dsh-deepseek-vision-router: Add an image preprocessing router to DeepSeek Harness
`dsh-deepseek-vision-router` is a plugin for DSH (DeepSeek Harness) designed to address the limitation where text-only DeepSeek main agents cannot directly process images. It implements image preprocessing via a dedicated `deepseek-vision` route: first sending the image to a configured vision model to generate a textual description, and then submitting the description to DeepSeek for processing. During this process, the official DeepSeek adapter continues to handle the main session, reasoning, streaming, and tool calls, while the plugin only stores visual provider and model settings without touching credentials or copying endpoints. The plugin supports configuring vision models in DSH settings and selecting the "DeepSeek + Vision" mode in the model menu to enable this configuration.
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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