Module facetorch.analyzer.core
-
-Expand source code -
-from typing import Optional, Union
-
-import torch
-import numpy as np
-from codetiming import Timer
-from PIL import Image
-from facetorch.analyzer.predictor.core import FacePredictor
-from facetorch.datastruct import ImageData, Response
-from facetorch.logger import LoggerJsonFile
-from importlib.metadata import version
-from hydra.utils import instantiate
-from omegaconf import OmegaConf
-
-logger = LoggerJsonFile().logger
-
-
-class FaceAnalyzer(object):
- @Timer(
- "FaceAnalyzer.__init__", "{name}: {milliseconds:.2f} ms", logger=logger.debug
- )
- def __init__(self, cfg: OmegaConf):
- """FaceAnalyzer is the main class that reads images, runs face detection, tensor unification and facial feature prediction.
- It also draws bounding boxes and facial landmarks over the image.
-
- The following components are used:
-
- 1. Reader - reads the image and returns an ImageData object containing the image tensor.
- 2. Detector - wrapper around a neural network that detects faces.
- 3. Unifier - processor that unifies sizes of all faces and normalizes them between 0 and 1.
- 4. Predictor dict - dict of wrappers around neural networks trained to analyze facial features.
- 5. Utilizer dict - dict of utilizer processors that can for example extract 3D face landmarks or draw boxes over the image.
-
- Args:
- cfg (OmegaConf): Config object with image reader, face detector, unifier and predictor configurations.
-
- Attributes:
- cfg (OmegaConf): Config object with image reader, face detector, unifier and predictor configurations.
- reader (BaseReader): Reader object that reads the image and returns an ImageData object containing the image tensor.
- detector (FaceDetector): FaceDetector object that wraps a neural network that detects faces.
- unifier (FaceUnifier): FaceUnifier object that unifies sizes of all faces and normalizes them between 0 and 1.
- predictors (Dict[str, FacePredictor]): Dict of FacePredictor objects that predict facial features. Key is the name of the predictor.
- utilizers (Dict[str, FaceUtilizer]): Dict of FaceUtilizer objects that can extract 3D face landmarks, draw boxes over the image, etc. Key is the name of the utilizer.
- logger (logging.Logger): Logger object that logs messages to the console or to a file.
-
- """
- self.cfg = cfg
- self.logger = instantiate(self.cfg.logger).logger
-
- self.logger.info("Initializing FaceAnalyzer")
- self.logger.debug("Config", extra=self.cfg.__dict__["_content"])
-
- self.logger.info("Initializing BaseReader")
- self.reader = instantiate(self.cfg.reader)
-
- self.logger.info("Initializing FaceDetector")
- self.detector = instantiate(self.cfg.detector)
-
- self.logger.info("Initializing FaceUnifier")
- if "unifier" in self.cfg:
- self.unifier = instantiate(self.cfg.unifier)
- else:
- self.unifier = None
-
- self.logger.info("Initializing FacePredictor objects")
- self.predictors = {}
- if "predictor" in self.cfg:
- for predictor_name in self.cfg.predictor:
- self.logger.info(f"Initializing FacePredictor {predictor_name}")
- self.predictors[predictor_name] = instantiate(
- self.cfg.predictor[predictor_name]
- )
-
- self.utilizers = {}
- if "utilizer" in self.cfg:
- self.logger.info("Initializing BaseUtilizer objects")
- for utilizer_name in self.cfg.utilizer:
- self.logger.info(f"Initializing BaseUtilizer {utilizer_name}")
- self.utilizers[utilizer_name] = instantiate(
- self.cfg.utilizer[utilizer_name]
- )
-
- @Timer("FaceAnalyzer.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
- def run(
- self,
- image_source: Optional[
- Union[str, torch.Tensor, np.ndarray, bytes, Image.Image]
- ] = None,
- path_image: Optional[str] = None,
- batch_size: int = 8,
- fix_img_size: bool = False,
- return_img_data: bool = False,
- include_tensors: bool = False,
- path_output: Optional[str] = None,
- tensor: Optional[torch.Tensor] = None,
- ) -> Union[Response, ImageData]:
- """Reads image, detects faces, unifies the detected faces, predicts facial features
- and returns analyzed data.
-
- Args:
- image_source (Optional[Union[str, torch.Tensor, np.ndarray, bytes, Image.Image]]): Input to be analyzed. If None, path_image or tensor must be provided. Default: None.
- path_image (Optional[str]): Path to the image to be analyzed. If None, tensor must be provided. Default: None.
- batch_size (int): Batch size for making predictions on the faces. Default is 8.
- fix_img_size (bool): If True, resizes the image to the size specified in reader. Default is False.
- return_img_data (bool): If True, returns all image data including tensors, otherwise only returns the faces. Default is False.
- include_tensors (bool): If True, removes tensors from the returned data object. Default is False.
- path_output (Optional[str]): Path where to save the image with detected faces. If None, the image is not saved. Default: None.
- tensor (Optional[torch.Tensor]): Image tensor to be analyzed. If None, path_image must be provided. Default: None.
-
- Returns:
- Union[Response, ImageData]: If return_img_data is False, returns a Response object containing the faces and their facial features. If return_img_data is True, returns the entire ImageData object.
-
- """
-
- def _predict_batch(
- data: ImageData, predictor: FacePredictor, predictor_name: str
- ) -> ImageData:
- n_faces = len(data.faces)
-
- for face_indx_start in range(0, n_faces, batch_size):
- face_indx_end = min(face_indx_start + batch_size, n_faces)
-
- face_batch_tensor = torch.stack(
- [face.tensor for face in data.faces[face_indx_start:face_indx_end]]
- )
- preds = predictor.run(face_batch_tensor)
- data.add_preds(preds, predictor_name, face_indx_start)
-
- return data
-
- self.logger.info("Running FaceAnalyzer")
-
- if path_image is None and tensor is None and image_source is None:
- raise ValueError("Either input, path_image or tensor must be provided.")
-
- if image_source is not None:
- self.logger.debug("Using image_source as input")
- reader_input = image_source
- elif path_image is not None:
- self.logger.debug(
- "Using path_image as input", extra={"path_image": path_image}
- )
- reader_input = path_image
- else:
- self.logger.debug("Using tensor as input")
- reader_input = tensor
-
- self.logger.info("Reading image", extra={"input": reader_input})
- data = self.reader.run(reader_input, fix_img_size=fix_img_size)
-
- path_output = None if path_output == "None" else path_output
- data.path_output = path_output
-
- try:
- data.version = version("facetorch")
- except Exception as e:
- self.logger.warning("Could not get version number", extra={"error": e})
-
- self.logger.info("Detecting faces")
- data = self.detector.run(data)
- n_faces = len(data.faces)
- self.logger.info(f"Number of faces: {n_faces}")
-
- if n_faces > 0 and self.unifier is not None:
- self.logger.info("Unifying faces")
- data = self.unifier.run(data)
-
- self.logger.info("Predicting facial features")
- for predictor_name, predictor in self.predictors.items():
- self.logger.info(f"Running FacePredictor: {predictor_name}")
- data = _predict_batch(data, predictor, predictor_name)
-
- self.logger.info("Utilizing facial features")
- for utilizer_name, utilizer in self.utilizers.items():
- self.logger.info(f"Running BaseUtilizer: {utilizer_name}")
- data = utilizer.run(data)
- else:
- if "save" in self.utilizers:
- self.utilizers["save"].run(data)
-
- if not include_tensors:
- self.logger.debug(
- "Removing tensors from response as include_tensors is False"
- )
- data.reset_tensors()
-
- response = Response(faces=data.faces, version=data.version)
-
- if return_img_data:
- self.logger.debug("Returning image data object", extra=data.__dict__)
- return data
- else:
- self.logger.debug("Returning response with faces", extra=response.__dict__)
- return responseReturns
Union[Response, ImageData]
-
@@ -651,7 +339,6 @@ -Expand source code -
-@Timer("FaceAnalyzer.run", "{name}: {milliseconds:.2f} ms", logger=logger.debug)
-def run(
- self,
- image_source: Optional[
- Union[str, torch.Tensor, np.ndarray, bytes, Image.Image]
- ] = None,
- path_image: Optional[str] = None,
- batch_size: int = 8,
- fix_img_size: bool = False,
- return_img_data: bool = False,
- include_tensors: bool = False,
- path_output: Optional[str] = None,
- tensor: Optional[torch.Tensor] = None,
-) -> Union[Response, ImageData]:
- """Reads image, detects faces, unifies the detected faces, predicts facial features
- and returns analyzed data.
-
- Args:
- image_source (Optional[Union[str, torch.Tensor, np.ndarray, bytes, Image.Image]]): Input to be analyzed. If None, path_image or tensor must be provided. Default: None.
- path_image (Optional[str]): Path to the image to be analyzed. If None, tensor must be provided. Default: None.
- batch_size (int): Batch size for making predictions on the faces. Default is 8.
- fix_img_size (bool): If True, resizes the image to the size specified in reader. Default is False.
- return_img_data (bool): If True, returns all image data including tensors, otherwise only returns the faces. Default is False.
- include_tensors (bool): If True, removes tensors from the returned data object. Default is False.
- path_output (Optional[str]): Path where to save the image with detected faces. If None, the image is not saved. Default: None.
- tensor (Optional[torch.Tensor]): Image tensor to be analyzed. If None, path_image must be provided. Default: None.
-
- Returns:
- Union[Response, ImageData]: If return_img_data is False, returns a Response object containing the faces and their facial features. If return_img_data is True, returns the entire ImageData object.
-
- """
-
- def _predict_batch(
- data: ImageData, predictor: FacePredictor, predictor_name: str
- ) -> ImageData:
- n_faces = len(data.faces)
-
- for face_indx_start in range(0, n_faces, batch_size):
- face_indx_end = min(face_indx_start + batch_size, n_faces)
-
- face_batch_tensor = torch.stack(
- [face.tensor for face in data.faces[face_indx_start:face_indx_end]]
- )
- preds = predictor.run(face_batch_tensor)
- data.add_preds(preds, predictor_name, face_indx_start)
-
- return data
-
- self.logger.info("Running FaceAnalyzer")
-
- if path_image is None and tensor is None and image_source is None:
- raise ValueError("Either input, path_image or tensor must be provided.")
-
- if image_source is not None:
- self.logger.debug("Using image_source as input")
- reader_input = image_source
- elif path_image is not None:
- self.logger.debug(
- "Using path_image as input", extra={"path_image": path_image}
- )
- reader_input = path_image
- else:
- self.logger.debug("Using tensor as input")
- reader_input = tensor
-
- self.logger.info("Reading image", extra={"input": reader_input})
- data = self.reader.run(reader_input, fix_img_size=fix_img_size)
-
- path_output = None if path_output == "None" else path_output
- data.path_output = path_output
-
- try:
- data.version = version("facetorch")
- except Exception as e:
- self.logger.warning("Could not get version number", extra={"error": e})
-
- self.logger.info("Detecting faces")
- data = self.detector.run(data)
- n_faces = len(data.faces)
- self.logger.info(f"Number of faces: {n_faces}")
-
- if n_faces > 0 and self.unifier is not None:
- self.logger.info("Unifying faces")
- data = self.unifier.run(data)
-
- self.logger.info("Predicting facial features")
- for predictor_name, predictor in self.predictors.items():
- self.logger.info(f"Running FacePredictor: {predictor_name}")
- data = _predict_batch(data, predictor, predictor_name)
-
- self.logger.info("Utilizing facial features")
- for utilizer_name, utilizer in self.utilizers.items():
- self.logger.info(f"Running BaseUtilizer: {utilizer_name}")
- data = utilizer.run(data)
- else:
- if "save" in self.utilizers:
- self.utilizers["save"].run(data)
-
- if not include_tensors:
- self.logger.debug(
- "Removing tensors from response as include_tensors is False"
- )
- data.reset_tensors()
-
- response = Response(faces=data.faces, version=data.version)
-
- if return_img_data:
- self.logger.debug("Returning image data object", extra=data.__dict__)
- return data
- else:
- self.logger.debug("Returning response with faces", extra=response.__dict__)
- return responseReturns
}).setContent('').open(); } -Index
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+Generated by pdoc 0.11.1.
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Generated by pdoc 0.10.0.
+Generated by pdoc 0.11.1.
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