dsh-deepseek-vision-router：给 DeepSeek Harness 加一条图像预处理路由
`dsh-deepseek-vision-router` 是 DSH（DeepSeek Harness）的一款插件，旨在解决文本专用 DeepSeek 主智能体无法直接处理图片的问题。它通过一条独立的 `deepseek-vision` 路由实现图像预处理：先将图片发送至已配置的视觉模型生成文字描述，再将描述交由 DeepSeek 处理。在此过程中，官方 DeepSeek adapter 继续负责主会话、推理、流式传输及工具调用，插件仅保存视觉提供商和模型设置，不触碰凭证或复制端点。 该插件支持在 DSH 设置中配置视觉模型，并在模型菜单中选择“DeepSeek + Vision”模式，配置变
Read MoreIntroduction to Python OpenCV: Denoising Methods in Image Preprocessing
In image preprocessing, denoising is a core step to eliminate noise (such as Gaussian, salt-and-pepper, Poisson noise) during acquisition/transmission and improve the accuracy of subsequent tasks. Python OpenCV provides multiple denoising methods: 1. **Mean Filtering**: A simple average of window pixels, fast but blurs edges. Suitable for Gaussian noise, implemented with `cv2.blur` (3×3 kernel). 2. **Median Filtering**: Replaces the center pixel with the median of window pixels. Effective against salt-and-pepper noise (0/255 specks), preserves edges well. Kernel size must be odd (e.g., 3×3), using `cv2.medianBlur`. 3. **Gaussian Filtering**: Weighted average using a Gaussian distribution kernel, balances denoising and edge preservation. Ideal for Gaussian noise, requires kernel size and standard deviation in `cv2.GaussianBlur`. 4. **Bilateral Filtering**: Combines spatial and color distance, excels at edge-preserving denoising with high computational cost. Suitable for high-precision scenarios (e.g., face images), implemented with `cv2.bilateralFilter`. **Selection Guidelines**: Gaussian noise → Gaussian filtering; salt-and-pepper noise → median filtering; mixed noise → Gaussian followed by median; high-frequency detail noise → bilateral filtering. Beginners are advised to start with Gaussian and median filters, adjusting based on... *(Note: The original text ends abruptly; the translation concludes at the logical cutoff point.)*
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