Commit 8ebf2e43 authored by narugo1992's avatar narugo1992
Browse files

dev(narugo): rename function

parent df0b4c2f

imgutils/clustering/__init__.py

+1 −1
Original line number Diff line number Diff line 
from .lpips import extract_feature, lpips_difference, batch_lpips_difference
 
from .lpips import lpips_extract_feature, lpips_difference

imgutils/clustering/lpips.py

+7 −6
Original line number Diff line number Diff line
@@ -27,8 +27,9 @@ def _lpips_feature_model(): 
    ))





def extract_feature(images: MultiImagesTyping) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:

    images = load_images(images)

def lpips_extract_feature(image: MultiImagesTyping) \

        -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:

    images = load_images(image)

    _encoded = np.stack([_image_encode(image) for image in images])

    features = _lpips_feature_model().run(["feat_0", "feat_1", "feat_2", "feat_3", "feat_4"], {'input': _encoded})

    return tuple(features)
@@ -46,7 +47,7 @@ _FEAT1_NAMES = ["feat_x_0", "feat_x_1", "feat_x_2", "feat_x_3", "feat_x_4"] 
_FEAT2_NAMES = ["feat_y_0", "feat_y_1", "feat_y_2", "feat_y_3", "feat_y_4"]





def batch_lpips_difference(feats1: Tuple[np.ndarray, ...], feats2: Tuple[np.ndarray, ...]) -> np.ndarray:

def _batch_lpips_difference(feats1: Tuple[np.ndarray, ...], feats2: Tuple[np.ndarray, ...]) -> np.ndarray:

    output, = _lpips_diff_model().run(

        ["output"],

        {
@@ -64,20 +65,20 @@ def _auto_feat(img: AutoFeatTyping): 
    if isinstance(img, (tuple, list)):

        return img

    else:

        return extract_feature(load_image(img))

        return lpips_extract_feature(load_image(img))





def lpips_difference(img1: AutoFeatTyping, img2: AutoFeatTyping) -> float:

    img1 = _auto_feat(img1)

    img2 = _auto_feat(img2)

    return batch_lpips_difference(img1, img2).item()

    return _batch_lpips_difference(img1, img2).item()





def lpips_clustering(images: MultiImagesTyping, threshold: float = 0.45) -> List[int]:

    images = load_images(images, mode='RGB')

    n = len(images)



    feat_list = [extract_feature(image) for image in tqdm(images, leave=False, desc='Extract features')]

    feat_list = [lpips_extract_feature(image) for image in tqdm(images, leave=False, desc='Extract features')]

    progress = tqdm(total=n * (n + 1) // 2, leave=False, desc='Metrics')



    @lru_cache(maxsize=n * (n + 1) // 2)