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acquisition-display-shell

acquisition-display-shell

Guides SimpleCV camera acquisition, virtual cameras, display windows, shell startup, streams, calibration, and headless runtime checks.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentación de la Skill

GitHub

name: acquisition-display-shell description: "Guides SimpleCV camera acquisition, virtual cameras, display windows, shell startup, streams, calibration, and headless runtime checks." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

Acquisition, Display, and Shell

Use this sub-skill when the difficult part is acquiring frames, displaying frames, starting the shell, handling interactive loops, or explaining optional hardware.

Read first

Read references/workflows.md for camera, display, shell, stream, and calibration patterns. Read references/troubleshooting.md for headless SDL, webcam, device, and shell-start failures. Run scripts/env_probe.py --help for safe environment probes that do not open a physical camera. Use the root ../../scripts/check_display_headless.py for a finite dummy-SDL Display smoke.

Use this for

  • Camera, VirtualCamera, JpegStreamCamera, StereoImage, and StereoCamera routing.
  • Display, Image.show(), display loops, Display(..., headless=True), and notebook display mode.
  • The simplecv interactive shell and shell help/banner behavior.
  • Stream, JPEG streamers, web display concepts, and finite display probes.
  • Calibration workflow planning from images or a physical camera.
  • Optional devices such as Kinect, Vimba/AVT cameras, scanners, digital cameras, and screenshot capture.

Route elsewhere

  • Static transforms or sample-image processing → ../image-processing-basics/SKILL.md.
  • Detector algorithms on a frame/image → ../feature-detection/SKILL.md.
  • Stateful masks and trackers → ../segmentation-tracking/SKILL.md.
  • Classifier training/testing → ../machine-learning-legacy/SKILL.md.

Safe acquisition workflow

  1. Check package import and OpenCV compatibility with ../../scripts/check_env.py.
  2. In headless environments, set SDL_VIDEODRIVER=dummy before importing pygame-backed display code.
  3. For static or automated tests, prefer VirtualCamera or sample images over Camera(0).
  4. Only instantiate Camera, Kinect, Vimba, scanner, or digital camera objects when the user explicitly confirms hardware availability.
  5. Keep loops finite in scripts; original examples often run while True or wait for mouse input.

Shell workflow

The simplecv console entry point starts an interactive shell. To smoke-check it in automation, use a timeout:

SDL_VIDEODRIVER=dummy timeout 10 simplecv --help

A printed banner plus prompt is a successful shell-start signal, not necessarily a command that should exit by itself.

Calibration workflow

Use calibration guidance when the user has a camera and a printed chessboard. The source calibration tool is interactive and should be treated as a workflow reference, not copied as an automatic script. For automated checks, use stored calibration fixtures only after the full skill is integrated.

Bundled helper

python sub-skills/acquisition-display-shell/scripts/env_probe.py
SDL_VIDEODRIVER=dummy python ../../scripts/check_display_headless.py

Use --repo-root only for an explicit target checkout.

Verification hooks

Good final candidates include the bounded simplecv --help shell smoke, headless Display construction, virtual-camera-only native tests, and stereo/calibration fixture tests when they are safe. Physical camera, Kinect, Vimba, scanner, and web/Flash examples are optional hardware/service workflows and should not block core CPU verification.

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