import json
import os.path
from functools import lru_cache
from typing import Union, List
import numpy as np
from PIL import Image
from huggingface_hub import hf_hub_download, HfFileSystem
try:
from typing import Literal
except (ModuleNotFoundError, ImportError):
from typing_extensions import Literal
from imgutils.data import MultiImagesTyping, load_images, ImageTyping
from imgutils.utils import open_onnx_model
hf_fs = HfFileSystem()
def _normalize(data, mean=(0.48145466, 0.4578275, 0.40821073), std=(0.26862954, 0.26130258, 0.27577711)):
mean, std = np.asarray(mean), np.asarray(std)
return (data - mean[:, None, None]) / std[:, None, None]
def _preprocess_image(image: Image.Image, size: int = 384):
image = image.resize((size, size), resample=Image.BILINEAR)
# noinspection PyTypeChecker
data = np.array(image).transpose(2, 0, 1).astype(np.float32) / 255.0
data = _normalize(data)
return data
@lru_cache()
def _open_feat_model(model):
return open_onnx_model(hf_hub_download(
f'deepghs/ccip_onnx',
f'{model}/model_feat.onnx',
))
@lru_cache()
def _open_metric_model(model):
return open_onnx_model(hf_hub_download(
f'deepghs/ccip_onnx',
f'{model}/model_metrics.onnx',
))
@lru_cache()
def _open_metrics(model):
with open(hf_hub_download(f'deepghs/ccip_onnx', f'{model}/metrics.json'), 'r') as f:
return json.load(f)
@lru_cache()
def _open_cluster_metrics(model):
with open(hf_hub_download(f'deepghs/ccip_onnx', f'{model}/cluster.json'), 'r') as f:
return json.load(f)
_VALID_MODEL_NAMES = [
os.path.basename(os.path.dirname(file)) for file in
hf_fs.glob('deepghs/ccip_onnx/*/model.ckpt')
]
_DEFAULT_MODEL_NAMES = 'ccip-caformer-24-randaug-pruned'
def ccip_extract_feature(image: ImageTyping, size: int = 384, model: str = _DEFAULT_MODEL_NAMES):
"""
Extracts the feature vector of the character from the given anime image.
:param image: The anime image containing a single character.
:type image: ImageTyping
:param size: The size of the input image to be used for feature extraction. (default: ``384``)
:type size: int
:param model: The name of the model to use for feature extraction. (default: ``ccip-caformer-24-randaug-pruned``)
The available model names are: ``ccip-caformer-24-randaug-pruned``,
``ccip-caformer-6-randaug-pruned_fp32``, ``ccip-caformer-5_fp32``.
:type model: str
:return: The feature vector of the character.
:rtype: numpy.ndarray
Examples::
>>> from imgutils.metrics import ccip_extract_feature
>>>
>>> feat = ccip_extract_feature('ccip/1.jpg')
>>> feat.shape, feat.dtype
((768,), dtype('float32'))
"""
return ccip_batch_extract_features([image], size, model)[0]
def ccip_batch_extract_features(images: MultiImagesTyping, size: int = 384, model: str = _DEFAULT_MODEL_NAMES):
"""
Extracts the feature vectors of multiple images using the specified model.
:param images: The input images from which to extract the feature vectors.
:type images: MultiImagesTyping
:param size: The size of the input image to be used for feature extraction. (default: ``384``)
:type size: int
:param model: The name of the model to use for feature extraction. (default: ``ccip-caformer-24-randaug-pruned``)
The available model names are: ``ccip-caformer-24-randaug-pruned``,
``ccip-caformer-6-randaug-pruned_fp32``, ``ccip-caformer-5_fp32``.
:type model: str
:return: The feature vectors of the input images.
:rtype: numpy.ndarray
Examples::
>>> from imgutils.metrics import ccip_batch_extract_features
>>>
>>> feat = ccip_batch_extract_features(['ccip/1.jpg', 'ccip/2.jpg', 'ccip/6.jpg'])
>>> feat.shape, feat.dtype
((3, 768), dtype('float32'))
"""
images = load_images(images, mode='RGB')
data = np.stack([_preprocess_image(item, size=size) for item in images]).astype(np.float32)
output, = _open_feat_model(model).run(['output'], {'input': data})
return output
_FeatureOrImage = Union[ImageTyping, np.ndarray]
def _p_feature(x: _FeatureOrImage, size: int = 384, model: str = _DEFAULT_MODEL_NAMES):
if isinstance(x, np.ndarray): # if feature
return x
else: # is image or path
return ccip_extract_feature(x, size, model)
def ccip_default_threshold(model: str = _DEFAULT_MODEL_NAMES) -> float:
"""
Retrieves the default threshold value obtained from model metrics in the Hugging Face model repository.
:param model: The name of the model to use for feature extraction. (default: ``ccip-caformer-24-randaug-pruned``)
The available model names are: ``ccip-caformer-24-randaug-pruned``,
``ccip-caformer-6-randaug-pruned_fp32``, ``ccip-caformer-5_fp32``.
:type model: str
:return: The default threshold value obtained from model metrics.
:rtype: float
Examples::
>>> from imgutils.metrics import ccip_default_threshold
>>>
>>> ccip_default_threshold()
0.17847511429108218
>>> ccip_default_threshold('ccip-caformer-6-randaug-pruned_fp32')
0.1951224011983088
>>> ccip_default_threshold('ccip-caformer-5_fp32')
0.18397327797685215
"""
return _open_metrics(model)['threshold']
def ccip_difference(x: _FeatureOrImage, y: _FeatureOrImage,
size: int = 384, model: str = _DEFAULT_MODEL_NAMES) -> float:
"""
Calculates the difference value between two anime characters based on their images or feature vectors.
:param x: The image or feature vector of the first anime character.
:type x: Union[ImageTyping, np.ndarray]
:param y: The image or feature vector of the second anime character.
:type y: Union[ImageTyping, np.ndarray]
:param size: The size of the input image to be used for feature extraction. (default: ``384``)
:type size: int
:param model: The name of the model to use for feature extraction. (default: ``ccip-caformer-24-randaug-pruned``)
The available model names are: ``ccip-caformer-24-randaug-pruned``,
``ccip-caformer-6-randaug-pruned_fp32``, ``ccip-caformer-5_fp32``.
:type model: str
:return: The difference value between the two anime characters.
:rtype: float
Examples::
>>> from imgutils.metrics import ccip_difference
>>>
>>> ccip_difference('ccip/1.jpg', 'ccip/2.jpg') # same character
0.16583099961280823
>>>
>>> # different characters
>>> ccip_difference('ccip/1.jpg', 'ccip/6.jpg')
0.42947039008140564
>>> ccip_difference('ccip/1.jpg', 'ccip/7.jpg')
0.4037521779537201
>>> ccip_difference('ccip/2.jpg', 'ccip/6.jpg')
0.4371533691883087
>>> ccip_difference('ccip/2.jpg', 'ccip/7.jpg')
0.40748104453086853
>>> ccip_difference('ccip/6.jpg', 'ccip/7.jpg')
0.392294704914093
"""
return ccip_batch_differences([x, y], size, model)[0, 1].item()
def ccip_batch_differences(images: List[_FeatureOrImage],
size: int = 384, model: str = _DEFAULT_MODEL_NAMES) -> np.ndarray:
"""
Calculates the pairwise differences between a given list of images or feature vectors representing anime characters.
:param images: The list of images or feature vectors representing anime characters.
:type images: List[Union[ImageTyping, np.ndarray]]
:param size: The size of the input image to be used for feature extraction. (default: ``384``)
:type size: int
:param model: The name of the model to use for feature extraction. (default: ``ccip-caformer-24-randaug-pruned``)
The available model names are: ``ccip-caformer-24-randaug-pruned``,
``ccip-caformer-6-randaug-pruned_fp32``, ``ccip-caformer-5_fp32``.
:type model: str
:return: The matrix of pairwise differences between the given images or feature vectors.
:rtype: np.ndarray
Examples::
>>> from imgutils.metrics import ccip_batch_differences
>>>
>>> ccip_batch_differences(['ccip/1.jpg', 'ccip/2.jpg', 'ccip/6.jpg', 'ccip/7.jpg'])
array([[6.5350548e-08, 1.6583106e-01, 4.2947042e-01, 4.0375218e-01],
[1.6583106e-01, 9.8025822e-08, 4.3715334e-01, 4.0748104e-01],
[4.2947042e-01, 4.3715334e-01, 3.2675274e-08, 3.9229470e-01],
[4.0375218e-01, 4.0748104e-01, 3.9229470e-01, 6.5350548e-08]],
dtype=float32)
"""
input_ = np.stack([_p_feature(img, size, model) for img in images]).astype(np.float32)
output, = _open_metric_model(model).run(['output'], {'input': input_})
return output