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Magika 是一款新颖的 AI 支持的文件类型检测工具，它依靠深度学习的最新进展来提供准确的检测。 在底层，Magika 采用了定制的、高度优化的 Keras 模型，该模型仅重约 1MB，即使在单个 CPU 上运行，也能在几毫秒内实现精确的文件识别。

在对超过 100 万个文件和超过 100 种内容类型（涵盖二进制和文本文件格式）的评估中，Magika 实现了 99% 以上的精确度和召回率。 Magika 被大规模使用，通过将 Gmail、云端硬盘和安全浏览文件路由到适当的安全和内容策略扫描仪，帮助提高 Google 用户的安全性。

你可以使用我们的Web演示来尝试 Magika，无需任何任何操作，该演示在你的浏览器中本地运行！

以下是 Magika 命令行输出的示例：

1、快速入门

Magika 在 PyPI 上以 Magika 的形式提供：

$ pip install magika

用法示例：

$ magika -r tests_data/
tests_data/README.md: Markdown document (text)
tests_data/basic/code.asm: Assembly (code)
tests_data/basic/code.c: C source (code)
tests_data/basic/code.css: CSS source (code)
tests_data/basic/code.js: JavaScript source (code)
tests_data/basic/code.py: Python source (code)
tests_data/basic/code.rs: Rust source (code)
...
tests_data/mitra/7-zip.7z: 7-zip archive data (archive)
tests_data/mitra/bmp.bmp: BMP image data (image)
tests_data/mitra/bzip2.bz2: bzip2 compressed data (archive)
tests_data/mitra/cab.cab: Microsoft Cabinet archive data (archive)
tests_data/mitra/elf.elf: ELF executable (executable)
tests_data/mitra/flac.flac: FLAC audio bitstream data (audio)
...
$ magika code.py --json
[
    {
        "path": "code.py",
        "dl": {
            "ct_label": "python",
            "score": 0.9940916895866394,
            "group": "code",
            "mime_type": "text/x-python",
            "magic": "Python script",
            "description": "Python source"
        },
        "output": {
            "ct_label": "python",
            "score": 0.9940916895866394,
            "group": "code",
            "mime_type": "text/x-python",
            "magic": "Python script",
            "description": "Python source"
        }
    }
]
$ cat doc.ini | magika -
-: INI configuration file (text)
$ magika -h
Usage: magika [OPTIONS] [FILE]...

  Magika - Determine type of FILEs with deep-learning.

Options:
  -r, --recursive                 When passing this option, magika scans every
                                  file within directories, instead of
                                  outputting "directory"
  --json                          Output in JSON format.
  --jsonl                         Output in JSONL format.
  -i, --mime-type                 Output the MIME type instead of a verbose
                                  content type description.
  -l, --label                     Output a simple label instead of a verbose
                                  content type description. Use --list-output-
                                  content-types for the list of supported
                                  output.
  -c, --compatibility-mode        Compatibility mode: output is as close as
                                  possible to `file` and colors are disabled.
  -s, --output-score              Output the prediction score in addition to
                                  the content type.
  -m, --prediction-mode [best-guess|medium-confidence|high-confidence]
  --batch-size INTEGER            How many files to process in one batch.
  --no-dereference                This option causes symlinks not to be
                                  followed. By default, symlinks are
                                  dereferenced.
  --colors / --no-colors          Enable/disable use of colors.
  -v, --verbose                   Enable more verbose output.
  -vv, --debug                    Enable debug logging.
  --generate-report               Generate report useful when reporting
                                  feedback.
  --version                       Print the version and exit.
  --list-output-content-types     Show a list of supported content types.
  --model-dir DIRECTORY           Use a custom model.
  -h, --help                      Show this message and exit.

  Magika version: "0.5.0"

  Default model: "standard_v1"

  Send any feedback to magika-dev@google.com or via GitHub issues.

有关详细文档，请参阅 python 文档。

Python API示例：

>>> from magika import Magika
>>> m = Magika()
>>> res = m.identify_bytes(b"# Example\nThis is an example of markdown!")
>>> print(res.output.ct_label)
markdown

有关详细文档，请参阅 python 文档。

2、开发环境搭建

开发设置
我们用poetry来开发和打包：

$ git clone https://github.com/google/magika
$ cd magika/python
$ poetry shell && poetry install
$ magika -r ../tests_data

运行测试：

$ cd magika/python
$ poetry shell
$ pytest tests/

y原文链接：Detect file content types with deep learning

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