pierreguillou commited on
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e6b0f00
1 Parent(s): 755d9f2

Update DocLayNet-large.py

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  1. DocLayNet-large.py +74 -51
DocLayNet-large.py CHANGED
@@ -56,6 +56,13 @@ _LICENSE = "https://github.com/DS4SD/DocLayNet/blob/main/LICENSE"
56
  # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
57
  # }
58
 
 
 
 
 
 
 
 
59
  # functions
60
  def load_image(image_path):
61
  image = Image.open(image_path).convert("RGB")
@@ -156,8 +163,10 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
156
  # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
157
  # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
158
 
159
- downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_0.zip")
160
- downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_1.zip")
 
 
161
  # downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_2.zip")
162
  # downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_3.zip")
163
 
@@ -166,7 +175,10 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
166
  name=datasets.Split.TRAIN,
167
  # These kwargs will be passed to _generate_examples
168
  gen_kwargs={
169
- "filepath": f"{downloaded_file}/large_dataset/train/",
 
 
 
170
  "split": "train",
171
  },
172
  ),
@@ -174,7 +186,10 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
174
  name=datasets.Split.VALIDATION,
175
  # These kwargs will be passed to _generate_examples
176
  gen_kwargs={
177
- "filepath": f"{downloaded_file}/large_dataset/val/",
 
 
 
178
  "split": "dev",
179
  },
180
  ),
@@ -182,58 +197,66 @@ class DocLayNet(datasets.GeneratorBasedBuilder):
182
  name=datasets.Split.TEST,
183
  # These kwargs will be passed to _generate_examples
184
  gen_kwargs={
185
- "filepath": f"{downloaded_file}/large_dataset/test/",
 
 
 
186
  "split": "test"
187
  },
188
  ),
189
  ]
190
 
191
- def _generate_examples(self, filepath, split):
 
192
  logger.info("⏳ Generating examples from = %s", filepath)
193
- ann_dir = os.path.join(filepath, "annotations")
194
- img_dir = os.path.join(filepath, "images")
195
- pdf_dir = os.path.join(filepath, "pdfs")
196
-
197
- for guid, file in enumerate(sorted(os.listdir(ann_dir))):
198
- texts = []
199
- bboxes_block = []
200
- bboxes_line = []
201
- categories = []
202
 
203
- # get json
204
- file_path = os.path.join(ann_dir, file)
205
- with open(file_path, "r", encoding="utf8") as f:
206
- data = json.load(f)
 
 
 
 
 
 
 
 
 
 
 
207
 
208
- # get image
209
- image_path = os.path.join(img_dir, file)
210
- image_path = image_path.replace("json", "png")
211
- image, size = load_image(image_path)
212
-
213
- # get pdf
214
- pdf_path = os.path.join(pdf_dir, file)
215
- pdf_path = pdf_path.replace("json", "pdf")
216
- with open(pdf_path, "rb") as pdf_file:
217
- pdf_bytes = pdf_file.read()
218
- pdf_encoded_string = base64.b64encode(pdf_bytes)
219
-
220
- for item in data["form"]:
221
- text_example, category_example, bbox_block_example, bbox_line_example = item["text"], item["category"], item["box"], item["box_line"]
222
- texts.append(text_example)
223
- categories.append(category_example)
224
- bboxes_block.append(bbox_block_example)
225
- bboxes_line.append(bbox_line_example)
226
-
227
- # get all metadadata
228
- page_hash = data["metadata"]["page_hash"]
229
- original_filename = data["metadata"]["original_filename"]
230
- page_no = data["metadata"]["page_no"]
231
- num_pages = data["metadata"]["num_pages"]
232
- original_width = data["metadata"]["original_width"]
233
- original_height = data["metadata"]["original_height"]
234
- coco_width = data["metadata"]["coco_width"]
235
- coco_height = data["metadata"]["coco_height"]
236
- collection = data["metadata"]["collection"]
237
- doc_category = data["metadata"]["doc_category"]
238
-
239
- yield guid, {"id": str(guid), "texts": texts, "bboxes_block": bboxes_block, "bboxes_line": bboxes_line, "categories": categories, "image": image, "pdf": pdf_encoded_string, "page_hash": page_hash, "original_filename": original_filename, "page_no": page_no, "num_pages": num_pages, "original_width": original_width, "original_height": original_height, "coco_width": coco_width, "coco_height": coco_height, "collection": collection, "doc_category": doc_category}
 
56
  # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
57
  # }
58
 
59
+ _URLs = {
60
+ "part_dataset_0": "https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_0.zip",
61
+ "part_dataset_1": "https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_1.zip",
62
+ "part_dataset_2": "https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_2.zip",
63
+ "part_dataset_3": "https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_3.zip",
64
+ }
65
+
66
  # functions
67
  def load_image(image_path):
68
  image = Image.open(image_path).convert("RGB")
 
163
  # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
164
  # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
165
 
166
+ archive_path = dl_manager.download_and_extract(_URLs)
167
+
168
+ downloaded_file = dl_manager.download_and_extract(archive_path["part_dataset_0"])
169
+ downloaded_file = dl_manager.download_and_extract("part_dataset_0")
170
  # downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_2.zip")
171
  # downloaded_file = dl_manager.download_and_extract("https://huggingface.co/datasets/pierreguillou/DocLayNet-large/resolve/main/data/part_dataset_3.zip")
172
 
 
175
  name=datasets.Split.TRAIN,
176
  # These kwargs will be passed to _generate_examples
177
  gen_kwargs={
178
+ "filepath_0": f"{archive_path["part_dataset_0"]}/large_dataset/train/",
179
+ "filepath_1": f"{archive_path["part_dataset_1"]}/large_dataset/train/",
180
+ "filepath_2": f"{archive_path["part_dataset_2"]}/large_dataset/train/",
181
+ "filepath_3": f"{archive_path["part_dataset_3"]}/large_dataset/train/",
182
  "split": "train",
183
  },
184
  ),
 
186
  name=datasets.Split.VALIDATION,
187
  # These kwargs will be passed to _generate_examples
188
  gen_kwargs={
189
+ "filepath_0": f"{archive_path["part_dataset_0"]}/large_dataset/val/",
190
+ "filepath_1": f"{archive_path["part_dataset_1"]}/large_dataset/val/",
191
+ "filepath_2": f"{archive_path["part_dataset_2"]}/large_dataset/val/",
192
+ "filepath_3": f"{archive_path["part_dataset_3"]}/large_dataset/val/",
193
  "split": "dev",
194
  },
195
  ),
 
197
  name=datasets.Split.TEST,
198
  # These kwargs will be passed to _generate_examples
199
  gen_kwargs={
200
+ "filepath_0": f"{archive_path["part_dataset_0"]}/large_dataset/test/",
201
+ "filepath_1": f"{archive_path["part_dataset_1"]}/large_dataset/test/",
202
+ "filepath_2": f"{archive_path["part_dataset_2"]}/large_dataset/test/",
203
+ "filepath_3": f"{archive_path["part_dataset_3"]}/large_dataset/test/",
204
  "split": "test"
205
  },
206
  ),
207
  ]
208
 
209
+ def _generate_examples(self, filepath_0, filepath_1, filepath_2, filepath_3, split):
210
+ filepath = (filepath_0, filepath_1, filepath_2, filepath_3)
211
  logger.info("⏳ Generating examples from = %s", filepath)
212
+ ann_dirs = [os.path.join(filepath_0, "annotations"), os.path.join(filepath_1, "annotations"), os.path.join(filepath_2, "annotations"), os.path.join(filepath_3, "annotations")]
213
+ img_dirs = [os.path.join(filepath_0, "images"), os.path.join(filepath_1, "images"), os.path.join(filepath_2, "images"), os.path.join(filepath_3, "images")]
214
+ pdf_dirs = [os.path.join(filepath_0, "pdfs"), os.path.join(filepath_1, "pdfs"), os.path.join(filepath_2, "pdfs"), os.path.join(filepath_3, "pdfs")]
215
+
216
+ for ann_dir, img_dir, pdf_dir in zip(ann_dirs, img_dirs, pdf_dirs):
217
+
218
+ ann_listdir = os.listdir(ann_dir)
 
 
219
 
220
+ for guid, file in enumerate(ann_listdir):
221
+ texts = []
222
+ bboxes_block = []
223
+ bboxes_line = []
224
+ categories = []
225
+
226
+ # get json
227
+ file_path = os.path.join(ann_dir, file)
228
+ with open(file_path, "r", encoding="utf8") as f:
229
+ data = json.load(f)
230
+
231
+ # get image
232
+ image_path = os.path.join(img_dir, file)
233
+ image_path = image_path.replace("json", "png")
234
+ image, size = load_image(image_path)
235
 
236
+ # get pdf
237
+ pdf_path = os.path.join(pdf_dir, file)
238
+ pdf_path = pdf_path.replace("json", "pdf")
239
+ with open(pdf_path, "rb") as pdf_file:
240
+ pdf_bytes = pdf_file.read()
241
+ pdf_encoded_string = base64.b64encode(pdf_bytes)
242
+
243
+ for item in data["form"]:
244
+ text_example, category_example, bbox_block_example, bbox_line_example = item["text"], item["category"], item["box"], item["box_line"]
245
+ texts.append(text_example)
246
+ categories.append(category_example)
247
+ bboxes_block.append(bbox_block_example)
248
+ bboxes_line.append(bbox_line_example)
249
+
250
+ # get all metadadata
251
+ page_hash = data["metadata"]["page_hash"]
252
+ original_filename = data["metadata"]["original_filename"]
253
+ page_no = data["metadata"]["page_no"]
254
+ num_pages = data["metadata"]["num_pages"]
255
+ original_width = data["metadata"]["original_width"]
256
+ original_height = data["metadata"]["original_height"]
257
+ coco_width = data["metadata"]["coco_width"]
258
+ coco_height = data["metadata"]["coco_height"]
259
+ collection = data["metadata"]["collection"]
260
+ doc_category = data["metadata"]["doc_category"]
261
+
262
+ yield guid, {"id": str(guid), "texts": texts, "bboxes_block": bboxes_block, "bboxes_line": bboxes_line, "categories": categories, "image": image, "pdf": pdf_encoded_string, "page_hash": page_hash, "original_filename": original_filename, "page_no": page_no, "num_pages": num_pages, "original_width": original_width, "original_height": original_height, "coco_width": coco_width, "coco_height": coco_height, "collection": collection, "doc_category": doc_category}