feat: configurations

vision_chunk.py

@@ -6,12 +6,13 @@ from pathlib import Path
 from typing import Literal, Optional, Tuple
 from PIL import Image
 
-FeatureMethod = Literal["orb", "sift", "surf", "akaze", "brisk"]
+FeatureMethod = Literal["orb", "sift", "akaze", "brisk"]
+DEFAULT_METHOD = "akaze"
 
 @dataclass
 class VisionChunk:
     image: Image.Image
-    feature_method: FeatureMethod = "orb"
+    feature_method: FeatureMethod = DEFAULT_METHOD
 
     keypoints: Optional[list] = field(default=None, init=False)
     descriptors: Optional[np.ndarray] = field(default=None, init=False)
@@ -26,7 +27,7 @@ class VisionChunk:
             self._detector = cv2.ORB_create(
                 nfeatures=1000,
                 scaleFactor=1.2,
-                nlevels=8,
+                nlevels=32,
                 edgeThreshold=31,
                 firstLevel=0,
                 WTA_K=2,
@@ -35,18 +36,11 @@ class VisionChunk:
             )
         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
+                nfeatures=1500,
+                nOctaveLayers=2,
+                contrastThreshold=0.01,
+                edgeThreshold=15,
+                sigma=3.3
             )
         elif self.feature_method == "akaze":
             self._detector = cv2.AKAZE_create(
@@ -59,8 +53,8 @@ class VisionChunk:
             )
         elif self.feature_method == "brisk":
             self._detector = cv2.BRISK_create(
-                thresh=30,
-                octaves=3,
+                thresh=70,
+                octaves=7,
                 patternScale=1.0
             )
         else:
@@ -69,15 +63,10 @@ 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':
+            if self.feature_method == 'sift':
+                self._matcher = cv2.BFMatcher(cv2.NORM_L2, crossCheck=False)
+            else:
                 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:
@@ -90,7 +79,7 @@ class VisionChunk:
         clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
         return clahe.apply(gray)
 
-    def compute_keypoints(self, force: bool = False) -> Tuple[list, Optional[np.ndarray]]:
+    def compute_keypoints(self, force: bool = False) -> Tuple[list[cv2.KeyPoint], Optional[np.ndarray]]:
         if self.keypoints is not None and self.descriptors is not None and not force:
             return self.keypoints, self.descriptors
 
@@ -103,11 +92,21 @@ class VisionChunk:
         
         # CLAHE предобработка
         preprocessed = self._preprocess(img_np)
-        kps, desc = detector.detectAndCompute(preprocessed, None)
+        keypoints, descriptors = detector.detectAndCompute(preprocessed, None)
 
-        self.keypoints = kps
-        self.descriptors = desc
-        return kps, desc
+        # Получаем массив response для всех точек
+        responses = np.array([kp.response for kp in keypoints])
+
+        # Находим индексы топ-100
+        top_indices = np.argsort(responses)[-2500:][::-1]
+
+        # Отбираем keypoints и descriptors
+        best_keypoints = [keypoints[i] for i in top_indices]
+        best_descriptors = descriptors[top_indices]
+
+        self.keypoints = best_keypoints
+        self.descriptors = best_descriptors
+        return self.keypoints, self.descriptors
 
     def detect_and_match_keypoints(
         self, 
@@ -193,7 +192,7 @@ class VisionChunk:
         return np.array(self.image)
 
     @classmethod
-    def load_image(cls, path: Path | str, feature_method: FeatureMethod = "orb") -> "VisionChunk":
+    def load_image(cls, path: Path | str, feature_method: FeatureMethod = DEFAULT_METHOD) -> "VisionChunk":
         path = Path(path)
         image = Image.open(path)
         return cls(image=image, feature_method=feature_method)