Upload erukaLabels.py

erukaLabels.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Untitled0.ipynb
+"""erukaLabels.ipynb
 
 Automatically generated by Colaboratory.
 
@@ -7,6 +7,7 @@ Original file is located at
     https://colab.research.google.com/drive/1p0VRh0b-OtHjNNLIcNUPb2BaoiE9Mh7O
 """
 
+
 # coding=utf-8
 import json
 import os
@@ -25,8 +26,8 @@ def normalize_bbox(bbox, size):
     return [
         int(1000 * bbox[0] / size[0]),
         int(1000 * bbox[1] / size[1]),
-        int(1000 * bbox[2] / size[0]),
-        int(1000 * bbox[3] / size[1]),
+        int(1000 * bbox[4] / size[0]),
+        int(1000 * bbox[5] / size[1]),
     ]
 
 logger = datasets.logging.get_logger(__name__)
@@ -58,7 +59,6 @@ class FunsdConfig(datasets.BuilderConfig):
         """
         super(FunsdConfig, self).__init__(**kwargs)
 
-
 class Funsd(datasets.GeneratorBasedBuilder):
     """Conll2003 dataset."""
 
@@ -89,13 +89,13 @@ class Funsd(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        downloaded_file = dl_manager.download_and_extract("https://guillaumejaume.github.io/FUNSD/dataset.zip")
+        downloaded_file = dl_manager.download_and_extract("dataset_eruka.zip")
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"}
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset_eruka/training_data/"}
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing_data/"}
+                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset_eruka/testing_data/"}
             ),
         ]
 
@@ -122,28 +122,25 @@ class Funsd(datasets.GeneratorBasedBuilder):
             with open(file_path, "r", encoding="utf8") as f:
                 data = json.load(f)
             image_path = os.path.join(img_dir, file)
-            image_path = image_path.replace("json", "png")
+
+            # changed
+            image_path = image_path.replace("json", "jpg")
             image, size = load_image(image_path)
-            for item in data["form"]:
+
+            #new
+            ddata_path = data["analyzeResult"]["pages"][0]["words"]
+
+            for item in ddata_path:
                 cur_line_bboxes = []
-                words, label = item["words"], item["label"]
-                words = [w for w in words if w["text"].strip() != ""]
+                words, label = [item["content"]], "other"
                 if len(words) == 0:
                     continue
-                if label == "other":
-                    for w in words:
-                        tokens.append(w["text"])
-                        ner_tags.append("O")
-                        cur_line_bboxes.append(normalize_bbox(w["box"], size))
-                else:
-                    tokens.append(words[0]["text"])
-                    ner_tags.append("B-" + label.upper())
-                    cur_line_bboxes.append(normalize_bbox(words[0]["box"], size))
-                    for w in words[1:]:
-                        tokens.append(w["text"])
-                        ner_tags.append("I-" + label.upper())
-                        cur_line_bboxes.append(normalize_bbox(w["box"], size))
+                tokens.append(words[0])
+                ner_tags.append("O")
+                cur_line_bboxes.append(normalize_bbox(item["polygon"], size))
+
                 cur_line_bboxes = self.get_line_bbox(cur_line_bboxes)
                 bboxes.extend(cur_line_bboxes)
             yield guid, {"id": str(guid), "tokens": tokens, "bboxes": bboxes, "ner_tags": ner_tags,
-                         "image": image}
+                         "image": image}
+