From c08a1332a7d5b170e4815afdc3442f6afcd0f28b Mon Sep 17 00:00:00 2001
From: 283375 <bilibili283375@163.com>
Date: Wed, 25 Jun 2025 23:37:21 +0800
Subject: [PATCH] chore!: removing unused code

---
 src/arcaea_offline_ocr/__init__.py |   2 -
 src/arcaea_offline_ocr/phash_db.py | 119 -----------------------------
 2 files changed, 121 deletions(-)
 delete mode 100644 src/arcaea_offline_ocr/phash_db.py

diff --git a/src/arcaea_offline_ocr/__init__.py b/src/arcaea_offline_ocr/__init__.py
index 18c5986..e69de29 100644
--- a/src/arcaea_offline_ocr/__init__.py
+++ b/src/arcaea_offline_ocr/__init__.py
@@ -1,2 +0,0 @@
-from .crop import *
-from .utils import *
diff --git a/src/arcaea_offline_ocr/phash_db.py b/src/arcaea_offline_ocr/phash_db.py
deleted file mode 100644
index 040b9d7..0000000
--- a/src/arcaea_offline_ocr/phash_db.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import sqlite3
-from typing import List, Union
-
-import cv2
-import numpy as np
-
-from .types import Mat
-
-
-def phash_opencv(img_gray, hash_size=8, highfreq_factor=4):
-    # type: (Union[Mat, np.ndarray], int, int) -> np.ndarray
-    """
-    Perceptual Hash computation.
-
-    Implementation follows
-    http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
-
-    Adapted from `imagehash.phash`, pure opencv implementation
-
-    The result is slightly different from `imagehash.phash`.
-    """
-    if hash_size < 2:
-        raise ValueError("Hash size must be greater than or equal to 2")
-
-    img_size = hash_size * highfreq_factor
-    image = cv2.resize(img_gray, (img_size, img_size), interpolation=cv2.INTER_LANCZOS4)
-    image = np.float32(image)
-    dct = cv2.dct(image)
-    dctlowfreq = dct[:hash_size, :hash_size]
-    med = np.median(dctlowfreq)
-    diff = dctlowfreq > med
-    return diff
-
-
-def hamming_distance_sql_function(user_input, db_entry) -> int:
-    return np.count_nonzero(
-        np.frombuffer(user_input, bool) ^ np.frombuffer(db_entry, bool)
-    )
-
-
-class ImagePhashDatabase:
-    def __init__(self, db_path: str):
-        with sqlite3.connect(db_path) as conn:
-            self.hash_size = int(
-                conn.execute(
-                    "SELECT value FROM properties WHERE key = 'hash_size'"
-                ).fetchone()[0]
-            )
-            self.highfreq_factor = int(
-                conn.execute(
-                    "SELECT value FROM properties WHERE key = 'highfreq_factor'"
-                ).fetchone()[0]
-            )
-            self.built_timestamp = int(
-                conn.execute(
-                    "SELECT value FROM properties WHERE key = 'built_timestamp'"
-                ).fetchone()[0]
-            )
-
-            self.ids: List[str] = [
-                i[0] for i in conn.execute("SELECT id FROM hashes").fetchall()
-            ]
-            self.hashes_byte = [
-                i[0] for i in conn.execute("SELECT hash FROM hashes").fetchall()
-            ]
-            self.hashes = [np.frombuffer(hb, bool) for hb in self.hashes_byte]
-
-            self.jacket_ids: List[str] = []
-            self.jacket_hashes = []
-            self.partner_icon_ids: List[str] = []
-            self.partner_icon_hashes = []
-
-            for _id, _hash in zip(self.ids, self.hashes):
-                id_splitted = _id.split("||")
-                if len(id_splitted) > 1 and id_splitted[0] == "partner_icon":
-                    self.partner_icon_ids.append(id_splitted[1])
-                    self.partner_icon_hashes.append(_hash)
-                else:
-                    self.jacket_ids.append(_id)
-                    self.jacket_hashes.append(_hash)
-
-    def calculate_phash(self, img_gray: Mat):
-        return phash_opencv(
-            img_gray, hash_size=self.hash_size, highfreq_factor=self.highfreq_factor
-        )
-
-    def lookup_hash(self, image_hash: np.ndarray, *, limit: int = 5):
-        image_hash = image_hash.flatten()
-        xor_results = [
-            (id, np.count_nonzero(image_hash ^ h))
-            for id, h in zip(self.ids, self.hashes)
-        ]
-        return sorted(xor_results, key=lambda r: r[1])[:limit]
-
-    def lookup_image(self, img_gray: Mat):
-        image_hash = self.calculate_phash(img_gray)
-        return self.lookup_hash(image_hash)[0]
-
-    def lookup_jackets(self, img_gray: Mat, *, limit: int = 5):
-        image_hash = self.calculate_phash(img_gray).flatten()
-        xor_results = [
-            (id, np.count_nonzero(image_hash ^ h))
-            for id, h in zip(self.jacket_ids, self.jacket_hashes)
-        ]
-        return sorted(xor_results, key=lambda r: r[1])[:limit]
-
-    def lookup_jacket(self, img_gray: Mat):
-        return self.lookup_jackets(img_gray)[0]
-
-    def lookup_partner_icons(self, img_gray: Mat, *, limit: int = 5):
-        image_hash = self.calculate_phash(img_gray).flatten()
-        xor_results = [
-            (id, np.count_nonzero(image_hash ^ h))
-            for id, h in zip(self.partner_icon_ids, self.partner_icon_hashes)
-        ]
-        return sorted(xor_results, key=lambda r: r[1])[:limit]
-
-    def lookup_partner_icon(self, img_gray: Mat):
-        return self.lookup_partner_icons(img_gray)[0]