add coco config

dataset_infos.json

@@ -1 +1 @@
-{"default": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}}
+{"default": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}, "YOLO": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image": {"decode": true, "id": null, "_type": "Image"}, "objects": {"feature": {"label": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "bbox": {"feature": {"dtype": "int32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "YOLO", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 60704, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 7537, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 47159, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 115400, "size_in_bytes": 376305464}, "COCO": {"description": "TODO", "citation": "    @dataset{clerice_thibault_2022_6827706,\n  author       = {Cl\u00e9rice, Thibault},\n  title        = {YALTAi: Tabular Dataset},\n  month        = jul,\n  year         = 2022,\n  publisher    = {Zenodo},\n  version      = {1.0.0},\n  doi          = {10.5281/zenodo.6827706},\n  url          = {https://doi.org/10.5281/zenodo.6827706}\n}\n", "homepage": "https://doi.org/10.5281/zenodo.6827706", "license": "Creative Commons Attribution 4.0 International", "features": {"image_id": {"dtype": "int64", "id": null, "_type": "Value"}, "image": {"decode": true, "id": null, "_type": "Image"}, "width": {"dtype": "int32", "id": null, "_type": "Value"}, "height": {"dtype": "int32", "id": null, "_type": "Value"}, "objects": [{"category_id": {"num_classes": 4, "names": ["Header", "Col", "Marginal", "text"], "id": null, "_type": "ClassLabel"}, "image_id": {"dtype": "string", "id": null, "_type": "Value"}, "id": {"dtype": "int64", "id": null, "_type": "Value"}, "area": {"dtype": "int64", "id": null, "_type": "Value"}, "bbox": {"feature": {"dtype": "float32", "id": null, "_type": "Value"}, "length": 4, "id": null, "_type": "Sequence"}, "segmentation": [[{"dtype": "float32", "id": null, "_type": "Value"}]], "iscrowd": {"dtype": "bool", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "yalt_ai_tabular_dataset", "config_name": "COCO", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 87171, "num_examples": 196, "dataset_name": "yalt_ai_tabular_dataset"}, "validation": {"name": "validation", "num_bytes": 11225, "num_examples": 22, "dataset_name": "yalt_ai_tabular_dataset"}, "test": {"name": "test", "num_bytes": 71491, "num_examples": 135, "dataset_name": "yalt_ai_tabular_dataset"}}, "download_checksums": {"https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1": {"num_bytes": 376190064, "checksum": "5b312faf097939302fb98ab0a8b35c007962d88978ea9dc28d2f560b89dc0657"}}, "download_size": 376190064, "post_processing_size": null, "dataset_size": 169887, "size_in_bytes": 376359951}}

yalt_ai_tabular_dataset.py

@@ -16,6 +16,7 @@
 
 import os
 from glob import glob
+from re import L
 
 import datasets
 from PIL import Image
@@ -44,15 +45,49 @@ _URL = "https://zenodo.org/record/6827706/files/yaltai-table.zip?download=1"
 _CATEGORIES = ["Header", "Col", "Marginal", "text"]
 
 
+class YaltAiTabularDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for YaltAiTabularDataset."""
+
+    def __init__(self, name, **kwargs):
+        """BuilderConfig for YaltAiTabularDataset."""
+        super(YaltAiTabularDatasetConfig, self).__init__(
+            version=datasets.Version("1.0.0"), name=name, description=None, **kwargs
+        )
+
+
 class YaltAiTabularDataset(datasets.GeneratorBasedBuilder):
     """Object Detection for historic manuscripts"""
 
-    VERSION = datasets.Version("1.0.0")
+    BUILDER_CONFIGS = [
+        YaltAiTabularDatasetConfig("YOLO"),
+        YaltAiTabularDatasetConfig("COCO"),
+    ]
 
     def _info(self):
-        return datasets.DatasetInfo(
-            features=datasets.Features(
+        if self.config.name == "COCO":
+            features = datasets.Features(
+                {
+                    "image_id": datasets.Value("int64"),
+                    "image": datasets.Image(),
+                    "width": datasets.Value("int32"),
+                    "height": datasets.Value("int32"),
+                    #   "url": datasets.Value("string"),
+                }
+            )
+            object_dict = {
+                "category_id": datasets.ClassLabel(names=_CATEGORIES),
+                "image_id": datasets.Value("string"),
+                "id": datasets.Value("int64"),
+                "area": datasets.Value("int64"),
+                "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                "segmentation": [[datasets.Value("float32")]],
+                "iscrowd": datasets.Value("bool"),
+            }
+            features["objects"] = [object_dict]
+        if self.config.name == "YOLO":
+            features = datasets.Features(
                 {
+                    # "image_id": datasets.Value("int32"),
                     "image": datasets.Image(),
                     "objects": datasets.Sequence(
                         {
@@ -63,7 +98,9 @@ class YaltAiTabularDataset(datasets.GeneratorBasedBuilder):
                         }
                     ),
                 }
-            ),
+            )
+        return datasets.DatasetInfo(
+            features=features,
             supervised_keys=None,
             description=_DESCRIPTION,
             homepage=_HOMEPAGE,
@@ -93,31 +130,122 @@ class YaltAiTabularDataset(datasets.GeneratorBasedBuilder):
         ]
 
     def _generate_examples(self, data_dir):
+        def create_annotation_from_yolo_format(
+            min_x,
+            min_y,
+            width,
+            height,
+            image_id,
+            category_id,
+            annotation_id,
+            segmentation=False,
+        ):
+            bbox = (float(min_x), float(min_y), float(width), float(height))
+            area = width * height
+            max_x = min_x + width
+            max_y = min_y + height
+            if segmentation:
+                seg = [[min_x, min_y, max_x, min_y, max_x, max_y, min_x, max_y]]
+            else:
+                seg = []
+            return {
+                "id": annotation_id,
+                "image_id": image_id,
+                "bbox": bbox,
+                "area": area,
+                "iscrowd": 0,
+                "category_id": category_id,
+                "segmentation": seg,
+            }
+
         image_dir = os.path.join(data_dir, "images")
         label_dir = os.path.join(data_dir, "labels")
         image_paths = sorted(glob(f"{image_dir}/*.jpg"))
         label_paths = sorted(glob(f"{label_dir}/*.txt"))
+        if self.config.name == "COCO":
+            for idx, (image_path, label_path) in enumerate(
+                zip(image_paths, label_paths)
+            ):
+                image_id = idx
+                annotations = []
+                image = Image.open(image_path)  # .convert("RGB")
+                w, h = image.size
+                with open(label_path, "r") as f:
+                    lines = f.readlines()
+                for line in lines:
+                    line = line.strip().split()
+                    # logger.warn(line)
+                    category_id = line[
+                        0
+                    ]  # int(line[0]) + 1  # you start with annotation id with '1'
+                    x_center = float(line[1])
+                    y_center = float(line[2])
+                    width = float(line[3])
+                    height = float(line[4])
+
+                    float_x_center = w * x_center
+                    float_y_center = h * y_center
+                    float_width = w * width
+                    float_height = h * height
+
+                    min_x = int(float_x_center - float_width / 2)
+                    min_y = int(float_y_center - float_height / 2)
+                    width = int(float_width)
+                    height = int(float_height)
+
+                    annotation = create_annotation_from_yolo_format(
+                        min_x,
+                        min_y,
+                        width,
+                        height,
+                        image_id,
+                        category_id,
+                        image_id,
+                        #  segmentation=opt.box2seg,
+                    )
+                    annotations.append(annotation)
+                    # annotation_id += 1
+
+                # image_id += 1  # if you finished annotation work, updates the image id.
+                example = {
+                    "image_id": image_id,
+                    "image": image,
+                    "width": w,
+                    "height": h,
+                    "objects": annotations,
+                }
+                yield idx, example
+        if self.config.name == "YOLO":
+            for idx, (image_path, label_path) in enumerate(
+                zip(image_paths, label_paths)
+            ):
+                im = Image.open(image_path)
+                width, height = im.size
+                image_id = idx
+                annotations = []
+                with open(label_path, "r") as f:
+                    lines = f.readlines()
+                objects = []
+                for line in lines:
+                    line = line.strip().split()
+                    bbox_class = int(line[0])
+                    bbox_xcenter = int(float(line[1]) * width)
+                    bbox_ycenter = int(float(line[2]) * height)
+                    bbox_width = int(float(line[3]) * width)
+                    bbox_height = int(float(line[4]) * height)
+                    objects.append(
+                        {
+                            "label": bbox_class,
+                            "bbox": [
+                                bbox_xcenter,
+                                bbox_ycenter,
+                                bbox_width,
+                                bbox_height,
+                            ],
+                        }
+                    )
 
-        for idx, (image_path, label_path) in enumerate(zip(image_paths, label_paths)):
-            im = Image.open(image_path)
-            width, height = im.size
-
-            with open(label_path, "r") as f:
-                lines = f.readlines()
-
-            objects = []
-            for line in lines:
-                line = line.strip().split()
-                bbox_class = int(line[0])
-                bbox_xcenter = int(float(line[1]) * width)
-                bbox_ycenter = int(float(line[2]) * height)
-                bbox_width = int(float(line[3]) * width)
-                bbox_height = int(float(line[4]) * height)
-                objects.append(
-                    {
-                        "label": bbox_class,
-                        "bbox": [bbox_xcenter, bbox_ycenter, bbox_width, bbox_height],
-                    }
-                )
-
-            yield idx, {"image": image_path, "objects": objects}
+                yield idx, {
+                    "image": image_path,
+                    "objects": objects,
+                }