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HivisionIDPhoto

HivisionIDPhoto is a Python-based open-source application that processes portrait images into identification photos using AI-assisted matting, face processing, background replacement and standardized layout generation.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

HivisionIDPhoto — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a portrait must be transformed into a formatted identification photo and you want an open-source Python implementation that can support local, hosted or server-oriented deployment.

Who it is for

  • Developers building identification-photo applications or services.
  • Teams seeking offline or self-hosted portrait-processing tooling.
  • Users who need a Gradio interface or a FastAPI-based integration for image processing.

Documented uses

  • Remove or separate a portrait subject from its background.
  • Create ID photos in different size specifications.
  • Change the background color and prepare print layouts.
  • Add face alignment, beauty and image-output options described by the project.
Before you choose

Requirements & limits

The repository is public and released under the Apache-2.0 license. The setup documentation lists Python 3.7 or newer and supports Linux, Windows and macOS; it recommends Python 3.10. Model weights must be downloaded separately.

  • Local CPU inference is documented, which may suit offline workflows.
  • The project exposes both Gradio and FastAPI-related deployment paths.
  • Multiple portrait-matting weights are listed, with different stated model origins and sizes.
  • Docker is listed in the repository metadata, but the available material does not show a Dockerfile or deployment configuration.
  • An HTTP-facing adaptation is Likely compatible because FastAPI is listed, although endpoint definitions and a demonstrated API workflow are not provided.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up HivisionIDPhoto locally

Prepare the Python application and portrait-matting model weights for local use.

Before you start

  • Python 3.7 or newer; the project is mainly tested with Python 3.10.
  • Linux, Windows or macOS.
  • A cloned copy of the public repository.
  • The project dependency files and at least one supported portrait-matting model weight.

Steps

  1. Clone the repository and enter its directory.

  2. Install the packages listed in requirements.txt and requirements-app.txt.

  3. Download all model weights with the documented model-download script, or place a selected model in hivision/creator/weights.

  4. Configure an optional face-detection model if that part of the workflow is required.

Expected outcome

The documented application environment and portrait-matting weights are prepared for the project's next local-use stage.

Guide limitations

  • These instructions establish setup only; the available material does not document a verified execution result.
  • The exact model choice and full face-detection configuration depend on the intended workflow.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
2 October 2026
Original website title
Zeyi-Lin/HivisionIDPhotos · Official homepage title · retained in its source language
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