Add data formatters for Synist and Bessin

Datasets/dataset_formatters/bessin_formatter.py

+#  contributors :
+#  - Denis Coquenet
+#
+#
+#  This software is a computer program written in Python  whose purpose is to
+#  provide public implementation of deep learning works, in pytorch.
+#
+#  This software is governed by the CeCILL-C license under French law and
+#  abiding by the rules of distribution of free software.  You can  use,
+#  modify and/ or redistribute the software under the terms of the CeCILL-C
+#  license as circulated by CEA, CNRS and INRIA at the following URL
+#  "http://www.cecill.info".
+#
+#  As a counterpart to the access to the source code and  rights to copy,
+#  modify and redistribute granted by the license, users are provided only
+#  with a limited warranty  and the software's author,  the holder of the
+#  economic rights,  and the successive licensors  have only  limited
+#  liability.
+#
+#  In this respect, the user's attention is drawn to the risks associated
+#  with loading,  using,  modifying and/or developing or reproducing the
+#  software by the user in light of its specific status of free software,
+#  that may mean  that it is complicated to manipulate,  and  that  also
+#  therefore means  that it is reserved for developers  and  experienced
+#  professionals having in-depth computer knowledge. Users are therefore
+#  encouraged to load and test the software's suitability as regards their
+#  requirements in conditions enabling the security of their systems and/or
+#  data to be ensured and,  more generally, to use and operate it in the
+#  same conditions as regards security.
+#
+#  The fact that you are presently reading this means that you have had
+#  knowledge of the CeCILL-C license and that you accept its terms.
+
+from Datasets.dataset_formatters.generic_dataset_formatter import OCRDatasetFormatter
+import os
+import numpy as np
+from Datasets.dataset_formatters.utils_dataset import natural_sort
+from PIL import Image
+import xml.etree.ElementTree as ET
+import re
+from tqdm import tqdm
+from collections import Counter
+
+
+def remove_spaces(text):
+    # remove begin/ending spaces
+    text = text.strip()
+    # replace \t with regular space
+    text = re.sub("\t", " ", text)
+    # remove consecutive spaces
+    text = re.sub(" +", " ", text)
+#    text = text.encode('ascii', 'ignore').decode("utf-8") 
+    return text
+
+
+class SynistDatasetFormatter(OCRDatasetFormatter):
+    def __init__(self, level, set_names=["train", "valid", "test"], dpi=150, sem_token=False):
+        super(SynistDatasetFormatter, self).__init__("bessin", level, "_sem" if sem_token else "", set_names)
+
+        self.dpi = dpi
+        self.counter = Counter()
+        self.map_datasets_files.update({
+            "bessin": {
+                # (1,050 for train, 100 for validation and 100 for test)
+                "line": {
+                    "needed_files": [],
+                    "arx_files": [],
+                    "format_function": self.format_bessin_zone
+                        }
+            }
+        })
+
+    def preformat_bessin_zone(self):
+        """
+        Extract all information from dataset and correct some annotations
+        """
+        
+        dataset = {
+            "train": list(),
+            "valid": list(),
+            "test": list()
+        }
+        img_folder_path = os.path.join("Datasets", "raw", "bessin", "images")
+        labels_folder_path = os.path.join("Datasets", "raw", "bessin", "labels")
+        
+        train_files = [
+            os.path.join(labels_folder_path, 'train', name)
+            for name in os.listdir(os.path.join(labels_folder_path, 'train'))]
+        valid_files = [
+            os.path.join(labels_folder_path, 'valid', name)
+            for name in os.listdir(os.path.join(labels_folder_path, 'valid'))]
+        test_files = [
+            os.path.join(labels_folder_path, 'test', name)
+            for name in os.listdir(os.path.join(labels_folder_path, 'test'))]
+
+        for set_name, files in zip(self.set_names, [train_files, valid_files, test_files]):
+            for i, label_file in enumerate(tqdm(files, desc='Pre-formatting '+set_name)):
+                with open(label_file, 'r') as f:
+                    text = remove_spaces(f.read())
+
+                dataset[set_name].append({
+                    "img_path": os.path.join(
+                        img_folder_path, set_name, label_file.split('/')[-1].replace('txt', 'jpg')),
+                    "label": text.strip()
+                })
+
+        return dataset
+
+    def format_bessin_zone(self):
+        """
+        Format synist page dataset
+        """
+        dataset = self.preformat_bessin_zone()
+        for set_name in self.set_names:
+            fold = os.path.join(self.target_fold_path, set_name)
+            for sample in tqdm(dataset[set_name], desc='Formatting '+set_name):
+                new_name = sample['img_path'].split('/')[-1]
+                new_img_path = os.path.join(fold, new_name)
+                self.load_resize_save(sample["img_path"], new_img_path)
+                zone = {
+                    "text": sample["label"],
+                }
+                self.charset = self.charset.union(set(zone["text"]))
+                self.gt[set_name][new_name] = zone
+                self.counter.update(zone['text'])
+
+
+if __name__ == "__main__":
+    formatter = SynistDatasetFormatter("line", sem_token=False)
+    formatter.format()
+    print("Character freq: ")
+    for k,v in formatter.counter.items():
+        print(k, v)
\ No newline at end of file

Datasets/dataset_formatters/extract_from_arkindex.py

@@ -23,6 +23,7 @@ from tqdm import tqdm
 
 from extraction_utils import arkindex_utils as ark
 from extraction_utils import utils
+from utils_dataset import assign_random_split, save_text, save_image, save_json
 
 logging.basicConfig(
     level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
@@ -53,6 +54,37 @@ SEM_MATCHING_TOKENS = {
 }
 
 
+def extract_transcription(element, no_entities=False):
+    tr = client.request('ListTranscriptions', id=element['id'], worker_version=None)['results']
+    tr = [one for one in tr if one['worker_version_id'] is None]
+
+    if len(tr) != 1:
+        return None
+
+    if no_entities:
+        text = tr[0]['text'].strip()
+
+    else:
+        for one_tr in tr:
+            ent = client.request('ListTranscriptionEntities', id=one_tr['id'])['results']
+            print(ent)
+            ent = [one for one in ent if one['worker_version_id'] is None]
+            if len(ent) == 0:
+                continue
+            else:
+                text = one_tr['text']
+
+        count = 0
+        for e in ent:
+            start_token = SEM_MATCHING_TOKENS_STR[e['entity']['metas']['subtype']]
+            end_token = SEM_MATCHING_TOKENS[start_token]
+            text = text[:count+e['offset']] + start_token + text[count+e['offset']:]
+            count += 1
+            text = text[:count+e['offset']+e['length']] + end_token + text[count+e['offset']+e['length']:]
+            count += 1
+    return text
+
+
 if __name__ == '__main__':
     args = utils.get_cli_args()
 
@@ -62,52 +94,76 @@ if __name__ == '__main__':
 
     # Login to arkindex.
     client = ArkindexClient(**options_from_env())
-
     corpus = ark.retrieve_corpus(client, args.corpus)
     subsets = ark.retrieve_subsets(
         client, corpus, args.parents_types, args.parents_names
     )
 
+    # Create out directories
+    split_dict = {}
+    split_names = [subset["name"] for subset in subsets] if args.use_existing_split else ["train", "valid", "test"]
+    for split in split_names:
+        os.makedirs(os.path.join(args.output_dir, IMAGES_DIR, split), exist_ok=True)
+        os.makedirs(os.path.join(args.output_dir, LABELS_DIR, split), exist_ok=True)
+        split_dict[split] = {}
+
     # Iterate over the subsets to find the page images and labels.
     for subset in subsets:
 
-        os.makedirs(os.path.join(args.output_dir, IMAGES_DIR, subset["name"]), exist_ok=True)
-        os.makedirs(os.path.join(args.output_dir, LABELS_DIR, subset["name"]), exist_ok=True)
-
+        # Iterate over the pages to create splits at page level.
         for page in tqdm(
-            client.paginate(
-                "ListElementChildren", id=subset["id"], type="page", recursive=True
-            ),
-            desc="Set " + subset["name"],
-        ):
-
-            image = iio.imread(page["zone"]["url"])
-            cv2.imwrite(
-                os.path.join(args.output_dir, IMAGES_DIR, subset['name'], f"{page['id']}.jpg"),
-                cv2.cvtColor(image, cv2.COLOR_BGR2RGB),
-            )
-
-            tr = client.request('ListTranscriptions', id=page['id'], worker_version=None)['results']
-            tr = [one for one in tr if one['worker_version_id'] is None]
-            assert len(tr) == 1, page['id']
-
-            for one_tr in tr:
-                ent = client.request('ListTranscriptionEntities', id=one_tr['id'])['results']
-                ent = [one for one in ent if one['worker_version_id'] is None]
-                if len(ent) == 0:
+                        client.paginate(
+                            "ListElementChildren", id=subset["id"], type="page", recursive=True
+                        )
+                    ):
+
+            if not args.use_existing_split:
+                split = assign_random_split(args.train_prob, args.val_prob)
+            else:
+                split = subset["name"]
+
+            # Extract only pages
+            if args.elements_types == ["page"]:
+
+                text = extract_transcription(element, no_entities=args.no_entities)
+                image = iio.imread(element["zone"]["url"])
+
+                if not text:
+                    logging.warning(f"Skipping {element['id']} (zero or multiple transcriptions with worker_version=None)")
                     continue
                 else:
-                    text = one_tr['text']
-
-            new_text = text
-            count = 0
-            for e in ent:
-                start_token = SEM_MATCHING_TOKENS_STR[e['entity']['metas']['subtype']]
-                end_token = SEM_MATCHING_TOKENS[start_token]
-                new_text = new_text[:count+e['offset']] + start_token + new_text[count+e['offset']:]
-                count += 1
-                new_text = new_text[:count+e['offset']+e['length']] + end_token + new_text[count+e['offset']+e['length']:]
-                count += 1
-
-            with open(os.path.join(args.output_dir, LABELS_DIR, subset['name'], f"{page['id']}.txt"), 'w') as f:
-                f.write(new_text)
+                    logging.info(f"Processed {element_type} {element['id']}")
+                    split_dict[split].append(page["id"])
+
+                    im_path = os.path.join(args.output_dir, IMAGES_DIR, split, f"{element_type}_{element['id']}.jpg")
+                    txt_path = os.path.join(args.output_dir, LABELS_DIR, split, f"{element_type}_{element['id']}.txt")
+
+                    save_text(txt_path, text)
+                    save_image(im_path, image)
+
+            # Extract page's children elements (text_zone, text_line)
+            else:
+                split_dict[split][page["id"]] = {}
+
+                for element_type in args.elements_types:
+                    split_dict[split][page["id"]][element_type] = []
+
+                    for element in client.paginate("ListElementChildren", id=page["id"], type=element_type, recursive=True):
+
+                        text = extract_transcription(element, no_entities=args.no_entities)
+                        image = iio.imread(element["zone"]["url"])
+                        
+                        if not text:
+                            logging.warning(f"Skipping {element_type} {element['id']} (zero or multiple transcriptions with worker_version=None)")
+                            continue
+                        else:
+                            logging.info(f"Processed {element_type} {element['id']}")
+                            split_dict[split][page["id"]][element_type].append(element['id'])
+
+                            im_path = os.path.join(args.output_dir, IMAGES_DIR, split, f"{element_type}_{element['id']}.jpg")
+                            txt_path = os.path.join(args.output_dir, LABELS_DIR, split, f"{element_type}_{element['id']}.txt")
+
+                            save_text(txt_path, text)
+                            save_image(im_path, image)
+
+    save_json(os.path.join(args.output_dir, "split.json"), split_dict)

Datasets/dataset_formatters/synist_formatter.py

+#  contributors :
+#  - Denis Coquenet
+#
+#
+#  This software is a computer program written in Python  whose purpose is to
+#  provide public implementation of deep learning works, in pytorch.
+#
+#  This software is governed by the CeCILL-C license under French law and
+#  abiding by the rules of distribution of free software.  You can  use,
+#  modify and/ or redistribute the software under the terms of the CeCILL-C
+#  license as circulated by CEA, CNRS and INRIA at the following URL
+#  "http://www.cecill.info".
+#
+#  As a counterpart to the access to the source code and  rights to copy,
+#  modify and redistribute granted by the license, users are provided only
+#  with a limited warranty  and the software's author,  the holder of the
+#  economic rights,  and the successive licensors  have only  limited
+#  liability.
+#
+#  In this respect, the user's attention is drawn to the risks associated
+#  with loading,  using,  modifying and/or developing or reproducing the
+#  software by the user in light of its specific status of free software,
+#  that may mean  that it is complicated to manipulate,  and  that  also
+#  therefore means  that it is reserved for developers  and  experienced
+#  professionals having in-depth computer knowledge. Users are therefore
+#  encouraged to load and test the software's suitability as regards their
+#  requirements in conditions enabling the security of their systems and/or
+#  data to be ensured and,  more generally, to use and operate it in the
+#  same conditions as regards security.
+#
+#  The fact that you are presently reading this means that you have had
+#  knowledge of the CeCILL-C license and that you accept its terms.
+
+from Datasets.dataset_formatters.generic_dataset_formatter import OCRDatasetFormatter
+import os
+import numpy as np
+from Datasets.dataset_formatters.utils_dataset import natural_sort
+from PIL import Image
+import xml.etree.ElementTree as ET
+import re
+from tqdm import tqdm
+from collections import Counter
+
+
+def remove_spaces(text):
+    # remove begin/ending spaces
+    text = text.strip()
+    # replace \t with regular space
+    text = re.sub("\t", " ", text)
+    # remove consecutive spaces
+    text = re.sub(" +", " ", text)
+#    text = text.encode('ascii', 'ignore').decode("utf-8") 
+    return text
+
+
+class SynistDatasetFormatter(OCRDatasetFormatter):
+    def __init__(self, level, set_names=["train", "valid", "test"], dpi=150, sem_token=False):
+        super(SynistDatasetFormatter, self).__init__("synist_synth", level, "_sem" if sem_token else "", set_names)
+
+        self.dpi = dpi
+        self.counter = Counter()
+        self.map_datasets_files.update({
+            "synist_synth": {
+                # (1,050 for train, 100 for validation and 100 for test)
+                "line": {
+                    "needed_files": [],
+                    "arx_files": [],
+                    "format_function": self.format_synist_page
+                        }
+            }
+        })
+
+    def preformat_synist_page(self):
+        """
+        Extract all information from dataset and correct some annotations
+        """
+        
+        dataset = {
+            "train": list(),
+            "valid": list(),
+            "test": list()
+        }
+        img_folder_path = os.path.join("Datasets", "raw", "synist_synth_lines", "images")
+        labels_folder_path = os.path.join("Datasets", "raw", "synist_synth_lines", "labels")
+        
+        train_files = [
+            os.path.join(labels_folder_path, 'train', name)
+            for name in os.listdir(os.path.join(labels_folder_path, 'train'))]
+        valid_files = [
+            os.path.join(labels_folder_path, 'valid', name)
+            for name in os.listdir(os.path.join(labels_folder_path, 'valid'))]
+        test_files = [
+            os.path.join(labels_folder_path, 'test', name)
+            for name in os.listdir(os.path.join(labels_folder_path, 'test'))]
+
+        for set_name, files in zip(self.set_names, [train_files, valid_files, test_files]):
+            for i, label_file in enumerate(tqdm(files, desc='Pre-formatting '+set_name)):
+                with open(label_file, 'r') as f:
+                    text = remove_spaces(f.read())
+
+                dataset[set_name].append({
+                    "img_path": os.path.join(
+                        img_folder_path, set_name, label_file.split('/')[-1].replace('txt', 'png')),
+                    "label": text.strip()
+                })
+
+        return dataset
+
+    def format_synist_page(self):
+        """
+        Format synist page dataset
+        """
+        dataset = self.preformat_synist_page()
+        for set_name in self.set_names:
+            fold = os.path.join(self.target_fold_path, set_name)
+            for sample in tqdm(dataset[set_name], desc='Formatting '+set_name):
+                new_name = sample['img_path'].split('/')[-1]
+                new_img_path = os.path.join(fold, new_name)
+                #self.load_resize_save(sample["img_path"], new_img_path, 300, self.dpi)
+                self.load_resize_save(sample["img_path"], new_img_path)
+                #self.load_flip_save(new_img_path, new_img_path)
+                page = {
+                    "text": sample["label"],
+                }
+                self.charset = self.charset.union(set(page["text"]))
+                self.gt[set_name][new_name] = page
+                self.counter.update(page['text'])
+
+
+if __name__ == "__main__":
+    formatter = SynistDatasetFormatter("line", sem_token=False)
+    formatter.format()
+    print(formatter.counter)
+    print(formatter.counter.most_common(80))
+    for k,v in formatter.counter.items():
+        print(k)
+        print(k.encode('utf-8'), v)