img0_name = img0.name
+        img1_name = img0.name
 
         # Load images
-        image0 = self._load_image_np(img0_path)
-        image1 = self._load_image_np(img1_path)
+        image0 = self._load_image_np(img0.path)
+        image1 = self._load_image_np(img1.path)
 
         # Resize images if needed
         image0_ = self._resize_image(self._quality, image0)
@@ -282,4 +285,4 @@ def _frame2tensor(self, image: np.ndarray, device: str = "cpu") -> torch.Tensor:
         if image.shape[1] > 2:
             image = K.color.bgr_to_rgb(image)
             image = K.color.rgb_to_grayscale(image)
-        return image
+        return image

src/deep_image_matching/matchers/matcher_base.py

@@ -17,7 +17,7 @@
 from ..thirdparty.hloc.extractors.superpoint import SuperPoint
 from ..thirdparty.LightGlue.lightglue import LightGlue
 from ..utils.geometric_verification import geometric_verification
-from ..utils.image import resize_image
+from ..utils.image import resize_image, Image
 from ..utils.tiling import Tiler
 from ..visualization import viz_matches_cv2, viz_matches_mpl
 
@@ -205,10 +205,12 @@ def match(
             self._feature_path = Path(feature_path)
 
         # Get features from h5 file
+        img0 = Image(img0)
+        img1 = Image(img1)
         img0_name = img0.name
         img1_name = img1.name
-        features0 = get_features(self._feature_path, img0.name)
-        features1 = get_features(self._feature_path, img1.name)
+        features0 = get_features(self._feature_path, img0_name)
+        features1 = get_features(self._feature_path, img1_name)
         timer_match.update("load h5 features")
 
         # Perform matching (on tiles or full images)
@@ -328,8 +330,8 @@ def match(
             self.viz_matches(
                 feature_path,
                 matches_path,
-                img0,
-                img1,
+                img0.path,
+                img1.path,
                 save_path=viz_dir / f"{img0_name}_{img1_name}.jpg",
                 img_format="jpg",
                 jpg_quality=70,
@@ -466,14 +468,14 @@ def viz_matches(
         jpg_quality = kwargs.get("jpg_quality", 80)
         hide_matching_track = kwargs.get("hide_matching_track", False)
 
-        img0 = Path(img0)
-        img1 = Path(img1)
+        img0 = Image(img0)
+        img1 = Image(img1)
         img0_name = img0.name
         img1_name = img1.name
 
         # Load images
-        image0 = load_image_np(img0, as_float=False, grayscale=True)
-        image1 = load_image_np(img1, as_float=False, grayscale=True)
+        image0 = load_image_np(img0.path, as_float=False, grayscale=True)
+        image1 = load_image_np(img1.path, as_float=False, grayscale=True)
 
         # Load features and matches
         features0 = get_features(feature_path, img0_name)
@@ -648,8 +650,8 @@ def match(
         else:
             self._feature_path = Path(feature_path)
 
-        img0 = Path(img0)
-        img1 = Path(img1)
+        img0 = Image(img0)
+        img1 = Image(img1)
         img0_name = img0.name
         img1_name = img1.name
 
@@ -672,7 +674,7 @@ def match(
         features1 = get_features(feature_path, img1_name)
 
         # Rescale threshold according the image original image size
-        img_shape = cv2.imread(str(img0)).shape
+        img_shape = cv2.imread(img0.path).shape
         scale_fct = np.floor(max(img_shape) / self.max_tile_size / 2)
         gv_threshold = self.config["general"]["gv_threshold"] * scale_fct
 
@@ -854,14 +856,14 @@ def viz_matches(
                 logger.warning("interactive_viz is ignored if fast_viz is True")
             assert save_path is not None, "output_dir must be specified if fast_viz is True"
 
-        img0 = Path(img0)
-        img1 = Path(img1)
+        img0 = Image(img0)
+        img1 = Image(img1)
         img0_name = img0.name
         img1_name = img1.name
 
         # Load images
-        image0 = load_image_np(img0, self.as_float, self.grayscale)
-        image1 = load_image_np(img1, self.as_float, self.grayscale)
+        image0 = load_image_np(img0.path, self.as_float, self.grayscale)
+        image1 = load_image_np(img1.path, self.as_float, self.grayscale)
 
         # Load features and matches
         features0 = get_features(feature_path, img0_name)
@@ -1181,4 +1183,4 @@ def sp2lg(feats: dict) -> dict:
 
 def rbd2np(data: dict) -> dict:
     """Remove batch dimension from elements in data"""
-    return {k: v[0].cpu().numpy() if isinstance(v, (torch.Tensor, np.ndarray, list)) else v for k, v in data.items()}
+    return {k: v[0].cpu().numpy() if isinstance(v, (torch.Tensor, np.ndarray, list)) else v for k, v in data.items()}

src/deep_image_matching/matchers/roma.py

@@ -13,6 +13,7 @@
 from ..io.h5 import get_features
 from ..thirdparty.RoMa.roma import roma_outdoor
 from ..utils.geometric_verification import geometric_verification
+from ..utils.image import Image
 from ..utils.tiling import Tiler
 from ..visualization import viz_matches_cv2
 from .matcher_base import DetectorFreeMatcherBase, tile_selection
@@ -115,8 +116,8 @@ def match(
             self._feature_path = Path(feature_path)
 
         # Get features from h5 file
-        img0 = Path(img0)
-        img1 = Path(img1)
+        img0 = Image(img0)
+        img1 = Image(img1)
         img0_name = img0.name
         img1_name = img1.name
 
@@ -139,7 +140,7 @@ def match(
         features1 = get_features(feature_path, img1_name)
 
         # Rescale threshold according the image original image size
-        img_shape = cv2.imread(str(img0)).shape
+        img_shape = cv2.imread(img0.path).shape
         tile_size = max(self.config["general"]["tile_size"])
         scale_fct = np.floor(max(img_shape) / tile_size / 2)
         gv_threshold = self.config["general"]["gv_threshold"] * scale_fct
@@ -172,8 +173,8 @@ def match(
     def _match_pairs(
         self,
         feature_path: Path,
-        img0_path: Path,
-        img1_path: Path,
+        img0: Image,
+        img1: Image
     ):
         """
         Perform matching between feature pairs.
@@ -187,20 +188,20 @@ def _match_pairs(
             np.ndarray: Array containing the indices of matched keypoints.
         """
 
-        img0_name = img0_path.name
-        img1_name = img1_path.name
+        img0_name = img0.name
+        img1_name = img1.name
 
         # Run inference
-        W_A, H_A = Image.open(img0_path).size
-        W_B, H_B = Image.open(img1_path).size
+        W_A, H_A = Image.open(img0.path).size
+        W_B, H_B = Image.open(img1.path).size
 
         #for path in [str(img0_path), str(img1_path)]:
         #    image = cv2.imread(path, cv2.IMREAD_UNCHANGED)
         #    if len(image.shape) == 2:
         #        image_rgb = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
         #        cv2.imwrite(path, image_rgb)
 
-        warp, certainty = self.matcher.match(str(img0_path), str(img1_path), device=self._device)
+        warp, certainty = self.matcher.match(img0.path, img1.path, device=self._device)
         matches, certainty = self.matcher.sample(warp, certainty)
         kptsA, kptsB = self.matcher.to_pixel_coordinates(matches, H_A, W_A, H_B, W_B)
         kptsA, kptsB = kptsA.cpu().numpy(), kptsB.cpu().numpy()
@@ -283,8 +284,8 @@ def write_tiles_disk(output_dir: Path, tiles: dict) -> None:
         timer.update("tile selection")
 
         # Read images and resize them if needed
-        image0 = cv2.imread(str(img0))
-        image1 = cv2.imread(str(img1))
+        image0 = cv2.imread(img0.path)
+        image1 = cv2.imread(img1.path)
         image0 = self._resize_image(self._quality, image0)
         image1 = self._resize_image(self._quality, image1)
 
@@ -431,4 +432,4 @@ def kps_in_image(kp, img_size, border_thr=2):
         if not self.keep_tiles:
             shutil.rmtree(tiles_dir)
 
-        return matches
+        return matches

src/deep_image_matching/utils/geometric_verification.py

@@ -180,4 +180,4 @@ def geometric_verification(
         confidence=0.9999,
         max_iters=10000,
     )
-    print(F)
+    print(F)