Metadata-Version: 1.0
Name: insightface
Version: 0.4.2
Summary: InsightFace Python Library
Home-page: https://github.com/deepinsight/insightface
Author: InsightFace Contributors
Author-email: contact@insightface.ai
License: MIT
Description: InsightFace Python Library
        ==========================
        
        License
        -------
        
        The code of InsightFace Python Library is released under the MIT
        License. There is no limitation for both academic and commercial usage.
        
        **The pretrained models we provided with this library are available for
        non-commercial research purposes only, including both auto-downloading
        models and manual-downloading models.**
        
        Install
        -------
        
        ::
        
            pip install -U insightface
        
        Quick Example
        -------------
        
        ::
        
            import cv2
            import numpy as np
            import insightface
            from insightface.app import FaceAnalysis
            from insightface.data import get_image as ins_get_image
        
            app = FaceAnalysis()
            app.prepare(ctx_id=0, det_size=(640, 640))
            img = ins_get_image('t1')
            faces = app.get(img)
            rimg = app.draw_on(img, faces)
            cv2.imwrite("./t1_output.jpg", rimg)
        
        This quick example will detect faces from the ``t1.jpg`` image and draw
        detection results on it.
        
        Inference Backend
        -----------------
        
        For ``insightface<=0.1.5``, we use MXNet as inference backend.
        
        (You may please download all models from
        `onedrive <https://1drv.ms/u/s!AswpsDO2toNKrUy0VktHTWgIQ0bn?e=UEF7C4>`__,
        and put them all under ``~/.insightface/models/`` directory to use this
        old version)
        
        Starting from insightface>=0.2, we use onnxruntime as inference backend.
        
        (You have to install ``onnxruntime-gpu`` to enable GPU inference)
        
        Model Zoo
        ---------
        
        In the latest version of insightface library, we provide following model
        packs:
        
        +------------------+-------------------+-----------------------+----------------+--------------+
        | Name             | Detection Model   | Recognition Model     | Alignment      | Attributes   |
        +==================+===================+=======================+================+==============+
        | **antelopev2**   | SCRFD-10GF        | ResNet100@Glint360K   | 2d106 & 3d68   | Gender&Age   |
        +------------------+-------------------+-----------------------+----------------+--------------+
        
        **Note that these models are available for non-commercial research
        purposes only.**
        
        For insightface>=0.3.3, models will be downloaded automatically once we
        init ``app = FaceAnalysis()`` instance.
        
        For insightface==0.3.2, you must first download the model package by
        command:
        
        ::
        
            insightface-cli model.download antelope
        
        or
        
        ::
        
            insightface-cli model.download antelopev2
        
        Use Your Own Licensed Model
        ---------------------------
        
        You can simply create a new model directory under
        ``~/.insightface/models/`` and replace the pretrained models we provide
        with your own models. And then call
        ``app = FaceAnalysis(name='your_model_zoo')`` to load these models.
        
        Call Models
        -----------
        
        The latest insightface libary only supports onnx models. Once you have
        trained detection or recognition models by PyTorch, MXNet or any other
        frameworks, you can convert it to the onnx format and then they can be
        called with insightface library.
        
        Call Detection Models
        ~~~~~~~~~~~~~~~~~~~~~
        
        ::
        
            import cv2
            import numpy as np
            import insightface
            from insightface.app import FaceAnalysis
            from insightface.data import get_image as ins_get_image
        
            # Method-1, use FaceAnalysis
            app = FaceAnalysis(allowed_modules=['detection']) # enable detection model only
            app.prepare(ctx_id=0, det_size=(640, 640))
        
            # Method-2, load model directly
            detector = insightface.model_zoo.get_model('your_detection_model.onnx')
            detector.prepare(ctx_id=0, det_size=(640, 640))
        
        Call Recognition Models
        ~~~~~~~~~~~~~~~~~~~~~~~
        
        ::
        
            import cv2
            import numpy as np
            import insightface
            from insightface.app import FaceAnalysis
            from insightface.data import get_image as ins_get_image
        
            handler = insightface.model_zoo.get_model('your_recognition_model.onnx')
            handler.prepare(ctx_id=0)
        
        
Platform: UNKNOWN
