Upload OCR

README.md

@@ -50,6 +50,18 @@ dataset_info:
   - name: boxes
     sequence:
       sequence: int32
+  splits:
+  - name: train
+    num_bytes: 38835143890
+    num_examples: 320000
+  - name: test
+    num_bytes: 4865648030
+    num_examples: 40000
+  - name: validation
+    num_bytes: 4871031282
+    num_examples: 40000
+  download_size: 38779484559
+  dataset_size: 48571823202
 ---
 
 # Dataset Card for RVL-CDIP

dataset_infos.json

@@ -0,0 +1,100 @@
+{
+  "default": {
+    "description": "The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images.\n",
+    "citation": "@inproceedings{harley2015icdar,\n    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},\n    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},\n    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},\n    year = {2015}\n}\n",
+    "homepage": "https://www.cs.cmu.edu/~aharley/rvl-cdip/",
+    "license": "https://www.industrydocuments.ucsf.edu/help/copyright/",
+    "features": {
+      "image": {
+        "decode": true,
+        "id": null,
+        "_type": "Image"
+      },
+      "label": {
+        "num_classes": 16,
+        "names": [
+          "letter",
+          "form",
+          "email",
+          "handwritten",
+          "advertisement",
+          "scientific report",
+          "scientific publication",
+          "specification",
+          "file folder",
+          "news article",
+          "budget",
+          "invoice",
+          "presentation",
+          "questionnaire",
+          "resume",
+          "memo"
+        ],
+        "id": null,
+        "_type": "ClassLabel"
+      },
+      "id": {
+        "_type": "Value",
+        "dtype": "string"
+      },
+      "words": {
+        "_type": "Sequence",
+        "feature": {
+          "dtype": "string",
+          "_type": "Value"
+        }
+      },
+      "boxes": {
+        "_type": "Sequence",
+        "feature": {
+          "_type": "Sequence",
+          "feature": {
+            "dtype": "int32",
+            "_type": "Value"
+          }
+        }
+      }
+    },
+    "post_processed": null,
+    "supervised_keys": {
+      "input": "image",
+      "output": "label"
+    },
+    "task_templates": [
+      {
+        "task": "image-classification",
+        "image_column": "image",
+        "label_column": "label"
+      }
+    ],
+    "builder_name": "rvl_cdip_easyOCR",
+    "config_name": "default",
+    "version": {
+      "version_str": "1.0.0",
+      "description": null,
+      "major": 1,
+      "minor": 0,
+      "patch": 0
+    },
+    "splits": {
+      "train": {
+        "name": "train",
+        "num_bytes": 38816373360,
+        "num_examples": 320000,
+        "dataset_name": "rvl_cdip_easyOCR"
+      },
+      "test": {
+        "name": "test",
+        "num_bytes": 4863300853,
+        "num_examples": 40000,
+        "dataset_name": "rvl_cdip_easyOCR"
+      },
+      "validation": {
+        "name": "validation",
+        "num_bytes": 4868685208,
+        "num_examples": 40000,
+        "dataset_name": "rvl_cdip_easyOCR"
+      }
+    }
+  }
+}

rvl_cdip_easyocr.py → rvl_cdip_easyOCR.py

@@ -19,7 +19,7 @@ import os
 import numpy as np
 from tqdm import tqdm
 import datasets
-from pathlib import Path
+
 
 _CITATION = """\
 @inproceedings{harley2015icdar,
@@ -52,12 +52,6 @@ _METADATA_URLS = {
     "val": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/val.txt",
 }
 
-_OCR_URLS = {
-    "train": "https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr/resolve/main/data/Easy_Train_Data.npy",
-    "test": "https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr/resolve/main/data/Easy_Test_Data.npy",
-    "val": "https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr/resolve/main/data/Easy_Valid_Data.npy",
-}
-
 _CLASSES = [
     "letter",
     "form",
@@ -77,27 +71,27 @@ _CLASSES = [
     "memo",
 ]
 
-_IMAGES_DIR = "images/"   
+_IMAGES_DIR = "images/"
 
 
 # class OCRConfig(datasets.BuilderConfig):
 #     """BuilderConfig for RedCaps."""
 
-#     def __init__(self, name, OCR_dir, **kwargs):
+#     def __init__(self, name, **kwargs):
 #         """BuilderConfig for RedCaps.
 #         Args:
 #           **kwargs: keyword arguments forwarded to super.
 #         """
 #         assert "description" not in kwargs
-#         super(OCRConfig, self).__init__(version=kwargs["version"], name=name, **kwargs)
-#         self.OCR_dir = OCR_dir
+#         super(OCRConfig, self).__init__(
+#             version=kwargs['version'], name=name, **kwargs
+#         )
 
 
 class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
     """Ryerson Vision Lab Complex Document Information Processing dataset."""
 
     VERSION = datasets.Version("1.0.0")
-    # BUILDER_CONFIGS = [OCRConfig("default",version=VERSION)]
     DEFAULT_CONFIG_NAME = "default"
 
     def _info(self):
@@ -127,7 +121,6 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
                 _URLS["rvl-cdip"]
             )  # only download images if need be
         labels_path = dl_manager.download(_METADATA_URLS)
-        ocrs_filepath = dl_manager.download(_OCR_URLS)
 
         return [
             datasets.SplitGenerator(
@@ -135,7 +128,6 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "archive_iterator": dl_manager.iter_archive(archive_path),
                     "labels_filepath": labels_path["train"],
-                    "ocrs_filepath": ocrs_filepath["train"],
                     "split": "train",
                 },
             ),
@@ -144,7 +136,6 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "archive_iterator": dl_manager.iter_archive(archive_path),
                     "labels_filepath": labels_path["test"],
-                    "ocrs_filepath": ocrs_filepath["test"],
                     "split": "test",
                 },
             ),
@@ -153,7 +144,6 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
                 gen_kwargs={
                     "archive_iterator": dl_manager.iter_archive(archive_path),
                     "labels_filepath": labels_path["val"],
-                    "ocrs_filepath": ocrs_filepath["val"],
                     "split": "validation",
                 },
             ),
@@ -170,19 +160,21 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
         return image_to_class_id
 
     @staticmethod
-    def _get_image_to_OCR(ocrs_filepath, split):
+    def _get_image_to_OCR(OCR_dir, split):
         def parse_easyOCR_box(box):
             # {'x0': 39, 'y0': 39, 'x1': 498, 'y1': 82, 'width': 459, 'height': 43}
             return (box["x0"], box["y0"], box["x1"], box["y1"])
 
+        if OCR_dir is None:
+            return {}
         image_to_OCR = {}
         data = np.load(
-            ocrs_filepath,
+            os.path.join(OCR_dir, f"Easy_{split[0].upper()+split[1:]}_Data.npy"),
             allow_pickle=True,
         )
         for ex in tqdm(data, desc="Loading OCR data"):
             w, h = ex["images"][0]["image_width"], ex["images"][0]["image_height"]
-            filename = Path(ex["images"][0]["file_name"]).stem
+            filename = ex["images"][0]["file_name"]
             words = ex["word-level annotations"][0]["ocred_text"]
             box_info = ex["word-level annotations"][0]["ocred_boxes"]
             boxes = [parse_easyOCR_box(box) for box in box_info]
@@ -194,18 +186,15 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
     def _path_to_OCR(image_to_OCR, file_path):
         # obtain text and boxes given file_path
         words, boxes = None, None
-        #imagesv/v/u/b/vub13c00/523466896+-6898.tif
-        #523466896+-6898.jpg
-        file_path = Path(file_path).stem
         if file_path in image_to_OCR:
             words, boxes = image_to_OCR[file_path]
         return words, boxes
 
-    def _generate_examples(self, archive_iterator, labels_filepath, ocrs_filepath, split):
+    def _generate_examples(self, archive_iterator, labels_filepath, split):
         with open(labels_filepath, encoding="utf-8") as f:
             data = f.read().splitlines()
 
-        image_to_OCR = self._get_image_to_OCR(ocrs_filepath, split)
+        image_to_OCR = self._get_image_to_OCR(self.config.data_dir, split)
         image_to_class_id = self._get_image_to_class_map(data)
 
         for file_path, file_obj in archive_iterator:
@@ -214,12 +203,12 @@ class RvlCdipEasyOcr(datasets.GeneratorBasedBuilder):
                     class_id = image_to_class_id[file_path]
                     label = _CLASSES[class_id]
                     words, boxes = self._path_to_OCR(image_to_OCR, file_path)
-                    if words is not None: #skipping all items for which we do not have OCR
-                        a = dict(
-                            id=file_path,
-                            image={"path": file_path, "bytes": file_obj.read()},
-                            label=label,
-                            words=words,
-                            boxes=boxes,
-                        )
-                        yield file_path, a
+                    a = dict(
+                        id=file_path,
+                        image={"path": file_path, "bytes": file_obj.read()},
+                        label=label,
+                        words=words,
+                        boxes=boxes,
+                    )
+                    yield file_path, a
+            

test_loader.py

@@ -1,18 +1,4 @@
-from datasets import (
-    load_dataset_builder,
-    get_dataset_config_names,
-    get_dataset_infos,
-    load_dataset,
-)
-
-
-data = load_dataset(
-    "jordyvl/rvl-cdip_easyOCR",
-)
-from pdb import set_trace
-
-set_trace()
-
+from datasets import load_dataset_builder, get_dataset_config_names, get_dataset_infos, load_dataset
 
 # print(get_dataset_infos('jordyvl/rvl-cdip_easyOCR'))
 # print(get_dataset_config_names("jordyvl/rvl-cdip_easyOCR"))
@@ -21,16 +7,12 @@ set_trace()
 
 builder = load_dataset_builder("rvl_cdip")
 
-from pdb import set_trace
-
-set_trace()
+from pdb import set_trace; set_trace()
 builder = load_dataset_builder("jordyvl/rvl-cdip_easyOCR")
 print(builder._info())
 print(builder.get_all_exported_dataset_infos())
 ds = builder.download_and_prepare()
-from pdb import set_trace
-
-set_trace()
+from pdb import set_trace; set_trace()
 # data = load_dataset(
 #     "jordyvl/rvl-cdip_easyOCR",
 #     split="test",
@@ -42,17 +24,16 @@ set_trace()
 #     #data_dir="/home/jordy/Downloads/OCRedText", # this is the path to the OCR data
 # )
 
-from pdb import set_trace
-
-set_trace()
+from pdb import set_trace; set_trace()
 
 
 data = load_dataset(
     "./rvl_cdip_easyOCR.py",
     split="test",
-    # cache_dir="/mnt/lerna/data/HFcache",
-    data_files={  # this is the path to the images if it does not download it
-        "binary": __file__  # "/mnt/lerna/data/HFcache/downloads/c8cc6f89129255a9adf3e97e319ebe2055cf97662135b3ad26c79e9432544db5",
+    #cache_dir="/mnt/lerna/data/HFcache",
+    data_files={ # this is the path to the images if it does not download it
+        "binary": __file__#"/mnt/lerna/data/HFcache/downloads/c8cc6f89129255a9adf3e97e319ebe2055cf97662135b3ad26c79e9432544db5",
     },
-    data_dir="/home/jordy/Downloads/OCRedText",  # this is the path to the OCR data
+    data_dir="/home/jordy/Downloads/OCRedText", # this is the path to the OCR data
 )
+