first version of pdftoimage and ocr

DUDE_imdb_loader.py

@@ -301,76 +301,88 @@ if __name__ == "__main__":
     )
     splits = datasets.keys()
 
-    split = "val"
-
-    OCR_paths = []
-    document_paths = []
-    for i, x in enumerate(dataset):
-        if x["data_split"] != split:
+    for split in splits:
+        if split != "val":
             continue
-        if x["document"] not in document_paths:
-            document_paths.append(x["document"])
-            OCR_paths.append(x["OCR"])
 
-    document_paths = document_paths[:30]
-    OCR_paths = OCR_paths[:30]
-
-    # 1. PDF to image dir and collect document metadata (num_pages, page_image_names)
+        OCR_paths = []
+        document_paths = []
+        for i, x in enumerate(dataset):
+            if x["data_split"] != split:
+                continue
+            if x["document"] not in document_paths:
+                document_paths.append(x["document"])
+                OCR_paths.append(x["OCR"])
+
+        document_paths = document_paths[:30]
+        OCR_paths = OCR_paths[:30]
+
+        # 1. PDF to image dir and collect document metadata (num_pages, page_image_names)
+
+        # document_ids = [document_filepath.split("/")[-1].replace(".pdf", "") for document_filepath in document_paths]
+        documents_metadata_filename = f"{split}-documents_metadata.json"
+        if os.path.exists(documents_metadata_filename):
+            documents_metadata = load_json(documents_metadata_filename)
+        else:
+            documents_metadata = {}
+            # for document_filepath in tqdm(document_paths):
+            #     documents_metadata[docId] = pdf_to_images(document_filepath)
+            num_jobs = 6
+            block_size = int(len(document_paths) / num_jobs) + 1
+            print(f"{block_size} * {num_jobs} = {block_size*num_jobs} ({len(document_paths)})")
+            documents_blocks = [
+                document_paths[block_size * i : block_size * i + block_size] for i in range(num_jobs)
+            ]
+            print(
+                "chunksize",
+                len(set([docId for doc_block in documents_blocks for docId in doc_block])),
+            )
+            parallel_results = Parallel(n_jobs=num_jobs)(
+                delayed(get_document_metadata_block)(documents_metadata, documents, document_paths[i])
+                for i in range(num_jobs)
+            )
 
-    # document_ids = [document_filepath.split("/")[-1].replace(".pdf", "") for document_filepath in document_paths]
-    documents_metadata_filename = "documents_metadata.json"
-    if os.path.exists(documents_metadata_filename):
-        documents_metadata = load_json(documents_metadata_filename)
-    else:
-        documents_metadata = {}
-        # for document_filepath in tqdm(document_paths):
-        #     documents_metadata[docId] = pdf_to_images(document_filepath)
-        num_jobs = 6
-        block_size = int(len(document_paths) / num_jobs) + 1
-        print(f"{block_size} * {num_jobs} = {block_size*num_jobs} ({len(document_paths)})")
-        documents_blocks = [
-            document_paths[block_size * i : block_size * i + block_size] for i in range(num_jobs)
-        ]
-        print(
-            "chunksize",
-            len(set([docId for doc_block in documents_blocks for docId in doc_block])),
+            for block_result in parallel_results:
+                for docId, metadata in tqdm(block_result.items()):
+                    if docId not in documents_metadata:
+                        documents_metadata[docId] = metadata
+
+            save_json(documents_metadata_filename, documents_metadata)
+
+        # 2. Process OCR to obtain doc_ocr_info
+        documents_ocr_filename = f"{split}-documents_ocr.json"
+
+        if os.path.exists(documents_ocr_filename):
+            documents_ocr_info = load_json(documents_ocr_filename)
+        else:
+            documents_ocr_info = {}
+            no_ocr = []
+            error_ocr = []
+
+            for i, document_filepath in enumerate(document_paths):
+                docId = document_filepath.split("/")[-1].replace(".pdf", "")
+            try:
+                ocr_tokens, ocr_boxes = get_ocr_information(OCR_paths[i], documents_metadata[docId]["num_pages"])
+                doc_ocr_info[docId] = {"ocr_tokens": ocr_tokens, "ocr_boxes": ocr_boxes}
+            except FileNotFoundError:
+                no_ocr.append(docId)
+            except KeyError:
+                error_ocr.append(docId)
+
+            save_json(documents_ocr_filename, documents_ocr_info)
+
+        imdb = create_imdb_from_json(
+            data,
+            documents_metadata=documents_metadata,
+            documents_ocr_information=doc_ocr_info,
+            split="train",
+            version="0.1",
+            include_answers=True,
         )
-        parallel_results = Parallel(n_jobs=num_jobs)(
-            delayed(get_document_metadata_block)(documents_metadata, documents, document_paths[i])
-            for i in range(num_jobs)
-        )
-
-        for block_result in parallel_results:
-            for docId, metadata in tqdm(block_result.items()):
-                if docId not in documents_metadata:
-                    documents_metadata[docId] = metadata
-
-        save_json(documents_metadata_filename, documents_metadata)
-
-    # 2. Process OCR to obtain doc_ocr_info
-    documents_ocr_filename = "documents_ocr.json"
-
-    if os.path.exists(documents_ocr_filename):
-        documents_ocr_info = load_json(documents_ocr_filename)
-    else:
-        documents_ocr_info = {}
-        no_ocr = []
-        error_ocr = []
-
-        for i, document_filepath in enumerate(document_paths):
-            docId = document_filepath.split("/")[-1].replace(".pdf", "")
-        try:
-            ocr_tokens, ocr_boxes = get_ocr_information(OCR_paths[i], documents_metadata[docId]["num_pages"])
-            doc_ocr_info[docId] = {"ocr_tokens": ocr_tokens, "ocr_boxes": ocr_boxes}
-        except FileNotFoundError:
-            no_ocr.append(docId)
-        except KeyError:
-            error_ocr.append(docId)
-
-        save_json(documents_ocr_filename, documents_ocr_info)
+        np.save(f"{split}_imdb.npy", imdb)
 
     import pdb; pdb.set_trace()  # breakpoint 930f4f6a //
-    
+
     # page_image_dir = '/'.join(dataset['val']['document'][0].split("/")[:-1]).replace('PDF', 'images')
     # if not os.path.exists(page_image_dir):
     #     os.makedirs(page_image_dir)
@@ -406,16 +418,9 @@ if __name__ == "__main__":
         version="0.1",
         include_answers=True,
     )
-    val_imdb = create_imdb_from_json(
-        val_data,
-        documents_metadata=documents_metadata,
-        documents_ocr_information=doc_ocr_info,
-        split="train",
-        version="0.1",
-        include_answers=True,
-    )
+
+
     np.save("Imdb/train_imdb.npy", train_imdb)
-    np.save("Imdb/val_imdb.npy", val_imdb)
 
     document_paths = []
     num_jobs = 6