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@@ -82,21 +82,22 @@ conda create -n MinerU python=3.10
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conda activate MinerU
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```
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-### Usage Instructions
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+### Installation and Configuration
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#### 1. Install Magic-PDF
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-Install using pip:
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-```bash
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-pip install magic-pdf
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-```
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-Alternatively, for built-in high-precision model parsing capabilities, use:
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+Install the full-feature package with pip:
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+>Note: The pip-installed package supports CPU-only and is ideal for quick tests.
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+>
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+>For CUDA/MPS acceleration in production, see [Acceleration Using CUDA or MPS](#4-Acceleration-Using-CUDA-or-MPS).
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+
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```bash
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pip install magic-pdf[full-cpu]
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```
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-The high-precision models depend on detectron2, which requires a compiled installation.
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-If you need to compile it yourself, refer to https://github.com/facebookresearch/detectron2/issues/5114
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-Or directly use our pre-compiled wheel packages (limited to python 3.10):
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+The full-feature package depends on detectron2, which requires a compilation installation.
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+If you need to compile it yourself, please refer to https://github.com/facebookresearch/detectron2/issues/5114
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+Alternatively, you can directly use our precompiled whl package (limited to Python 3.10):
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+
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```bash
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pip install detectron2 --extra-index-url https://myhloli.github.io/wheels/
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```
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@@ -123,31 +124,8 @@ In magic-pdf.json, configure "models-dir" to point to the directory where the mo
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```
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-#### 4. Usage via Command Line
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-
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-###### simple
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-
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-```bash
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-magic-pdf pdf-command --pdf "pdf_path" --inside_model true
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-```
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-After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
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-You can find the corresponding xxx_model.json file in the markdown directory.
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-If you intend to do secondary development on the post-processing pipeline, you can use the command:
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-```bash
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-magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
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-```
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-In this way, you won't need to re-run the model data, making debugging more convenient.
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-
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-
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-###### more
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-
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-```bash
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-magic-pdf --help
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-```
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-
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-
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-#### 5. Acceleration Using CUDA or MPS
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-
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+#### 4. Acceleration Using CUDA or MPS
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+If you have an available Nvidia GPU or are using a Mac with Apple Silicon, you can leverage acceleration with CUDA or MPS respectively.
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##### CUDA
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You need to install the corresponding PyTorch version according to your CUDA version.
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@@ -172,13 +150,39 @@ You also need to modify the value of "device-mode" in the configuration file mag
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}
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```
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-#### 6. Usage via Api
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+
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+### Usage
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+
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+#### 1.Usage via Command Line
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+
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+###### simple
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+
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+```bash
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+magic-pdf pdf-command --pdf "pdf_path" --inside_model true
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+```
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+After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".
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+You can find the corresponding xxx_model.json file in the markdown directory.
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+If you intend to do secondary development on the post-processing pipeline, you can use the command:
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+```bash
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+magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
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+```
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+In this way, you won't need to re-run the model data, making debugging more convenient.
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+
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+
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+###### more
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+
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+```bash
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+magic-pdf --help
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+```
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+
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+
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+#### 2. Usage via Api
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###### Local
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```python
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image_writer = DiskReaderWriter(local_image_dir)
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image_dir = str(os.path.basename(local_image_dir))
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-jso_useful_key = {"_pdf_type": "", "model_list": model_json}
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+jso_useful_key = {"_pdf_type": "", "model_list": []}
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pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
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pipe.pipe_classify()
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pipe.pipe_parse()
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@@ -191,7 +195,7 @@ s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
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image_dir = "s3://img_bucket/"
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s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
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pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
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-jso_useful_key = {"_pdf_type": "", "model_list": model_json}
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+jso_useful_key = {"_pdf_type": "", "model_list": []}
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pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
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pipe.pipe_classify()
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pipe.pipe_parse()
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