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data/images/goteborgs_poliskammare_fore_1900_images_1.tar.gz

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data/images/goteborgs_poliskammare_fore_1900_images_2.tar.gz

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data/page_xmls/goteborgs_poliskammare_fore_1900_page_xmls_1.tar.gz

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data/page_xmls/goteborgs_poliskammare_fore_1900_page_xmls_2.tar.gz

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goteborgs_poliskammare_fore_1900.py

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+# 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({"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/goteborgs_poliskammare_fore_1900/resolve/main/data/images/goteborgs_poliskammare_fore_1900_images_{i}.tar.gz"
+                                for i in range(1, 3)
+                            ]
+                        )
+        xmls = dl_manager.download_and_extract(
+                            [
+                                f"https://huggingface.co/datasets/Riksarkivet/goteborgs_poliskammare_fore_1900/resolve/main/data/page_xmls/goteborgs_poliskammare_fore_1900_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,
+                        {"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