Update new_dataset_script.py

new_dataset_script.py

@@ -79,40 +79,33 @@ class NewDataset(datasets.GeneratorBasedBuilder):
     DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option1": datasets.Value("string"),
-                    "answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
-            features = datasets.Features(
-                {
-                    "sentence": datasets.Value("string"),
-                    "option2": datasets.Value("string"),
-                    "second_domain_answer": datasets.Value("string")
-                    # These are the features of your dataset like images, labels ...
-                }
-            )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
-            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
-            # supervised_keys=("sentence", "label"),
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
+    features = datasets.Features({
+        "image_id": datasets.Value("string"),
+        "species": datasets.Value("string"),
+        "scientific_name": datasets.Value("string"),
+        "pics_array": datasets.Array3D(dtype="uint8", shape=(3, 768, 1024)), # Assuming images are RGB with shape 768x1024
+        "image_resolution": {
+            "width": datasets.Value("int32"),
+            "height": datasets.Value("int32"),
+        },
+        "annotations": datasets.Sequence({
+            "category_id": datasets.Value("int32"),
+            "bounding_box": {
+                "x_min": datasets.Value("float32"),
+                "y_min": datasets.Value("float32"),
+                "x_max": datasets.Value("float32"),
+                "y_max": datasets.Value("float32"),
+            },
+        }),
+    })
+    return datasets.DatasetInfo(
+        description=_DESCRIPTION,
+        features=features,  # Here we define them because they are different between the two configurations
+        homepage=_HOMEPAGE,
+        license=_LICENSE,
+        citation=_CITATION,
+    )
+
 
     def _split_generators(self, dl_manager):
     # Download and extract the dataset using Hugging Face's datasets library
@@ -173,64 +166,54 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         ),
     ]
 
-    import os
-from PIL import Image
-import numpy as np
-import pandas as pd
+ 
 
 # ... other necessary imports and class definitions
-
-def _generate_examples(self, filepaths, species_info, data_dir, split):
-    """Yields examples as (key, example) tuples."""
-    for file_name in filepaths:
-        # Extract the base name without the file extension for image_id
-        image_id = os.path.splitext(file_name)[0]
-        
-        # Construct the full image file path and label file path
-        image_path = os.path.join(data_dir, f"{image_id}.jpg")
-        label_path = os.path.join(data_dir, f"{image_id}.txt")
-        
-        # Open image and convert to array
-        with Image.open(image_path) as img:
-            pics_array = np.array(img)
-            width, height = img.size
-        
-        # Extract species and scientific name from CSV file
-        species_row = species_info.loc[species_info['FileName'] == file_name]
-        species = species_row['Species'].values[0] if not species_row.empty else None
-        scientific_name = species_row['ScientificName'].values[0] if not species_row.empty else None
-
-        # Parse YOLO annotations
-        annotations = self._parse_yolo_labels(label_path, width, height)
-
-        # Yield the final structured example
-        yield image_id, {
-            "image_id": image_id,
-            "species": species,
-            "scientific_name": scientific_name,
-            "pics_array": pics_array,
-            "image_resolution": {"width": width, "height": height},
-            "annotations": annotations
-        }
-
-def _parse_yolo_labels(self, label_path, width, height):
+    def _parse_yolo_labels(self, label_path, width, height):
     annotations = []
     with open(label_path, 'r') as file:
         yolo_data = file.readlines()
 
     for line in yolo_data:
         class_id, x_center_rel, y_center_rel, width_rel, height_rel = map(float, line.split())
-        x_center_abs = x_center_rel * width
-        y_center_abs = y_center_rel * height
-        bbox_width = width_rel * width
-        bbox_height = height_rel * height
+        x_min = (x_center_rel - width_rel / 2) * width
+        y_min = (y_center_rel - height_rel / 2) * height
+        x_max = (x_center_rel + width_rel / 2) * width
+        y_max = (y_center_rel + height_rel / 2) * height
         annotations.append({
             "category_id": int(class_id),
             "bounding_box": {
-                "x_center": x_center_abs,
-                "y_center": y_center_abs,
-                "width": bbox_width,
-                "height": bbox_height
+                "x_min": x_min,
+                "y_min": y_min,
+                "x_max": x_max,
+                "y_max": y_max
             }
         })
-    return annotations
+    return annotations
+
+    def _generate_examples(self, filepaths, species_info, data_dir, split):
+        """Yields examples as (key, example) tuples."""
+        for file_name in filepaths:
+            image_id = os.path.splitext(file_name)[0]  # Extract the base name without the file extension
+            image_path = os.path.join(data_dir, f"{image_id}.jpg")
+            label_path = os.path.join(data_dir, f"{image_id}.txt")
+            
+            with Image.open(image_path) as img:
+                pics_array = np.array(img)
+                width, height = img.size
+            
+            species_row = species_info.loc[species_info['FileName'] == file_name]
+            species = species_row['Species'].values[0] if not species_row.empty else None
+            scientific_name = species_row['ScientificName'].values[0] if not species_row.empty else None
+    
+            annotations = self._parse_yolo_labels(label_path, width, height)
+    
+            yield image_id, {
+                "image_id": image_id,
+                "species": species,
+                "scientific_name": scientific_name,
+                "pics_array": pics_array,
+                "image_resolution": {"width": width, "height": height},
+                "annotations": annotations
+            }
+