base on Rembg is a tool to remove images background # Rembg [](https://img.shields.io/badge/License-MIT-blue.svg) [](https://huggingface.co/spaces/KenjieDec/RemBG) [](https://bgremoval.streamlit.app/) [](https://colab.research.google.com/github/danielgatis/rembg/blob/main/rembg.ipynb) [](https://repomapr.com/danielgatis/rembg) <p align="center"> <img src="logo.png" alt="Rembg Logo" width="600" /> </p> Rembg is a tool to remove image backgrounds. It can be used as a CLI, Python library, HTTP server, or Docker container. **If this project has helped you, please consider making a [donation](https://www.buymeacoffee.com/danielgatis).** ## Sponsors <table> <tr> <td align="center" vertical-align="center"> <a href="https://photoroom.com/api/remove-background?utm_source=rembg&utm_medium=github_webpage&utm_campaign=sponsor" > <img src="https://font-cdn.photoroom.com/media/api-logo.png" width="120px;" alt="Unsplash" /> </a> </td> <td align="center" vertical-align="center"> <b>PhotoRoom Remove Background API</b> <br /> <a href="https://photoroom.com/api/remove-background?utm_source=rembg&utm_medium=github_webpage&utm_campaign=sponsor">https://photoroom.com/api</a> <br /> <p width="200px"> Fast and accurate background remover API<br/> </p> </td> </tr> </table> ## Requirements ```text python: >=3.11, <3.14 ``` ## Installation Choose **one** of the following backends based on your hardware: ### CPU support ```bash pip install "rembg[cpu]" # for library pip install "rembg[cpu,cli]" # for library + cli ``` ### GPU support (NVIDIA/CUDA) First, check if your system supports `onnxruntime-gpu` by visiting [onnxruntime.ai](https://onnxruntime.ai/getting-started) and reviewing the installation matrix. <p style="display: flex;align-items: center;justify-content: center;"> <img alt="onnxruntime-installation-matrix" src="./onnxruntime-installation-matrix.png" width="400" /> </p> If your system is compatible, run: ```bash pip install "rembg[gpu]" # for library pip install "rembg[gpu,cli]" # for library + cli ``` > **Note:** NVIDIA GPUs may require `onnxruntime-gpu`, CUDA, and `cudnn-devel`. See [#668](https://github.com/danielgatis/rembg/issues/668#issuecomment-2689830314) for details. If `rembg[gpu]` doesn't work and you can't install CUDA or `cudnn-devel`, use `rembg[cpu]` with `onnxruntime` instead. ### GPU support (AMD/ROCm) ROCm support requires the `onnxruntime-rocm` package. Install it by following [AMD's documentation](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/install-onnx.html). Once `onnxruntime-rocm` is installed and working, install rembg with ROCm support: ```bash pip install "rembg[rocm]" # for library pip install "rembg[rocm,cli]" # for library + cli ``` ## Usage as a CLI After installation, you can use rembg by typing `rembg` in your terminal. The `rembg` command has 4 subcommands, one for each input type: - `i` - single files - `p` - folders (batch processing) - `s` - HTTP server - `b` - RGB24 pixel binary stream You can get help about the main command using: ```shell rembg --help ``` You can also get help for any subcommand: ```shell rembg <COMMAND> --help ``` ### rembg `i` Used for processing single files. **Remove background from a remote image:** ```shell curl -s http://input.png | rembg i > output.png ``` **Remove background from a local file:** ```shell rembg i path/to/input.png path/to/output.png ``` **Specify a model:** ```shell rembg i -m u2netp path/to/input.png path/to/output.png ``` **Return only the mask:** ```shell rembg i -om path/to/input.png path/to/output.png ``` **Apply alpha matting:** ```shell rembg i -a path/to/input.png path/to/output.png ``` **Pass extra parameters (SAM example):** ```shell rembg i -m sam -x '{ "sam_prompt": [{"type": "point", "data": [724, 740], "label": 1}] }' examples/plants-1.jpg examples/plants-1.out.png ``` **Pass extra parameters (custom model):** ```shell rembg i -m u2net_custom -x '{"model_path": "~/.u2net/u2net.onnx"}' path/to/input.png path/to/output.png ``` ### rembg `p` Used for batch processing entire folders. **Process all images in a folder:** ```shell rembg p path/to/input path/to/output ``` **Watch mode (process new/changed files automatically):** ```shell rembg p -w path/to/input path/to/output ``` ### rembg `s` Used to start an HTTP server. ```shell rembg s --host 0.0.0.0 --port 7000 --log_level info ``` For complete API documentation, visit: `http://localhost:7000/api` **Remove background from an image URL:** ```shell curl -s "http://localhost:7000/api/remove?url=http://input.png" -o output.png ``` **Remove background from an uploaded image:** ```shell curl -s -F file=@/path/to/input.jpg "http://localhost:7000/api/remove" -o output.png ``` ### rembg `b` Process a sequence of RGB24 images from stdin. This is intended to be used with programs like FFmpeg that output RGB24 pixel data to stdout. ```shell rembg b <width> <height> -o <output_specifier> ``` **Arguments:** | Argument | Description | |----------|-------------| | `width` | Width of input image(s) | | `height` | Height of input image(s) | | `output_specifier` | Printf-style specifier for output filenames (e.g., `output-%03u.png` produces `output-000.png`, `output-001.png`, etc.). Omit to write to stdout. | **Example with FFmpeg:** ```shell ffmpeg -i input.mp4 -ss 10 -an -f rawvideo -pix_fmt rgb24 pipe:1 | rembg b 1280 720 -o folder/output-%03u.png ``` > **Note:** The width and height must match FFmpeg's output dimensions. The flags `-an -f rawvideo -pix_fmt rgb24 pipe:1` are required for FFmpeg compatibility. ## Usage as a Library **Input and output as bytes:** ```python from rembg import remove with open('input.png', 'rb') as i: with open('output.png', 'wb') as o: input = i.read() output = remove(input) o.write(output) ``` **Input and output as a PIL image:** ```python from rembg import remove from PIL import Image input = Image.open('input.png') output = remove(input) output.save('output.png') ``` **Input and output as a NumPy array:** ```python from rembg import remove import cv2 input = cv2.imread('input.png') output = remove(input) cv2.imwrite('output.png', output) ``` **Force output as bytes:** ```python from rembg import remove with open('input.png', 'rb') as i: with open('output.png', 'wb') as o: input = i.read() output = remove(input, force_return_bytes=True) o.write(output) ``` **Batch processing with session reuse (recommended for performance):** ```python from pathlib import Path from rembg import remove, new_session session = new_session() for file in Path('path/to/folder').glob('*.png'): input_path = str(file) output_path = str(file.parent / (file.stem + ".out.png")) with open(input_path, 'rb') as i: with open(output_path, 'wb') as o: input = i.read() output = remove(input, session=session) o.write(output) ``` For more examples, see the [examples](USAGE.md) page. ## Usage with Docker ### CPU Only Replace the `rembg` command with `docker run danielgatis/rembg`: ```shell docker run -v .:/data danielgatis/rembg i /data/input.png /data/output.png ``` ### NVIDIA CUDA GPU Acceleration **Requirements:** Your host must have the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) installed. CUDA acceleration requires `cudnn-devel`, so you need to build the Docker image yourself. See [#668](https://github.com/danielgatis/rembg/issues/668#issuecomment-2689914205) for details. **Build the image:** ```shell docker build -t rembg-nvidia-cuda-cudnn-gpu -f Dockerfile_nvidia_cuda_cudnn_gpu . ``` > **Note:** This image requires ~11GB of disk space (CPU version is ~1.6GB). Models are not included. **Run the container:** ```shell sudo docker run --rm -it --gpus all -v /dev/dri:/dev/dri -v $PWD:/data rembg-nvidia-cuda-cudnn-gpu i -m birefnet-general /data/input.png /data/output.png ``` **Tips:** - You can create your own NVIDIA CUDA image and install `rembg[gpu,cli]` in it. - Use `-v /path/to/models/:/root/.u2net` to store model files outside the container, avoiding re-downloads. ## Models All models are automatically downloaded and saved to `~/.u2net/` on first use. ### Available Models - u2net ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for general use cases. - u2netp ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2netp.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A lightweight version of u2net model. - u2net_human_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_human_seg.onnx), [source](https://github.com/xuebinqin/U-2-Net)): A pre-trained model for human segmentation. - u2net_cloth_seg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/u2net_cloth_seg.onnx), [source](https://github.com/levindabhi/cloth-segmentation)): A pre-trained model for Cloths Parsing from human portrait. Here clothes are parsed into 3 category: Upper body, Lower body and Full body. - silueta ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/silueta.onnx), [source](https://github.com/xuebinqin/U-2-Net/issues/295)): Same as u2net but the size is reduced to 43Mb. - isnet-general-use ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-general-use.onnx), [source](https://github.com/xuebinqin/DIS)): A new pre-trained model for general use cases. - isnet-anime ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/isnet-anime.onnx), [source](https://github.com/SkyTNT/anime-segmentation)): A high-accuracy segmentation for anime character. - sam ([download encoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-encoder-quant.onnx), [download decoder](https://github.com/danielgatis/rembg/releases/download/v0.0.0/vit_b-decoder-quant.onnx), [source](https://github.com/facebookresearch/segment-anything)): A pre-trained model for any use cases. - birefnet-general ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-epoch_244.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A pre-trained model for general use cases. - birefnet-general-lite ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-general-bb_swin_v1_tiny-epoch_232.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A light pre-trained model for general use cases. - birefnet-portrait ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-portrait-epoch_150.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A pre-trained model for human portraits. - birefnet-dis ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-DIS-epoch_590.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A pre-trained model for dichotomous image segmentation (DIS). - birefnet-hrsod ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-HRSOD_DHU-epoch_115.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A pre-trained model for high-resolution salient object detection (HRSOD). - birefnet-cod ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-COD-epoch_125.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A pre-trained model for concealed object detection (COD). - birefnet-massive ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/BiRefNet-massive-TR_DIS5K_TR_TEs-epoch_420.onnx), [source](https://github.com/ZhengPeng7/BiRefNet)): A pre-trained model with massive dataset. - bria-rmbg ([download](https://github.com/danielgatis/rembg/releases/download/v0.0.0/bria-rmbg-2.0.onnx), [source](https://huggingface.co/briaai/RMBG-2.0)): A state-of-the-art background removal model by BRIA AI. ## FAQ ### When will this library support Python version 3.xx? This library depends on [onnxruntime](https://pypi.org/project/onnxruntime). Python version support is determined by onnxruntime's compatibility. ## Support If you find this project useful, consider buying me a coffee (or a beer): <a href="https://www.buymeacoffee.com/danielgatis" target="_blank"><img src="https://bmc-cdn.nyc3.digitaloceanspaces.com/BMC-button-images/custom_images/orange_img.png" alt="Buy Me A Coffee" style="height: auto !important;width: auto !important;"></a> ## Star History [](https://star-history.com/#danielgatis/rembg&Date) ## License Copyright (c) 2020-present [Daniel Gatis](https://github.com/danielgatis) Licensed under the [MIT License](./LICENSE.txt). ", Assign "at most 3 tags" to the expected json: {"id":"2846","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"