feat: load script

data/annotations.xml

@@ -28,7 +28,7 @@
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-  <image id="39" name="images/39.png" width="1542" height="854">
+  <image id="39" name="39.png" width="1542" height="854">
     <box label="strawberry" source="manual" occluded="1" xtl="383.65" ytl="486.35" xbr="560.15" ybr="741.95" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="0" xtl="299.38" ytl="363.71" xbr="464.90" ybr="679.06" z_order="0">
@@ -894,7 +894,7 @@
     <box label="strawberry" source="manual" occluded="1" xtl="508.57" ytl="398.75" xbr="692.92" ybr="658.93" z_order="0">
     </box>
   </image>
-  <image id="4" name="images/4.png" width="733" height="733">
+  <image id="4" name="4.png" width="733" height="733">
     <box label="strawberry" source="manual" occluded="1" xtl="553.61" ytl="203.30" xbr="634.97" ybr="290.27" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="1" xtl="28.18" ytl="205.15" xbr="132.41" ybr="280.80" z_order="0">
@@ -926,7 +926,7 @@
     <box label="strawberry" source="manual" occluded="0" xtl="603.17" ytl="452.52" xbr="733.00" ybr="675.55" z_order="0">
     </box>
   </image>
-  <image id="5" name="images/5.png" width="640" height="640">
+  <image id="5" name="5.png" width="640" height="640">
     <box label="strawberry" source="manual" occluded="0" xtl="321.40" ytl="416.76" xbr="437.72" ybr="549.78" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="1" xtl="290.41" ytl="414.72" xbr="370.36" ybr="513.04" z_order="0">
@@ -942,7 +942,7 @@
     <box label="strawberry" source="manual" occluded="1" xtl="354.47" ytl="339.50" xbr="454.80" ybr="443.80" z_order="0">
     </box>
   </image>
-  <image id="6" name="images/6.png" width="1600" height="900">
+  <image id="6" name="6.png" width="1600" height="900">
     <box label="strawberry" source="manual" occluded="1" xtl="1117.06" ytl="455.36" xbr="1356.15" ybr="707.34" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="0" xtl="1318.45" ytl="425.69" xbr="1572.42" ybr="738.19" z_order="0">
@@ -960,7 +960,7 @@
     <box label="strawberry" source="manual" occluded="0" xtl="938.66" ytl="610.45" xbr="1152.03" ybr="862.60" z_order="0">
     </box>
   </image>
-  <image id="7" name="images/7.png" width="1000" height="1000">
+  <image id="7" name="7.png" width="1000" height="1000">
     <box label="strawberry" source="manual" occluded="0" xtl="188.95" ytl="639.95" xbr="394.36" ybr="875.33" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="0" xtl="554.15" ytl="609.52" xbr="740.42" ybr="839.17" z_order="0">
@@ -974,7 +974,7 @@
     <box label="strawberry" source="manual" occluded="1" xtl="810.20" ytl="512.69" xbr="965.85" ybr="644.73" z_order="0">
     </box>
   </image>
-  <image id="8" name="images/8.png" width="2048" height="1152">
+  <image id="8" name="8.png" width="2048" height="1152">
     <box label="strawberry" source="manual" occluded="1" xtl="116.49" ytl="873.80" xbr="261.93" ybr="1040.68" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="0" xtl="743.93" ytl="755.83" xbr="821.33" ybr="841.31" z_order="0">
@@ -1036,7 +1036,7 @@
     <box label="strawberry" source="manual" occluded="0" xtl="626.66" ytl="889.73" xbr="741.48" ybr="1021.90" z_order="0">
     </box>
   </image>
-  <image id="9" name="images/9.png" width="1600" height="1200">
+  <image id="9" name="9.png" width="1600" height="1200">
     <box label="strawberry" source="manual" occluded="0" xtl="731.59" ytl="633.82" xbr="1023.70" ybr="964.51" z_order="0">
     </box>
     <box label="strawberry" source="manual" occluded="1" xtl="841.40" ytl="546.69" xbr="1019.80" ybr="744.95" z_order="0">

ripe-strawberries-detection.py

@@ -0,0 +1,158 @@
+from xml.etree import ElementTree as ET
+
+import datasets
+
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+title = {ripe-strawberries-detection},
+author = {TrainingDataPro},
+year = {2023}
+}
+"""
+
+_DESCRIPTION = """\
+The dataset consists of photos of strawberries for the identification and recognition of
+ripe berries. 
+The images are annotated with **bounding boxes** that accurately demarcate the location
+of the ripe strawberries within the image.
+Each image in the dataset showcases a strawberry plantation, and includes a diverse
+range of backgrounds, lighting conditions, and orientations. The photos are captured
+from various angles and distances, providing a realistic representation of strawberries.
+The dataset can be utilised for enabling advancements in strawberry production, quality
+control, and greater precision in agricultural practices.
+
+"""
+_NAME = "ripe-strawberries-detection"
+
+_HOMEPAGE = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}"
+
+_LICENSE = ""
+
+_DATA = f"https://huggingface.co/datasets/TrainingDataPro/{_NAME}/resolve/main/data/"
+
+_LABELS = ["strawberry"]
+
+
+class BotoxInjectionsBeforeAndAfter(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("int32"),
+                    "name": datasets.Value("string"),
+                    "image": datasets.Image(),
+                    "mask": datasets.Image(),
+                    "width": datasets.Value("uint16"),
+                    "height": datasets.Value("uint16"),
+                    "shapes": datasets.Sequence(
+                        {
+                            "label": datasets.ClassLabel(
+                                num_classes=len(_LABELS),
+                                names=_LABELS,
+                            ),
+                            "type": datasets.Value("string"),
+                            "points": datasets.Sequence(
+                                datasets.Sequence(
+                                    datasets.Value("float"),
+                                ),
+                            ),
+                            "rotation": datasets.Value("float"),
+                            "attributes": datasets.Sequence(
+                                {
+                                    "name": datasets.Value("string"),
+                                    "text": datasets.Value("string"),
+                                }
+                            ),
+                        }
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        images = dl_manager.download(f"{_DATA}images.tar.gz")
+        masks = dl_manager.download(f"{_DATA}boxes.tar.gz")
+        annotations = dl_manager.download(f"{_DATA}annotations.xml")
+        images = dl_manager.iter_archive(images)
+        masks = dl_manager.iter_archive(masks)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "images": images,
+                    "masks": masks,
+                    "annotations": annotations,
+                },
+            ),
+        ]
+
+    @staticmethod
+    def parse_shape(shape: ET.Element) -> dict:
+        label = shape.get("label")
+        shape_type = shape.tag
+        rotation = shape.get("rotation", 0.0)
+
+        points = None
+
+        if shape_type == "points":
+            points = tuple(map(float, shape.get("points").split(",")))
+
+        elif shape_type == "box":
+            points = [
+                (float(shape.get("xtl")), float(shape.get("ytl"))),
+                (float(shape.get("xbr")), float(shape.get("ybr"))),
+            ]
+
+        elif shape_type == "polygon":
+            points = [
+                tuple(map(float, point.split(",")))
+                for point in shape.get("points").split(";")
+            ]
+
+        attributes = []
+
+        for attr in shape:
+            attr_name = attr.get("name")
+            attr_text = attr.text
+            attributes.append({"name": attr_name, "text": attr_text})
+
+        shape_data = {
+            "label": label,
+            "type": shape_type,
+            "points": points,
+            "rotation": rotation,
+            "attributes": attributes,
+        }
+
+        return shape_data
+
+    def _generate_examples(self, images, masks, annotations):
+        tree = ET.parse(annotations)
+        root = tree.getroot()
+
+        for idx, (
+            (image_path, image),
+            (mask_path, mask),
+        ) in enumerate(zip(images, masks)):
+            image_name = image_path.split("/")[-1]
+            img = root.find(f"./image[@name='{image_name}']")
+
+            image_id = img.get("id")
+            name = img.get("name")
+            width = img.get("width")
+            height = img.get("height")
+            shapes = [self.parse_shape(shape) for shape in img]
+
+            yield idx, {
+                "id": image_id,
+                "name": name,
+                "image": {"path": image_path, "bytes": image.read()},
+                "mask": {"path": mask_path, "bytes": mask.read()},
+                "width": width,
+                "height": height,
+                "shapes": shapes,
+            }