From 4f129a0be7676a270a3565ab604f8741b1333a99 Mon Sep 17 00:00:00 2001
From: Refound-445 <2877834692@qq.com>
Date: Sat, 16 Nov 2024 17:45:31 +0800
Subject: [PATCH] up
---
.../README.md | 80 +++++---
.../nonebot_plugin_nailongremove/__init__.py | 2 +-
.../nonebot_plugin_nailongremove/config.py | 28 +--
.../frame_source.py | 6 +-
.../nonebot_plugin_nailongremove/handler.py | 140 +++++++------
.../model/__init__.py | 18 +-
.../model/classification.py | 29 +--
.../model/hf_detection.py | 186 ++++++++++++++++++
.../model/target_detection.py | 140 ++++++++-----
.../model/utils/common.py | 158 +++++++++++++--
.../model/utils/update.py | 40 ++--
.../model/utils/yolox.py | 2 +-
.../pyproject.toml | 5 +-
13 files changed, 608 insertions(+), 226 deletions(-)
create mode 100644 packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/hf_detection.py
diff --git a/packages/nonebot-plugin-nailongremove-base/README.md b/packages/nonebot-plugin-nailongremove-base/README.md
index e922b7e..d7bc284 100644
--- a/packages/nonebot-plugin-nailongremove-base/README.md
+++ b/packages/nonebot-plugin-nailongremove-base/README.md
@@ -187,41 +187,50 @@ pip install nonebot-plugin-nailongremove-base -U
在 nonebot2 项目的 `.env` 文件中添加下表中的必填配置
-| 配置项 | 必填 | 默认值 | 说明 |
-| :-------------------------: | :--: | :--------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
-| **全局配置** | | | |
-| `PROXY` | 否 | `None` | 下载模型等文件时使用的代理地址 |
-| **响应配置** | | | |
-| `NAILONG_BYPASS_SUPERUSER` | 否 | `True` | 是否不检查超级用户发送的图片 |
-| `NAILONG_BYPASS_ADMIN` | 否 | `True` | 是否不检查群组管理员发送的图片 |
-| `NAILONG_NEED_ADMIN` | 否 | `False` | 当自身不为群组管理员时是否不检查群内所有图片 |
-| `NAILONG_LIST_SCENES` | 否 | `[]` | 聊天场景 ID 黑白名单列表
在单级聊天下为该聊天 ID,如 QQ 群号;
在多级聊天下为以 `_` 分割的各级聊天 ID,如频道下的子频道或频道下私聊 |
-| `NAILONG_BLACKLIST` | 否 | `True` | 是否使用黑名单模式 |
-| `NAILONG_USER_BLACKLIST` | 否 | `[]` | 用户 ID 黑名单列表 |
-| `NAILONG_PRIORITY` | 否 | `100` | Matcher 优先级 |
-| **行为配置** | | | |
-| `NAILONG_RECALL` | 否 | `True` | 是否撤回消息 |
-| `NAILONG_MUTE_SECONDS` | 否 | `0` | 设置禁言时间,默认为 0 即不禁言
单位:秒 |
-| `NAILONG_TIP` | 否 | `{"nailong": "本群禁止发奶龙!"}` | 发送的提示,使用 [Alconna 的消息模板](https://nonebot.dev/docs/best-practice/alconna/uniseg#%E4%BD%BF%E7%94%A8%E6%B6%88%E6%81%AF%E6%A8%A1%E6%9D%BF),可用变量见下,可以根据标签自定义对应值,如遇其中没有的标签会回退到 `nailong` |
-| `NAILONG_FAILED_TIP` | 否 | `{"nailong": "{:Reply($message_id)}呜,不要发奶龙了嘛 🥺 👉👈"}` | 撤回失败或禁用撤回时发送的提示,同上 |
-| `NAILONG_CHECK_ALL_FRAMES` | 否 | `False` | 使用模型 1 时是否检查图片中的所有帧,启用该项后消息模板中的 `$checked_result` 变量当原图为动图时会变为动图 |
-| **模型通用配置** | | | |
-| `NAILONG_MODEL_DIR` | 否 | `./data/nailongremove` | 模型的下载位置 |
-| `NAILONG_MODEL` | 否 | `1` | 选择需要加载的模型,可用模型见下 |
-| `NAILONG_AUTO_UPDATE_MODEL` | 否 | `True` | 是否自动更新模型 |
-| `NAILONG_CONCURRENCY` | 否 | `1` | 当图片为动图时,针对该图片并发识别图片帧的最大并发数 |
-| `NAILONG_ONNX_PROVIDERS` | 否 | `["CPUExecutionProvider"]` | 加载 onnx 模型使用的 provider 列表,请参考上方安装文档 |
-| **模型 1 特定配置** | | | |
-| `NAILONG_MODEL1_TYPE` | 否 | `tiny` | 模型 1 使用的模型类型,可用 `tiny` / `m` |
-| `NAILONG_MODEL1_YOLOX_SIZE` | 否 | `None` | 针对模型 1,自定义模型输入可能会有尺寸更改 |
-| `NAILONG_MODEL1_SCORE` | 否 | `{"nailong": 0.5}` | 模型 1 置信度阈值,范围 `0` ~ `1`,可以根据标签自定义对应值,设置对应标签的阈值以检测该标签,设为 `null` 或者不填可以忽略该标签 |
-| **杂项配置** | | | |
-| `NAILONG_GITHUB_TOKEN` | 否 | `None` | GitHub Access Token,遇到模型下载或更新问题时可尝试填写 |
+| 配置项 | 必填 | 默认值 | 说明 |
+|:-----------------------------------:| :--: |:-------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
+| **全局配置** | | | |
+| `PROXY` | 否 | `None` | 下载模型等文件时使用的代理地址 |
+| **响应配置** | | | |
+| `NAILONG_BYPASS_SUPERUSER` | 否 | `True` | 是否不检查超级用户发送的图片 |
+| `NAILONG_BYPASS_ADMIN` | 否 | `True` | 是否不检查群组管理员发送的图片 |
+| `NAILONG_NEED_ADMIN` | 否 | `False` | 当自身不为群组管理员时是否不检查群内所有图片 |
+| `NAILONG_LIST_SCENES` | 否 | `[]` | 聊天场景 ID 黑白名单列表
在单级聊天下为该聊天 ID,如 QQ 群号;
在多级聊天下为以 `_` 分割的各级聊天 ID,如频道下的子频道或频道下私聊 |
+| `NAILONG_BLACKLIST` | 否 | `True` | 是否使用黑名单模式 |
+| `NAILONG_USER_BLACKLIST` | 否 | `[]` | 用户 ID 黑名单列表 |
+| `NAILONG_PRIORITY` | 否 | `100` | Matcher 优先级 |
+| **行为配置** | | | |
+| `NAILONG_RECALL` | 否 | `True` | 是否撤回消息 |
+| `NAILONG_MUTE_SECONDS` | 否 | `0` | 设置禁言时间,默认为 0 即不禁言
单位:秒 |
+| `NAILONG_TIP` | 否 | `{"nailong": "本群禁止发奶龙!"}` | 发送的提示,使用 [Alconna 的消息模板](https://nonebot.dev/docs/best-practice/alconna/uniseg#%E4%BD%BF%E7%94%A8%E6%B6%88%E6%81%AF%E6%A8%A1%E6%9D%BF),可用变量见下,可以根据标签自定义对应值,如遇其中没有的标签会回退到 `nailong` |
+| `NAILONG_FAILED_TIP` | 否 | `{"nailong": "{:Reply($message_id)}呜,不要发奶龙了嘛 🥺 👉👈"}` | 撤回失败或禁用撤回时发送的提示,同上 |
+| `NAILONG_CHECK_ALL_FRAMES` | 否 | `False` | 使用模型 1 时是否检查图片中的所有帧,需要同时设置`NAILONG_CHECK_MODE`为0,启用该项后消息模板中的 `$checked_result` 变量当原图为动图时会变为动图 |
+| `NAILONG_CHECK_MODE` | 否 | `0` | 选择对GIF动图的检测方式
0.检测所有帧
1.只检测第一帧
2.随机抽帧检测 |
+| **相似度检测配置** | | | |
+| `NAILONG_SIMILARITY_ON` | 否 | `False` | 是否启用处理图片前对本地存储进行相似度检测(该功能仍在更新中,目前可能耗能较大且处理较慢) |
+| `NAILONG_SIMILARITY_MAX_STORAGE` | 否 | `10` | 本地存储报错图片最大上限,到达上限会压缩并删除上次记录 |
+| `NAILONG_SIMILARITY_MAX_BATCH_SIZE` | 否 | `10` | 本地存储相似度检测时处理的最大批数量 |
+| **模型通用配置** | | | |
+| `NAILONG_MODEL_DIR` | 否 | `./data/nailongremove` | 模型的下载位置 |
+| `NAILONG_MODEL` | 否 | `1` | 选择需要加载的模型,可用模型见下 |
+| `NAILONG_AUTO_UPDATE_MODEL` | 否 | `True` | 是否自动更新模型 |
+| `NAILONG_CONCURRENCY` | 否 | `1` | 当图片为动图时,针对该图片并发识别图片帧的最大并发数 |
+| `NAILONG_ONNX_PROVIDERS` | 否 | `["CPUExecutionProvider"]` | 加载 onnx 模型使用的 provider 列表,请参考上方安装文档 |
+| **模型 1 特定配置** | | | |
+| `NAILONG_MODEL1_TYPE` | 否 | `tiny` | 模型 1 使用的模型类型,可用 `tiny` / `m` |
+| `NAILONG_MODEL1_YOLOX_SIZE` | 否 | `None` | 针对模型 1,自定义模型输入可能会有尺寸更改 |
+| **模型 2 特定配置** | | | |
+| `NAILONG_MODEL2_ONLINE` | 否 | `False` | 针对模型 2,是否启用在线推理,此模式目前不适用`NAILONG_CHECK_MODE`为0 |
+| **模型 1&2 特定配置** | | | |
+| `NAILONG_MODEL1_SCORE` | 否 | `{"nailong": 0.5}` | 模型 1&2 置信度阈值,范围 `0` ~ `1`,可以根据标签自定义对应值,设置对应标签的阈值以检测该标签,设为 `null` 或者不填可以忽略该标签 |
+| **杂项配置** | | | |
+| `NAILONG_GITHUB_TOKEN` | 否 | `None` | GitHub Access Token,遇到模型下载或更新问题时可尝试填写 |
### 可用模型
- `0`:基于 Renet50 图像分类模型训练推理,感谢 @spawner1145 提供的模型,原链接:[spawner1145/NailongRecognize](https://github.com/spawner1145/NailongRecognize.git)
- `1`:基于 YOLOX 目标检测模型训练推理,感谢 @NKXingXh 提供的模型,原链接:[nkxingxh/NailongDetection](https://github.com/nkxingxh/NailongDetection)
+- `2`:基于 YOLOv11 目标检测模型训练推理,感谢 @Hakureirm 提供的模型,原链接:[Hakureirm/NailongKiller](https://huggingface.co/spaces/Hakureirm/NailongKiller)
### 消息模板可用变量
@@ -238,16 +247,23 @@ pip install nonebot-plugin-nailongremove-base -U
只要有人发奶龙表情包被识别出来,就会被撤回并提醒。
+本地存储报错图片(`SUPERUSERS`);发送"这是[种类]"+图片,例如:"这是nailong+图片",便会自动存储到本地,开启相似度检测后,在下一次检测图片会优先识别本地已存储的图片。
+
## 📞 联系
-- Nonebot2 官方交流群:768887710(基础的安装部署问题可在这里询问)
+- 机器人插件学习交流群:200980266(安装部署,机器人 BUG 模型精度等问题反馈来这里哟)
- 人工智能学习交流群:949992679(学习交流 AI 相关技术可以来这里捏)
-- 机器人插件学习交流群:200980266(机器人 BUG 模型精度等问题反馈来这里哟)
欢迎大家进群一起学习交流~
## 📝 更新日志
+### 2.3.2
+
+- 更新对GIF动图的三种帧处理模式,通过`NAILONG_CHECK_MODE`自行选择
+- 更新对于报错图片临时处理方案,通过设置`NAILONG_SIMILARITY_ON`开启浏览本地存储相似度匹配,通过`SUPERUSERS`发送"这是[种类]"+图片,可将报错图片保存到本地记录
+- `NAILONG_MODEL`加入model2,基于YOLOv11训练的模型,目前仅支持奶龙识别
+
### 2.3.1
- 修改插件依赖以避免一些问题,影响了安装过程,请查看安装文档了解
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/__init__.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/__init__.py
index 0277030..e0ec33a 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/__init__.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/__init__.py
@@ -9,7 +9,7 @@ require("nonebot_plugin_uninfo")
from . import handler as handler
from .config import Config
-__version__ = "2.3.1.post1"
+__version__ = "2.3.0"
__plugin_meta__ = PluginMetadata(
name="自动撤回奶龙",
description="一个基于图像分类模型的简单插件~",
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/config.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/config.py
index 5cb4c7a..785d39e 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/config.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/config.py
@@ -13,6 +13,7 @@ DEFAULT_LABEL = "nailong"
class ModelType(int, Enum):
CLASSIFICATION = 0
TARGET_DETECTION = 1
+ HF_DETECTION = 2
class Model1Type(StrEnum):
@@ -54,13 +55,18 @@ class Config(BaseModel):
nailong_model: ModelType = ModelType.TARGET_DETECTION
nailong_auto_update_model: bool = True
nailong_concurrency: int = 1
- nailong_onnx_providers: List[str] = ["CPUExecutionProvider"]
+ nailong_onnx_try_to_use_gpu: bool = True
nailong_model1_type: Model1Type = Model1Type.TINY
nailong_model1_yolox_size: Optional[Tuple[int, int]] = None
nailong_model1_score: Dict[str, Optional[float]] = {
DEFAULT_LABEL: 0.5,
}
+ nailong_model2_online: bool = False
+ nailong_check_mode: int = 0
+ nailong_similarity_on: bool = False
+ nailong_similarity_max_storage: int = 10
+ nailong_similarity_max_batch_size: int = 10
nailong_github_token: Optional[str] = None
@@ -71,7 +77,9 @@ class Config(BaseModel):
mode="before",
)
def transform_to_dict(cls, v: Any): # noqa: N805
- return v if isinstance(v, dict) else {DEFAULT_LABEL: v}
+ if not isinstance(v, dict):
+ return {DEFAULT_LABEL: v}
+ return v
@field_validator(
"nailong_tip",
@@ -84,21 +92,5 @@ class Config(BaseModel):
raise ValueError(f"Please ensure default label {DEFAULT_LABEL} in dict")
return v
- @field_validator("nailong_onnx_providers", mode="before")
- def transform_to_list(cls, v: Any): # noqa: N805
- return v if isinstance(v, list) else [v]
-
- @field_validator("nailong_onnx_providers", mode="after")
- def validate_provider_available(cls, v: Any): # noqa: N805
- try:
- from onnxruntime.capi import _pybind_state as c
- except ImportError:
- pass
- else:
- available_providers: List[str] = c.get_available_providers() # type: ignore
- if any(p not in available_providers for p in v):
- raise ValueError(f"Provider {v} not available in onnxruntime")
- return v
-
config = get_plugin_config(Config)
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/frame_source.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/frame_source.py
index dce3c65..d00e52a 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/frame_source.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/frame_source.py
@@ -128,8 +128,8 @@ async def _(source: PilImageFrameSource, frames: Iterator[np.ndarray]) -> Segmen
def repack_save(
- source: FrameSource,
- frames: Iterator[np.ndarray],
+ source: FrameSource,
+ frames: Iterator[np.ndarray],
) -> Awaitable[Segment]:
if (k := type(source)) not in repack_savers:
raise NotImplementedError
@@ -180,7 +180,7 @@ async def extract_source(seg: Segment) -> FrameSource:
async def iter_sources_in_message(
- message: UniMessage,
+ message: UniMessage,
) -> AsyncIterator[Tuple[FrameSource, Segment]]:
for seg in message:
try:
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/handler.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/handler.py
index a210f65..66357f6 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/handler.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/handler.py
@@ -1,16 +1,17 @@
+import re
from typing import Any, Awaitable, Callable, Iterable, List, TypeVar
-
from nonebot import logger, on_message
from nonebot.adapters import Bot as BaseBot, Event as BaseEvent
from nonebot.permission import SUPERUSER
from nonebot.rule import Rule
-from nonebot_plugin_alconna.uniseg import UniMessage, UniMsg
+from nonebot_plugin_alconna.uniseg import UniMessage, UniMsg, Text
from nonebot_plugin_uninfo import QryItrface, Uninfo
from .config import DEFAULT_LABEL, config
from .frame_source import iter_sources_in_message, source_extractors
from .model import check
from .uniapi import mute, recall
+from .model.utils.common import process_gif_and_save_jpgs
T = TypeVar("T")
@@ -20,7 +21,7 @@ def judge_list(lst: Iterable[T], val: T, blacklist: bool) -> bool:
async def execute_functions_any_ok(
- func: Iterable[Callable[[], Awaitable[Any]]],
+ func: Iterable[Callable[[], Awaitable[Any]]],
) -> bool:
ok = False
for f in func:
@@ -35,36 +36,36 @@ async def execute_functions_any_ok(
async def nailong_rule(
- bot: BaseBot,
- event: BaseEvent,
- session: Uninfo,
- ss_interface: QryItrface,
- msg: UniMsg,
+ bot: BaseBot,
+ event: BaseEvent,
+ session: Uninfo,
+ ss_interface: QryItrface,
+ msg: UniMsg,
) -> bool:
return (
# check if it's a group chat
- bool(session.member) # this prop only exists in group chats
- # user blacklist
- and (session.user.id not in config.nailong_user_blacklist)
- # scene blacklist or whitelist
- and judge_list(
- config.nailong_list_scenes,
- session.scene_path,
- config.nailong_blacklist,
- )
- # bypass superuser
- and ((not config.nailong_bypass_superuser) or (not await SUPERUSER(bot, event)))
- # bypass group admin
- and (
- (not config.nailong_bypass_admin)
- or ((not session.member.role) or session.member.role.level <= 1)
- )
- # msg has supported seg
- and (any(True for x in msg if type(x) in source_extractors))
- # self is admin
- and (
- (not config.nailong_need_admin)
- or bool(
+ bool(session.member) # this prop only exists in group chats
+ # user blacklist
+ and (session.user.id not in config.nailong_user_blacklist)
+ # scene blacklist or whitelist
+ and judge_list(
+ config.nailong_list_scenes,
+ session.scene_path,
+ config.nailong_blacklist,
+ )
+ # bypass superuser
+ and ((not config.nailong_bypass_superuser) or (not await SUPERUSER(bot, event)))
+ # bypass group admin
+ and (
+ (not config.nailong_bypass_admin)
+ or ((not session.member.role) or session.member.role.level <= 1)
+ )
+ # msg has supported seg
+ and (any(True for x in msg if type(x) in source_extractors))
+ # self is admin
+ and (
+ (not config.nailong_need_admin)
+ or bool(
(
self_info := await ss_interface.get_member(
session.scene.type,
@@ -75,41 +76,64 @@ async def nailong_rule(
and self_info.role
and self_info.role.level > 1,
)
- )
+ )
)
nailong = on_message(rule=Rule(nailong_rule), priority=config.nailong_priority)
+input_shape = config.nailong_model1_yolox_size or config.nailong_model1_type.yolox_size
@nailong.handle()
async def handle_function(bot: BaseBot, ev: BaseEvent, msg: UniMsg, session: Uninfo):
+ save_img = False
+ if await SUPERUSER(bot, ev):
+ for seg in msg:
+ if type(seg) == Text and "这是" in seg.text:
+ save_img = True
+ label = re.search(r"这是(\S+)", seg.text.replace(" ", "")).group(1)
+ break
async for source, seg in iter_sources_in_message(msg):
- try:
- check_res = await check(source)
- except Exception:
- logger.exception(f"Failed to check {seg!r}")
- continue
- if not check_res.ok:
- continue
+ if save_img:
+ frames = []
+ temp_iter = iter(source)
+ while True:
+ try:
+ temp_image = next(temp_iter)
+ frames.append(temp_image)
+ except StopIteration:
+ break
+ zip_filename = process_gif_and_save_jpgs(frames, label, input_shape)
+ if zip_filename is None:
+ await nailong.finish(f"已保存数据到目录{config.nailong_model_dir}\\records\\{label},标签:{label}")
+ else:
+ await nailong.finish(
+ f"记录数据超过{config.nailong_similarity_max_storage},已清除原记录数据,压缩并保存至{zip_filename}\n已保存数据到目录{config.nailong_model_dir}\\records\\{label},标签:{label}")
+ else:
+ try:
+ check_res = await check(source)
+ except Exception:
+ logger.exception(f"Failed to check {seg!r}")
+ continue
+ if not check_res.ok or check_res.label not in config.nailong_tip:
+ continue
- functions: List[Callable[[], Awaitable[Any]]] = []
- if config.nailong_recall:
- functions.append(lambda: recall(bot, ev))
- if config.nailong_mute_seconds > 0:
- functions.append(lambda: mute(bot, ev, config.nailong_mute_seconds))
- punish_ok = functions and (await execute_functions_any_ok(functions))
-
- template_dict = config.nailong_tip if punish_ok else config.nailong_failed_tip
- template_str = template_dict[
- check_res.label if (check_res.label in template_dict) else DEFAULT_LABEL
- ]
- mapping = {
- "$event": ev,
- "$target": msg.get_target(),
- "$message_id": msg.get_message_id(),
- "$msg": msg,
- "$ss": session,
- **check_res.extra_vars,
- }
- await UniMessage.template(template_str).format_map(mapping).finish()
+ functions: List[Callable[[], Awaitable[Any]]] = []
+ if config.nailong_recall:
+ functions.append(lambda: recall(bot, ev))
+ if config.nailong_mute_seconds > 0:
+ functions.append(lambda: mute(bot, ev, config.nailong_mute_seconds))
+ punish_ok = functions and (await execute_functions_any_ok(functions))
+ template_dict = config.nailong_tip if punish_ok else config.nailong_failed_tip
+ template_str = template_dict[
+ check_res.label if (check_res.label in template_dict) else DEFAULT_LABEL
+ ]
+ mapping = {
+ "$event": ev,
+ "$target": msg.get_target(),
+ "$message_id": msg.get_message_id(),
+ "$msg": msg,
+ "$ss": session,
+ **check_res.extra_vars,
+ }
+ await UniMessage.template(template_str).format_map(mapping).finish()
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/__init__.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/__init__.py
index c852398..6ef8585 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/__init__.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/__init__.py
@@ -22,17 +22,11 @@ if config.nailong_model is ModelType.CLASSIFICATION:
raise_extra_import_error(e, "model0")
elif config.nailong_model is ModelType.TARGET_DETECTION:
- try:
- from .target_detection import check as check
- except ImportError as e:
- raise ImportError(
- "To avoid dependency issues, please install onnxruntime manually.\n"
- "If you have a compatible GPU, "
- "please run `pip install onnxruntime-gpu` in your project's environment, "
- "then edit plugin's `NAILONG_ONNX_PROVIDERS` config to use it;\n"
- "Otherwise run `pip install onnxruntime` in your project's environment "
- "and use CPU to compute.",
- ) from e
+ pass
+
+elif config.nailong_model is ModelType.HF_DETECTION:
+ from .hf_detection import check as check
+
else:
- raise NotImplementedError # never reach here
+ raise ValueError("Invalid model type")
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/classification.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/classification.py
index a7a1981..de70cbe 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/classification.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/classification.py
@@ -9,8 +9,9 @@ from torchvision import transforms
from ..config import DEFAULT_LABEL
from ..frame_source import FrameSource
-from .utils.common import CheckResult, CheckSingleResult, race_check
+from .utils.common import CheckResult, CheckSingleResult, race_check, similarity_process
from .utils.update import GitHubRepoModelUpdater
+from ..config import config
model_path = GitHubRepoModelUpdater(
"spawner1145",
@@ -35,17 +36,23 @@ SIZE = 224
@run_sync
-def check_single(image: np.ndarray) -> CheckSingleResult[None]:
- if image.shape[0] < SIZE or image.shape[1] < SIZE:
+def check_single(image: np.ndarray, is_gif: bool = False) -> CheckSingleResult[None]:
+ if is_gif:
+ res = similarity_process(image, dsize=(SIZE, SIZE))
+ if res is not None:
+ return res
return CheckSingleResult.not_ok(None)
- image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
- image = cv2.resize(image, (SIZE, SIZE))
- image = transform(image)
- image = image.unsqueeze(0) # type: ignore
- with torch.no_grad():
- output = model(image.to(device)) # type: ignore
- _, pred = torch.max(output, 1)
- return CheckSingleResult(ok=pred.item() == 1, label=DEFAULT_LABEL, extra=None)
+ else:
+ if image.shape[0] < SIZE or image.shape[1] < SIZE:
+ return CheckSingleResult.not_ok(None)
+ image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+ image = cv2.resize(image, (SIZE, SIZE))
+ image = transform(image)
+ image = image.unsqueeze(0) # type: ignore
+ with torch.no_grad():
+ output = model(image.to(device)) # type: ignore
+ _, pred = torch.max(output, 1)
+ return CheckSingleResult(ok=pred.item() == 1, label=DEFAULT_LABEL, extra=None)
async def check(source: FrameSource):
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/hf_detection.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/hf_detection.py
new file mode 100644
index 0000000..33cce2c
--- /dev/null
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/hf_detection.py
@@ -0,0 +1,186 @@
+import asyncio
+import datetime
+import os
+
+import cv2
+from PIL import Image
+import torch
+import numpy as np
+from cookit import with_semaphore
+from nonebot.utils import run_sync
+from ..config import config
+from ..frame_source import FrameSource, repack_save
+from .utils.common import CheckResult, CheckSingleResult, race_check, similarity_process
+import itertools
+from nonebot import logger
+
+if config.nailong_model2_online:
+ from gradio_client import Client, handle_file
+ import base64
+ import io
+ import shutil
+
+ FILENAME = "nailong_yolo11.pt"
+ client = Client("Hakureirm/NailongKiller")
+ logger.info(f"Using model {FILENAME} online")
+else:
+ from ultralytics import YOLO
+ from huggingface_hub import hf_hub_download, hf_api
+
+ REPO_ID = "Hakureirm/NailongKiller"
+ FILENAME = "nailong_yolo11.pt"
+
+ model_path = os.path.join(str(config.nailong_model_dir), FILENAME)
+ if config.nailong_auto_update_model or not os.path.exists(model_path):
+ api = hf_api.HfApi()
+ file_path = os.path.join(str(config.nailong_model_dir), FILENAME)
+ model_info = api.model_info(REPO_ID)
+
+
+ def get_file_last_modified_time(file_path):
+ try:
+ timestamp = os.path.getmtime(file_path)
+ last_modified_time = datetime.datetime.fromtimestamp(timestamp, tz=datetime.timezone.utc)
+ return last_modified_time
+ except FileNotFoundError:
+ return None
+
+
+ local_time = get_file_last_modified_time(file_path)
+ if local_time is None or model_info.last_modified >= local_time:
+ hf_hub_download(repo_id=REPO_ID, filename=FILENAME, local_dir=config.nailong_model_dir)
+ logger.info(f"Update model {FILENAME} successfully!")
+
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+ model = YOLO(model_path).to(device)
+ logger.info(f"Using model {FILENAME}")
+
+input_shape = config.nailong_model1_yolox_size or config.nailong_model1_type.yolox_size
+
+
+@run_sync
+def _check_single(frame: np.ndarray, is_gif: bool = False) -> CheckSingleResult:
+ if is_gif:
+ res = similarity_process(frame, dsize=input_shape)
+ if res is not None:
+ return CheckSingleResult(ok=res.ok, label=res.label, extra=frame)
+ return CheckSingleResult(ok=False, label=None, extra=frame)
+ else:
+ if config.nailong_model2_online:
+ input_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+ if not os.path.exists(os.path.join(str(config.nailong_model_dir), "online_temp")):
+ os.makedirs(os.path.join(str(config.nailong_model_dir), "online_temp"))
+ image_path = os.path.join(str(config.nailong_model_dir), "online_temp",
+ "temp_{}.jpg".format(datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")))
+ while os.path.exists(image_path):
+ basename = os.path.basename(image_path)
+ image_path = os.path.join(str(config.nailong_model_dir), "online_temp", f"exist-{basename}")
+ input_image.save(image_path, format='JPEG')
+ result_image, result_info = client.predict(
+ img=handle_file(image_path),
+ api_name="/predict"
+ )
+ os.remove(image_path)
+ if "检测到的目标数量: " in result_info and int(
+ result_info.split("检测到的目标数量: ")[1].split("\n")[0]) < 1:
+ return CheckSingleResult(ok=False, label=None, extra=frame)
+ if isinstance(result_image, str):
+ if result_image.startswith('data:image'):
+ img_data = base64.b64decode(result_image.split(',')[1])
+ img = Image.open(io.BytesIO(img_data))
+ result_image = np.array(img)
+ else:
+ img_data = result_image
+ img = Image.open(img_data)
+ result_image = np.array(img)
+ shutil.rmtree(os.path.dirname(os.path.dirname(img_data)))
+ result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB)
+ return CheckSingleResult(ok=True, label="nailong", extra=result_image)
+ else:
+ input_image = Image.fromarray(frame)
+ original_size = input_image.size
+
+ max_size = max(original_size)
+ pad_w = max_size - original_size[0]
+ pad_h = max_size - original_size[1]
+
+ padded_img = Image.new('RGB', (max_size, max_size), (114, 114, 114))
+ padded_img.paste(input_image, (pad_w // 2, pad_h // 2))
+
+ img_array = np.array(padded_img)
+
+ results = model.predict(
+ img_array,
+ conf=config.nailong_model1_score['nailong'],
+ iou=0.5,
+ max_det=100,
+ verbose=False
+ )
+ cls = results[0].boxes.cls
+ if len(cls) < 1:
+ return CheckSingleResult(ok=False, label=None, extra=frame)
+ result_img = results[0].plot()
+
+ if pad_w > 0 or pad_h > 0:
+ result_img = result_img[pad_h // 2:pad_h // 2 + original_size[1],
+ pad_w // 2:pad_w // 2 + original_size[0]]
+ return CheckSingleResult(ok=True, label='nailong', extra=result_img)
+
+
+async def check_single(frame: np.ndarray, is_gif: bool = False) -> CheckSingleResult:
+ if is_gif:
+ res = await _check_single(frame, True)
+ return CheckSingleResult(
+ ok=res.ok,
+ label=res.label,
+ extra=res.extra,
+ )
+ else:
+ res = await _check_single(frame)
+ return CheckSingleResult(
+ ok=res.ok,
+ label=res.label,
+ extra=res.extra,
+ )
+
+
+async def check(source: FrameSource) -> CheckResult:
+ label = None
+ extra_vars = {}
+ if config.nailong_check_all_frames and config.nailong_check_mode == 0:
+ if config.nailong_similarity_on:
+ tem_source = itertools.tee(source, 1)[0]
+ sem = asyncio.Semaphore(config.nailong_concurrency)
+ results = await asyncio.gather(
+ *(with_semaphore(sem)(check_single)(frame, True) for frame in tem_source),
+ )
+ ok = any(r.ok for r in results)
+ else:
+ ok = False
+ if not ok:
+ sem = asyncio.Semaphore(config.nailong_concurrency)
+ results = await asyncio.gather(
+ *(with_semaphore(sem)(check_single)(frame) for frame in source),
+ )
+ ok = any(r.ok for r in results)
+ if ok:
+ all_labels = {r.label for r in results if r.label}
+ label = next(
+ (x for x in config.nailong_model1_score if x in all_labels),
+ None,
+ )
+ extra_vars["$checked_result"] = await repack_save(
+ source,
+ (r.extra for r in results),
+ )
+ else:
+ res = await race_check(check_single, source)
+ ok = bool(res)
+ if res:
+ label = res.label
+ extra_vars["$checked_result"] = await repack_save(
+ source,
+ iter((res.extra,)),
+ )
+ return CheckResult(ok, label, extra_vars)
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/target_detection.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/target_detection.py
index 9baed92..b95e93e 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/target_detection.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/target_detection.py
@@ -3,19 +3,19 @@ from dataclasses import dataclass
from typing import Optional
from typing_extensions import override
-# import torch before onnxruntime
-import torch as torch # isort: skip
-import onnxruntime # isort: skip
-
import numpy as np
+import onnxruntime
from cookit import with_semaphore
from nonebot.utils import run_sync
-from ..config import config
-from ..frame_source import FrameSource, repack_save
-from .utils.common import CheckResult, CheckSingleResult, race_check
-from .utils.update import GitHubLatestReleaseModelUpdater, ModelInfo, UpdaterGroup
-from .utils.yolox import demo_postprocess, multiclass_nms, preprocess, vis
+from plugins.nonebot_plugin_nailongremove.config import config
+from plugins.nonebot_plugin_nailongremove.frame_source import FrameSource, repack_save
+from plugins.nonebot_plugin_nailongremove.model.utils.common import CheckResult, CheckSingleResult, race_check, \
+ similarity_process
+from plugins.nonebot_plugin_nailongremove.model.utils.update import GitHubLatestReleaseModelUpdater, ModelInfo, \
+ UpdaterGroup
+from plugins.nonebot_plugin_nailongremove.model.utils.yolox import demo_postprocess, multiclass_nms, preprocess, vis
+import itertools
model_filename_sfx = f"_{config.nailong_model1_type.value}.onnx"
@@ -47,7 +47,15 @@ labels = labels_path.read_text("u8").splitlines()
session = onnxruntime.InferenceSession(
model_path,
- providers=config.nailong_onnx_providers,
+ providers=(
+ [
+ "TensorrtExecutionProvider",
+ "CUDAExecutionProvider",
+ "CPUExecutionProvider",
+ ]
+ if config.nailong_onnx_try_to_use_gpu
+ else ["CPUExecutionProvider"]
+ ),
)
input_shape = config.nailong_model1_yolox_size or config.nailong_model1_type.yolox_size
@@ -80,60 +88,84 @@ class FrameInfo:
@run_sync
-def _check_single(frame: np.ndarray) -> CheckSingleResult[Optional[Detections]]:
- img, ratio = preprocess(frame, input_shape)
- ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
- output = session.run(None, ort_inputs)
- predictions = demo_postprocess(output[0], input_shape)[0]
+def _check_single(frame: np.ndarray, is_gif: bool = False) -> CheckSingleResult[Optional[Detections]]:
+ if is_gif:
+ res = similarity_process(frame, dsize=input_shape)
+ if res is not None:
+ return res
+ return CheckSingleResult.not_ok(None)
+ else:
+ img, ratio = preprocess(frame, input_shape)
+ ort_inputs = {session.get_inputs()[0].name: img[None, :, :, :]}
+ output = session.run(None, ort_inputs)
+ predictions = demo_postprocess(output[0], input_shape)[0]
- boxes = predictions[:, :4]
- scores = predictions[:, 4:5] * predictions[:, 5:]
+ boxes = predictions[:, :4]
+ scores = predictions[:, 4:5] * predictions[:, 5:]
- boxes_xyxy = np.ones_like(boxes)
- boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
- boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
- boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
- boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
- boxes_xyxy /= ratio
- dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
- if dets is None:
+ boxes_xyxy = np.ones_like(boxes)
+ boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.0
+ boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.0
+ boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.0
+ boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.0
+ boxes_xyxy /= ratio
+ dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
+ if dets is None:
+ return CheckSingleResult.not_ok(None)
+
+ final_boxes, final_scores, final_cls_ids = (
+ dets[:, :4], # type: ignore
+ dets[:, 4], # type: ignore
+ dets[:, 5], # type: ignore
+ )
+ for c, s in zip(final_cls_ids, final_scores):
+ label = labels[int(c)]
+ expected = config.nailong_model1_score.get(label)
+ if (expected is not None) and s >= expected:
+ return CheckSingleResult(
+ ok=True,
+ label=label,
+ extra=Detections(final_boxes, final_scores, final_cls_ids),
+ )
return CheckSingleResult.not_ok(None)
- final_boxes, final_scores, final_cls_ids = (
- dets[:, :4], # type: ignore
- dets[:, 4], # type: ignore
- dets[:, 5], # type: ignore
- )
- for c, s in zip(final_cls_ids, final_scores):
- label = labels[int(c)]
- expected = config.nailong_model1_score.get(label)
- if (expected is not None) and s >= expected:
- return CheckSingleResult(
- ok=True,
- label=label,
- extra=Detections(final_boxes, final_scores, final_cls_ids),
- )
- return CheckSingleResult.not_ok(None)
-
-async def check_single(frame: np.ndarray) -> CheckSingleResult[FrameInfo]:
- res = await _check_single(frame)
- return CheckSingleResult(
- ok=res.ok,
- label=res.label,
- extra=FrameInfo(frame, res.extra),
- )
+async def check_single(frame: np.ndarray, is_gif: bool = False) -> CheckSingleResult[FrameInfo]:
+ if is_gif:
+ res = await _check_single(frame, True)
+ return CheckSingleResult(
+ ok=res.ok,
+ label=res.label,
+ extra=FrameInfo(frame, res.extra),
+ )
+ else:
+ res = await _check_single(frame)
+ return CheckSingleResult(
+ ok=res.ok,
+ label=res.label,
+ extra=FrameInfo(frame, res.extra),
+ )
async def check(source: FrameSource) -> CheckResult:
label = None
extra_vars = {}
- if config.nailong_check_all_frames:
- sem = asyncio.Semaphore(config.nailong_concurrency)
- results = await asyncio.gather(
- *(with_semaphore(sem)(check_single)(frame) for frame in source),
- )
- ok = any(r.ok for r in results)
+ if config.nailong_check_all_frames and config.nailong_check_mode == 0:
+ if config.nailong_similarity_on:
+ tem_source = itertools.tee(source, 1)[0]
+ sem = asyncio.Semaphore(config.nailong_concurrency)
+ results = await asyncio.gather(
+ *(with_semaphore(sem)(check_single)(frame, True) for frame in tem_source),
+ )
+ ok = any(r.ok for r in results)
+ else:
+ ok = False
+ if not ok:
+ sem = asyncio.Semaphore(config.nailong_concurrency)
+ results = await asyncio.gather(
+ *(with_semaphore(sem)(check_single)(frame) for frame in source),
+ )
+ ok = any(r.ok for r in results)
if ok:
all_labels = {r.label for r in results if r.label}
label = next(
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/common.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/common.py
index ef8dc5e..d38f104 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/common.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/common.py
@@ -1,7 +1,17 @@
import asyncio
+import datetime
+import glob
+import os
+import random
+import shutil
from dataclasses import dataclass, field
from typing import Any, Awaitable, Callable, Dict, Generic, Optional, TypeVar
+import itertools
+import cv2
+import torch
+from PIL import Image
from typing_extensions import TypeAlias
+import torch.nn.functional as F
import numpy as np
@@ -10,6 +20,8 @@ from ...frame_source import FrameSource
T = TypeVar("T")
+device = torch.device("cuda" if config.nailong_onnx_try_to_use_gpu and torch.cuda.is_available() else "cpu")
+
@dataclass
class CheckSingleResult(Generic[T]):
@@ -33,36 +45,150 @@ class CheckResult:
return cls(ok=False, label=None, extra_vars={})
-FrameChecker: TypeAlias = Callable[[np.ndarray], Awaitable[CheckSingleResult[T]]]
+FrameChecker: TypeAlias = Callable[[[np.ndarray], bool], Awaitable[CheckSingleResult[T]]]
async def race_check(
- checker: FrameChecker[T],
- frames: FrameSource,
- concurrency: int = config.nailong_concurrency,
+ checker: FrameChecker[T],
+ frames: FrameSource,
+ concurrency: int = config.nailong_concurrency,
) -> Optional[CheckSingleResult[T]]:
iterator = iter(frames)
+ if config.nailong_similarity_on:
+ temp_frames = itertools.tee(frames, 1)[0]
async def worker() -> CheckSingleResult:
+ if config.nailong_similarity_on:
+ while True:
+ try:
+ frame = next(temp_frames)
+ except StopIteration:
+ break
+ res = await checker(frame, True)
+ if res.ok:
+ return res
while True:
try:
frame = next(iterator)
except StopIteration:
return CheckSingleResult.not_ok(None)
- res = await checker(frame)
+ res = await checker(frame, False)
if res.ok:
return res
- tasks = [asyncio.create_task(worker()) for _ in range(concurrency)]
- while True:
- if not tasks:
- break
- done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
- for t in done:
- if (res := t.result()).ok:
- for pt in pending:
- pt.cancel()
+ if config.nailong_check_mode == 0:
+ tasks = [asyncio.create_task(worker()) for _ in range(concurrency)]
+ while True:
+ if not tasks:
+ break
+ done, pending = await asyncio.wait(tasks, return_when=asyncio.FIRST_COMPLETED)
+ for t in done:
+ if (res := t.result()).ok:
+ for pt in pending:
+ pt.cancel()
+ return res
+ tasks = pending
+ elif config.nailong_check_mode == 1:
+ frame = next(iterator)
+ if config.nailong_similarity_on:
+ res = await checker(frame, True)
+ if res.ok:
return res
- tasks = pending
-
+ res = await checker(frame, False)
+ if res.ok:
+ return res
+ elif config.nailong_check_mode == 2:
+ records = []
+ while True:
+ try:
+ frame = next(iterator)
+ records.append(frame)
+ except StopIteration:
+ break
+ frame = records[random.randint(0, len(records) - 1)]
+ if config.nailong_similarity_on:
+ res = await checker(frame, True)
+ if res.ok:
+ return res
+ res = await checker(frame, False)
+ if res.ok:
+ return res
return None
+
+
+def similarity_process(image1: np.ndarray, dsize) -> Optional[CheckSingleResult]:
+ path = list(glob.glob(os.path.join(config.nailong_model_dir, 'records/*/*.jpg')))
+ if len(path) == 0:
+ return None
+ image1 = cv2.cvtColor(image1, cv2.COLOR_BGR2RGB)
+ image1 = cv2.resize(image1, dsize, interpolation=cv2.INTER_LINEAR)
+ image1_tensor = torch.tensor(image1, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)
+ image1_tensor = image1_tensor.reshape(1, -1).to(device)
+ for i in range(0, len(path), config.nailong_similarity_max_batch_size):
+ temp_paths = path[i:(
+ i + config.nailong_similarity_max_batch_size if i + config.nailong_similarity_max_batch_size < len(
+ path) else len(path))]
+ image2s = []
+ for image_path in temp_paths:
+ image2 = cv2.imread(image_path)
+ image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2RGB)
+ image2 = cv2.resize(image2, dsize, interpolation=cv2.INTER_LINEAR)
+ image2s.append(image2)
+ image2_tensor = torch.tensor(np.array(image2s), dtype=torch.float32).permute(0, 3, 1, 2)
+ image2_tensor = image2_tensor.reshape(image2_tensor.shape[0], -1).to(device)
+ similarities = F.cosine_similarity(image1_tensor, image2_tensor)
+ indices = torch.nonzero(similarities > 0.99)
+ index = indices[0].item() if indices.numel() > 0 else None
+ if index is not None:
+ image_path = path[index]
+ label = os.path.split(image_path)[-2].split('\\')[-1]
+ return CheckSingleResult(ok=True, label=label, extra=None)
+ return None
+
+
+def process_gif_and_save_jpgs(frames, label, dsize, similarity_threshold=0.85):
+ if len(list(glob.glob(
+ os.path.join(str(config.nailong_model_dir), 'records/*/*.jpg')))) >= config.nailong_similarity_max_storage:
+ zip_filename = shutil.make_archive(os.path.join(str(config.nailong_model_dir), '{}_records'.format(
+ datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))), 'zip',
+ os.path.join(str(config.nailong_model_dir), 'records'))
+ shutil.rmtree(os.path.join(str(config.nailong_model_dir), 'records'))
+ else:
+ zip_filename = None
+ output_dir = os.path.join(str(config.nailong_model_dir), 'records', label)
+ if not os.path.exists(output_dir):
+ os.makedirs(output_dir)
+ frame_count = [i for i in range(len(frames))]
+ while len(frame_count) > 0:
+ frame_num1 = frame_count[0]
+ frame_count.remove(frame_num1)
+ frame1 = frames[frame_num1]
+ frame_filename = os.path.join(output_dir, "frame{}_{}.jpg".format(frame_num1,
+ datetime.datetime.now().strftime(
+ "%Y-%m-%d_%H-%M-%S")))
+ while os.path.exists(frame_filename):
+ frame_filename = "exist-" + frame_filename
+ cv2.imwrite(frame_filename, frame1)
+ # frame1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2RGB)
+ frame1 = cv2.resize(frame1, dsize)
+ image1_tensor = torch.tensor(frame1, dtype=torch.float32).permute(2, 0, 1).unsqueeze(0)
+ image1_tensor = image1_tensor.reshape(1, -1).to(device)
+ max_length = len(list(frame_count))
+ indexs = []
+ for i in range(0, max_length, config.nailong_similarity_max_batch_size):
+ frame2_num = frame_count[i:(
+ i + config.nailong_similarity_max_batch_size if i + config.nailong_similarity_max_batch_size < max_length else max_length)]
+ frame2 = [frames[i] for i in frame2_num]
+ # frame2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2RGB)
+ frame2 = [cv2.resize(t, dsize) for t in frame2]
+ image2_tensor = torch.tensor(np.array(frame2), dtype=torch.float32).permute(0, 3, 1, 2)
+ image2_tensor = image2_tensor.reshape(image2_tensor.shape[0], -1).to(device)
+ similarities = F.cosine_similarity(image1_tensor, image2_tensor)
+ indices = torch.nonzero(similarities > similarity_threshold)
+ index = indices.squeeze().tolist() if indices.numel() > 0 else None
+ if type(index) is int:
+ index = [index]
+ if index is not None:
+ indexs.extend([frame2_num[i] for i in index])
+ frame_count = [i for i in frame_count if i not in indexs]
+ return zip_filename
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/update.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/update.py
index f221f89..d27b8f1 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/update.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/update.py
@@ -56,10 +56,10 @@ def create_parent_dir(path: Path, create: bool = True):
def find_file(
- path: Path,
- checker: Union[Callable[[Path], bool], str, None] = None,
- recursive: bool = False,
- last_modified: bool = True,
+ path: Path,
+ checker: Union[Callable[[Path], bool], str, None] = None,
+ recursive: bool = False,
+ last_modified: bool = True,
) -> Optional[Path]:
if isinstance(checker, str) and checker:
if (p := path / checker).exists():
@@ -99,10 +99,12 @@ class ModelInfo(Generic[T]):
class ModelUpdater(ABC):
@abstractmethod
- def find_from_local(self) -> Optional[Path]: ...
+ def find_from_local(self) -> Optional[Path]:
+ ...
@abstractmethod
- def get_info(self) -> ModelInfo: ...
+ def get_info(self) -> ModelInfo:
+ ...
@property
def root_dir(self) -> Path:
@@ -119,8 +121,8 @@ class ModelUpdater(ABC):
def check_local_ver(self, info: ModelInfo) -> Optional[str]:
if (
- self.get_path(info.filename).exists()
- and (ver_path := self.get_ver_path(info.filename)).exists()
+ self.get_path(info.filename).exists()
+ and (ver_path := self.get_ver_path(info.filename)).exists()
):
return ver_path.read_text(encoding="u8").strip()
return None
@@ -162,10 +164,10 @@ class ModelUpdater(ABC):
return
def validate_with_unlink(
- self,
- path: Path,
- info: ModelInfo,
- clear_ver: bool = True,
+ self,
+ path: Path,
+ info: ModelInfo,
+ clear_ver: bool = True,
) -> Any:
try:
return self.validate(path, info)
@@ -177,9 +179,9 @@ class ModelUpdater(ABC):
def _get(self, force_update: bool = False) -> Path:
if (
- (not force_update)
- and (not config.nailong_auto_update_model)
- and (local := self.find_from_local())
+ (not force_update)
+ and (not config.nailong_auto_update_model)
+ and (local := self.find_from_local())
):
logger.info("Update skipped")
return local
@@ -299,10 +301,10 @@ class GitHubRepoModelUpdater(GitHubModelUpdater):
class GitHubLatestReleaseModelUpdater(GitHubModelUpdater):
def __init__(
- self,
- owner: str,
- repo: str,
- local_filename_checker: Optional[Callable[[str], bool]] = None,
+ self,
+ owner: str,
+ repo: str,
+ local_filename_checker: Optional[Callable[[str], bool]] = None,
) -> None:
super().__init__()
self.owner = owner
diff --git a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/yolox.py b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/yolox.py
index 2aa1df3..a19d5ca 100644
--- a/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/yolox.py
+++ b/packages/nonebot-plugin-nailongremove-base/nonebot_plugin_nailongremove/model/utils/yolox.py
@@ -92,7 +92,7 @@ _COLORS = (
],
)
.astype(np.float32)
- .reshape(-1, 3)
+ .reshape(-1, 3)
) # fmt: skip
diff --git a/packages/nonebot-plugin-nailongremove-base/pyproject.toml b/packages/nonebot-plugin-nailongremove-base/pyproject.toml
index a328f27..a93d44f 100644
--- a/packages/nonebot-plugin-nailongremove-base/pyproject.toml
+++ b/packages/nonebot-plugin-nailongremove-base/pyproject.toml
@@ -13,13 +13,16 @@ dependencies = [
"nonebot-plugin-uninfo>=0.5.0",
"opencv-python>=4.5",
"numpy>=1.19",
- "keras>=2.4",
"pillow>=9",
"cookit[pydantic]>=0.8.1",
"httpx>=0.27.2",
"githubkit>=0.11.14",
"yarl>=1.17.1",
"tqdm>=4.66.6",
+ "huggingface-hub>=0.26",
+ "ultralytics>=8.3",
+ "gradio_client>=1.4"
+
]
license = { text = "MIT" }
readme = "README.md"