Run a local desktop app that removes photo backgrounds without sending images to a web service.
Composite layered images with opacity, ordering, and mask tuning sliders for product mockups.
Connect to Claude Desktop or Cursor so an agent can call remove_background and apply_mask on local files.
Batch process photos to transparent PNGs from a launcher script on macOS, Linux, or Windows.
Launch scripts build a Python 3.10+ venv and download model weights on first run, which takes a few minutes.
FP-Background_Obliterator is a Python tool for removing the background from photos and compositing layered images, all running locally on the user's own computer. The author writes that no image data leaves the machine, which is the main selling point compared to web services. The background cutout itself is done by a neural network called InSPyReNet, and the rest of the image handling uses OpenCV. The project can be used in two ways. The first is an interactive desktop application built with PyQt6. It has a layer panel similar to image editing apps, with reordering, grouping into folders, per-layer opacity, and visibility toggles. It supports rectangular selections, copy and paste with the system clipboard, and three sliders for tuning the cutout edge: a threshold from 0 to 255, an erode or dilate shift between minus 20 and plus 20 pixels, and a blur radius from 0 to 20 for softer borders. It reads JPG, PNG, and WEBP, and exports either a transparent PNG or a flat-background JPG. The second way to use it is as a Model Context Protocol server. MCP is a standard for letting AI assistants call external tools, and the README gives copy-paste configurations for Claude Desktop, the Cursor IDE, and the Windsurf IDE. Once connected, the agent gets five tools it can call: remove_background, get_mask, apply_mask, edit_image for things like brightness and cropping, and a composite layers tool for combining images. Each tool documents its arguments, including the same threshold, shift, and blur controls available in the GUI. Launching is handled by scripts that build a local Python virtual environment and install dependencies. macOS and Linux users run launch_headless.sh or launch_ui.sh, and Windows users run the equivalent .bat files. On Apple Silicon the tool uses Metal Performance Shaders, on NVIDIA hardware it uses CUDA, and otherwise it falls back to CPU. Python 3.10 or newer is required. The repository has 1 star and is MIT licensed.
Generated 2026-05-22 · Model: sonnet-4-6 · Verify against the repo before relying on details.