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基于海明距离的相似图像识别 #63

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merged 1 commit into from
Dec 26, 2022
+16 −9
Merged

基于海明距离的相似图像识别 #63

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20 changes: 13 additions & 7 deletions aiotieba/database.py
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Original file line number Diff line number Diff line change
Expand Up @@ -558,7 +558,7 @@ async def _create_table_imghash(self) -> None:
async with conn.cursor() as cursor:
await cursor.execute(
f"CREATE TABLE IF NOT EXISTS `imghash_{self.fname}` \
(`img_hash` CHAR(16) PRIMARY KEY, `raw_hash` CHAR(40) UNIQUE NOT NULL, `permission` TINYINT NOT NULL DEFAULT 0, `note` VARCHAR(64) NOT NULL DEFAULT '', `record_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP, \
(`img_hash` CHAR(16) PRIMARY KEY, `img_hash_uint64` BIGINT UNSIGNED UNIQUE NOT NULL, `raw_hash` CHAR(40) UNIQUE NOT NULL, `permission` TINYINT NOT NULL DEFAULT 0, `note` VARCHAR(64) NOT NULL DEFAULT '', `record_time` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP, \
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这个uint64确实是个好想法，比字符串索引快得多得多

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使用定长blob(max_length_of_phash)或定长uint16/32/64存储bytes array不是常识？
只有前端给用户看的bytes才需要给他看bin2hex后的字符串

INDEX `permission`(permission), INDEX `record_time`(record_time))"
)

Expand All @@ -580,8 +580,8 @@ async def add_imghash(self, img_hash: str, raw_hash: str, /, permission: int = 0
async with self._pool.acquire() as conn:
async with conn.cursor() as cursor:
await cursor.execute(
f"REPLACE INTO `imghash_{self.fname}` VALUES (%s,%s,%s,%s,DEFAULT)",
(img_hash, raw_hash, permission, note),
f"REPLACE INTO `imghash_{self.fname}` VALUES (%s,CONV(%s,16,10),%s,%s,%s,DEFAULT)",
(img_hash, img_hash, raw_hash, permission, note),
)
except aiomysql.Error as err:
LOG.warning(f"{err}. forum={self.fname} img_hash={img_hash}")
Expand Down Expand Up @@ -612,12 +612,13 @@ async def del_imghash(self, img_hash: str) -> bool:
LOG.info(f"Succeeded. forum={self.fname} img_hash={img_hash}")
return True

async def get_imghash(self, img_hash: str) -> int:
async def get_imghash(self, img_hash: str, hamming_distance: int=0) -> int:
"""
获取表imghash_{fname}中img_hash的封锁级别

Args:
img_hash (str): 图像的phash
hamming_distance: 最大海明距离 默认为0(图像phash完全一致)

Returns:
int: 封锁级别
Expand All @@ -626,9 +627,14 @@ async def get_imghash(self, img_hash: str) -> int:
try:
async with self._pool.acquire() as conn:
async with conn.cursor() as cursor:
await cursor.execute(
f"SELECT `permission` FROM `imghash_{self.fname}` WHERE `img_hash`=%s", (img_hash,)
)
if hamming_distance > 0:
await cursor.execute(
f"SELECT `permission`, BIT_COUNT(`img_hash_uint64` ^ CONV(%s,16,10)) AS hd FROM `imghash_{self.fname}` HAVING hd <= %s ORDER BY hd ASC", (img_hash, hamming_distance)
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复制粘贴 https://stackoverflow.com/questions/21037578/mysql-hamming-distance-between-two-phash

)
else:
await cursor.execute(
f"SELECT `permission` FROM `imghash_{self.fname}` WHERE `img_hash`=%s", (img_hash,)
)
except aiomysql.Error as err:
LOG.warning(f"{err}. forum={self.fname} img_hash={img_hash}")
return False
Expand Down
5 changes: 3 additions & 2 deletions aiotieba/reviewer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -419,19 +419,20 @@ def compute_imghash(self, image: "np.ndarray") -> str:

return img_hash

async def get_imghash(self, image: "np.ndarray") -> int:
async def get_imghash(self, image: "np.ndarray", hamming_distance: int=0) -> int:
"""
获取图像的封锁级别

Args:
image (np.ndarray): 图像
hamming_distance: 最大海明距离 默认为0(图像phash完全一致)

Returns:
int: 封锁级别
"""

if img_hash := self.compute_imghash(image):
return await self.db.get_imghash(img_hash)
return await self.db.get_imghash(img_hash, hamming_distance)
return 0

async def get_imghash_full(self, image: "np.ndarray") -> Tuple[int, str]:
Expand Down