Commit d83d2ee3 authored by narugo1992's avatar narugo1992
Browse files

dev(narugo): create benchmark image

parent d35d53df

docs/source/api_doc/detect/face_detect.benchmark.py

+9 −6
Original line number Diff line number Diff line
@@ -5,13 +5,14 @@ from imgutils.detect import detect_faces 




class FaceDetectBenchmark(BaseBenchmark):

    def __init__(self, level):

    def __init__(self, level, version):

        BaseBenchmark.__init__(self)

        self.level = level

        self.version = version



    def load(self):

        from imgutils.detect.face import _open_face_detect_model

        _ = _open_face_detect_model(level=self.level)

        _ = _open_face_detect_model(level=self.level, version=self.version)



    def unload(self):

        from imgutils.detect.face import _open_face_detect_model
@@ -19,16 +20,18 @@ class FaceDetectBenchmark(BaseBenchmark): 


    def run(self):

        image_file = random.choice(self.all_images)

        _ = detect_faces(image_file, level=self.level)

        _ = detect_faces(image_file, level=self.level, version=self.version)





if __name__ == '__main__':

    create_plot_cli(

        [

            ('face (yolov8s)', FaceDetectBenchmark('s')),

            ('face (yolov8n)', FaceDetectBenchmark('n')),

            ('face v1 (yolov8s)', FaceDetectBenchmark('s', 'v1')),

            ('face v1 (yolov8n)', FaceDetectBenchmark('n', 'v1')),

            ('face v0 (yolov8s)', FaceDetectBenchmark('s', 'v0')),

            ('face v0 (yolov8n)', FaceDetectBenchmark('n', 'v0')),

        ],

        title='Benchmark for Anime Face Detections',

        run_times=10,

        try_times=5,

        try_times=20,

    )()

docs/source/api_doc/detect/face_detect.benchmark.py.svg

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docs/source/api_doc/detect/head_detect.benchmark.py

+1 −1
Original line number Diff line number Diff line
@@ -30,5 +30,5 @@ if __name__ == '__main__': 
        ],

        title='Benchmark for Anime Head Detections',

        run_times=10,

        try_times=5,

        try_times=20,

    )()

docs/source/api_doc/detect/head_detect.benchmark.py.svg

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imgutils/detect/face.py

+10 −4
Original line number Diff line number Diff line
@@ -23,16 +23,21 @@ from ._yolo import _image_preprocess, _data_postprocess 
from ..data import ImageTyping, load_image, rgb_encode

from ..utils import open_onnx_model



_VERSIONS = {

    'v0': '',

    'v1': 'v1_',

}





@lru_cache()

def _open_face_detect_model(level: str = 's'):

def _open_face_detect_model(level: str = 's', version: str = 'v1'):

    return open_onnx_model(hf_hub_download(

        'deepghs/imgutils-models',

        f'face_detect/face_detect_best_{level}.onnx'

        f'face_detect/face_detect_{_VERSIONS[version]}best_{level}.onnx'

    ))





def detect_faces(image: ImageTyping, level: str = 's', max_infer_size=640,

def detect_faces(image: ImageTyping, level: str = 's', version: str = 'v1', max_infer_size=640,

                 conf_threshold: float = 0.45, iou_threshold: float = 0.7) \

        -> List[Tuple[Tuple[int, int, int, int], str, float]]:

    """
@@ -43,6 +48,7 @@ def detect_faces(image: ImageTyping, level: str = 's', max_infer_size=640, 
    :param level: The model level being used can be either `s` or `n`.

        The `n` model runs faster with smaller system overface, while the `s` model achieves higher accuracy.

        The default value is `s`.

    :param version: Version of model, default is ``v1``. Available versions are ``v0`` and ``v1``.

    :param max_infer_size: The maximum image size used for model inference, if the image size exceeds this limit,

        the image will be resized and used for inference. The default value is `640` pixels.

    :param conf_threshold: The confidence threshold, only detection results with confidence scores above
@@ -73,5 +79,5 @@ def detect_faces(image: ImageTyping, level: str = 's', max_infer_size=640, 
    new_image, old_size, new_size = _image_preprocess(image, max_infer_size)



    data = rgb_encode(new_image)[None, ...]

    output, = _open_face_detect_model(level).run(['output0'], {'images': data})

    output, = _open_face_detect_model(level, version).run(['output0'], {'images': data})

    return _data_postprocess(output[0], conf_threshold, iou_threshold, old_size, new_size, ['face'])