diff --git a/Convention/Image/OpenCV.py b/Convention/Image/OpenCV.py
new file mode 100644
index 0000000..31e3a79
--- /dev/null
+++ b/Convention/Image/OpenCV.py
@@ -0,0 +1,862 @@
+from ..Runtime.Config   import *
+
+try:
+    import cv2              as     cv2
+    import cv2.data         as     cv2data
+    from cv2.typing         import *
+except ImportError as e:
+    ImportingThrow(e, "OpenCV", ["opencv-python", "opencv-python-headless"])
+try:
+    import numpy            as     np
+except ImportError as e:
+    ImportingThrow(e, "OpenCV", ["numpy"])
+try:
+    from PIL                import ImageFile as ImageFile
+    from PIL                import Image as Image
+except ImportError as e:
+    ImportingThrow(e, "OpenCV", ["pillow"])
+
+from ..Runtime.File     import ToolFile
+
+_Unwrapper2Str = lambda x: str(x)
+_Wrapper2File = lambda x: ToolFile(x)
+
+VideoWriter = cv2.VideoWriter
+def mp4_with_MPEG4_fourcc() -> int:
+    return VideoWriter.fourcc(*"mp4v")
+def avi_with_Xvid_fourcc() -> int: 
+    return VideoWriter.fourcc(*"XVID")
+def avi_with_DivX_fourcc() -> int:
+    return VideoWriter.fourcc(*"DIVX")
+def avi_with_MJPG_fourcc() -> int:
+    return VideoWriter.fourcc(*"MJPG")
+def mp4_or_avi_with_H264_fourcc() -> int:
+    return VideoWriter.fourcc(*"X264")
+def avi_with_H265_fourcc() -> int:
+    return VideoWriter.fourcc(*"H264")
+def wmv_with_WMV1_fourcc() -> int:
+    return VideoWriter.fourcc(*"WMV1")
+def wmv_with_WMV2_fourcc() -> int:
+    return VideoWriter.fourcc(*"WMV2")
+def oggTheora_with_THEO_fourcc() -> int:
+    return VideoWriter.fourcc(*"THEO")
+def flv_with_FLV1_fourcc() -> int:
+    return VideoWriter.fourcc(*"FLV1")
+class VideoWriterInstance(VideoWriter):
+    def __init__(
+        self, 
+        file_name:  Union[ToolFile, str], 
+        fourcc:     int,
+        fps:        float, 
+        frame_size: tuple[int, int],
+        is_color:   bool = True
+        ):
+        super().__init__(_Unwrapper2Str(file_name), fourcc, fps, frame_size, is_color)
+    def __del__(self):
+        self.release()
+
+def wait_key(delay:int):
+    return cv2.waitKey(delay)
+def until_esc():
+    return wait_key(0)
+
+def is_current_key(key:str, *, wait_delay:int = 1):
+    return wait_key(wait_delay) & 0xFF == ord(key[0])
+
+class BasicViewable:
+    def __init__(self, filename_or_index:Union[str, ToolFile, int]):
+        self._capture: cv2.VideoCapture     = None
+        self.stats:     bool                = True
+        self.Retarget(filename_or_index)
+    def __del__(self):
+        self.Release()
+    
+    def __bool__(self):
+        return self.stats
+    
+    def IsOpened(self):
+        return self._capture.isOpened()
+        
+    def Release(self):
+        if self._capture is not None:
+            self._capture.release()
+    def Retarget(self, filename_or_index:Union[str, ToolFile, int]):
+        self.Release()
+        if isinstance(filename_or_index, int):
+            self._capture = cv2.VideoCapture(filename_or_index)
+        else:
+            self._capture = cv2.VideoCapture(_Unwrapper2Str(filename_or_index))
+        return self
+    
+    def NextFrame(self) -> MatLike:
+        self.stats, frame =self._capture.read()
+        if self.stats:
+            return frame
+        else:
+            return None
+    
+    def GetCaptrueInfo(self, id:int):
+        return self._capture.get(id)
+    def GetPropPosMsec(self):
+        return self.GetCaptrueInfo(0)
+    def GetPropPosFrames(self):
+        return self.GetCaptrueInfo(1)
+    def GetPropAviRatio(self):
+        return self.GetCaptrueInfo(2)
+    def GetPropFrameWidth(self):
+        return self.GetCaptrueInfo(3)
+    def GetPropFrameHeight(self):
+        return self.GetCaptrueInfo(4)
+    def GetPropFPS(self):
+        return self.GetCaptrueInfo(5)
+    def GetPropFourcc(self):
+        return self.GetCaptrueInfo(6)
+    def GetPropFrameCount(self):
+        return self.GetCaptrueInfo(7)
+    def GetPropFormat(self):
+        return self.GetCaptrueInfo(8)
+    def GetPropMode(self):
+        return self.GetCaptrueInfo(9)
+    def GetPropBrightness(self):
+        return self.GetCaptrueInfo(10)
+    def GetPropContrast(self):
+        return self.GetCaptrueInfo(11)
+    def GetPropSaturation(self):
+        return self.GetCaptrueInfo(12)
+    def GetPropHue(self):
+        return self.GetCaptrueInfo(13)
+    def GetPropGain(self):
+        return self.GetCaptrueInfo(14)
+    def GetPropExposure(self):
+        return self.GetCaptrueInfo(15)
+    def GetPropConvertRGB(self):
+        return self.GetCaptrueInfo(16)
+        
+    def SetupCapture(self, id:int, value):
+        self._capture.set(id, value)
+        return self
+    def SetPropPosMsec(self, value:int):
+        return self.SetupCapture(0, value)
+    def SetPropPosFrames(self, value:int):
+        return self.SetupCapture(1, value)
+    def SetPropAviRatio(self, value:float):
+        return self.SetupCapture(2, value)
+    def SetPropFrameWidth(self, value:int):
+        return self.SetupCapture(3, value)
+    def SetPropFrameHeight(self, value:int):
+        return self.SetupCapture(4, value)
+    def SetPropFPS(self, value:int):
+        return self.SetupCapture(5, value)
+    def SetPropFourcc(self, value):
+        return self.SetupCapture(6, value)
+    def SetPropFrameCount(self, value):
+        return self.SetupCapture(7, value)
+    def SetPropFormat(self, value):
+        return self.SetupCapture(8, value)
+    def SetPropMode(self, value):
+        return self.SetupCapture(9, value)
+    def SetPropBrightness(self, value):
+        return self.SetupCapture(10, value)
+    def SetPropContrast(self, value):
+        return self.SetupCapture(11, value)
+    def SetPropSaturation(self, value):
+        return self.SetupCapture(12, value)
+    def SetPropHue(self, value):
+        return self.SetupCapture(13, value)
+    def SetPropGain(self, value):
+        return self.SetupCapture(14, value)
+    def SetPropExposure(self, value):
+        return self.SetupCapture(15, value)
+    def SetPropConvertRGB(self, value:int):
+        return self.SetupCapture(16, value)
+    def SetPropRectification(self, value:int):
+        return self.SetupCapture(17, value)
+    
+    @property
+    def FrameSize(self) -> Tuple[float, float]:
+        return self.GetPropFrameWidth(), self.GetPropFrameHeight()
+    
+class BasicCamera(BasicViewable):
+    def __init__(self, index:int = 0):
+        self.writer:    VideoWriter = None
+        super().__init__(int(index))
+    
+    @override
+    def Release(self):
+        super().Release()
+        if self.writer is not None:
+            self.writer.release()
+    
+    def CurrentFrame(self):
+        return self.NextFrame()
+    
+    def recording(
+        self, 
+        stop_pr:    Callable[[], bool], 
+        writer:     VideoWriter,
+        ):
+        self.writer = writer
+        while self.IsOpened():
+            if stop_pr():
+                break
+            frame = self.CurrentFrame()
+            cv2.imshow("__recording__", frame)
+            writer.write(frame)
+        cv2.destroyWindow("__recording__")
+        return self
+
+class ImageObject:
+    def __init__(
+        self,
+        image:          Optional[Union[
+            str,
+            Self,
+            BasicCamera,
+            ToolFile, 
+            MatLike, 
+            np.ndarray, 
+            ImageFile.ImageFile,
+            Image.Image
+            ]],
+        flags:          int             = -1):
+        self.__image:   MatLike         = None
+        self.__camera:  BasicCamera = None
+        self.current:   MatLike         = None
+        if isinstance(image, BasicCamera):
+            self.lock_from_camera(image)
+        else:
+            self.load_image(image, flags)
+
+    @property
+    def camera(self) -> BasicCamera:
+        if self.__camera is None or self.__camera.IsOpened() is False:
+            return None
+        else:
+            return self.__camera
+    @property
+    def image(self) -> MatLike:
+        if self.current is not None:
+            return self.current
+        elif self.camera is None:
+            return self.__image
+        else:
+            return self.__camera.CurrentFrame()
+
+    @image.setter
+    def image(self, image:          Optional[Union[
+            str,
+            Self,
+            ToolFile, 
+            MatLike, 
+            np.ndarray, 
+            ImageFile.ImageFile,
+            Image.Image
+            ]]):
+        self.load_image(image)
+
+    def load_from_nparray(
+        self,
+        array_: np.ndarray,
+        code:   int = cv2.COLOR_RGB2BGR,
+        *args, **kwargs
+        ):
+        self.__image = cv2.cvtColor(array_, code, *args, **kwargs)
+        return self
+    def load_from_PIL_image(
+        self,
+        image:  Image.Image,
+        code:   int = cv2.COLOR_RGB2BGR,
+        *args, **kwargs
+    ):
+        self.load_from_nparray(np.array(image), code, *args, **kwargs)
+        return self
+    def load_from_PIL_ImageFile(
+        self,
+        image:  ImageFile.ImageFile,
+        rect:   Optional[Tuple[float, float, float, float]] = None
+    ):
+        return self.load_from_PIL_image(image.crop(rect))
+    def load_from_cv2_image(self, image:  MatLike):
+        self.__image = image
+        return self
+    def lock_from_camera(self, camera: BasicCamera):
+        self.__camera = camera
+        return self
+
+    @property
+    def dimension(self) -> int:
+        return self.image.ndim
+    
+    @property
+    def shape(self) -> Tuple[int, int, int]:
+        '''height, width, depth'''
+        return self.image.shape
+    @property
+    def height(self) -> int:
+        return self.shape[0]
+    @property
+    def width(self) -> int:
+        return self.shape[1]
+
+    def is_enable(self):
+        return self.image is not None
+    def is_invalid(self):
+        return self.is_enable() is False
+    def __bool__(self):
+        return self.is_enable()
+    def __MatLike__(self):
+        return self.image
+
+    def load_image(
+        self, 
+        image:          Optional[Union[
+            str,
+            ToolFile, 
+            Self,
+            MatLike, 
+            np.ndarray, 
+            ImageFile.ImageFile,
+            Image.Image
+            ]],
+        flags:          int = -1
+        ):
+        """加载图片"""
+        if image is None:
+            self.__image = None
+            return self
+        elif isinstance(image, type(self)):
+            self.__image = image.image
+        elif isinstance(image, MatLike):
+            self.__image = image
+        elif isinstance(image, np.ndarray):
+            self.load_from_nparray(image, flags)
+        elif isinstance(image, ImageFile.ImageFile):
+            self.load_from_PIL_ImageFile(image, flags)
+        elif isinstance(image, Image.Image):
+            self.load_from_PIL_image(image, flags)
+        else:
+            self.__image = cv2.imread(_Unwrapper2Str(image), flags)
+        return self
+    def save_image(self, save_path:Union[str, ToolFile], is_path_must_exist = False):
+        """保存图片"""
+        if is_path_must_exist:
+            _Wrapper2File(save_path).try_create_parent_path()
+        if self.is_enable():
+            cv2.imwrite(_Unwrapper2Str(save_path), self.image)
+        return self
+
+    def show_image(
+        self, 
+        window_name:        str                         = "Image", 
+        delay:              Union[int,str]              = 0,
+        image_show_func:    Callable[[Self], None]      = None,
+        *args, **kwargs
+        ):
+        """显示图片"""
+        if self.is_invalid():
+            return self
+        if self.camera is not None:
+            while (wait_key(1) & 0xFF != ord(str(delay)[0])) and self.camera is not None:
+                # dont delete this line, self.image is camera flame now, see<self.current = None>
+                self.current = self.image
+                if image_show_func is not None:
+                    image_show_func(self)
+                if self.current is not None:
+                    cv2.imshow(window_name, self.current)
+                # dont delete this line, see property<image>
+                self.current = None
+        else:
+            cv2.imshow(window_name, self.image)
+            cv2.waitKey(delay = int(delay), *args, **kwargs)
+        if cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) > 0:
+            cv2.destroyWindow(window_name)
+        return self
+
+    # 分离通道
+    def split(self):
+        """分离通道"""
+        return cv2.split(self.image)
+    def split_to_image_object(self):
+        """分离通道"""
+        return [ImageObject(channel) for channel in self.split()]
+    @property
+    def channels(self):
+        return self.split()
+    @property
+    def blue_channel(self):
+        return self.channels[0]
+    @property
+    def green_channel(self):
+        return self.channels[1]
+    @property
+    def red_channel(self):
+        return self.channels[2]
+    @property
+    def alpha_channel(self):
+        return self.channels[3]
+    def get_blue_image(self):
+        return ImageObject(self.blue_channel)
+    def get_green_image(self):
+        return ImageObject(self.green_channel)
+    def get_red_image(self):
+        return ImageObject(self.red_channel)
+    def get_alpha_image(self):
+        return ImageObject(self.alpha_channel)
+
+    # 混合通道
+    def merge_channels_from_list(self, channels:List[MatLike]):
+        """合并通道"""
+        self.image = cv2.merge(channels)
+        return self
+    def merge_channels(self, blue:MatLike, green:MatLike, red:MatLike):
+        """合并通道"""
+        return self.merge_channels_from_list([blue, green, red])
+    def merge_channel_list(self, bgr:List[MatLike]):
+        """合并通道"""
+        return self.merge_channels_from_list(bgr)
+
+    # Transform
+    def get_resize_image(self, width:int, height:int):
+        if self.is_enable():
+            return cv2.resize(self.image, (width, height))
+        return None
+    def get_rotate_image(self, angle:float):
+        if self.is_invalid():
+            return None
+        (h, w) = self.image.shape[:2]
+        center = (w // 2, h // 2)
+        M = cv2.getRotationMatrix2D(center, angle, 1.0)
+        return cv2.warpAffine(self.image, M, (w, h))
+    def resize_image(self, width:int, height:int):
+        """调整图片大小"""
+        new_image = self.get_resize_image(width, height)
+        if new_image is not None:
+            self.image = new_image
+        return self
+    def rotate_image(self, angle:float):
+        """旋转图片"""
+        new_image = self.get_rotate_image(angle)
+        if new_image is not None:
+            self.image = new_image
+        return self
+    
+    # 图片翻折
+    def flip(self, flip_code:int):
+        """翻转图片"""
+        if self.is_enable():
+            self.image = cv2.flip(self.image, flip_code)
+        return self
+    def horizon_flip(self):
+        """水平翻转图片"""
+        return self.flip(1)
+    def vertical_flip(self):
+        """垂直翻转图片"""
+        return self.flip(0)
+    def both_flip(self):
+        """双向翻转图片"""
+        return self.flip(-1)
+
+    # 色彩空间猜测
+    def guess_color_space(self) -> Optional[str]:
+        """猜测色彩空间"""
+        if self.is_invalid():
+            return None
+        image = self.image
+        # 计算每个通道的像素值分布
+        hist_b = cv2.calcHist([image], [0], None, [256], [0, 256])
+        hist_g = cv2.calcHist([image], [1], None, [256], [0, 256])
+        hist_r = cv2.calcHist([image], [2], None, [256], [0, 256])
+
+        # 计算每个通道的像素值总和
+        sum_b = np.sum(hist_b)
+        sum_g = np.sum(hist_g)
+        sum_r = np.sum(hist_r)
+
+        # 根据像素值总和判断色彩空间
+        if sum_b > sum_g and sum_b > sum_r:
+            #print("The image might be in BGR color space.")
+            return "BGR"
+        elif sum_g > sum_b and sum_g > sum_r:
+            #print("The image might be in GRAY color space.")
+            return "GRAY"
+        else:
+            #print("The image might be in RGB color space.")
+            return "RGB"
+
+    # 颜色转化
+    def get_convert(self, color_convert:int):
+        """颜色转化"""
+        if self.is_invalid():
+            return None
+        return cv2.cvtColor(self.image, color_convert)
+    def convert_to(self, color_convert:int):
+        """颜色转化"""
+        if self.is_invalid():
+            return None
+        self.image = self.get_convert(color_convert)
+    
+    def is_grayscale(self):
+        return self.dimension == 2
+    def get_grayscale(self):
+        if self.is_invalid():
+            return None
+        return cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
+    def convert_to_grayscale(self):
+        """将图片转换为灰度图"""
+        self.image = self.get_grayscale()
+        return self
+
+    def get_convert_flag(
+        self, 
+        targetColorTypeName:Literal[
+            "BGR", "RGB", "GRAY", "YCrCb"
+            ]
+        ) -> Optional[int]:
+        """获取颜色转化标志"""
+        flag = self.guess_color_space()
+        if flag is None:
+            return None
+        
+        if targetColorTypeName == "BGR":
+            if flag == "RGB":
+                return cv2.COLOR_RGB2BGR
+            elif flag == "GRAY":
+                return cv2.COLOR_GRAY2BGR
+            elif flag == "YCrCb":
+                return cv2.COLOR_YCrCb2BGR
+        elif targetColorTypeName == "RGB":
+            if flag == "BGR":
+                return cv2.COLOR_BGR2RGB
+            elif flag == "GRAY":
+                return cv2.COLOR_GRAY2RGB
+            elif flag == "YCrCb":
+                return cv2.COLOR_YCrCb2RGB
+        elif targetColorTypeName == "GRAY":
+            if flag == "RGB":
+                return cv2.COLOR_RGB2GRAY
+            elif flag == "RGB":
+                return cv2.COLOR_BGR2GRAY
+        return None
+
+    # 原址裁切
+    def sub_image(self, x:int, y:int ,width:int ,height:int):
+        """裁剪图片"""
+        if self.is_invalid():
+            return self
+        self.image = self.image[y:y+height, x:x+width]
+        return self
+
+    # 直方图
+    def equalizeHist(self, is_cover = False) -> MatLike:
+        """直方图均衡化"""
+        if self.is_invalid():
+            return self
+        result:MatLike = cv2.equalizeHist(self.image)
+        if is_cover:
+            self.image = result
+        return result
+    def calcHist(
+        self, 
+        channel:    Union[List[int], int],
+        mask:       Optional[MatLike]       = None,
+        hist_size:  Sequence[int]           = [256],
+        ranges:     Sequence[float]         = [0, 256]
+        ) -> MatLike:
+        """计算直方图"""
+        if self.is_invalid():
+            return None
+        return cv2.calcHist(
+            [self.image],
+            channel if isinstance(channel, list) else [channel],
+            mask, 
+            hist_size, 
+            ranges)
+
+    # 子集操作
+    def sub_image_with_rect(self, rect:Tuple[float, float, float, float]):
+        """裁剪图片"""
+        if self.is_invalid():
+            return self
+        self.image = self.image[rect[1]:rect[1]+rect[3], rect[0]:rect[0]+rect[2]]
+        return self
+    def sub_image_with_box(self, box:Tuple[float, float, float, float]):
+        """裁剪图片"""
+        if self.is_invalid():
+            return self
+        self.image = self.image[box[1]:box[3], box[0]:box[2]]
+        return self
+    def sub_cover_with_rect(self, image:Union[Self, MatLike], rect:Tuple[float, float, float, float]):
+        """覆盖图片"""
+        if self.is_invalid():
+            raise ValueError("Real Image is none")
+        if isinstance(image, MatLike):
+            image = ImageObject(image)
+        self.image[rect[1]:rect[1]+rect[3], rect[0]:rect[0]+rect[2]] = image.image
+        return self
+    def sub_cover_with_box(self, image:Union[Self, MatLike], box:Tuple[float, float, float, float]):
+        """覆盖图片"""
+        if self.is_invalid():
+            raise ValueError("Real Image is none")
+        if isinstance(image, MatLike):
+            image = ImageObject(image)
+        self.image[box[1]:box[3], box[0]:box[2]] = image.image
+        return self
+
+    def operator_cv(self, func:Callable[[MatLike], Any], *args, **kwargs):
+        func(self.image, *args, **kwargs)
+        return self
+
+    def stack(self, *args:Self, **kwargs) -> Self:
+        images = [ image for image in args]
+        images.append(self)
+        return ImageObject(np.stack([np.uint8(image.image) for image in images], *args, **kwargs))
+    def vstack(self, *args:Self) -> Self:
+        images = [ image for image in args]
+        images.append(self)
+        return ImageObject(np.vstack([np.uint8(image.image) for image in images]))
+    def hstack(self, *args:Self) -> Self:
+        images = [ image for image in args]
+        images.append(self)
+        return ImageObject(np.hstack([np.uint8(image.image) for image in images]))
+    
+    def merge_with_blending(self, other:Self, weights:Tuple[float, float]):
+        return ImageObject(cv2.addWeighted(self.image, weights[0], other.image, weights[1], 0))
+    
+    def add(self, image_or_value:Union[Self, int]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.add(self.image, image_or_value)
+        else:
+            self.image = cv2.add(self.image, image_or_value.image)
+        return self
+    def __add__(self, image_or_value:Union[Self, int]):
+        return ImageObject(self.image.copy()).add(image_or_value)
+    def subtract(self, image_or_value:Union[Self, int]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.subtract(self.image, image_or_value)
+        else:
+            self.image = cv2.subtract(self.image, image_or_value.image)
+        return self
+    def __sub__(self, image_or_value:Union[Self, int]):
+        return ImageObject(self.image.copy()).subtract(image_or_value)
+    def multiply(self, image_or_value:Union[Self, int]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.multiply(self.image, image_or_value)
+        else:
+            self.image = cv2.multiply(self.image, image_or_value.image)
+        return self
+    def __mul__(self, image_or_value:Union[Self, int]):
+        return ImageObject(self.image.copy()).multiply(image_or_value)
+    def divide(self, image_or_value:Union[Self, int]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.divide(self.image, image_or_value)
+        else:
+            self.image = cv2.divide(self.image, image_or_value.image)
+        return self
+    def __truediv__(self, image_or_value:Union[Self, int]):
+        return ImageObject(self.image.copy()).divide(image_or_value)
+    def bitwise_and(self, image_or_value:Union[Self, int]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.bitwise_and(self.image, image_or_value)
+        else:
+            self.image = cv2.bitwise_and(self.image, image_or_value.image)
+        return self
+    def bitwise_or(self, image_or_value:Union[Self, int]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.bitwise_or(self.image, image_or_value)
+        else:
+            self.image = cv2.bitwise_or(self.image, image_or_value.image)
+        return self
+    def bitwise_xor(self, image_or_value:Union[Self]):
+        if isinstance(image_or_value, int):
+            self.image = cv2.bitwise_xor(self.image, image_or_value)
+        else:
+            self.image = cv2.bitwise_xor(self.image, image_or_value.image)
+        return self
+    def bitwise_not(self):
+        self.image = cv2.bitwise_not(self.image)
+        return self
+    def __neg__(self):
+        return ImageObject(self.image.copy()).bitwise_not()
+    
+class NoiseImageObject(ImageObject):
+    def __init__(
+        self,
+        height:     int,
+        weight:     int,
+        *,
+        mean:       float   = 0,
+        sigma:      float   = 25,
+        dtype               = np.uint8
+        ):
+        super().__init__(NoiseImageObject.get_new_noise(
+            None, height, weight, mean=mean, sigma=sigma, dtype=dtype
+            ))
+    
+    @classmethod
+    def get_new_noise(
+        raw_image:  Optional[MatLike],
+        height:     int,
+        weight:     int,
+        *,
+        mean:       float   = 0,
+        sigma:      float   = 25,
+        dtype               = np.uint8
+        ) -> MatLike:
+        noise = raw_image
+        if noise is None:
+            noise = np.zeros((height, weight), dtype=dtype)
+        cv2.randn(noise, mean, sigma)
+        return cv2.cvtColor(noise, cv2.COLOR_GRAY2BGR)
+
+def Unwrapper(image:Optional[Union[
+            str,
+            ImageObject,
+            ToolFile, 
+            MatLike, 
+            np.ndarray, 
+            ImageFile.ImageFile,
+            Image.Image
+            ]]) -> MatLike:
+    return image.image if isinstance(image, ImageObject) else ImageObject(image).image
+
+def Wrapper(image:Optional[Union[
+            str,
+            ImageObject,
+            ToolFile, 
+            MatLike, 
+            np.ndarray, 
+            ImageFile.ImageFile,
+            Image.Image
+            ]]) -> ImageObject:
+    return ImageObject(image)
+
+class light_cv_window:
+    def __init__(self, name:str):
+        self.__my_window_name = name
+        cv2.namedWindow(self.__my_window_name)
+    def __del__(self):
+        self.destroy()
+
+    def show_image(self, image:Union[ImageObject, MatLike]):
+        if self.__my_window_name is None:
+            self.__my_window_name = "window"
+        if isinstance(image, ImageObject):
+            image = image.image
+        cv2.imshow(self.__my_window_name, image)
+        return self
+    def destroy(self):
+        if self.__my_window_name is not None and cv2.getWindowProperty(self.__my_window_name, cv2.WND_PROP_VISIBLE) > 0:
+            cv2.destroyWindow(self.__my_window_name)
+        return self
+    
+    @property
+    def window_rect(self):
+        return cv2.getWindowImageRect(self.__my_window_name)
+    @window_rect.setter
+    def window_rect(self, rect:Tuple[float, float, float, float]):
+        self.set_window_rect(rect[0], rect[1], rect[2], rect[3])
+    
+    def set_window_size(self, weight:int, height:int):
+        cv2.resizeWindow(self.__my_window_name, weight, height)
+        return self
+    def get_window_size(self) -> Tuple[float, float]:
+        rect = self.window_rect
+        return rect[2], rect[3]
+    
+    def get_window_property(self, prop_id:int):
+        return cv2.getWindowProperty(self.__my_window_name, prop_id)
+    def set_window_property(self, prop_id:int, prop_value:int):
+        cv2.setWindowProperty(self.__my_window_name, prop_id, prop_value)
+        return self
+    def get_prop_frame_width(self):
+        return self.window_rect[2]
+    def get_prop_frame_height(self):
+        return self.window_rect[3]
+    def is_full_window(self):
+        return cv2.getWindowProperty(self.__my_window_name, cv2.WINDOW_FULLSCREEN) > 0
+    def set_full_window(self):
+        cv2.setWindowProperty(self.__my_window_name, cv2.WINDOW_FULLSCREEN, 1)
+        return self
+    def set_normal_window(self):
+        cv2.setWindowProperty(self.__my_window_name, cv2.WINDOW_FULLSCREEN, 0)
+        return self
+    def is_using_openGL(self):
+        return cv2.getWindowProperty(self.__my_window_name, cv2.WINDOW_OPENGL) > 0
+    def set_using_openGL(self):
+        cv2.setWindowProperty(self.__my_window_name, cv2.WINDOW_OPENGL, 1)
+        return self
+    def set_not_using_openGL(self):
+        cv2.setWindowProperty(self.__my_window_name, cv2.WINDOW_OPENGL, 0)
+        return self
+    def is_autosize(self):
+        return cv2.getWindowProperty(self.__my_window_name, cv2.WINDOW_AUTOSIZE) > 0
+    def set_autosize(self):
+        cv2.setWindowProperty(self.__my_window_name, cv2.WINDOW_AUTOSIZE, 1)
+        return self
+    def set_not_autosize(self):
+        cv2.setWindowProperty(self.__my_window_name, cv2.WINDOW_AUTOSIZE, 0)
+        return self
+    
+    def set_window_rect(self, x:int, y:int, weight:int, height:int):
+        cv2.moveWindow(self.__my_window_name, x, y)
+        return self.set_window_size(weight, height)
+
+    def set_window_pos(self, x:int, y:int):
+        cv2.moveWindow(self.__my_window_name, x, y)
+        return self
+
+    def wait_key(self, wait_time:int=0):
+        return cv2.waitKey(wait_time)
+
+def get_haarcascade_frontalface(name_or_default:Optional[str]=None):
+    if name_or_default is None:
+        name_or_default = "haarcascade_frontalface_default"
+    return cv2.CascadeClassifier(cv2data.haarcascades+'haarcascade_frontalface_default.xml')
+
+def detect_human_face(
+    image:          ImageObject,
+    detecter:       cv2.CascadeClassifier, 
+    scaleFactor:    float                   = 1.1,
+    minNeighbors:   int                     = 4,
+    *args, **kwargs):
+    '''return is Rect[]'''
+    return detecter.detectMultiScale(image.image, scaleFactor, minNeighbors, *args, **kwargs)
+
+class internal_detect_faces_oop(Callable[[ImageObject], None]):
+    def __init__(self):
+        self.face_cascade = get_haarcascade_frontalface()
+    def __call__(self, image:ImageObject):
+        gray = image.convert_to_grayscale()
+        faces = self.face_cascade.detectMultiScale(gray, 1.3, 5)
+        for (x,y,w,h) in faces:
+            image.operator_cv(cv2.rectangle,(x,y),(x+w,y+h),(255,0,0),2)
+    
+def easy_detect_faces(camera:BasicCamera):
+    ImageObject(camera).show_image("window", 'q', internal_detect_faces_oop())
+    
+# 示例使用
+if __name__ == "__main__":
+    img_obj = ImageObject("path/to/your/image.jpg")
+    img_obj.show_image()
+    img_obj.resize_image(800, 600)
+    img_obj.rotate_image(45)
+    img_obj.convert_to_grayscale()
+    img_obj.save_image("path/to/save/image.jpg")
+
+# Override tool_file to tool_file_ex
+
+class tool_file_cvex(ToolFile):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        
+    @override
+    def load_as_image(self) -> ImageObject:
+        self.data = ImageObject(self.get_path())
+        return self.data
+    
+    @override
+    def save(self, path = None):
+        image:ImageObject   = self.data
+        image.save_image(path if path is not None else self.get_path())
+        return self
+
diff --git a/Convention/Image/init.py b/Convention/Image/init.py
new file mode 100644
index 0000000..e69de29