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