Source code for annotations.FaceDetection.sppasfacedetect

# -*- coding: UTF-8 -*-
:author:   Brigitte Bigi
:summary:  SPPAS integration of the Face detection automatic annotation.

.. _This file is part of SPPAS: <>

     ___   __    __    __    ___
    /     |  \  |  \  |  \  /              the automatic
    \__   |__/  |__/  |___| \__             annotation and
       \  |     |     |   |    \             analysis
    ___/  |     |     |   | ___/              of speech

    Copyright (C) 2011-2021  Brigitte Bigi
    Laboratoire Parole et Langage, Aix-en-Provence, France

    Use of this software is governed by the GNU Public License, version 3.

    SPPAS is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    SPPAS is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with SPPAS. If not, see <>.

    This banner notice must not be removed.



import os
import logging
import traceback

from sppas.src.config import cfg
from sppas.src.config import annots
from sppas.src.config import sppasEnableFeatureError

from sppas.src.imgdata import image_extensions
from sppas.src.imgdata import sppasImage
from sppas.src.imgdata import sppasCoordsImageWriter
from sppas.src.videodata import video_extensions
from sppas.src.videodata import sppasCoordsVideoWriter

from ..annotationsexc import AnnotationOptionError
from ..annotationsexc import NoInputError
from ..baseannot import sppasBaseAnnotation
from ..autils import SppasFiles

from .imgfacedetect import ImageFaceDetection
from .videofacedetect import VideoFaceDetection

# ---------------------------------------------------------------------------

[docs]class sppasFaceDetection(sppasBaseAnnotation): """SPPAS integration of the automatic face detection systems. Can detect faces in an image or in all images of a video. For this annotation, the user can't change the output pattern; it is intentional. The pattern is either '-face' or '-portrait' if the option portrait is enabled. """
[docs] def __init__(self, log=None): """Create a new automatic annotation instance. :param log: (sppasLog) Human-readable logs. :raise: sppasEnableFeatureError """ if cfg.feature_installed("facedetect") is False: raise sppasEnableFeatureError("facedetect") super(sppasFaceDetection, self).__init__("facedetect.json", log) # Face detection in an image self.__fdi = ImageFaceDetection() self.__img_writer = sppasCoordsImageWriter() # Face detection in a video -- actually in all images of a video self.__fdv = VideoFaceDetection(self.__fdi) self.__video_writer = sppasCoordsVideoWriter(self.__img_writer)
# -----------------------------------------------------------------------
[docs] def load_resources(self, model1, *args, **kwargs): """Fix the model files. Currently, both HaarCascade classifiers and DNN are supported. Add as many models as wished; their results are combined. :param model1: (str) Filename of a model :param args: other models for face detection """ self.__fdi.load_model(model1, *args)
# ----------------------------------------------------------------------- # Methods to fix options # -----------------------------------------------------------------------
[docs] def fix_options(self, options): """Fix all options. :param options: (sppasOption) """ for opt in options: key = opt.get_key() if key == "nbest": self.set_max_faces(opt.get_value()) elif key == "score": self.set_min_score(opt.get_value()) elif key == "csv": self.set_out_csv(opt.get_value()) elif key == "tag": self.set_img_tag(opt.get_value()) elif key == "crop": self.set_img_crop(opt.get_value()) elif key == "portrait": self.set_img_portrait(opt.get_value()) elif key == "folder": self.set_out_folder(opt.get_value()) elif key == "width": self.set_img_width(opt.get_value()) elif key == "height": self.set_img_height(opt.get_value()) elif "pattern" in key: self._options[key] = opt.get_value() else: raise AnnotationOptionError(key)
# ----------------------------------------------------------------------- # Getters and Setters # -----------------------------------------------------------------------
[docs] def set_max_faces(self, value): """Fix the maximum number of expected faces in an image. :param value: (int) Number of faces """ value = int(value) self.__fdv.set_filter_best(value) self.__fdi.filter_best(value) self._options["nbest"] = value
# -----------------------------------------------------------------------
[docs] def set_min_score(self, value): """Fix the minimum score to accept a face in an image. :param value: (float) Min confidence score of face detection result """ value = float(value) self.__fdv.set_filter_confidence(value) self.__fdi.filter_confidence(value) self._options["score"] = value
# -----------------------------------------------------------------------
[docs] def set_out_csv(self, out_csv=False): """The result includes a CSV file. :param out_csv: (bool) Create a CSV file when detecting """ self.__video_writer.set_options(csv=out_csv) self._options["csv"] = out_csv
# -----------------------------------------------------------------------
[docs] def set_img_tag(self, value=True): """Surround the faces with a square. :param value: (bool) Tag the images """ value = bool(value) self.__video_writer.set_options(tag=value) self._options["tag"] = value
# -----------------------------------------------------------------------
[docs] def set_img_crop(self, value=True): """Create an image/video for each detected person. :param value: (bool) Crop the images """ value = bool(value) self.__video_writer.set_options(crop=value) self._options["crop"] = value
# -----------------------------------------------------------------------
[docs] def set_img_width(self, value): """Width of the resulting images/video. :param value: (int) Number of pixels """ self.__video_writer.set_options(width=value) self._options["width"] = value
# -----------------------------------------------------------------------
[docs] def set_img_height(self, value): """Height of the resulting images/video. :param value: (int) Number of pixel """ self.__video_writer.set_options(height=value) self._options["height"] = value
# -----------------------------------------------------------------------
[docs] def set_img_portrait(self, value): """Result is the portrait instead of the face. :param value: (bool) True """ value = bool(value) self._options["portrait"] = value self.__fdv.set_portrait(value)
# -----------------------------------------------------------------------
[docs] def set_out_folder(self, out_folder=False): """The result includes a folder with image files -- if video input. :param out_folder: (bool) Create a folder with image files when detecting """ self.__video_writer.set_options(folder=out_folder) self._options["folder"] = out_folder
# ----------------------------------------------------------------------- # Apply the annotation on a given file # -----------------------------------------------------------------------
[docs] def image_face_detect(self, image, output=None): """Get the image, detect faces and write results. :param image: (str) Image filename :param output: (str) The output name for the image :return: (list) the coordinates of all detected faces or created filenames """ # Get the image from the input image = sppasImage(filename=image) # Search for coordinates of faces self.__fdi.detect(image) # Make the output list of coordinates if self._options["portrait"] is True: try: self.__fdi.to_portrait(image) except Exception as e: self.logfile.print_message( "Faces can't be scaled to portrait: {}".format(str(e)), indent=2, status=annots.error) coords = [c.copy() for c in self.__fdi] # Save result as a list of coordinates (csv), a tagged image # and/or a list of images (face or portrait) in a folder if output is not None: output_file = self.fix_out_file_ext(output, out_format="IMAGE") new_files = self.__img_writer.write(image, coords, output_file, self.get_output_pattern()) return new_files return coords
# -----------------------------------------------------------------------
[docs] def get_inputs(self, input_files): """Return the media filenames. :param input_files: (list) :raise: NoInputError :return: (str) Name of the media file """ media_ext = self.get_input_extensions() media = None for filename in input_files: fn, fe = os.path.splitext(filename) if media is None and fe in media_ext[0]: return filename logging.error("Neither a video nor an image was found.") raise NoInputError
# -----------------------------------------------------------------------
[docs] def run(self, input_files, output=None): """Run the automatic annotation process on a single input. :param input_files: (list of str) Video or image file :param output: (str) the output name :returns: (list of sppasCoords) Coordinates of detected faces or filenames """ media_file = self.get_inputs(input_files) # Input is either a video or an image self.__video_writer.set_image_extension(self._out_extensions["IMAGE"]) self.__video_writer.set_video_extension(self._out_extensions["VIDEO"]) self.__video_writer.close() fn, ext = os.path.splitext(media_file) result = list() if ext in video_extensions: try: result = self.__fdv.video_face_detect(media_file, self.__video_writer, output) except: print(traceback.format_exc()) raise self.__video_writer.close() elif ext in image_extensions: result = self.image_face_detect(media_file, output) return result
# -----------------------------------------------------------------------
[docs] def get_output_pattern(self): """Pattern this annotation uses in an output filename.""" pattern = "-face" if self._options["portrait"] is True: pattern = "-portrait" return self._options.get("outputpattern", pattern)
# -----------------------------------------------------------------------
[docs] @staticmethod def get_input_extensions(): """Extensions that the annotation expects for its input filename. Priority is given to video files, then image files. """ out_ext = list(SppasFiles.get_informat_extensions("VIDEO")) for img_ext in SppasFiles.get_informat_extensions("IMAGE"): out_ext.append(img_ext) return [out_ext]