Update README.md

README.md

@@ -67,3 +67,91 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+```python
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+import numpy as np
+
+# 1. Load the dataset
+# Note: Since this is a private repo, ensure you have run `huggingface-cli login`
+repo_id = "bdanko/loaf_resolution_512"
+print(f"Downloading {repo_id}...")
+dataset = load_dataset(repo_id, split="train")
+
+# 2. Grab the first example
+example = dataset[0]
+
+# Hugging Face automatically decodes the Parquet bytes into a PIL Image
+img = example["image"] 
+objects = example["objects"]
+
+# Print image-level metadata
+print("\n--- Image Metadata ---")
+print(f"File Name: {example['file_name']}")
+print(f"Image ID: {example['image_id']}")
+print(f"Dimensions: {example['width']}x{example['height']}")
+print(f"Camera Height (Image): {example['camera_height_img']}")
+
+# 3. Set up the matplotlib plot
+fig, ax = plt.subplots(1, figsize=(10, 8))
+ax.imshow(img)
+ax.axis("off")
+ax.set_title(f"Sample: {example['file_name']}", fontsize=14)
+
+print("\n--- Object Metadata ---")
+
+# 4. Iterate through all objects in this image
+# We use zip to unpack all the parallel lists stored inside the 'objects' dictionary
+num_objects = len(objects["annotation_id"])
+
+for i in range(num_objects):
+    ann_id = objects["annotation_id"][i]
+    cat_id = objects["category_id"][i]
+    bbox = objects["bbox"][i]
+    seg = objects["segmentation"][i]
+    
+    # Custom / 3D annotations
+    ritbox = objects["ritbox"][i]
+    rot_box = objects["rotated_box"][i]
+    world_loc = objects["world_location"][i]
+    person_loc = objects["person_location"][i]
+    cam_height_ann = objects["camera_height_ann"][i]
+    
+    print(f"\nObject {i+1} (Ann ID: {ann_id} | Category: {cat_id})")
+    print(f"  - 2D BBox: {bbox}")
+    print(f"  - Ritbox: {ritbox}")
+    print(f"  - Rotated Box: {rot_box}")
+    print(f"  - World Location: {world_loc}")
+    print(f"  - Person Location: {person_loc}")
+    print(f"  - Camera Height: {cam_height_ann}")
+
+    # --- Plotting the 2D Bounding Box ---
+    # COCO bbox format is [top_left_x, top_left_y, width, height]
+    if len(bbox) == 4:
+        x, y, w, h = bbox
+        rect = patches.Rectangle(
+            (x, y), w, h, 
+            linewidth=2, edgecolor='red', facecolor='none', label=f"Cat {cat_id}"
+        )
+        ax.add_patch(rect)
+        ax.text(x, y - 5, f"Cat {cat_id}", color='red', fontsize=10, weight='bold')
+
+    # --- Plotting the Segmentation Polygon ---
+    # COCO segmentation is a flat list: [x1, y1, x2, y2, ...]
+    if len(seg) > 0:
+        # Reshape flat list into a list of (x, y) coordinate pairs
+        poly_coords = np.array(seg).reshape(-1, 2)
+        polygon = patches.Polygon(
+            poly_coords, 
+            linewidth=2, edgecolor='cyan', facecolor='cyan', alpha=0.3
+        )
+        ax.add_patch(polygon)
+
+plt.tight_layout()
+plt.show()
+```
+
+
+![image](https://cdn-uploads.huggingface.co/production/uploads/6739246f8f51fde3c5ca7663/7b-CyZqtH1Qcz1IyOpwko.png)