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Tools Usage

Language: English | 简体中文

Several tools are provided with MetDetPy to support related functions.

Menu

Tool User Guides

Other Tools

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Tool User Guides

Detection Tools

MetDetPy provides two detection tools: MetDetPy for video meteor detection and MetDetPhoto for image meteor detection. Each tool has its own characteristics and is suitable for different use cases.

For detailed usage information about these detection tools, please refer to the Detection Tools User Guide.

ClipToolkit

ClipToolkit can be used to create multiple video segments from a single video or a stack of images from these video segments at once. For detailed usage information, please see the ClipToolkit User Guide.

Evaluate

Evaluate is an integrated performance evaluation and regression testing tool. It can be used to generate result reports, evaluate the utilization of device resources, and compare differences between results.

To evaluate how MetDetPy performs on your video, you can simply run evaluate.py :

python evaluate.py json [--cfg CFG] [--load LOAD] [--save SAVE] [--metrics] [--debug]

Arguments

  • json: A JSON file in MDRF format, which needs to contain the necessary information related to the video (video file and mask file paths, start and end times) to initiate. Its format should meet the requirements specified in Meteor Detection Recording Format (MDRF).

  • --cfg: Configuration file. By default, it uses the default configuration, which is m3det_normal.json.

  • --load: If this is enabled with a path to another JSON, evaluate.py will directly load its results for comparison as the current detection result instead of running detection through the video.

  • --save: The path and filename where the detection results will be saved.

  • --metrics: Depending on the category of the provided JSON file, it performs regression testing (comparing with other prediction results) or calculates detection precision and recall (comparing with ground truth). To apply this option, the json file needs to contain results information.

  • --debug: When starting evaluate.py with this option, detailed debug information will be provided.

Example

(To be updated)

make_package

Use make_package.py to freeze the MetDetPy programs with either Nuitka or PyInstaller. Nuitka is the default backend.

python make_package.py [--backend {nuitka,pyinstaller}]
     [--apply-upx] [--apply-zip] [--onefile]
     [--mingw64] [--macos-sign-identity IDENTITY]
     [--windowed] [--icon ICON_PATH]

  • --backend: select nuitka or pyinstaller. Defaults to nuitka.

  • --apply-upx: apply UPX to squeeze the size of the executable program.

  • --apply-zip: generate a ZIP package after packaging.

  • --onefile: generate one executable per program instead of a directory bundle.

  • --mingw64: use the MinGW64 compiler with the Nuitka backend on Windows.

  • --macos-sign-identity: macOS signing identity for the Nuitka backend.

  • --windowed: use windowed mode with the PyInstaller backend.

  • --icon: executable icon used by the PyInstaller backend.

Common Notes

Executables and the optional ZIP package are generated in the dist directory.

Notice:

  1. When using Nuitka, install nuitka>=2.0.0 and an available C/C++ compiler. For PyInstaller, install pyinstaller>=6.0.
  2. Due to the nature of Python packaging, these tools cannot generate cross-platform executables; build the executable on the target platform.
  3. If matplotlib or scipy exists in the environment, they may be included in the packaged output. To reduce package size, prepare a clean environment or avoid installing heavy optional dependencies.