# ladda upp datasetet i en zip av imgs och en zip av xml, skapa flera archive iterators och använd dom (men hur blir det med ordningen?) import os import xml.etree.ElementTree as ET from glob import glob from pathlib import Path, PurePath import cv2 import numpy as np from datasets import ( BuilderConfig, DatasetInfo, Features, GeneratorBasedBuilder, Image, Split, SplitGenerator, Value, ) from PIL import Image as PILImage class HTRDatasetConfig(BuilderConfig): """BuilderConfig for HTRDataset""" def __init__(self, **kwargs): super(HTRDatasetConfig, self).__init__(**kwargs) class HTRDataset(GeneratorBasedBuilder): BUILDER_CONFIGS = [ HTRDatasetConfig( name="htr_dataset", version="1.0.0", description="Line dataset for text recognition of historical swedish", ) ] def _info(self): features = Features({"unique_key": Value("string"), "image": Image(), "transcription": Value("string")}) return DatasetInfo(features=features) def _split_generators(self, dl_manager): """ images = dl_manager.download_and_extract( [ f"https://huggingface.co/datasets/Riksarkivet/alvsborgs_losen/resolve/main/data/images/alvsborgs_losen_imgs_{i}.tar.gz" for i in range(1, 3) ] ) xmls = dl_manager.download_and_extract( [ f"https://huggingface.co/datasets/Riksarkivet/alvsborgs_losen/resolve/main/data/page_xmls/alvsborgs_losen_page_xmls_{i}.tar.gz" for i in range(1, 3) ] ) """ images = dl_manager.download_and_extract( [ f"https://huggingface.co/datasets/Riksarkivet/frihetstidens_utskottshandlingar/resolve/main/data/images/frihetstidens_utskottshandlingar_images_{i}.tar.gz" for i in range(1, 3) ] ) xmls = dl_manager.download_and_extract( [ f"https://huggingface.co/datasets/Riksarkivet/frihetstidens_utskottshandlingar/resolve/main/data/page_xmls/frihetstidens_utskottshandlingar_page_xmls_{i}.tar.gz" for i in range(1, 3) ] ) image_extensions = [ "*.jpg", "*.jpeg", "*.png", "*.gif", "*.bmp", "*.tif", "*.tiff", "*.JPG", "*.JPEG", "*.PNG", "*.GIF", "*.BMP", "*.TIF", "*.TIFF", ] imgs_nested = [glob(os.path.join(x, "**", ext), recursive=True) for ext in image_extensions for x in images] imgs_flat = [item for sublist in imgs_nested for item in sublist] sorted_imgs = sorted(imgs_flat, key=lambda x: Path(x).stem) xmls_nested = [glob(os.path.join(x, "**", "*.xml"), recursive=True) for x in xmls] xmls_flat = [item for sublist in xmls_nested for item in sublist] sorted_xmls = sorted(xmls_flat, key=lambda x: Path(x).stem) assert len(sorted_imgs) == len(sorted_xmls) imgs_xmls = [] for img, xml in zip(sorted_imgs, sorted_xmls): imgs_xmls.append((img, xml)) return [ SplitGenerator( name=Split.TRAIN, gen_kwargs={"imgs_xmls": imgs_xmls}, ) ] def _generate_examples(self, imgs_xmls): for img, xml in imgs_xmls: assert Path(img).stem == Path(xml).stem img_filename = Path(img).stem volume = PurePath(img).parts[-2] lines_data = self.parse_pagexml(xml) # Convert the bytes to a NumPy array image_array = cv2.imread(img) for i, line in enumerate(lines_data): line_id = str(i).zfill(4) try: cropped_image = self.crop_line_image(image_array, line["coords"]) except Exception as e: print(e) continue # Logging to ensure data types and shapes cropped_image_np = np.array(cropped_image, dtype=np.uint8) # Ensure transcription is a string and not None transcription = str(line["transcription"]) if transcription is None or not isinstance(transcription, str) or transcription == "": print(f"Invalid transcription: {transcription}") continue # Generate and log the unique key unique_key = f"{volume}_{img_filename}_{line_id}" try: yield ( unique_key, {"unique_key": unique_key, "image": cropped_image, "transcription": transcription}, ) except Exception as e: print(f"Error yielding example {unique_key}: {e}") def parse_pagexml(self, xml): try: tree = ET.parse(xml) root = tree.getroot() except ET.ParseError as e: print(e) return [] namespaces = {"ns": "http://schema.primaresearch.org/PAGE/gts/pagecontent/2013-07-15"} page = root.find("ns:Page", namespaces) if page is None: print("no page") return [] text_regions = page.findall("ns:TextRegion", namespaces) lines_data = [] for region in text_regions: lines = region.findall("ns:TextLine", namespaces) for line in lines: try: line_id = line.get("id") coords = line.find("ns:Coords", namespaces).get("points") coords = [tuple(map(int, p.split(","))) for p in coords.split()] transcription = line.find("ns:TextEquiv/ns:Unicode", namespaces).text lines_data.append({"line_id": line_id, "coords": coords, "transcription": transcription}) except Exception as e: print(e) continue return lines_data def crop_line_image(self, img, coords): coords = np.array(coords) # img = HTRDataset.np_to_cv2(image) mask = np.zeros(img.shape[0:2], dtype=np.uint8) try: cv2.drawContours(mask, [coords], -1, (255, 255, 255), -1, cv2.LINE_AA) except Exception as e: print(e) res = cv2.bitwise_and(img, img, mask=mask) rect = cv2.boundingRect(coords) wbg = np.ones_like(img, np.uint8) * 255 cv2.bitwise_not(wbg, wbg, mask=mask) # overlap the resulted cropped image on the white background dst = wbg + res cropped = dst[rect[1] : rect[1] + rect[3], rect[0] : rect[0] + rect[2]] cropped = HTRDataset.cv2_to_pil(cropped) return cropped def np_to_cv2(image_array): image = cv2.imdecode(image_array, cv2.IMREAD_COLOR) image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) return image_rgb # Convert OpenCV image to PIL Image def cv2_to_pil(cv2_image): # Convert BGR to RGB cv2_image_rgb = cv2.cvtColor(cv2_image, cv2.COLOR_BGR2RGB) # Convert NumPy array to PIL image pil_image = PILImage.fromarray(cv2_image_rgb) return pil_image