+
+
+
+
+
+
+"""
+Overview:
+ CCIP-based metrics for anime character training.
+
+ See `imgutils.metrics.ccip <https://deepghs.github.io/imgutils/main/api_doc/metrics/ccip.html>`_ for more information.
+"""
+from typing import List, Optional
+
+from PIL import Image
+from imgutils.metrics import ccip_extract_feature, ccip_default_threshold, ccip_batch_differences
+
+from ..utils import load_images, ImagesTyping, tqdm
+
+_DEFAULT_CCIP_MODEL = 'ccip-caformer-24-randaug-pruned'
+
+
+[docs]class CCIPMetrics:
+ """
+ Class for calculating similarity scores between images using the CCIP (Content-Consistent Image Pairwise) metric.
+
+ The `CCIPMetrics` class allows you to calculate the similarity score between a set of images and a reference dataset using the CCIP metric.
+
+ :param images: The reference dataset of images for initializing CCIP metrics.
+ :type images: ImagesTyping
+ :param model: The CCIP model to use for feature extraction. Default is 'ccip-caformer-24-randaug-pruned'.
+ :type model: str
+ :param threshold: The threshold for the CCIP metric. If not provided, the default threshold for the chosen model is used.
+ :type threshold: Optional[float]
+ :param silent: If True, suppresses progress bars and additional output during initialization and calculation.
+ :type silent: bool
+ :param tqdm_desc: Description for the tqdm progress bar during initialization and calculation.
+ :type tqdm_desc: str
+ """
+
+[docs] def __init__(self, images: ImagesTyping, model: str = _DEFAULT_CCIP_MODEL,
+ threshold: Optional[float] = None, silent: bool = False, tqdm_desc: str = None):
+ image_list: List[Image.Image] = load_images(images)
+ if not image_list:
+ raise FileNotFoundError(f'Images for initializing CCIP metrics not provided - {images}.')
+
+ self.silent = silent
+ self.tqdm_desc = tqdm_desc or self.__class__.__name__
+ self._ccip_model = model
+ self._features = [
+ ccip_extract_feature(img, model=self._ccip_model)
+ for img in tqdm(image_list, silent=self.silent, desc=f'{self.tqdm_desc} Initializing')
+ ]
+ self._threshold = ccip_default_threshold(self._ccip_model) if threshold is None else threshold
+
+[docs] def score(self, images: ImagesTyping, silent: bool = None) -> float:
+ """
+ Calculate the similarity score between the reference dataset and a set of input images.
+
+ This method calculates the similarity score between the reference dataset (used for initialization) and a set of input images using the CCIP metric.
+
+ :param images: The set of input images for calculating CCIP metrics.
+ :type images: ImagesTyping
+ :param silent: If True, suppresses progress bars and additional output during calculation.
+ :type silent: bool
+
+ :return: The similarity score between the reference dataset and the input images.
+ :rtype: float
+ """
+ image_list: List[Image.Image] = load_images(images)
+ if not image_list:
+ raise FileNotFoundError(f'Images for calculating CCIP metrics not provided - {images}.')
+
+ _features = [
+ ccip_extract_feature(img, model=self._ccip_model)
+ for img in tqdm(image_list, silent=self.silent if silent is None else silent,
+ desc=f'{self.tqdm_desc} Calculating')
+ ]
+
+ diffs = ccip_batch_differences([*self._features, *_features])
+ matrix = diffs[:len(self._features), len(self._features):]
+
+ return (matrix < self._threshold).mean().item()
+
+import os.path
+from typing import List, Iterator, Union
+
+from PIL import UnidentifiedImageError, Image
+from imgutils.data import load_image
+
+ImagesTyping = Union[Image.Image, str, List[Union[Image.Image, str]]]
+
+
+def _yield_images(images: ImagesTyping) -> Iterator[Image.Image]:
+ """
+ Yield PIL.Image objects from various sources.
+
+ This internal function yields PIL.Image objects from a variety of sources, including PIL.Image objects, file paths, and lists of images. It supports recursive loading of images from directories.
+
+ :param images: An image or a list of images (PIL.Image, file paths, or a combination).
+ :type images: ImagesTyping
+
+ :return: An iterator yielding PIL.Image objects.
+ :rtype: Iterator[Image.Image]
+ """
+ if isinstance(images, list):
+ for item in images:
+ yield from _yield_images(item)
+ elif isinstance(images, str) and os.path.isdir(images):
+ for root, dirs, files in os.walk(images):
+ for file in files:
+ yield from _yield_images(os.path.join(images, root, file))
+ else:
+ try:
+ image = load_image(images)
+ image.load()
+ yield image
+ except UnidentifiedImageError:
+ pass
+
+
+[docs]def load_images(images: ImagesTyping) -> List[Image.Image]:
+ """
+ Load multiple PIL.Image objects from various sources.
+
+ This function loads multiple PIL.Image objects from a variety of sources, including PIL.Image objects, file paths, and lists of images. It supports recursive loading of images from directories.
+
+ :param images: An image or a list of images (PIL.Image, file paths, or a combination).
+ :type images: ImagesTyping
+
+ :return: A list of PIL.Image objects.
+ :rtype: List[Image.Image]
+ """
+ return list(_yield_images(images))
+
+import io
+
+from tqdm.auto import tqdm as _origin_tqdm
+
+__all__ = ['tqdm']
+
+
+[docs]def tqdm(*args, silent: bool = False, **kwargs):
+ """
+ An enhanced version of tqdm (progress bar) with an option to silence the output.
+
+ This function modifies the behavior of tqdm to allow silencing the progress bar.
+
+ :param args: Positional arguments to be passed to tqdm.
+ :param silent: If True, the progress bar content will not be displayed.
+ :type silent: bool
+ :param kwargs: Additional keyword arguments to be passed to tqdm.
+ :return: tqdm progress bar.
+ :rtype: tqdm.std.tqdm
+ """
+ with io.StringIO() as sio:
+ if silent:
+ kwargs['file'] = sio
+
+ return _origin_tqdm(*args, **kwargs)
+CCIP-based metrics for anime character training.
+See imgutils.metrics.ccip for more information.
+sdeval.fidelity.ccip.CCIPMetrics(images: Union[PIL.Image.Image, str, List[Union[PIL.Image.Image, str]]], model: str = 'ccip-caformer-24-randaug-pruned', threshold: Optional[float] = None, silent: bool = False, tqdm_desc: str = None)[source]¶Class for calculating similarity scores between images using the CCIP (Content-Consistent Image Pairwise) metric.
+The CCIPMetrics class allows you to calculate the similarity score between a set of images and a reference dataset using the CCIP metric.
+images (ImagesTyping) – The reference dataset of images for initializing CCIP metrics.
model (str) – The CCIP model to use for feature extraction. Default is ‘ccip-caformer-24-randaug-pruned’.
threshold (Optional[float]) – The threshold for the CCIP metric. If not provided, the default threshold for the chosen model is used.
silent (bool) – If True, suppresses progress bars and additional output during initialization and calculation.
tqdm_desc (str) – Description for the tqdm progress bar during initialization and calculation.
__init__(images: Union[PIL.Image.Image, str, List[Union[PIL.Image.Image, str]]], model: str = 'ccip-caformer-24-randaug-pruned', threshold: Optional[float] = None, silent: bool = False, tqdm_desc: str = None)[source]¶Initialize self. See help(type(self)) for accurate signature.
+score(images: Union[PIL.Image.Image, str, List[Union[PIL.Image.Image, str]]], silent: bool = None) → float[source]¶Calculate the similarity score between the reference dataset and a set of input images.
+This method calculates the similarity score between the reference dataset (used for initialization) and a set of input images using the CCIP metric.
+images (ImagesTyping) – The set of input images for calculating CCIP metrics.
silent (bool) – If True, suppresses progress bars and additional output during calculation.
The similarity score between the reference dataset and the input images.
+float
+Metrics for evaluating training fidelity.
+sdeval.utils.images.load_images(images: Union[PIL.Image.Image, str, List[Union[PIL.Image.Image, str]]]) → List[PIL.Image.Image][source]¶Load multiple PIL.Image objects from various sources.
+This function loads multiple PIL.Image objects from a variety of sources, including PIL.Image objects, file paths, and lists of images. It supports recursive loading of images from directories.
+images (ImagesTyping) – An image or a list of images (PIL.Image, file paths, or a combination).
+A list of PIL.Image objects.
+List[Image.Image]
+sdeval.utils.tqdm_.tqdm(*args, silent: bool = False, **kwargs)[source]¶An enhanced version of tqdm (progress bar) with an option to silence the output.
+This function modifies the behavior of tqdm to allow silencing the progress bar.
+args – Positional arguments to be passed to tqdm.
silent (bool) – If True, the progress bar content will not be displayed.
kwargs – Additional keyword arguments to be passed to tqdm.
tqdm progress bar.
+tqdm.std.tqdm
+API Documentation
@@ -168,8 +170,11 @@| + |
| + |
M |
@@ -208,6 +241,8 @@
|
-
|
+ + |
API Documentation
@@ -194,6 +196,15 @@API Documentation
@@ -201,7 +203,7 @@sdeval.config.metasdeval.fidelitysdeval.fidelity.ccipsdeval.utilssdeval.utils.imagessdeval.utils.tqdm_API Documentation
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diff --git a/main/tutorials/quick_start/index.html b/main/tutorials/quick_start/index.html index 06d1575..15d19c0 100644 --- a/main/tutorials/quick_start/index.html +++ b/main/tutorials/quick_start/index.html @@ -104,6 +104,8 @@API Documentation