farshid-mcp-imageProcessing

<!-- mcp-name: io.github.pirahansiah/farshid-mcp-imageProcessing --> # farshid-mcp-imageProcessing A comprehensive **OpenCV image-processing MCP server** for VS Code Copilot Agent Mode (or any MCP client). Exposes ~40 tools across webcam capture, image I/O, transforms, color, filtering, edges, thresholding, morphology, contours/shapes, feature matching, object detection (faces / eyes / bodies / QR), drawing, image arithmetic, template matching, and video processing. - **PyPI:** [`farshid-mcp-imageProcessing`](https://pypi.org/project/farshid-mcp-imageProcessing/) - **MCP Registry:** `io.github.pirahansiah/farshid-mcp-imageProcessing` - **Python:** 3.14+ - **OS:** latest Windows 11, latest macOS, latest mainstream Linux (Ubuntu 24.04+/Fedora 41+) ## Install (PyPI) ```bash pip install farshid-mcp-imageProcessing farshid-mcp-imageprocessing # runs the stdio MCP server ``` ## Register in VS Code Add this to your user or workspace `mcp.json`: ```jsonc { "servers": { "imageProcessing": { "command": "farshid-mcp-imageprocessing", "type": "stdio" } } } ``` Or, if you cloned the repo and want to run from source with the local `.venv`: ```bash git clone https://github.com/pirahansiah/farshid-mcp-imageProcessing cd farshid-mcp-imageProcessing # Windows (PowerShell): py -3.14 -m venv .venv ; .\.venv\Scripts\Activate.ps1 # macOS / Linux: python3.14 -m venv .venv && source .venv/bin/activate pip install -U pip pip install -e . ``` `opencv-contrib-python` is used so the bundled Haar cascades and extra algorithms are available. ## Quick start: the `/cv` Copilot prompt This repo ships a workspace prompt file at [.github/prompts/cv.prompt.md](.github/prompts/cv.prompt.md). In VS Code Copilot Chat (Agent mode), type: ``` /cv take image from webcam and save it as gray scale 240 * 240 ``` The agent will call `webcam_save`, `image_to_grayscale`, and `image_resize` from this server to produce the requested file under `./.farshid/cv/`. ## Tool catalog ### Webcam / capture - `webcam_capture(camera_index=0)` → returns a PNG image - `webcam_save(output_path="", camera_index=0)` - `webcam_preview(camera_index=0, seconds=10)` (local desktop window) - `webcam_record(output_path, seconds=5, camera_index=0, fps=20)` ### Image I/O & info - `image_show(path)` — return image to chat - `image_info(path)` — shape, dtype, mean, file size - `image_convert(input_path, output_path, quality=95)` ### Geometric transforms - `image_resize(... width|height|scale, interpolation)` - `image_crop(input_path, output_path, x, y, width, height)` - `image_rotate(input_path, output_path, angle, scale=1, keep_size=False)` - `image_flip(input_path, output_path, direction)` - `image_pad(... top, bottom, left, right, border_type, color)` ### Color - `image_to_grayscale` - `color_convert(target=gray|hsv|hls|lab|ycrcb|rgb|bgr)` - `adjust_brightness_contrast` - `histogram_equalize(method=clahe|global)` - `histogram_data(bins=32)` ### Filtering - `blur_gaussian(ksize, sigma)` - `blur_median(ksize)` - `blur_bilateral(d, sigma_color, sigma_space)` - `sharpen(amount)` - `denoise(strength)` ### Edges / gradients - `edges_canny(threshold1, threshold2)` - `edges_sobel(ksize)` - `edges_laplacian(ksize)` ### Thresholding & morphology - `threshold(method=otsu|binary|binary_inv|adaptive_mean|adaptive_gaussian)` - `morphology(op=erode|dilate|open|close|gradient|tophat|blackhat)` ### Contours & shapes - `find_contours(input_path, output_path?, thresh, min_area)` - `detect_circles(...)` — Hough - `detect_lines(...)` — Probabilistic Hough - `detect_corners(...)` — Shi-Tomasi ### Feature matching - `feature_match(image1, image2, output_path?)` — ORB + BFMatcher ### Object detection (Haar) - `detect_faces` - `detect_eyes` - `detect_bodies` - `detect_qrcode` ### Drawing - `draw_rectangle`, `draw_circle`, `draw_line`, `draw_text` ### Composition / arithmetic - `image_blend(image1, image2, output_path, alpha)` - `image_diff(image1, image2, output_path?)` → mean/max diff - `image_concat(images, output_path, direction)` - `template_match(image_path, template_path, output_path?, threshold)` ### Video - `video_info(path)` - `video_extract_frames(video_path, output_dir, every_n, max_frames, ext)` - `video_thumbnail(video_path, output_path, time_seconds)` ## Build & publish ```bash pip install -U build twine mcp-publisher python -m build twine upload dist/* mcp-publisher login github mcp-publisher publish .mcp/server.json ``` ## OS notes - **Windows 11 (latest):** webcam works out of the box; ensure *Settings → Privacy & security → Camera → Let desktop apps access your camera* is **On**. - **macOS (latest):** the first webcam call triggers a system Camera permission prompt; grant it to the terminal/VS Code process. - **Linux (latest):** requires a working `/dev/video*` device. Headless servers without a display cannot use `webcam_preview` (it opens an OpenCV window). ## Notes - Never use `print()` in tool functions: stdout is the MCP protocol channel. Use `sys.stderr` (the `_log` helper at the bottom of `server.py`). - `webcam_preview` opens a real desktop window — only works where the server has a display (not over plain SSH or in a headless container). - All paths support `~` expansion. Output directories are created automatically. - Tools that return annotated images take an optional `output_path`; when omitted they only return the JSON metadata.