refactor!: image hash database provider

src/arcaea_offline_ocr/__init__.py

@@ -1,4 +1,3 @@
 from .crop import *
 from .device import *
-from .ocr import *
 from .utils import *

src/arcaea_offline_ocr/b30/chieri/v4/ocr.py

@@ -4,12 +4,6 @@ import cv2
 import numpy as np
 
 from ....crop import crop_xywh
-from ....ocr import (
-    FixRects,
-    ocr_digits_by_contour_knn,
-    preprocess_hog,
-    resize_fill_square,
-)
 from ....phash_db import ImagePhashDatabase
 from ....types import Mat
 from ...shared import B30OcrResultItem
@@ -28,36 +22,21 @@ from .colors import (
     PURE_BG_MIN_HSV,
 )
 from .rois import ChieriBotV4Rois
+from ....providers.knn import OcrKNearestTextProvider
 
 
 class ChieriBotV4Ocr:
     def __init__(
         self,
-        score_knn: cv2.ml.KNearest,
-        pfl_knn: cv2.ml.KNearest,
+        score_knn_provider: OcrKNearestTextProvider,
+        pfl_knn_provider: OcrKNearestTextProvider,
         phash_db: ImagePhashDatabase,
         factor: float = 1.0,
     ):
-        self.__score_knn = score_knn
-        self.__pfl_knn = pfl_knn
         self.__phash_db = phash_db
         self.__rois = ChieriBotV4Rois(factor)
-
-    @property
-    def score_knn(self):
-        return self.__score_knn
-
-    @score_knn.setter
-    def score_knn(self, knn_digits_model: cv2.ml.KNearest):
-        self.__score_knn = knn_digits_model
-
-    @property
-    def pfl_knn(self):
-        return self.__pfl_knn
-
-    @pfl_knn.setter
-    def pfl_knn(self, knn_digits_model: cv2.ml.KNearest):
-        self.__pfl_knn = knn_digits_model
+        self.pfl_knn_provider = pfl_knn_provider
+        self.score_knn_provider = score_knn_provider
 
     @property
     def phash_db(self):
@@ -125,7 +104,9 @@ class ChieriBotV4Ocr:
             if rect[3] > score_roi.shape[0] * 0.5:
                 continue
             score_roi = cv2.fillPoly(score_roi, [contour], 0)
-        return ocr_digits_by_contour_knn(score_roi, self.score_knn)
+
+        ocr_result = self.score_knn_provider.result(score_roi)
+        return int(ocr_result) if ocr_result else 0
 
     def find_pfl_rects(
         self, component_pfl_processed: Mat
@@ -203,25 +184,9 @@ class ChieriBotV4Ocr:
             pure_far_lost = []
             for pfl_roi_rect in pfl_rects:
                 roi = crop_xywh(pfl_roi, pfl_roi_rect)
-                digit_contours, _ = cv2.findContours(
-                    roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
-                )
-                digit_rects = [cv2.boundingRect(c) for c in digit_contours]
-                digit_rects = FixRects.connect_broken(
-                    digit_rects, roi.shape[1], roi.shape[0]
-                )
-                digit_rects = FixRects.split_connected(roi, digit_rects)
-                digit_rects = sorted(digit_rects, key=lambda r: r[0])
-                digits = []
-                for digit_rect in digit_rects:
-                    digit = crop_xywh(roi, digit_rect)
-                    digit = resize_fill_square(digit, 20)
-                    digits.append(digit)
-                samples = preprocess_hog(digits)
+                result = self.pfl_knn_provider.result(roi)
+                pure_far_lost.append(int(result) if result else None)
 
-                _, results, _, _ = self.pfl_knn.findNearest(samples, 4)
-                results = [str(int(i)) for i in results.ravel()]
-                pure_far_lost.append(int("".join(results)))
             return tuple(pure_far_lost)
         except Exception:
             return (None, None, None)

src/arcaea_offline_ocr/builders/__init__.py

@@ -0,0 +1,6 @@
+from .ihdb import ImageHashDatabaseBuildTask, ImageHashesDatabaseBuilder
+
+__all__ = [
+    "ImageHashDatabaseBuildTask",
+    "ImageHashesDatabaseBuilder",
+]

src/arcaea_offline_ocr/builders/ihdb.py

@@ -0,0 +1,112 @@
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from typing import TYPE_CHECKING, Callable, List
+
+import cv2
+
+from arcaea_offline_ocr.core import hashers
+from arcaea_offline_ocr.providers import ImageCategory
+from arcaea_offline_ocr.providers.ihdb import (
+    PROP_KEY_BUILT_AT,
+    PROP_KEY_HASH_SIZE,
+    PROP_KEY_HIGH_FREQ_FACTOR,
+    ImageHashDatabaseIdProvider,
+    ImageHashType,
+)
+
+if TYPE_CHECKING:
+    from sqlite3 import Connection
+
+    from arcaea_offline_ocr.types import Mat
+
+
+def _default_imread_gray(image_path: str):
+    return cv2.cvtColor(cv2.imread(image_path, cv2.IMREAD_COLOR), cv2.COLOR_BGR2GRAY)
+
+
+@dataclass
+class ImageHashDatabaseBuildTask:
+    image_path: str
+    image_id: str
+    category: ImageCategory
+    imread_function: Callable[[str], "Mat"] = _default_imread_gray
+
+
+@dataclass
+class _ImageHash:
+    image_id: str
+    category: ImageCategory
+    image_hash_type: ImageHashType
+    hash: bytes
+
+
+class ImageHashesDatabaseBuilder:
+    @staticmethod
+    def __insert_property(conn: "Connection", key: str, value: str):
+        return conn.execute(
+            "INSERT INTO properties (key, value) VALUES (?, ?)",
+            (key, value),
+        )
+
+    @classmethod
+    def build(
+        cls,
+        conn: "Connection",
+        tasks: List[ImageHashDatabaseBuildTask],
+        *,
+        hash_size: int = 16,
+        high_freq_factor: int = 4,
+    ):
+        hashes: List[_ImageHash] = []
+
+        for task in tasks:
+            img_gray = task.imread_function(task.image_path)
+
+            for hash_type, hash_mat in [
+                (
+                    ImageHashType.AVERAGE,
+                    hashers.average(img_gray, hash_size),
+                ),
+                (
+                    ImageHashType.DCT,
+                    hashers.dct(img_gray, hash_size, high_freq_factor),
+                ),
+                (
+                    ImageHashType.DIFFERENCE,
+                    hashers.difference(img_gray, hash_size),
+                ),
+            ]:
+                hashes.append(
+                    _ImageHash(
+                        image_id=task.image_id,
+                        image_hash_type=hash_type,
+                        category=task.category,
+                        hash=ImageHashDatabaseIdProvider.hash_mat_to_bytes(hash_mat),
+                    )
+                )
+
+        conn.execute("CREATE TABLE properties (`key` VARCHAR, `value` VARCHAR)")
+        conn.execute(
+            """CREATE TABLE hashes (
+`id` VARCHAR,
+`category` INTEGER,
+`hash_type` INTEGER,
+`hash` BLOB
+)"""
+        )
+
+        now = datetime.now(tz=timezone.utc)
+        timestamp = int(now.timestamp() * 1000)
+
+        cls.__insert_property(conn, PROP_KEY_HASH_SIZE, str(hash_size))
+        cls.__insert_property(conn, PROP_KEY_HIGH_FREQ_FACTOR, str(high_freq_factor))
+        cls.__insert_property(conn, PROP_KEY_BUILT_AT, str(timestamp))
+
+        conn.executemany(
+            "INSERT INTO hashes (`id`, `category`, `hash_type`, `hash`) VALUES (?, ?, ?, ?)",
+            [
+                (it.image_id, it.category.value, it.image_hash_type.value, it.hash)
+                for it in hashes
+            ],
+        )
+        conn.commit()

src/arcaea_offline_ocr/dependencies/ihdb/__init__.py

@@ -1,18 +0,0 @@
-from .builder import ImageHashesDatabaseBuilder
-from .index import ImageHashesDatabase, ImageHashesDatabasePropertyMissingError
-from .models import (
-    ImageHashBuildTask,
-    ImageHashHashType,
-    ImageHashResult,
-    ImageHashCategory,
-)
-
-__all__ = [
-    "ImageHashesDatabase",
-    "ImageHashesDatabasePropertyMissingError",
-    "ImageHashHashType",
-    "ImageHashResult",
-    "ImageHashCategory",
-    "ImageHashesDatabaseBuilder",
-    "ImageHashBuildTask",
-]

src/arcaea_offline_ocr/dependencies/ihdb/builder.py

@@ -1,85 +0,0 @@
-import logging
-from datetime import datetime, timezone
-from sqlite3 import Connection
-from typing import List
-
-from arcaea_offline_ocr.core import hashers
-
-from .index import ImageHashesDatabase
-from .models import ImageHash, ImageHashBuildTask, ImageHashHashType
-
-logger = logging.getLogger(__name__)
-
-
-class ImageHashesDatabaseBuilder:
-    @staticmethod
-    def __insert_property(conn: Connection, key: str, value: str):
-        return conn.execute(
-            "INSERT INTO properties (key, value) VALUES (?, ?)",
-            (key, value),
-        )
-
-    @classmethod
-    def build(
-        cls,
-        conn: Connection,
-        tasks: List[ImageHashBuildTask],
-        *,
-        hash_size: int = 16,
-        high_freq_factor: int = 4,
-    ):
-        rows: List[ImageHash] = []
-
-        for task in tasks:
-            try:
-                img_gray = task.imread_function(task.image_path)
-
-                for hash_type, hash_mat in [
-                    (
-                        ImageHashHashType.AVERAGE,
-                        hashers.average(img_gray, hash_size),
-                    ),
-                    (
-                        ImageHashHashType.DCT,
-                        hashers.dct(img_gray, hash_size, high_freq_factor),
-                    ),
-                    (
-                        ImageHashHashType.DIFFERENCE,
-                        hashers.difference(img_gray, hash_size),
-                    ),
-                ]:
-                    rows.append(
-                        ImageHash(
-                            hash_type=hash_type,
-                            category=task.category,
-                            label=task.label,
-                            hash=ImageHashesDatabase.hash_mat_to_bytes(hash_mat),
-                        )
-                    )
-            except Exception:
-                logger.exception("Error processing task %r", task)
-
-        conn.execute("CREATE TABLE properties (`key` VARCHAR, `value` VARCHAR)")
-        conn.execute(
-            "CREATE TABLE hashes (`hash_type` INTEGER, `category` INTEGER, `label` VARCHAR, `hash` BLOB)"
-        )
-
-        now = datetime.now(tz=timezone.utc)
-        timestamp = int(now.timestamp() * 1000)
-
-        cls.__insert_property(conn, ImageHashesDatabase.KEY_HASH_SIZE, str(hash_size))
-        cls.__insert_property(
-            conn, ImageHashesDatabase.KEY_HIGH_FREQ_FACTOR, str(high_freq_factor)
-        )
-        cls.__insert_property(
-            conn, ImageHashesDatabase.KEY_BUILT_TIMESTAMP, str(timestamp)
-        )
-
-        conn.executemany(
-            "INSERT INTO hashes (hash_type, category, label, hash) VALUES (?, ?, ?, ?)",
-            [
-                (row.hash_type.value, row.category.value, row.label, row.hash)
-                for row in rows
-            ],
-        )
-        conn.commit()

src/arcaea_offline_ocr/dependencies/ihdb/index.py

@@ -1,144 +0,0 @@
-import sqlite3
-from datetime import datetime, timezone
-from typing import Any, Callable, List, Optional, TypeVar
-
-from arcaea_offline_ocr.core import hashers
-from arcaea_offline_ocr.types import Mat
-
-from .models import ImageHashHashType, ImageHashResult, ImageHashCategory
-
-T = TypeVar("T")
-
-
-def _sql_hamming_distance(hash1: bytes, hash2: bytes):
-    assert len(hash1) == len(hash2), "hash size does not match!"
-    count = sum(1 for byte1, byte2 in zip(hash1, hash2) if byte1 != byte2)
-    return count
-
-
-class ImageHashesDatabasePropertyMissingError(Exception):
-    pass
-
-
-class ImageHashesDatabase:
-    KEY_HASH_SIZE = "hash_size"
-    KEY_HIGH_FREQ_FACTOR = "high_freq_factor"
-    KEY_BUILT_TIMESTAMP = "built_timestamp"
-
-    def __init__(self, conn: sqlite3.Connection):
-        self.conn = conn
-        self.conn.create_function("HAMMING_DISTANCE", 2, _sql_hamming_distance)
-
-        self._hash_size: int = -1
-        self._high_freq_factor: int = -1
-        self._built_time: Optional[datetime] = None
-
-        self._hashes_count = {
-            ImageHashCategory.JACKET: 0,
-            ImageHashCategory.PARTNER_ICON: 0,
-        }
-
-        self._hash_length: int = -1
-
-        self._initialize()
-
-    @property
-    def hash_size(self):
-        return self._hash_size
-
-    @property
-    def high_freq_factor(self):
-        return self._high_freq_factor
-
-    @property
-    def hash_length(self):
-        return self._hash_length
-
-    def _initialize(self):
-        def query_property(key, convert_func: Callable[[Any], T]) -> Optional[T]:
-            result = self.conn.execute(
-                "SELECT value FROM properties WHERE key = ?",
-                (key,),
-            ).fetchone()
-            return convert_func(result[0]) if result is not None else None
-
-        def set_hashes_count(category: ImageHashCategory):
-            self._hashes_count[category] = self.conn.execute(
-                "SELECT COUNT(DISTINCT label) FROM hashes WHERE category = ?",
-                (category.value,),
-            ).fetchone()[0]
-
-        hash_size = query_property(self.KEY_HASH_SIZE, lambda x: int(x))
-        if hash_size is None:
-            raise ImageHashesDatabasePropertyMissingError("hash_size")
-        self._hash_size = hash_size
-
-        high_freq_factor = query_property(self.KEY_HIGH_FREQ_FACTOR, lambda x: int(x))
-        if high_freq_factor is None:
-            raise ImageHashesDatabasePropertyMissingError("high_freq_factor")
-        self._high_freq_factor = high_freq_factor
-
-        self._built_time = query_property(
-            self.KEY_BUILT_TIMESTAMP,
-            lambda ts: datetime.fromtimestamp(int(ts) / 1000, tz=timezone.utc),
-        )
-
-        set_hashes_count(ImageHashCategory.JACKET)
-        set_hashes_count(ImageHashCategory.PARTNER_ICON)
-
-        self._hash_length = self._hash_size**2
-
-    def lookup_hash(
-        self, category: ImageHashCategory, hash_type: ImageHashHashType, hash: bytes
-    ) -> List[ImageHashResult]:
-        cursor = self.conn.execute(
-            "SELECT"
-            " label,"
-            " HAMMING_DISTANCE(hash, ?) AS distance"
-            " FROM hashes"
-            " WHERE category = ? AND hash_type = ?"
-            " ORDER BY distance ASC LIMIT 10",
-            (hash, category.value, hash_type.value),
-        )
-
-        results = []
-        for label, distance in cursor.fetchall():
-            results.append(
-                ImageHashResult(
-                    hash_type=hash_type,
-                    category=category,
-                    label=label,
-                    confidence=(self.hash_length - distance) / self.hash_length,
-                )
-            )
-
-        return results
-
-    @staticmethod
-    def hash_mat_to_bytes(hash: Mat) -> bytes:
-        return bytes([255 if b else 0 for b in hash.flatten()])
-
-    def identify_image(self, category: ImageHashCategory, img) -> List[ImageHashResult]:
-        results = []
-
-        ahash = hashers.average(img, self.hash_size)
-        dhash = hashers.difference(img, self.hash_size)
-        phash = hashers.dct(img, self.hash_size, self.high_freq_factor)
-
-        results.extend(
-            self.lookup_hash(
-                category, ImageHashHashType.AVERAGE, self.hash_mat_to_bytes(ahash)
-            )
-        )
-        results.extend(
-            self.lookup_hash(
-                category, ImageHashHashType.DIFFERENCE, self.hash_mat_to_bytes(dhash)
-            )
-        )
-        results.extend(
-            self.lookup_hash(
-                category, ImageHashHashType.DCT, self.hash_mat_to_bytes(phash)
-            )
-        )
-
-        return results

src/arcaea_offline_ocr/dependencies/ihdb/models.py

@@ -1,46 +0,0 @@
-import dataclasses
-from enum import IntEnum
-from typing import Callable
-
-import cv2
-
-from arcaea_offline_ocr.types import Mat
-
-
-class ImageHashHashType(IntEnum):
-    AVERAGE = 0
-    DIFFERENCE = 1
-    DCT = 2
-
-
-class ImageHashCategory(IntEnum):
-    JACKET = 0
-    PARTNER_ICON = 1
-
-
-@dataclasses.dataclass
-class ImageHash:
-    hash_type: ImageHashHashType
-    category: ImageHashCategory
-    label: str
-    hash: bytes
-
-
-@dataclasses.dataclass
-class ImageHashResult:
-    hash_type: ImageHashHashType
-    category: ImageHashCategory
-    label: str
-    confidence: float
-
-
-def _default_imread_gray(image_path: str):
-    return cv2.cvtColor(cv2.imread(image_path, cv2.IMREAD_COLOR), cv2.COLOR_BGR2GRAY)
-
-
-@dataclasses.dataclass
-class ImageHashBuildTask:
-    image_path: str
-    category: ImageHashCategory
-    label: str
-    imread_function: Callable[[str], Mat] = _default_imread_gray

src/arcaea_offline_ocr/device/common.py

@@ -5,10 +5,10 @@ from typing import Optional
 @dataclass
 class DeviceOcrResult:
     rating_class: int
-    pure: int
-    far: int
-    lost: int
     score: int
+    pure: Optional[int] = None
+    far: Optional[int] = None
+    lost: Optional[int] = None
     max_recall: Optional[int] = None
     song_id: Optional[str] = None
     song_id_possibility: Optional[float] = None

src/arcaea_offline_ocr/device/ocr.py

@@ -1,15 +1,8 @@
 import cv2
 import numpy as np
 
-from ..crop import crop_xywh
-from ..ocr import (
-    FixRects,
-    ocr_digit_samples_knn,
-    ocr_digits_by_contour_knn,
-    preprocess_hog,
-    resize_fill_square,
-)
 from ..phash_db import ImagePhashDatabase
+from ..providers.knn import OcrKNearestTextProvider
 from ..types import Mat
 from .common import DeviceOcrResult
 from .rois.extractor import DeviceRoisExtractor
@@ -21,38 +14,37 @@ class DeviceOcr:
         self,
         extractor: DeviceRoisExtractor,
         masker: DeviceRoisMasker,
-        knn_model: cv2.ml.KNearest,
+        knn_provider: OcrKNearestTextProvider,
         phash_db: ImagePhashDatabase,
     ):
         self.extractor = extractor
         self.masker = masker
-        self.knn_model = knn_model
+        self.knn_provider = knn_provider
         self.phash_db = phash_db
 
     def pfl(self, roi_gray: Mat, factor: float = 1.25):
-        contours, _ = cv2.findContours(
-            roi_gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE
-        )
-        filtered_contours = [c for c in contours if cv2.contourArea(c) >= 5 * factor]
-        rects = [cv2.boundingRect(c) for c in filtered_contours]
-        rects = FixRects.connect_broken(rects, roi_gray.shape[1], roi_gray.shape[0])
+        def contour_filter(cnt):
+            return cv2.contourArea(cnt) >= 5 * factor
 
-        filtered_rects = [r for r in rects if r[2] >= 5 * factor and r[3] >= 6 * factor]
-        filtered_rects = FixRects.split_connected(roi_gray, filtered_rects)
-        filtered_rects = sorted(filtered_rects, key=lambda r: r[0])
+        contours = self.knn_provider.contours(roi_gray)
+        contours_filtered = self.knn_provider.contours(
+            roi_gray, contours_filter=contour_filter
+        )
 
         roi_ocr = roi_gray.copy()
-        filtered_contours_flattened = {tuple(c.flatten()) for c in filtered_contours}
+        contours_filtered_flattened = {tuple(c.flatten()) for c in contours_filtered}
         for contour in contours:
-            if tuple(contour.flatten()) in filtered_contours_flattened:
+            if tuple(contour.flatten()) in contours_filtered_flattened:
                 continue
             roi_ocr = cv2.fillPoly(roi_ocr, [contour], [0])
-        digit_rois = [
-            resize_fill_square(crop_xywh(roi_ocr, r), 20) for r in filtered_rects
-        ]
 
-        samples = preprocess_hog(digit_rois)
-        return ocr_digit_samples_knn(samples, self.knn_model)
+        ocr_result = self.knn_provider.result(
+            roi_ocr,
+            contours_filter=lambda cnt: cv2.contourArea(cnt) >= 5 * factor,
+            rects_filter=lambda rect: rect[2] >= 5 * factor and rect[3] >= 6 * factor,
+        )
+
+        return int(ocr_result) if ocr_result else 0
 
     def pure(self):
         return self.pfl(self.masker.pure(self.extractor.pure))
@@ -65,13 +57,14 @@ class DeviceOcr:
 
     def score(self):
         roi = self.masker.score(self.extractor.score)
-        contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+        contours = self.knn_provider.contours(roi)
         for contour in contours:
             if (
                 cv2.boundingRect(contour)[3] < roi.shape[0] * 0.6
             ):  # h < score_component_h * 0.6
                 roi = cv2.fillPoly(roi, [contour], [0])
-        return ocr_digits_by_contour_knn(roi, self.knn_model)
+        ocr_result = self.knn_provider.result(roi)
+        return int(ocr_result) if ocr_result else 0
 
     def rating_class(self):
         roi = self.extractor.rating_class
@@ -85,9 +78,10 @@ class DeviceOcr:
         return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]
 
     def max_recall(self):
-        return ocr_digits_by_contour_knn(
-            self.masker.max_recall(self.extractor.max_recall), self.knn_model
+        ocr_result = self.knn_provider.result(
+            self.masker.max_recall(self.extractor.max_recall)
         )
+        return int(ocr_result) if ocr_result else None
 
     def clear_status(self):
         roi = self.extractor.clear_status

src/arcaea_offline_ocr/providers/__init__.py

@@ -0,0 +1,12 @@
+from .base import ImageCategory, ImageIdProvider, ImageIdProviderResult, OcrTextProvider
+from .ihdb import ImageHashDatabaseIdProvider
+from .knn import OcrKNearestTextProvider
+
+__all__ = [
+    "ImageCategory",
+    "ImageHashDatabaseIdProvider",
+    "OcrKNearestTextProvider",
+    "ImageIdProvider",
+    "OcrTextProvider",
+    "ImageIdProviderResult",
+]

src/arcaea_offline_ocr/providers/base.py

@@ -0,0 +1,38 @@
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from enum import IntEnum
+from typing import TYPE_CHECKING, Any, Sequence, Optional
+
+if TYPE_CHECKING:
+    from ..types import Mat
+
+
+class OcrTextProvider(ABC):
+    @abstractmethod
+    def result_raw(self, img: "Mat", /, *args, **kwargs) -> Any: ...
+    @abstractmethod
+    def result(self, img: "Mat", /, *args, **kwargs) -> Optional[str]: ...
+
+
+class ImageCategory(IntEnum):
+    JACKET = 0
+    PARTNER_ICON = 1
+
+
+@dataclass(kw_only=True)
+class ImageIdProviderResult:
+    image_id: str
+    category: ImageCategory
+    confidence: float
+
+
+class ImageIdProvider(ABC):
+    @abstractmethod
+    def result(
+        self, img: "Mat", category: ImageCategory, /, *args, **kwargs
+    ) -> ImageIdProviderResult: ...
+
+    @abstractmethod
+    def results(
+        self, img: "Mat", category: ImageCategory, /, *args, **kwargs
+    ) -> Sequence[ImageIdProviderResult]: ...

src/arcaea_offline_ocr/providers/ihdb.py

@@ -0,0 +1,194 @@
+import sqlite3
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from enum import IntEnum
+from typing import TYPE_CHECKING, Any, Callable, List, Optional, TypeVar
+
+from arcaea_offline_ocr.core import hashers
+
+from .base import ImageCategory, ImageIdProvider, ImageIdProviderResult
+
+if TYPE_CHECKING:
+    from arcaea_offline_ocr.types import Mat
+
+
+T = TypeVar("T")
+PROP_KEY_HASH_SIZE = "hash_size"
+PROP_KEY_HIGH_FREQ_FACTOR = "high_freq_factor"
+PROP_KEY_BUILT_AT = "built_at"
+
+
+def _sql_hamming_distance(hash1: bytes, hash2: bytes):
+    assert len(hash1) == len(hash2), "hash size does not match!"
+    count = sum(1 for byte1, byte2 in zip(hash1, hash2) if byte1 != byte2)
+    return count
+
+
+class ImageHashType(IntEnum):
+    AVERAGE = 0
+    DIFFERENCE = 1
+    DCT = 2
+
+
+@dataclass(kw_only=True)
+class ImageHashDatabaseIdProviderResult(ImageIdProviderResult):
+    image_hash_type: ImageHashType
+
+
+class MissingPropertiesError(Exception):
+    keys: List[str]
+
+    def __init__(self, keys, *args):
+        super().__init__(*args)
+        self.keys = keys
+
+
+class ImageHashDatabaseIdProvider(ImageIdProvider):
+    def __init__(self, conn: sqlite3.Connection):
+        self.conn = conn
+        self.conn.create_function("HAMMING_DISTANCE", 2, _sql_hamming_distance)
+
+        self.properties = {
+            PROP_KEY_HASH_SIZE: -1,
+            PROP_KEY_HIGH_FREQ_FACTOR: -1,
+            PROP_KEY_BUILT_AT: None,
+        }
+
+        self._hashes_count = {
+            ImageCategory.JACKET: 0,
+            ImageCategory.PARTNER_ICON: 0,
+        }
+
+        self._hash_length: int = -1
+
+        self._initialize()
+
+    @property
+    def hash_size(self) -> int:
+        return self.properties[PROP_KEY_HASH_SIZE]
+
+    @property
+    def high_freq_factor(self) -> int:
+        return self.properties[PROP_KEY_HIGH_FREQ_FACTOR]
+
+    @property
+    def built_at(self) -> Optional[datetime]:
+        return self.properties.get(PROP_KEY_BUILT_AT)
+
+    @property
+    def hash_length(self):
+        return self._hash_length
+
+    def _initialize(self):
+        def get_property(key, converter: Callable[[Any], T]) -> Optional[T]:
+            result = self.conn.execute(
+                "SELECT value FROM properties WHERE key = ?",
+                (key,),
+            ).fetchone()
+            return converter(result[0]) if result is not None else None
+
+        def set_hashes_count(category: ImageCategory):
+            self._hashes_count[category] = self.conn.execute(
+                "SELECT COUNT(DISTINCT `id`) FROM hashes WHERE category = ?",
+                (category.value,),
+            ).fetchone()[0]
+
+        properties_converter_map = {
+            PROP_KEY_HASH_SIZE: lambda x: int(x),
+            PROP_KEY_HIGH_FREQ_FACTOR: lambda x: int(x),
+            PROP_KEY_BUILT_AT: lambda ts: datetime.fromtimestamp(
+                int(ts) / 1000, tz=timezone.utc
+            ),
+        }
+        required_properties = [PROP_KEY_HASH_SIZE, PROP_KEY_HIGH_FREQ_FACTOR]
+
+        missing_properties = []
+        for property_key, converter in properties_converter_map.items():
+            value = get_property(property_key, converter)
+            if value is None:
+                if property_key in required_properties:
+                    missing_properties.append(property_key)
+
+                continue
+
+            self.properties[property_key] = value
+
+        if missing_properties:
+            raise MissingPropertiesError(keys=missing_properties)
+
+        set_hashes_count(ImageCategory.JACKET)
+        set_hashes_count(ImageCategory.PARTNER_ICON)
+
+        self._hash_length = self.hash_size**2
+
+    def lookup_hash(
+        self, category: ImageCategory, hash_type: ImageHashType, hash: bytes
+    ) -> List[ImageHashDatabaseIdProviderResult]:
+        cursor = self.conn.execute(
+            """
+SELECT
+    `id`,
+    HAMMING_DISTANCE(hash, ?) AS distance
+FROM hashes
+WHERE category = ? AND hash_type = ?
+ORDER BY distance ASC LIMIT 10""",
+            (hash, category.value, hash_type.value),
+        )
+
+        results = []
+        for id_, distance in cursor.fetchall():
+            results.append(
+                ImageHashDatabaseIdProviderResult(
+                    image_id=id_,
+                    category=category,
+                    confidence=(self.hash_length - distance) / self.hash_length,
+                    image_hash_type=hash_type,
+                )
+            )
+
+        return results
+
+    @staticmethod
+    def hash_mat_to_bytes(hash: "Mat") -> bytes:
+        return bytes([255 if b else 0 for b in hash.flatten()])
+
+    def results(self, img: "Mat", category: ImageCategory, /):
+        results: List[ImageHashDatabaseIdProviderResult] = []
+
+        results.extend(
+            self.lookup_hash(
+                category,
+                ImageHashType.AVERAGE,
+                self.hash_mat_to_bytes(hashers.average(img, self.hash_size)),
+            )
+        )
+        results.extend(
+            self.lookup_hash(
+                category,
+                ImageHashType.DIFFERENCE,
+                self.hash_mat_to_bytes(hashers.difference(img, self.hash_size)),
+            )
+        )
+        results.extend(
+            self.lookup_hash(
+                category,
+                ImageHashType.DCT,
+                self.hash_mat_to_bytes(
+                    hashers.dct(img, self.hash_size, self.high_freq_factor)
+                ),
+            )
+        )
+
+        return results
+
+    def result(
+        self,
+        img: "Mat",
+        category: ImageCategory,
+        /,
+        *,
+        hash_type: ImageHashType = ImageHashType.DCT,
+    ):
+        return [
+            it for it in self.results(img, category) if it.image_hash_type == hash_type
+        ][0]

src/arcaea_offline_ocr/providers/knn.py

@@ -1,18 +1,19 @@
+import logging
 import math
-from typing import Optional, Sequence, Tuple
+from typing import TYPE_CHECKING, Callable, Optional, Sequence, Tuple
 
 import cv2
 import numpy as np
 
-from .crop import crop_xywh
-from .types import Mat
+from ..crop import crop_xywh
+from .base import OcrTextProvider
 
-__all__ = [
-    "FixRects",
-    "preprocess_hog",
-    "ocr_digits_by_contour_get_samples",
-    "ocr_digits_by_contour_knn",
-]
+if TYPE_CHECKING:
+    from cv2.ml import KNearest
+
+    from ..types import Mat
+
+logger = logging.getLogger(__name__)
 
 
 class FixRects:
@@ -68,7 +69,7 @@ class FixRects:
 
     @staticmethod
     def split_connected(
-        img_masked: Mat,
+        img_masked: "Mat",
         rects: Sequence[Tuple[int, int, int, int]],
         rect_wh_ratio: float = 1.05,
         width_range_ratio: float = 0.1,
@@ -118,7 +119,7 @@ class FixRects:
         return return_rects
 
 
-def resize_fill_square(img: Mat, target: int = 20):
+def resize_fill_square(img: "Mat", target: int = 20):
     h, w = img.shape[:2]
     if h > w:
         new_h = target
@@ -152,29 +153,88 @@ def preprocess_hog(digit_rois):
 
 def ocr_digit_samples_knn(__samples, knn_model: cv2.ml.KNearest, k: int = 4):
     _, results, _, _ = knn_model.findNearest(__samples, k)
-    result_list = [int(r) for r in results.ravel()]
-    result_str = "".join(str(r) for r in result_list if r > -1)
-    return int(result_str) if result_str else 0
+    return [int(r) for r in results.ravel()]
 
 
-def ocr_digits_by_contour_get_samples(__roi_gray: Mat, size: int):
-    roi = __roi_gray.copy()
-    contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
-    rects = [cv2.boundingRect(c) for c in contours]
-    rects = FixRects.connect_broken(rects, roi.shape[1], roi.shape[0])
-    rects = FixRects.split_connected(roi, rects)
-    rects = sorted(rects, key=lambda r: r[0])
-    # digit_rois = [cv2.resize(crop_xywh(roi, rect), size) for rect in rects]
-    digit_rois = [resize_fill_square(crop_xywh(roi, rect), size) for rect in rects]
-    return preprocess_hog(digit_rois)
+class OcrKNearestTextProvider(OcrTextProvider):
+    _ContourFilter = Callable[["Mat"], bool]
+    _RectsFilter = Callable[[Sequence[int]], bool]
 
+    def __init__(self, model: "KNearest"):
+        self.model = model
 
-def ocr_digits_by_contour_knn(
-    __roi_gray: Mat,
-    knn_model: cv2.ml.KNearest,
-    *,
-    k=4,
-    size: int = 20,
-) -> int:
-    samples = ocr_digits_by_contour_get_samples(__roi_gray, size)
-    return ocr_digit_samples_knn(samples, knn_model, k)
+    def contours(
+        self, img: "Mat", /, *, contours_filter: Optional[_ContourFilter] = None
+    ):
+        cnts, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+        if contours_filter:
+            cnts = list(filter(contours_filter, cnts))
+
+        return cnts
+
+    def result_raw(
+        self,
+        img: "Mat",
+        /,
+        *,
+        fix_rects: bool = True,
+        contours_filter: Optional[_ContourFilter] = None,
+        rects_filter: Optional[_RectsFilter] = None,
+    ):
+        """
+        :param img: grayscaled roi
+        """
+
+        try:
+            cnts, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+            if contours_filter:
+                cnts = list(filter(contours_filter, cnts))
+
+            rects = [cv2.boundingRect(cnt) for cnt in cnts]
+            if fix_rects and rects_filter:
+                rects = FixRects.connect_broken(rects, img.shape[1], img.shape[0])  # type: ignore
+                rects = list(filter(rects_filter, rects))
+                rects = FixRects.split_connected(img, rects)
+            elif fix_rects:
+                rects = FixRects.connect_broken(rects, img.shape[1], img.shape[0])  # type: ignore
+                rects = FixRects.split_connected(img, rects)
+            elif rects_filter:
+                rects = list(filter(rects_filter, rects))
+
+            rects = sorted(rects, key=lambda r: r[0])
+
+            digits = []
+            for rect in rects:
+                digit = crop_xywh(img, rect)
+                digit = resize_fill_square(digit, 20)
+                digits.append(digit)
+            samples = preprocess_hog(digits)
+            return ocr_digit_samples_knn(samples, self.model)
+        except Exception:
+            logger.exception("Error occurred during KNearest OCR")
+            return None
+
+    def result(
+        self,
+        img: "Mat",
+        /,
+        *,
+        fix_rects: bool = True,
+        contours_filter: Optional[_ContourFilter] = None,
+        rects_filter: Optional[_RectsFilter] = None,
+    ):
+        """
+        :param img: grayscaled roi
+        """
+
+        raw = self.result_raw(
+            img,
+            fix_rects=fix_rects,
+            contours_filter=contours_filter,
+            rects_filter=rects_filter,
+        )
+        return (
+            "".join(["".join(str(r) for r in raw if r > -1)])
+            if raw is not None
+            else None
+        )