def _samples_from_book(self, root, img, page_id) -> Iterable[Dict[str, Any]]: ns = {"ns": root.nsmap[root.prefix]} page = root.find(".//ns:Page", namespaces=ns) imgfile = page.attrib.get("imageFilename") if not split_all_ext(img)[0].endswith(split_all_ext(imgfile)[0]): logger.warning( "Mapping of image file to xml file invalid: {} vs {} (comparing basename {} vs {})".format( img, imgfile, split_all_ext(img)[0], split_all_ext(imgfile)[0] ) ) img_w = int(page.attrib.get("imageWidth")) for textline in root.findall(".//ns:TextLine", namespaces=ns): if self.skip_commented and len(textline.attrib.get("comments", "")): continue orientation = float(textline.getparent().attrib.get("orientation", default=0)) yield { "page_id": page_id, "ns": ns, "rtype": textline.getparent().attrib.get("type", default=""), "xml_element": textline, "image_path": img, "id": "{}/{}".format(page_id, textline.attrib.get("id")), "base_name": textline.attrib.get("id"), "coords": textline.find("./ns:Coords", namespaces=ns).attrib.get("points"), "orientation": orientation, "img_width": img_w, "text": None, }
def create_train_dataset(args, dataset_args=None): gt_extension = args.gt_extension if args.gt_extension is not None else DataSetType.gt_extension(args.dataset) # Training dataset print("Resolving input files") input_image_files = sorted(glob_all(args.files)) if not args.text_files: if gt_extension: gt_txt_files = [split_all_ext(f)[0] + gt_extension for f in input_image_files] else: gt_txt_files = [None] * len(input_image_files) else: gt_txt_files = sorted(glob_all(args.text_files)) input_image_files, gt_txt_files = keep_files_with_same_file_name(input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of file: {} and {}".format(img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception("Some image are occurring more than once in the data set.") dataset = create_dataset( args.dataset, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, skip_invalid=not args.no_skip_invalid_gt, args=dataset_args if dataset_args else {}, ) print("Found {} files in the dataset".format(len(dataset))) return dataset
def prepare_for_mode(self, mode: PipelineMode): logger.info("Resolving input files") input_image_files = sorted(glob_all(self.images)) if not self.texts: gt_txt_files = [split_all_ext(f)[0] + self.gt_extension for f in input_image_files] else: gt_txt_files = sorted(glob_all(self.texts)) if mode in INPUT_PROCESSOR: input_image_files, gt_txt_files = keep_files_with_same_file_name(input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception(f"Expected identical basenames of file: {img} and {gt}") else: input_image_files = None if mode in {PipelineMode.TRAINING, PipelineMode.EVALUATION}: if len(set(gt_txt_files)) != len(gt_txt_files): logger.warning( "Some ground truth text files occur more than once in the data set " "(ignore this warning, if this was intended)." ) if len(set(input_image_files)) != len(input_image_files): logger.warning( "Some images occur more than once in the data set. " "This warning should usually not be ignored." ) self.images = input_image_files self.texts = gt_txt_files
def _samples_from_book(self, root, img): ns = {"ns": root.nsmap[None]} imgfile = root.xpath('//ns:Page', namespaces=ns)[0].attrib["imageFilename"] if not split_all_ext(img)[0].endswith(split_all_ext(imgfile)[0]): raise Exception("Mapping of image file to xml file invalid: {} vs {} (comparing basename {} vs {})".format( img, imgfile, split_all_ext(img)[0], split_all_ext(imgfile)[0])) img_w = int(root.xpath('//ns:Page', namespaces=ns)[0].attrib["imageWidth"]) for l in root.xpath('//ns:TextLine', namespaces=ns): try: orientation = float(l.xpath('../@orientation', namespaces=ns).pop()) except (ValueError, IndexError): orientation = 0 yield { 'ns': ns, "rtype": l.xpath('../@type', namespaces=ns).pop(), 'xml_element': l, "image_path": img, "id": l.xpath('./@id', namespaces=ns).pop(), "coords": l.xpath('./ns:Coords/@points', namespaces=ns).pop(), "orientation": orientation, "img_width": img_w, "text": None, }
def main(): parser = argparse.ArgumentParser() parser.add_argument("--files", nargs="+", required=True, help="The image files to predict with its gt and pred") parser.add_argument("--html_output", type=str, required=True, help="Where to write the html file") parser.add_argument("--open", action="store_true", help="Automatically open the file") args = parser.parse_args() img_files = sorted(glob_all(args.files)) gt_files = [split_all_ext(f)[0] + ".gt.txt" for f in img_files] pred_files = [split_all_ext(f)[0] + ".pred.txt" for f in img_files] with open(args.html_output, 'w') as html: html.write(""" <!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"/> </head> <body> <ul>""") for img, gt, pred in zip(img_files, gt_files, pred_files): html.write("<li><p><img src=\"file://{}\"></p><p>{}</p><p>{}</p>\n".format( img.replace('\\', '/').replace('/', '\\\\'), open(gt).read(), open(pred).read() )) html.write("</ul></body></html>") if args.open: webbrowser.open(args.html_output)
def prepare_for_mode(self, mode: PipelineMode) -> 'PipelineParams': from calamari_ocr.ocr.dataset.datareader.factory import DataReaderFactory assert (self.type is not None) params_out = deepcopy(self) # Training dataset logger.info("Resolving input files") if isinstance(self.type, str): try: self.type = DataSetType.from_string(self.type) except ValueError: # Not a valid type, must be custom if self.type not in DataReaderFactory.CUSTOM_READERS: raise KeyError( f"DataSetType {self.type} is neither a standard DataSetType or preset as custom " f"reader ({list(DataReaderFactory.CUSTOM_READERS.keys())})" ) if not isinstance(self.type, str) and self.type not in { DataSetType.RAW, DataSetType.GENERATED_LINE }: input_image_files = sorted(glob_all( self.files)) if self.files else None if not self.text_files: if self.gt_extension: gt_txt_files = [ split_all_ext(f)[0] + self.gt_extension for f in input_image_files ] else: gt_txt_files = None else: gt_txt_files = sorted(glob_all(self.text_files)) if mode in INPUT_PROCESSOR: input_image_files, gt_txt_files = keep_files_with_same_file_name( input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext( os.path.basename(img))[0] != split_all_ext( os.path.basename(gt))[0]: raise Exception( "Expected identical basenames of file: {} and {}" .format(img, gt)) else: input_image_files = None if mode in {PipelineMode.Training, PipelineMode.Evaluation}: if len(set(gt_txt_files)) != len(gt_txt_files): logger.warning( "Some ground truth text files occur more than once in the data set " "(ignore this warning, if this was intended).") if len(set(input_image_files)) != len(input_image_files): logger.warning( "Some images occur more than once in the data set. " "This warning should usually not be ignored.") params_out.files = input_image_files params_out.text_files = gt_txt_files return params_out
def data_reader_from_params(mode: PipelineMode, params: PipelineParams) -> DataReader: assert (params.type is not None) from calamari_ocr.ocr.dataset.dataset_factory import create_data_reader # Training dataset logger.info("Resolving input files") if params.type not in {DataSetType.RAW, DataSetType.GENERATED_LINE}: input_image_files = sorted(glob_all( params.files)) if params.files else None if not params.text_files: if params.gt_extension: gt_txt_files = [ split_all_ext(f)[0] + params.gt_extension for f in input_image_files ] else: gt_txt_files = None else: gt_txt_files = sorted(glob_all(params.text_files)) if mode in INPUT_PROCESSOR: input_image_files, gt_txt_files = keep_files_with_same_file_name( input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext( os.path.basename(img))[0] != split_all_ext( os.path.basename(gt))[0]: raise Exception( "Expected identical basenames of file: {} and {}". format(img, gt)) else: input_image_files = None if mode in {PipelineMode.Training, PipelineMode.Evaluation}: if len(set(gt_txt_files)) != len(gt_txt_files): logger.warning( "Some ground truth text files occur more than once in the data set " "(ignore this warning, if this was intended).") if len(set(input_image_files)) != len(input_image_files): logger.warning( "Some images occur more than once in the data set. " "This warning should usually not be ignored.") else: input_image_files = params.files gt_txt_files = params.text_files dataset = create_data_reader( params.type, mode, images=input_image_files, texts=gt_txt_files, skip_invalid=params.skip_invalid, args=params.data_reader_args if params.data_reader_args else FileDataReaderArgs(), ) logger.info(f"Found {len(dataset)} files in the dataset") return dataset
def main(): parser = argparse.ArgumentParser() parser.add_argument("--files", nargs="+", type=str, required=True, help="The image files to copy") parser.add_argument("--target_dir", type=str, required=True, help="") parser.add_argument("--index_files", action="store_true") parser.add_argument("--convert_images", type=str, help="Convert the image to a given type (by default use original format). E. g. jpg, png, tif, ...") parser.add_argument("--gt_ext", type=str, default=".gt.txt") parser.add_argument("--index_ext", type=str, default=".index") args = parser.parse_args() if args.convert_images and not args.convert_images.startswith("."): args.convert_images = "." + args.convert_images args.target_dir = os.path.expanduser(args.target_dir) print("Resolving files") image_files = glob_all(args.files) gt_files = [split_all_ext(p)[0] + ".gt.txt" for p in image_files] if len(image_files) == 0: raise Exception("No files found") if not os.path.isdir(args.target_dir): os.makedirs(args.target_dir) for i, (img, gt) in tqdm(enumerate(zip(image_files, gt_files)), total=len(gt_files), desc="Copying"): if not os.path.exists(img) or not os.path.exists(gt): # skip non existing examples continue # img with optional convert try: ext = split_all_ext(img)[1] target_ext = args.convert_images if args.convert_images else ext target_name = os.path.join(args.target_dir, "{:08}{}".format(i, target_ext)) if ext == target_ext: shutil.copyfile(img, target_name) else: data = skimage_io.imread(img) skimage_io.imsave(target_name, data) except: continue # gt txt target_name = os.path.join(args.target_dir, "{:08}{}".format(i, args.gt_ext)) shutil.copyfile(gt, target_name) if args.index_files: target_name = os.path.join(args.target_dir, "{:08}{}".format(i, args.index_ext)) with open(target_name, "w") as f: f.write(str(i))
def create_test_dataset( cfg: CfgNode, dataset_args=None ) -> Union[List[Union[RawDataSet, FileDataSet, AbbyyDataSet, PageXMLDataset, Hdf5DataSet, ExtendedPredictionDataSet, GeneratedLineDataset]], None]: if cfg.DATASET.VALID.TEXT_FILES: assert len(cfg.DATASET.VALID.PATH) == len(cfg.DATASET.VALID.TEXT_FILES) if cfg.DATASET.VALID.PATH: validation_dataset_list = [] print("Resolving validation files") for i, valid_path in enumerate(cfg.DATASET.VALID.PATH): validation_image_files = glob_all(valid_path) dataregistry.register( i, os.path.basename(os.path.dirname(valid_path)), len(validation_image_files)) if not cfg.DATASET.VALID.TEXT_FILES: val_txt_files = [ split_all_ext(f)[0] + cfg.DATASET.VALID.GT_EXTENSION for f in validation_image_files ] else: val_txt_files = sorted( glob_all(cfg.DATASET.VALID.TEXT_FILES[i])) validation_image_files, val_txt_files = keep_files_with_same_file_name( validation_image_files, val_txt_files) for img, gt in zip(validation_image_files, val_txt_files): if split_all_ext( os.path.basename(img))[0] != split_all_ext( os.path.basename(gt))[0]: raise Exception( "Expected identical basenames of validation file: {} and {}" .format(img, gt)) if len(set(val_txt_files)) != len(val_txt_files): raise Exception( "Some validation images are occurring more than once in the data set." ) validation_dataset = create_dataset( cfg.DATASET.VALID.TYPE, DataSetMode.TRAIN, images=validation_image_files, texts=val_txt_files, skip_invalid=not cfg.DATALOADER.NO_SKIP_INVALID_GT, args=dataset_args, ) print("Found {} files in the validation dataset".format( len(validation_dataset))) validation_dataset_list.append(validation_dataset) else: validation_dataset_list = None return validation_dataset_list
def _samples_gt_from_book(self, root, img, skipcommented=True): ns = {"ns": root.nsmap[None]} imgfile = root.xpath('//ns:Page', namespaces=ns)[0].attrib["imageFilename"] if (self.mode == DataSetMode.TRAIN or self.mode == DataSetMode.PRED_AND_EVAL) and not split_all_ext(img)[0].endswith(split_all_ext(imgfile)[0]): raise Exception("Mapping of image file to xml file invalid: {} vs {} (comparing basename {} vs {})".format( img, imgfile, split_all_ext(img)[0], split_all_ext(imgfile)[0])) img_w = int(root.xpath('//ns:Page', namespaces=ns)[0].attrib["imageWidth"]) textlines = root.xpath('//ns:TextLine', namespaces=ns) for textline in textlines: tequivs = textline.xpath('./ns:TextEquiv[@index="{}"]'.format(self.text_index), namespaces=ns) if len(tequivs) > 1: logger.warning("PageXML is invalid: TextLine includes TextEquivs with non unique ids") parat = textline.attrib if skipcommented and "comments" in parat and parat["comments"]: continue if tequivs is not None and len(tequivs) > 0: l = tequivs[0] text = l.xpath('./ns:Unicode', namespaces=ns).pop().text else: l = None text = None if not text: if self.skip_invalid: continue elif self._non_existing_as_empty: text = "" else: raise Exception("Empty text field") try: orientation = float(textline.xpath('../@orientation', namespaces=ns).pop()) except (ValueError, IndexError): orientation = 0 yield { 'ns': ns, "rtype": textline.xpath('../@type', namespaces=ns).pop(), 'xml_element': l, "image_path": img, "id": textline.xpath('./@id', namespaces=ns).pop(), "text": text, "coords": textline.xpath('./ns:Coords/@points', namespaces=ns).pop(), "orientation": orientation, "img_width": img_w }
def main(): parser = ArgumentParser() parser.add_argument("--checkpoint", type=str, required=True, help="The checkpoint used to resume") parser.add_argument("--validation", type=str, nargs="+", help="Validation line files used for early stopping") parser.add_argument("files", type=str, nargs="+", help="The files to use for training") args = parser.parse_args() # Train dataset input_image_files = glob_all(args.files) gt_txt_files = [split_all_ext(f)[0] + ".gt.txt" for f in input_image_files] if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception( "Some image are occurring more than once in the data set.") dataset = FileDataSet(input_image_files, gt_txt_files) print("Found {} files in the dataset".format(len(dataset))) # Validation dataset if args.validation: validation_image_files = glob_all(args.validation) val_txt_files = [ split_all_ext(f)[0] + ".gt.txt" for f in validation_image_files ] if len(set(val_txt_files)) != len(val_txt_files): raise Exception( "Some validation images are occurring more than once in the data set." ) validation_dataset = FileDataSet(validation_image_files, val_txt_files) print("Found {} files in the validation dataset".format( len(validation_dataset))) else: validation_dataset = None with open(args.checkpoint + '.json', 'r') as f: checkpoint_params = json_format.Parse(f.read(), CheckpointParams()) trainer = Trainer(checkpoint_params, dataset, validation_dataset=validation_dataset, restore=args.checkpoint) trainer.train(progress_bar=True)
def store(self): extension = self.params.pred_extension if self._last_page_id: self._store_page(extension, self._last_page_id) self._last_page_id = None else: for xml in tqdm( self.params.xmlfiles, desc="Writing PageXML files", total=len(self.params.xmlfiles), ): page = self.pages(split_all_ext(xml)[0]) with open(split_all_ext(xml)[0] + extension, "w", encoding="utf-8") as f: f.write(etree.tounicode(page.getroottree(), pretty_print=True))
def to_prediction(self): self.files = sorted(glob_all(self.files)) pred = deepcopy(self) pred.files = [ split_all_ext(f)[0] + self.pred_extension for f in self.files ] return pred
def _generate_epoch(self, text_only) -> Generator[InputSample, None, None]: filenames = list(self.params.files) if self.mode == PipelineMode.TRAINING: shuffle(filenames) for filename in filenames: basename = split_all_ext(filename)[0] with h5py.File(filename, 'r') as f: codec = list(map(chr, f['codec'])) if text_only: for i, (text, idx) in enumerate( zip(f['transcripts'], range(len(f['transcripts'])))): text = "".join([codec[c] for c in text]) fold_id = idx % self.params.n_folds if self.params.n_folds > 0 else -1 yield InputSample( None, text, SampleMeta(id=f"{basename}/{i}", fold_id=fold_id)) else: gen = zip(f['images'], f['images_dims'], f['transcripts'], range(len(f['images']))) if self.mode == PipelineMode.TRAINING: gen = list(gen) shuffle(gen) for i, (image, shape, text, idx) in enumerate(gen): image = np.reshape(image, shape) text = "".join([codec[c] for c in text]) fold_id = idx % self.params.n_folds if self.params.n_folds > 0 else -1 yield InputSample( image, text, SampleMeta(id=f"{basename}/{i}", fold_id=fold_id))
def __init__( self, mode: DataSetMode, files: List[str] = None, xmlfiles: List[str] = None, skip_invalid=False, remove_invalid=True, binary=False, non_existing_as_empty=False, ): """ Create a dataset from a Path as String Parameters ---------- files : [], required image files skip_invalid : bool, optional skip invalid files remove_invalid : bool, optional remove invalid files """ super().__init__(mode, skip_invalid, remove_invalid) self.xmlfiles = xmlfiles if xmlfiles else [] self.files = files if files else [] self._non_existing_as_empty = non_existing_as_empty if len(self.xmlfiles) == 0: from calamari_ocr.ocr.datasets import DataSetType self.xmlfiles = [ split_all_ext(p)[0] + DataSetType.gt_extension(DataSetType.ABBYY) for p in files ] if len(self.files) == 0: self.files = [None] * len(self.xmlfiles) self.book = XMLReader(self.files, self.xmlfiles, skip_invalid, remove_invalid).read() self.binary = binary for p, page in enumerate(self.book.pages): for l, line in enumerate(page.getLines()): for f, fo in enumerate(line.formats): self.add_sample({ "image_path": page.imgFile, "xml_path": page.xmlFile, "id": "{}_{}_{}_{}".format( os.path.splitext(page.xmlFile if page. xmlFile else page.imgFile)[0], p, l, f), "line": line, "format": fo, })
def __init__( self, mode: PipelineMode, params: PageXML, ): super().__init__(mode, params) self.pages = {} for img, xml in zip(params.images, params.xml_files): loader = PageXMLDatasetLoader( self.mode, params.non_existing_as_empty, params.text_index, params.skip_invalid, params.skip_commented, ) for sample in loader.load(img, xml): self.add_sample(sample) self.pages[split_all_ext(xml)[0]] = loader.root # store which pagexml was stored last, to check when a file is ready to be written during sequential prediction self._last_page_id = None # counter for word tag ids self._next_word_id = 0
def __init__(self, json_path: str, auto_update=True, dry_run=False): self.json_path = json_path if json_path.endswith( '.json') else json_path + '.json' self.json_path = os.path.abspath( os.path.expanduser(os.path.expandvars(self.json_path))) self.ckpt_path = os.path.splitext(self.json_path)[0] self.dry_run = dry_run self.dirname = os.path.dirname(self.ckpt_path) self.basename = os.path.basename(split_all_ext(self.ckpt_path)[0]) # do not parse as proto, since some parameters might have changed with open(self.json_path, 'r') as f: self.dict = json.load(f) self.version = self.dict['version'] if 'version' in self.dict else 0 if self.version != SavedCalamariModel.VERSION: if auto_update: self.update_checkpoint() else: raise Exception( "Version of checkpoint is {} but {} is required. Please upgrade the model or " "set the auto update flag.".format( self.version, SavedCalamariModel.VERSION)) else: logger.info(f"Checkpoint version {self.version} is up-to-date.") from calamari_ocr.ocr.training.params import TrainerParams if 'scenario' in self.dict: self.trainer_params = TrainerParams.from_dict(self.dict) else: self.trainer_params = TrainerParams.from_dict( {'scenario': self.dict}) self.scenario_params = self.trainer_params.scenario
def main(): parser = argparse.ArgumentParser() parser.add_argument("--files", type=str, default=[], nargs="+", required=True, help="Protobuf files to convert") parser.add_argument("--logits", action="store_true", help="Do write logits") args = parser.parse_args() files = glob_all(args.files) for file in tqdm(files, desc="Converting"): predictions = Predictions() with open(file, 'rb') as f: predictions.ParseFromString(f.read()) if not args.logits: for prediction in predictions.predictions: prediction.logits.rows = 0 prediction.logits.cols = 0 prediction.logits.data[:] = [] out_json_path = split_all_ext(file)[0] + ".json" with open(out_json_path, 'w') as f: f.write( MessageToJson(predictions, including_default_value_fields=True))
def store(self): for page in tqdm(self.book.pages, desc="Writing Abbyy files", total=len(self.book.pages)): XMLWriter.write( page, split_all_ext(page.xmlFile)[0] + self.params.pred_extension)
def __init__( self, mode: PipelineMode, params: Abbyy, ): super().__init__(mode, params) self.book = XMLReader(self.params.images, self.params.xml_files, self.params.skip_invalid).read() for p, page in enumerate(self.book.pages): for l, line in enumerate(page.getLines()): for f, fo in enumerate(line.formats): self.add_sample({ "image_path": page.imgFile, "xml_path": page.xmlFile, "id": "{}_{}_{}_{}".format( split_all_ext(page.xmlFile or page.imgFile)[0], p, l, f), "line": line, "format": fo, })
def __init__( self, mode: DataSetMode, files, xmlfiles: List[str] = None, skip_invalid=False, remove_invalid=True, non_existing_as_empty=False, args: dict = None, ): """ Create a dataset from a Path as String Parameters ---------- files : [], required image files skip_invalid : bool, optional skip invalid files remove_invalid : bool, optional remove invalid files """ super().__init__( mode, skip_invalid, remove_invalid, ) if xmlfiles is None: xmlfiles = [] if args is None: args = {} self.args = args self.text_index = args.get('text_index', 0) self._non_existing_as_empty = non_existing_as_empty if len(xmlfiles) == 0: xmlfiles = [split_all_ext(p)[0] + ".xml" for p in files] if len(files) == 0: files = [None] * len(xmlfiles) self.files = files self.xmlfiles = xmlfiles self.pages = [] for img, xml in zip(files, xmlfiles): loader = PageXMLDatasetLoader(self.mode, self._non_existing_as_empty, self.text_index, self.skip_invalid) for sample in loader.load(img, xml): self.add_sample(sample) self.pages.append(loader.root) # store which pagexml was stored last, to check when a file is ready to be written during sequential prediction self._last_page_id = None
def store(self, extension): if self._last_page_id: self._store_page(extension, self._last_page_id) self._last_page_id = None else: for xml, page in tqdm(zip(self.xmlfiles, self.pages), desc="Writing PageXML files", total=len(self.xmlfiles)): with open(split_all_ext(xml)[0] + extension, 'w') as f: f.write(etree.tounicode(page.getroottree()))
def prepare_for_mode(self, mode: PipelineMode): self.images = sorted(glob_all(self.images)) self.xml_files = sorted(glob_all(self.xml_files)) if not self.xml_files: self.xml_files = [split_all_ext(f)[0] + self.gt_extension for f in self.images] if not self.images: self.images = [None] * len(self.xml_files) if len(self.images) != len(self.xml_files): raise ValueError(f"Different number of image and xml files, {len(self.images)} != {len(self.xml_files)}") for img_path, xml_path in zip(self.images, self.xml_files): if img_path and xml_path: img_bn, xml_bn = split_all_ext(img_path)[0], split_all_ext(xml_path)[0] if img_bn != xml_bn: logger.warning( f"Filenames are not matching, got base names \n image: {img_bn}\n xml: {xml_bn}\n." )
def main(): parser = argparse.ArgumentParser() parser.add_argument("--files", nargs="+", required=True, help="List of all image files with corresponding gt.txt files") parser.add_argument("--dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--line_height", type=int, default=48, help="The line height") parser.add_argument("--pad", type=int, default=16, help="Padding (left right) of the line") args = parser.parse_args() print("Resolving files") image_files = glob_all(args.files) gt_files = [split_all_ext(p)[0] + ".gt.txt" for p in image_files] ds = create_dataset( args.dataset, DataSetMode.TRAIN, images=image_files, texts=gt_files, non_existing_as_empty=True) print("Loading {} files".format(len(image_files))) ds.load_samples(processes=1, progress_bar=True) images, texts = ds.train_samples(skip_empty=True) statistics = { "n_lines": len(images), "chars": [len(c) for c in texts], "widths": [img.shape[1] / img.shape[0] * args.line_height + 2 * args.pad for img in images if img is not None and img.shape[0] > 0 and img.shape[1] > 0], "total_line_width": 0, "char_counts": {}, } for image, text in zip(images, texts): for c in text: if c in statistics["char_counts"]: statistics["char_counts"][c] += 1 else: statistics["char_counts"][c] = 1 statistics["av_line_width"] = np.average(statistics["widths"]) statistics["max_line_width"] = np.max(statistics["widths"]) statistics["min_line_width"] = np.min(statistics["widths"]) statistics["total_line_width"] = np.sum(statistics["widths"]) statistics["av_chars"] = np.average(statistics["chars"]) statistics["max_chars"] = np.max(statistics["chars"]) statistics["min_chars"] = np.min(statistics["chars"]) statistics["total_chars"] = np.sum(statistics["chars"]) statistics["av_px_per_char"] = statistics["av_line_width"] / statistics["av_chars"] statistics["codec_size"] = len(statistics["char_counts"]) del statistics["chars"] del statistics["widths"] print(statistics)
def store(self, extension): for filename, data in self.prediction.items(): texts = data['transcripts'] codec = data['codec'] basename, ext = split_all_ext(filename) with h5py.File(basename + extension, 'w') as file: dt = h5py.special_dtype(vlen=np.dtype('int32')) file.create_dataset('transcripts', (len(texts), ), dtype=dt) file['transcripts'][...] = texts file.create_dataset('codec', data=list(map(ord, codec)))
def main(): parser = argparse.ArgumentParser( description="Write split of folds to separate directories" ) parser.add_argument("--files", nargs="+", help="List all image files that shall be processed. Ground truth fils with the same " "base name but with '.gt.txt' as extension are required at the same location") parser.add_argument("--n_folds", type=int, required=True, help="The number of fold, that is the number of models to train") parser.add_argument("--output_dir", type=str, required=True, help="Where to write the folds") parser.add_argument("--keep_original_filename", action="store_true", help="By default the copied new files get a new 8 digit name. Use this flag to keep the " "original name but be aware, that this might override lines with the same name") args = parser.parse_args() logger.info("Creating folds") images = glob_all(args.files) texts = [split_all_ext(p)[0] + '.gt.txt' for p in images] data_reader = FileDataReader(PipelineMode.Training, images=images, texts=texts, skip_invalid=True) cross_fold = CrossFold(n_folds=args.n_folds, data_reader=data_reader, output_dir=args.output_dir) logger.info("Copying files") for fold_id, fold_files in enumerate(cross_fold.folds): fold_out_dir = os.path.join(args.output_dir, str(fold_id)) if not os.path.exists(fold_out_dir): os.makedirs(fold_out_dir) for file_id, file in tqdm(enumerate(fold_files), total=len(fold_files), desc="Fold {}".format(fold_id)): img_file = file base, ext = split_all_ext(file) txt_file = base + ".gt.txt" output_basename = os.path.basename(base) if args.keep_original_filename else "{:08d}".format(file_id) if os.path.exists(img_file) and os.path.exists(txt_file): output_file = os.path.join(fold_out_dir, "{}{}".format(output_basename, ext)) shutil.copyfile(img_file, output_file) output_file = os.path.join(fold_out_dir, "{}{}".format(output_basename, ".gt.txt")) shutil.copyfile(txt_file, output_file) else: logger.info("Warning: Does not exist {} or {}".format(img_file, txt_file))
def __init__(self, texts=list()): super().__init__(DataSetMode.EVAL) for text in texts: text_bn, text_ext = split_all_ext(text) self.add_sample({ "image_path": None, "pred_path": text, "id": text_bn, })
def prepare_for_mode(self, mode: PipelineMode): self.images = sorted(glob_all(self.images)) self.xml_files = sorted(self.xml_files) if not self.xml_files: self.xml_files = [ split_all_ext(f)[0] + self.gt_extension for f in self.images ] if not self.images: self.xml_files = sorted(glob_all(self.xml_files)) self.images = [None] * len(self.xml_files)
def __init__(self, mode, params: ExtendedPredictionDataParams): super().__init__(mode, params) for text in params.files: text_bn, text_ext = split_all_ext(text) sample = { "image_path": None, "pred_path": text, "id": text_bn, } self._load_sample(sample, False) self.add_sample(sample)
def _samples_gt_from_book(self, root, img, skipcommented=True): ns = {"ns": root.nsmap[None]} imgfile = root.xpath('//ns:Page', namespaces=ns)[0].attrib["imageFilename"] if self.mode == DataSetMode.TRAIN and not split_all_ext( img)[0].endswith(split_all_ext(imgfile)[0]): raise Exception( "Mapping of image file to xml file invalid: {} vs {} (comparing basename {} vs {})" .format(img, imgfile, split_all_ext(img)[0], split_all_ext(imgfile)[0])) img_w = int( root.xpath('//ns:Page', namespaces=ns)[0].attrib["imageWidth"]) tequivs = root.xpath('//ns:TextEquiv[@index="{}"]'.format( self.text_index), namespaces=ns) for l in tequivs: parat = l.getparent().attrib if skipcommented and "comments" in parat and parat["comments"]: continue text = l.xpath('./ns:Unicode', namespaces=ns).pop().text if not text: if self.skip_invalid: continue elif self._non_existing_as_empty: text = "" else: raise Exception("Empty text field") yield { 'ns': ns, "rtype": l.xpath('../../@type', namespaces=ns).pop(), 'xml_element': l, "image_path": img, "id": l.xpath('../@id', namespaces=ns).pop(), "text": text, "coords": l.xpath('../ns:Coords/@points', namespaces=ns).pop(), "img_width": img_w }
def __init__( self, mode: DataSetMode, files, xmlfiles=list(), skip_invalid=False, remove_invalid=True, binary=False, non_existing_as_empty=False, ): """ Create a dataset from a Path as String Parameters ---------- files : [], required image files skip_invalid : bool, optional skip invalid files remove_invalid : bool, optional remove invalid files """ super().__init__(mode, skip_invalid, remove_invalid) self._non_existing_as_empty = non_existing_as_empty if not xmlfiles or len(xmlfiles) == 0: xmlfiles = [split_all_ext(p)[0] + ".xml" for p in files] if not files or len(files) == 0: files = [None] * len(xmlfiles) self.book = XMLReader(files, xmlfiles, skip_invalid, remove_invalid).read() self.binary = binary for p, page in enumerate(self.book.pages): for l, line in enumerate(page.getLines()): for f, fo in enumerate(line.formats): self.add_sample({ "image_path": page.imgFile, "xml_path": page.xmlFile, "id": "{}_{}_{}_{}".format( os.path.splitext(page.xmlFile if page. xmlFile else page.imgFile)[0], p, l, f), "line": line, "format": fo, })
def store(self): extension = self.params.pred_extension for filename, data in self.prediction.items(): texts = data["transcripts"] codec = data["codec"] basename, ext = split_all_ext(filename) with h5py.File(basename + extension, "w") as file: dt = h5py.special_dtype(vlen=np.dtype("int32")) file.create_dataset("transcripts", (len(texts), ), dtype=dt) file["transcripts"][...] = texts file.create_dataset("codec", data=list(map(ord, codec)))
def __init__(self, mode: DataSetMode, files, xmlfiles=list(), skip_invalid=False, remove_invalid=True, binary=False, non_existing_as_empty=False, ): """ Create a dataset from a Path as String Parameters ---------- files : [], required image files skip_invalid : bool, optional skip invalid files remove_invalid : bool, optional remove invalid files """ super().__init__( mode, skip_invalid, remove_invalid) self._non_existing_as_empty = non_existing_as_empty if not xmlfiles or len(xmlfiles) == 0: xmlfiles = [split_all_ext(p)[0] + ".xml" for p in files] if not files or len(files) == 0: files = [None] * len(xmlfiles) self.book = XMLReader(files, xmlfiles, skip_invalid, remove_invalid).read() self.binary = binary for p, page in enumerate(self.book.pages): for l, line in enumerate(page.getLines()): for f, fo in enumerate(line.formats): self.add_sample({ "image_path": page.imgFile, "xml_path": page.xmlFile, "id": "{}_{}_{}_{}".format(os.path.splitext(page.xmlFile if page.xmlFile else page.imgFile)[0], p, l, f), "line": line, "format": fo, })
def main(): parser = argparse.ArgumentParser( description="Write split of folds to separate directories" ) parser.add_argument("--files", nargs="+", help="List all image files that shall be processed. Ground truth fils with the same " "base name but with '.gt.txt' as extension are required at the same location") parser.add_argument("--n_folds", type=int, required=True, help="The number of fold, that is the number of models to train") parser.add_argument("--output_dir", type=str, required=True, help="Where to write the folds") parser.add_argument("--keep_original_filename", action="store_true", help="By default the copied new files get a new 8 digit name. Use this flag to keep the " "original name but be aware, that this might override lines with the same name") args = parser.parse_args() print("Creating folds") cross_fold = CrossFold(n_folds=args.n_folds, source_files=args.files, output_dir=args.output_dir) print("Copying files") for fold_id, fold_files in enumerate(cross_fold.folds): fold_out_dir = os.path.join(args.output_dir, str(fold_id)) if not os.path.exists(fold_out_dir): os.makedirs(fold_out_dir) for file_id, file in tqdm(enumerate(fold_files), total=len(fold_files), desc="Fold {}".format(fold_id)): img_file = file base, ext = split_all_ext(file) txt_file = base + ".gt.txt" output_basename = os.path.basename(base) if args.keep_original_filename else "{:08d}".format(file_id) if os.path.exists(img_file) and os.path.exists(txt_file): output_file = os.path.join(fold_out_dir, "{}{}".format(output_basename, ext)) shutil.copyfile(img_file, output_file) output_file = os.path.join(fold_out_dir, "{}{}".format(output_basename, ".gt.txt")) shutil.copyfile(txt_file, output_file) else: print("Waring: Does not exist {} or {}".format(img_file, txt_file))
def __init__(self, mode: DataSetMode, files, xmlfiles=list(), skip_invalid=False, remove_invalid=True, non_existing_as_empty=False, args=dict(), ): """ Create a dataset from a Path as String Parameters ---------- files : [], required image files skip_invalid : bool, optional skip invalid files remove_invalid : bool, optional remove invalid files """ super().__init__( mode, skip_invalid, remove_invalid, ) self.text_index = args.get('text_index', 0) self._non_existing_as_empty = non_existing_as_empty if not xmlfiles or len(xmlfiles) == 0: xmlfiles = [split_all_ext(p)[0] + ".xml" for p in files] if not files or len(files) == 0: files = [None] * len(xmlfiles) self.files = files self.xmlfiles = xmlfiles self.pages = [self.read_page_xml(img, xml) for img, xml in zip(files, xmlfiles)]
def main(): parser = argparse.ArgumentParser() parser.add_argument("--files", type=str, default=[], nargs="+", required=True, help="Protobuf files to convert") parser.add_argument("--logits", action="store_true", help="Do write logits") args = parser.parse_args() files = glob_all(args.files) for file in tqdm(files, desc="Converting"): predictions = Predictions() with open(file, 'rb') as f: predictions.ParseFromString(f.read()) if not args.logits: for prediction in predictions.predictions: prediction.logits.rows = 0 prediction.logits.cols = 0 prediction.logits.data[:] = [] out_json_path = split_all_ext(file)[0] + ".json" with open(out_json_path, 'w') as f: f.write(MessageToJson(predictions, including_default_value_fields=True))
def store(self): for page in tqdm(self.book.pages, desc="Writing Abbyy files", total=len(self.book.pages)): XMLWriter.write(page, split_all_ext(page.xmlFile)[0] + ".pred.abbyy.xml")
def main(): parser = argparse.ArgumentParser() parser.add_argument("--files", nargs="+", required=True, help="All img files, an appropriate .gt.txt must exist") parser.add_argument("--n_eval", type=float, required=True, help="The (relative or absolute) count of training files (or -1 to use the remaining)") parser.add_argument("--n_train", type=float, required=True, help="The (relative or absolute) count of training files (or -1 to use the remaining)") parser.add_argument("--output_dir", type=str, required=True, help="Where to write the splits") parser.add_argument("--eval_sub_dir", type=str, default="eval") parser.add_argument("--train_sub_dir", type=str, default="train") args = parser.parse_args() img_files = sorted(glob_all(args.files)) if len(img_files) == 0: raise Exception("No files were found") gt_txt_files = [split_all_ext(p)[0] + ".gt.txt" for p in img_files] if args.n_eval < 0: pass elif args.n_eval < 1: args.n_eval = int(args.n_eval) * len(img_files) else: args.n_eval = int(args.n_eval) if args.n_train < 0: pass elif args.n_train < 1: args.n_train = int(args.n_train) * len(img_files) else: args.n_train = int(args.n_train) if args.n_eval < 0 and args.n_train < 0: raise Exception("Either n_eval or n_train may be < 0") if args.n_eval < 0: args.n_eval = len(img_files) - args.n_train elif args.n_train < 0: args.n_train = len(img_files) - args.n_eval if args.n_eval + args.n_train > len(img_files): raise Exception("Got {} eval and {} train files = {} in total, but only {} files are in the dataset".format( args.n_eval, args.n_train, args.n_eval + args.n_train, len(img_files) )) def copy_files(imgs, txts, out_dir): assert(len(imgs) == len(txts)) if not os.path.exists(out_dir): os.makedirs(out_dir) for img, txt in tqdm(zip(imgs, txts), total=len(imgs), desc="Writing to {}".format(out_dir)): if not os.path.exists(img): print("Image file at {} not found".format(img)) continue if not os.path.exists(txt): print("Ground truth file at {} not found".format(txt)) continue shutil.copyfile(img, os.path.join(out_dir, os.path.basename(img))) shutil.copyfile(txt, os.path.join(out_dir, os.path.basename(txt))) copy_files(img_files[:args.n_eval], gt_txt_files[:args.n_eval], os.path.join(args.output_dir, args.eval_sub_dir)) copy_files(img_files[args.n_eval:], gt_txt_files[args.n_eval:], os.path.join(args.output_dir, args.train_sub_dir))
def __init__(self, mode: DataSetMode, images=None, texts=None, skip_invalid=False, remove_invalid=True, non_existing_as_empty=False): """ Create a dataset from a list of files Images or texts may be empty to create a dataset for prediction or evaluation only. Parameters ---------- images : list of str, optional image files texts : list of str, optional text files skip_invalid : bool, optional skip invalid files remove_invalid : bool, optional remove invalid files non_existing_as_empty : bool, optional tread non existing files as empty. This is relevant for evaluation a dataset """ super().__init__(mode, skip_invalid=skip_invalid, remove_invalid=remove_invalid) self._non_existing_as_empty = non_existing_as_empty images = [] if images is None else images texts = [] if texts is None else texts if mode == DataSetMode.PREDICT: texts = [None] * len(images) if mode == DataSetMode.EVAL: images = [None] * len(texts) for image, text in zip(images, texts): try: if image is None and text is None: raise Exception("An empty data point is not allowed. Both image and text file are None") img_bn, text_bn = None, None if image: img_path, img_fn = os.path.split(image) img_bn, img_ext = split_all_ext(img_fn) if not self._non_existing_as_empty and not os.path.exists(image): raise Exception("Image at '{}' must exist".format(image)) if text: if not self._non_existing_as_empty and not os.path.exists(text): raise Exception("Text file at '{}' must exist".format(text)) text_path, text_fn = os.path.split(text) text_bn, text_ext = split_all_ext(text_fn) if image and text and img_bn != text_bn: raise Exception("Expected image base name equals text base name but got '{}' != '{}'".format( img_bn, text_bn )) except Exception as e: if self.skip_invalid: print("Invalid data: {}".format(e)) continue else: raise e self.add_sample({ "image_path": image, "text_path": text, "id": img_bn if image else text_bn, })
def main(): parser = ArgumentParser() parser.add_argument("--checkpoint", type=str, required=True, help="The checkpoint used to resume") # validation files parser.add_argument("--validation", type=str, nargs="+", help="Validation line files used for early stopping") parser.add_argument("--validation_text_files", nargs="+", default=None, help="Optional list of validation GT files if they are in other directory") parser.add_argument("--validation_extension", default=None, help="Default extension of the gt files (expected to exist in same dir)") parser.add_argument("--validation_dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) # input files parser.add_argument("--files", nargs="+", help="List all image files that shall be processed. Ground truth fils with the same " "base name but with '.gt.txt' as extension are required at the same location") parser.add_argument("--text_files", nargs="+", default=None, help="Optional list of GT files if they are in other directory") parser.add_argument("--gt_extension", default=None, help="Default extension of the gt files (expected to exist in same dir)") parser.add_argument("--dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--no_skip_invalid_gt", action="store_true", help="Do no skip invalid gt, instead raise an exception.") args = parser.parse_args() if args.gt_extension is None: args.gt_extension = DataSetType.gt_extension(args.dataset) if args.validation_extension is None: args.validation_extension = DataSetType.gt_extension(args.validation_dataset) # Training dataset print("Resolving input files") input_image_files = sorted(glob_all(args.files)) if not args.text_files: gt_txt_files = [split_all_ext(f)[0] + args.gt_extension for f in input_image_files] else: gt_txt_files = sorted(glob_all(args.text_files)) input_image_files, gt_txt_files = keep_files_with_same_file_name(input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of file: {} and {}".format(img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception("Some image are occurring more than once in the data set.") dataset = create_dataset( args.dataset, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, skip_invalid=not args.no_skip_invalid_gt ) print("Found {} files in the dataset".format(len(dataset))) # Validation dataset if args.validation: print("Resolving validation files") validation_image_files = glob_all(args.validation) if not args.validation_text_files: val_txt_files = [split_all_ext(f)[0] + args.validation_extension for f in validation_image_files] else: val_txt_files = sorted(glob_all(args.validation_text_files)) validation_image_files, val_txt_files = keep_files_with_same_file_name(validation_image_files, val_txt_files) for img, gt in zip(validation_image_files, val_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of validation file: {} and {}".format(img, gt)) if len(set(val_txt_files)) != len(val_txt_files): raise Exception("Some validation images are occurring more than once in the data set.") validation_dataset = create_dataset( args.validation_dataset, DataSetMode.TRAIN, images=validation_image_files, texts=val_txt_files, skip_invalid=not args.no_skip_invalid_gt) print("Found {} files in the validation dataset".format(len(validation_dataset))) else: validation_dataset = None print("Resuming training") with open(args.checkpoint + '.json', 'r') as f: checkpoint_params = json_format.Parse(f.read(), CheckpointParams()) trainer = Trainer(checkpoint_params, dataset, validation_dataset=validation_dataset, weights=args.checkpoint) trainer.train(progress_bar=True)
def store(self): for xml, page in tqdm(zip(self.xmlfiles, self.pages), desc="Writing PageXML files", total=len(self.xmlfiles)): with open(split_all_ext(xml)[0] + ".pred.xml", 'w') as f: f.write(etree.tounicode(page.getroottree()))
def main(): parser = ArgumentParser() parser.add_argument("--dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--gt", nargs="+", required=True, help="Ground truth files (.gt.txt extension)") parser.add_argument("--pred", nargs="+", default=None, help="Prediction files if provided. Else files with .pred.txt are expected at the same " "location as the gt.") parser.add_argument("--pred_dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--pred_ext", type=str, default=".pred.txt", help="Extension of the predicted text files") parser.add_argument("--n_confusions", type=int, default=10, help="Only print n most common confusions. Defaults to 10, use -1 for all.") parser.add_argument("--n_worst_lines", type=int, default=0, help="Print the n worst recognized text lines with its error") parser.add_argument("--xlsx_output", type=str, help="Optionally write a xlsx file with the evaluation results") parser.add_argument("--num_threads", type=int, default=1, help="Number of threads to use for evaluation") parser.add_argument("--non_existing_file_handling_mode", type=str, default="error", help="How to handle non existing .pred.txt files. Possible modes: skip, empty, error. " "'Skip' will simply skip the evaluation of that file (not counting it to errors). " "'Empty' will handle this file as would it be empty (fully checking for errors)." "'Error' will throw an exception if a file is not existing. This is the default behaviour.") parser.add_argument("--no_progress_bars", action="store_true", help="Do not show any progress bars") parser.add_argument("--checkpoint", type=str, default=None, help="Specify an optional checkpoint to parse the text preprocessor (for the gt txt files)") # page xml specific args parser.add_argument("--pagexml_gt_text_index", default=0) parser.add_argument("--pagexml_pred_text_index", default=1) args = parser.parse_args() print("Resolving files") gt_files = sorted(glob_all(args.gt)) if args.pred: pred_files = sorted(glob_all(args.pred)) else: pred_files = [split_all_ext(gt)[0] + args.pred_ext for gt in gt_files] args.pred_dataset = args.dataset if args.non_existing_file_handling_mode.lower() == "skip": non_existing_pred = [p for p in pred_files if not os.path.exists(p)] for f in non_existing_pred: idx = pred_files.index(f) del pred_files[idx] del gt_files[idx] text_preproc = None if args.checkpoint: with open(args.checkpoint if args.checkpoint.endswith(".json") else args.checkpoint + '.json', 'r') as f: checkpoint_params = json_format.Parse(f.read(), CheckpointParams()) text_preproc = text_processor_from_proto(checkpoint_params.model.text_preprocessor) non_existing_as_empty = args.non_existing_file_handling_mode.lower() != "error " gt_data_set = create_dataset( args.dataset, DataSetMode.EVAL, texts=gt_files, non_existing_as_empty=non_existing_as_empty, args={'text_index': args.pagexml_gt_text_index}, ) pred_data_set = create_dataset( args.pred_dataset, DataSetMode.EVAL, texts=pred_files, non_existing_as_empty=non_existing_as_empty, args={'text_index': args.pagexml_pred_text_index}, ) evaluator = Evaluator(text_preprocessor=text_preproc) r = evaluator.run(gt_dataset=gt_data_set, pred_dataset=pred_data_set, processes=args.num_threads, progress_bar=not args.no_progress_bars) # TODO: More output print("Evaluation result") print("=================") print("") print("Got mean normalized label error rate of {:.2%} ({} errs, {} total chars, {} sync errs)".format( r["avg_ler"], r["total_char_errs"], r["total_chars"], r["total_sync_errs"])) # sort descending print_confusions(r, args.n_confusions) print_worst_lines(r, gt_data_set.samples(), pred_data_set.text_samples(), args.n_worst_lines) if args.xlsx_output: write_xlsx(args.xlsx_output, [{ "prefix": "evaluation", "results": r, "gt_files": gt_files, "gts": gt_data_set.text_samples(), "preds": pred_data_set.text_samples() }])
def main(): parser = argparse.ArgumentParser() parser.add_argument("--eval_imgs", type=str, nargs="+", required=True, help="The evaluation files") parser.add_argument("--eval_dataset", type=DataSetType.from_string, choices=list(DataSetType), default=DataSetType.FILE) parser.add_argument("--checkpoint", type=str, nargs="+", default=[], help="Path to the checkpoint without file extension") parser.add_argument("-j", "--processes", type=int, default=1, help="Number of processes to use") parser.add_argument("--verbose", action="store_true", help="Print additional information") parser.add_argument("--voter", type=str, nargs="+", default=["sequence_voter", "confidence_voter_default_ctc", "confidence_voter_fuzzy_ctc"], help="The voting algorithm to use. Possible values: confidence_voter_default_ctc (default), " "confidence_voter_fuzzy_ctc, sequence_voter") parser.add_argument("--batch_size", type=int, default=10, help="The batch size for prediction") parser.add_argument("--dump", type=str, help="Dump the output as serialized pickle object") parser.add_argument("--no_skip_invalid_gt", action="store_true", help="Do no skip invalid gt, instead raise an exception.") args = parser.parse_args() # allow user to specify json file for model definition, but remove the file extension # for further processing args.checkpoint = [(cp[:-5] if cp.endswith(".json") else cp) for cp in args.checkpoint] # load files gt_images = sorted(glob_all(args.eval_imgs)) gt_txts = [split_all_ext(path)[0] + ".gt.txt" for path in sorted(glob_all(args.eval_imgs))] dataset = create_dataset( args.eval_dataset, DataSetMode.TRAIN, images=gt_images, texts=gt_txts, skip_invalid=not args.no_skip_invalid_gt ) print("Found {} files in the dataset".format(len(dataset))) if len(dataset) == 0: raise Exception("Empty dataset provided. Check your files argument (got {})!".format(args.files)) # predict for all models n_models = len(args.checkpoint) predictor = MultiPredictor(checkpoints=args.checkpoint, batch_size=args.batch_size, processes=args.processes) do_prediction = predictor.predict_dataset(dataset, progress_bar=True) voters = [] all_voter_sentences = [] all_prediction_sentences = [[] for _ in range(n_models)] for voter in args.voter: # create voter voter_params = VoterParams() voter_params.type = VoterParams.Type.Value(voter.upper()) voters.append(voter_from_proto(voter_params)) all_voter_sentences.append([]) for prediction, sample in do_prediction: for sent, p in zip(all_prediction_sentences, prediction): sent.append(p.sentence) # vote results for voter, voter_sentences in zip(voters, all_voter_sentences): voter_sentences.append(voter.vote_prediction_result(prediction).sentence) # evaluation text_preproc = text_processor_from_proto(predictor.predictors[0].model_params.text_preprocessor) evaluator = Evaluator(text_preprocessor=text_preproc) evaluator.preload_gt(gt_dataset=dataset, progress_bar=True) def single_evaluation(predicted_sentences): if len(predicted_sentences) != len(dataset): raise Exception("Mismatch in number of gt and pred files: {} != {}. Probably, the prediction did " "not succeed".format(len(dataset), len(predicted_sentences))) pred_data_set = create_dataset( DataSetType.RAW, DataSetMode.EVAL, texts=predicted_sentences) r = evaluator.run(pred_dataset=pred_data_set, progress_bar=True, processes=args.processes) return r full_evaluation = {} for id, data in [(str(i), sent) for i, sent in enumerate(all_prediction_sentences)] + list(zip(args.voter, all_voter_sentences)): full_evaluation[id] = {"eval": single_evaluation(data), "data": data} if args.verbose: print(full_evaluation) if args.dump: import pickle with open(args.dump, 'wb') as f: pickle.dump({"full": full_evaluation, "gt_txts": gt_txts, "gt": dataset.text_samples()}, f)
def run(args): # check if loading a json file if len(args.files) == 1 and args.files[0].endswith("json"): import json with open(args.files[0], 'r') as f: json_args = json.load(f) for key, value in json_args.items(): setattr(args, key, value) # parse whitelist whitelist = args.whitelist if len(whitelist) == 1: whitelist = list(whitelist[0]) whitelist_files = glob_all(args.whitelist_files) for f in whitelist_files: with open(f) as txt: whitelist += list(txt.read()) if args.gt_extension is None: args.gt_extension = DataSetType.gt_extension(args.dataset) if args.validation_extension is None: args.validation_extension = DataSetType.gt_extension(args.validation_dataset) # Training dataset print("Resolving input files") input_image_files = sorted(glob_all(args.files)) if not args.text_files: gt_txt_files = [split_all_ext(f)[0] + args.gt_extension for f in input_image_files] else: gt_txt_files = sorted(glob_all(args.text_files)) input_image_files, gt_txt_files = keep_files_with_same_file_name(input_image_files, gt_txt_files) for img, gt in zip(input_image_files, gt_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of file: {} and {}".format(img, gt)) if len(set(gt_txt_files)) != len(gt_txt_files): raise Exception("Some image are occurring more than once in the data set.") dataset = create_dataset( args.dataset, DataSetMode.TRAIN, images=input_image_files, texts=gt_txt_files, skip_invalid=not args.no_skip_invalid_gt ) print("Found {} files in the dataset".format(len(dataset))) # Validation dataset if args.validation: print("Resolving validation files") validation_image_files = glob_all(args.validation) if not args.validation_text_files: val_txt_files = [split_all_ext(f)[0] + args.validation_extension for f in validation_image_files] else: val_txt_files = sorted(glob_all(args.validation_text_files)) validation_image_files, val_txt_files = keep_files_with_same_file_name(validation_image_files, val_txt_files) for img, gt in zip(validation_image_files, val_txt_files): if split_all_ext(os.path.basename(img))[0] != split_all_ext(os.path.basename(gt))[0]: raise Exception("Expected identical basenames of validation file: {} and {}".format(img, gt)) if len(set(val_txt_files)) != len(val_txt_files): raise Exception("Some validation images are occurring more than once in the data set.") validation_dataset = create_dataset( args.validation_dataset, DataSetMode.TRAIN, images=validation_image_files, texts=val_txt_files, skip_invalid=not args.no_skip_invalid_gt) print("Found {} files in the validation dataset".format(len(validation_dataset))) else: validation_dataset = None params = CheckpointParams() params.max_iters = args.max_iters params.stats_size = args.stats_size params.batch_size = args.batch_size params.checkpoint_frequency = args.checkpoint_frequency if args.checkpoint_frequency >= 0 else args.early_stopping_frequency params.output_dir = args.output_dir params.output_model_prefix = args.output_model_prefix params.display = args.display params.skip_invalid_gt = not args.no_skip_invalid_gt params.processes = args.num_threads params.data_aug_retrain_on_original = not args.only_train_on_augmented params.early_stopping_frequency = args.early_stopping_frequency params.early_stopping_nbest = args.early_stopping_nbest params.early_stopping_best_model_prefix = args.early_stopping_best_model_prefix params.early_stopping_best_model_output_dir = \ args.early_stopping_best_model_output_dir if args.early_stopping_best_model_output_dir else args.output_dir params.model.data_preprocessor.type = DataPreprocessorParams.DEFAULT_NORMALIZER params.model.data_preprocessor.line_height = args.line_height params.model.data_preprocessor.pad = args.pad # Text pre processing (reading) params.model.text_preprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(params.model.text_preprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(params.model.text_preprocessor.children.add(), groups=args.text_regularization) strip_processor_params = params.model.text_preprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER # Text post processing (prediction) params.model.text_postprocessor.type = TextProcessorParams.MULTI_NORMALIZER default_text_normalizer_params(params.model.text_postprocessor.children.add(), default=args.text_normalization) default_text_regularizer_params(params.model.text_postprocessor.children.add(), groups=args.text_regularization) strip_processor_params = params.model.text_postprocessor.children.add() strip_processor_params.type = TextProcessorParams.STRIP_NORMALIZER if args.seed > 0: params.model.network.backend.random_seed = args.seed if args.bidi_dir: # change bidirectional text direction if desired bidi_dir_to_enum = {"rtl": TextProcessorParams.BIDI_RTL, "ltr": TextProcessorParams.BIDI_LTR, "auto": TextProcessorParams.BIDI_AUTO} bidi_processor_params = params.model.text_preprocessor.children.add() bidi_processor_params.type = TextProcessorParams.BIDI_NORMALIZER bidi_processor_params.bidi_direction = bidi_dir_to_enum[args.bidi_dir] bidi_processor_params = params.model.text_postprocessor.children.add() bidi_processor_params.type = TextProcessorParams.BIDI_NORMALIZER bidi_processor_params.bidi_direction = TextProcessorParams.BIDI_AUTO params.model.line_height = args.line_height network_params_from_definition_string(args.network, params.model.network) params.model.network.clipping_mode = NetworkParams.ClippingMode.Value("CLIP_" + args.gradient_clipping_mode.upper()) params.model.network.clipping_constant = args.gradient_clipping_const params.model.network.backend.fuzzy_ctc_library_path = args.fuzzy_ctc_library_path params.model.network.backend.num_inter_threads = args.num_inter_threads params.model.network.backend.num_intra_threads = args.num_intra_threads # create the actual trainer trainer = Trainer(params, dataset, validation_dataset=validation_dataset, data_augmenter=SimpleDataAugmenter(), n_augmentations=args.n_augmentations, weights=args.weights, codec_whitelist=whitelist, preload_training=not args.train_data_on_the_fly, preload_validation=not args.validation_data_on_the_fly, ) trainer.train( auto_compute_codec=not args.no_auto_compute_codec, progress_bar=not args.no_progress_bars )
def run_for_single_line(args): # lines/network/pretraining as base dir args.base_dir = os.path.join(args.base_dir, "all" if args.n_lines < 0 else str(args.n_lines)) pretrain_prefix = "scratch" if args.weights and len(args.weights) > 0: pretrain_prefix = ",".join([split_all_ext(os.path.basename(path))[0] for path in args.weights]) args.base_dir = os.path.join(args.base_dir, args.network, pretrain_prefix) if not os.path.exists(args.base_dir): os.makedirs(args.base_dir) tmp_dir = os.path.join(args.base_dir, "tmp") if not os.path.exists(tmp_dir): os.makedirs(tmp_dir) best_models_dir = os.path.join(args.base_dir, "models") if not os.path.exists(best_models_dir): os.makedirs(best_models_dir) prediction_dir = os.path.join(args.base_dir, "predictions") if not os.path.exists(prediction_dir): os.makedirs(prediction_dir) # select number of files files = args.train_files if args.n_lines > 0: all_files = glob_all(args.train_files) files = random.sample(all_files, args.n_lines) # run the cross-fold-training setattr(args, "max_parallel_models", args.max_parallel_models) setattr(args, "best_models_dir", best_models_dir) setattr(args, "temporary_dir", tmp_dir) setattr(args, "keep_temporary_files", False) setattr(args, "files", files) setattr(args, "best_model_label", "{id}") if not args.skip_train: cross_fold_train.main(args) dump_file = os.path.join(tmp_dir, "prediction.pkl") # run the prediction if not args.skip_eval: # locate the eval script (must be in the same dir as "this") predict_script_path = os.path.join(this_absdir, "experiment_eval.py") if len(args.single_fold) > 0: models = [os.path.join(best_models_dir, "{}.ckpt.json".format(sf)) for sf in args.single_fold] for m in models: if not os.path.exists(m): raise Exception("Expected model at '{}', but file does not exist".format(m)) else: models = [os.path.join(best_models_dir, d) for d in sorted(os.listdir(best_models_dir)) if d.endswith("json")] if len(models) != args.n_folds: raise Exception("Expected {} models, one for each fold respectively, but only {} models were found".format( args.n_folds, len(models) )) for line in run(prefix_run_command([ "python3", "-u", predict_script_path, "-j", str(args.num_threads), "--batch_size", str(args.batch_size), "--dump", dump_file, "--eval_imgs"] + args.eval_files + [ ] + (["--verbose"] if args.verbose else []) + [ "--checkpoint"] + models + [ ], args.run, {"threads": args.num_threads}), verbose=args.verbose): # Print the output of the thread if args.verbose: print(line) import pickle with open(dump_file, 'rb') as f: prediction = pickle.load(f) return prediction