feat: statistics

vision_chunk.py

@@ -6,7 +6,7 @@ from pathlib import Path
 from typing import Literal, Optional, Tuple
 from PIL import Image
 
-FeatureMethod = Literal["orb", "sift", "surf"]
+FeatureMethod = Literal["orb", "sift", "surf", "akaze", "brisk"]
 
 @dataclass
 class VisionChunk:
@@ -33,6 +33,36 @@ class VisionChunk:
                 patchSize=31,
                 fastThreshold=20,
             )
+        elif self.feature_method == "sift":
+            self._detector = cv2.SIFT_create(
+                nfeatures=1000,
+                nOctaveLayers=3,
+                contrastThreshold=0.04,
+                edgeThreshold=10,
+                sigma=1.6
+            )
+        elif self.feature_method == "surf":
+            self._detector = cv2.xfeatures2d.SURF_create(
+                hessianThreshold=400.0,
+                nOctaveLayers=3,
+                # nOctaveLayers=4,
+                upright=False
+            )
+        elif self.feature_method == "akaze":
+            self._detector = cv2.AKAZE_create(
+                descriptor_type=cv2.AKAZE_DESCRIPTOR_MLDB,
+                descriptor_size=0,
+                descriptor_channels=3,
+                threshold=0.001,
+                nOctaves=4,
+                diffusivity=cv2.KAZE_DIFF_PM_G2
+            )
+        elif self.feature_method == "brisk":
+            self._detector = cv2.BRISK_create(
+                thresh=30,
+                octaves=3,
+                patternScale=1.0
+            )
         else:
             raise ValueError(f"Unsupported feature method: {self.feature_method}")
         return self._detector
@@ -40,6 +70,14 @@ class VisionChunk:
     def _get_matcher(self) -> cv2.DescriptorMatcher:
         if self._matcher is None:
             self._matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False)
+            return self._matcher
+            if self.feature_method == 'orb':
+                self._matcher = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=False)
+            elif self.feature_method == 'sift':
+                FLANN_INDEX_LSH = 6
+                index_params = dict(algorithm=FLANN_INDEX_LSH, table_number=6, key_size=12, multi_probe_level=1)
+                search_params = dict(checks=50)
+                self._matcher = cv2.FlannBasedMatcher(index_params, search_params)
         return self._matcher
 
     def _preprocess(self, img_np: np.ndarray) -> np.ndarray: