Update README.md

README.md

@@ -56,6 +56,68 @@ Note that the annotations follow the YOLO instance segmentation format.
 
 Please refer to [this page](https://docs.ultralytics.com/datasets/segment/) for more info.
 
+## 🧪 How to use
+```python
+from datasets import load_dataset
+from PIL import Image, ImageDraw
+import numpy as np
+import random
+
+# --- Configuration ---
+DATASET_NAME = "FBK-TeV/RaspGrade"
+SAMPLE_INDEX = 0  # Index of the sample to visualize from the 'valid' split
+OUTPUT_IMAGE = 'annotated_hub_image_fixed_colors.png'
+ALPHA = 128  # Transparency level for masks
+
+# Define a color map for different classes
+CLASS_COLORS = {
+    0: (255, 0, 0, ALPHA),    # Red
+    1: (0, 255, 0, ALPHA),    # Green
+    2: (0, 0, 255, ALPHA),    # Blue
+    3: (255, 255, 0, ALPHA),  # Yellow
+    4: (255, 0, 255, ALPHA)   # Magenta
+    # Add more colors for other class IDs if needed
+}
+
+def convert_normalized_polygon_to_pixels(polygon_normalized, width, height):
+    polygon_pixels = (np.array(polygon_normalized).reshape(-1, 2) * np.array([width, height])).astype(int).flatten().tolist()
+    return polygon_pixels
+
+if __name__ == "__main__":
+    try:
+        dataset = load_dataset(DATASET_NAME)
+        if 'valid' not in dataset:
+            raise ValueError(f"Split 'valid' not found in dataset '{DATASET_NAME}'")
+        valid_dataset = dataset['valid']
+        if SAMPLE_INDEX >= len(valid_dataset):
+            raise ValueError(f"Sample index {SAMPLE_INDEX} is out of bounds for the 'valid' split (size: {len(valid_dataset)})")
+
+        sample = valid_dataset[SAMPLE_INDEX]
+        original_image = sample['image'].convert("RGBA")
+        width, height = original_image.size
+
+        mask = Image.new('RGBA', (width, height), (0, 0, 0, 0))
+        mask_draw = ImageDraw.Draw(mask, 'RGBA')
+
+        labels = sample['labels']
+        if isinstance(labels, list):
+            for annotation in labels:
+                if len(annotation) > 4:  # Assuming YOLO format includes class and polygon
+                    class_id = int(annotation[0])
+                    polygon_normalized = np.array(annotation[1:]).astype(float).reshape(-1, 2).flatten().tolist()
+                    polygon_pixels = convert_normalized_polygon_to_pixels(polygon_normalized, width, height)
+                    color = CLASS_COLORS.get(class_id, (255, 255, 255, ALPHA)) # Default to white if class_id not in map
+                    mask_draw.polygon(polygon_pixels, fill=color)
+
+        annotated_image = Image.alpha_composite(original_image, mask)
+
+        annotated_image.save(OUTPUT_IMAGE)
+        print(f"Annotated image with fixed colors saved as {OUTPUT_IMAGE}")
+        annotated_image.show()
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+```
 
 ## 🙏 Acknowledgement
 <style>