SPPAS 4.20

def load_resources(self, *args, **kwargs):
    """Fix the model and proto files.

        :param args: models for landmark (Kazemi/LBF/AAM)

        """
    for model in args:
        self.__fli.add_model(model)

def fix_options(self, options):
    """Fix all options.

        :param options: (sppasOption)

        """
    for opt in options:
        key = opt.get_key()
        if 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 == '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)

def set_out_csv(self, out_csv=False):
    """The result includes a CSV file.

        :param out_csv: (bool) Create a CSV file with the coordinates

        """
    out_csv = bool(out_csv)
    self.__video_writer.set_options(csv=out_csv)
    self._options['csv'] = out_csv
    self.__video_writer.set_options(xra=not out_csv)
    self._options['xra'] = not out_csv

def set_img_tag(self, value=True):
    """Draw the landmark points to the image.

        :param value: (bool) Tag the images

        """
    value = bool(value)
    self.__img_writer.set_options(tag=value)
    self.__video_writer.set_options(tag=value)
    self._options['tag'] = value

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

        """
    out_folder = bool(out_folder)
    self.__video_writer.set_options(folder=out_folder)
    self._options['folder'] = out_folder

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

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

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

def image_face_sights(self, image_file, csv_faces=None, output=None):
    """Get the image, get or detect faces, eval sights and write results.

        :param image_file: (str) Image filename
        :param csv_faces: (str) CSV filename
        :param output: (str) The output name for the image

        :return: (list) the sights of all detected faces or created filenames

        """
    image = sppasImage(filename=image_file)
    coords = list()
    if csv_faces is not None:
        if csv_faces.lower().endswith('.csv') is True:
            cr = sppasCoordsReader(csv_faces)
            if len(cr.coords) > 0:
                coords = cr.coords
    if len(coords) == 0:
        raise sppasError(ERR_NO_FACE)
    all_sights = list()
    for face_coord in coords:
        success = self.__fli.detect_sights(image, face_coord)
        if success is True:
            sights = self.__fli.get_sights()
            all_sights.append(sights)
    if output is not None:
        output_file = self.fix_out_file_ext(output, out_format='IMAGE')
        new_files = self.__img_writer.write(image, all_sights, output_file, pattern='')
        return new_files
    return all_sights

def get_inputs(self, input_files):
    """Return the media and the csv filenames.

        :param input_files: (list)
        :raise: NoInputError
        :return: (str, str) Names of the 2 expected files

        """
    ext = self.get_input_extensions()
    media_ext = ext[0]
    annot_ext = ext[1]
    media = None
    annot = None
    for filename in input_files:
        fn, fe = os.path.splitext(filename)
        if media is None and fe in media_ext:
            media = filename
        elif annot is None and fe.lower() in annot_ext:
            annot = filename
    if media is None:
        logging.error('Neither a video nor an image was found.')
        raise NoInputError
    if annot is None:
        logging.error('The CSV or XRA file with identities/faces was not found.')
        raise NoInputError
    return (media, annot)

def run(self, input_files, output=None):
    """Run the automatic annotation process on an input.

        :param input_files: (list of str) Input file is an image or a video
        :param output: (str) the output file name
        :returns: (list of points or filenames) detected sights or created filenames

        """
    media_file, annot_file = self.get_inputs(input_files)
    fn, ext = os.path.splitext(media_file)
    self.__video_writer.set_image_extension(self._out_extensions['IMAGE'])
    self.__video_writer.set_video_extension(self._out_extensions['VIDEO'])
    result_files = list()
    if ext in video_extensions:
        try:
            result_files = self.__flv.video_face_sights(media_file, annot_file, self.__video_writer, output)
            self.__video_writer.close()
        except:
            import traceback
            print(traceback.format_exc())
            raise
    if ext in image_extensions:
        result_files = self.image_face_sights(media_file, annot_file, output)
    return result_files

def get_output_pattern(self):
    """Pattern this annotation uses in an output filename."""
    return self._options.get('outputpattern', '-sights')

def get_input_patterns(self):
    """Pattern this annotation expects for its input filename."""
    return [self._options.get('inputpattern1', ''), self._options.get('inputpattern2', '-ident')]

@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, ['.xra', '.csv']]