| """Generate notebooks/demo.ipynb programmatically.""" |
| from pathlib import Path |
|
|
| import nbformat as nbf |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| nb = nbf.v4.new_notebook() |
| cells = [] |
|
|
| cells.append(nbf.v4.new_markdown_cell( |
| "# ViViD-5k demo — load, visualize, count berries\n" |
| "Dataset: [XZhi/ViViD-5k](https://huggingface.co/datasets/XZhi/ViViD-5k) · " |
| "Paper: [arXiv:2605.24353](https://arxiv.org/abs/2605.24353) · " |
| "Code: [GrapeSAM](https://github.com/tong-xz/GrapeSAM)")) |
|
|
| cells.append(nbf.v4.new_code_cell( |
| "%pip install -q huggingface_hub datasets pillow matplotlib numpy")) |
|
|
| cells.append(nbf.v4.new_markdown_cell("## Download one sample (image + berry keypoints)")) |
| cells.append(nbf.v4.new_code_cell( |
| 'from huggingface_hub import hf_hub_download\n' |
| 'import numpy as np\n' |
| 'from PIL import Image\n\n' |
| 'repo = "XZhi/ViViD-5k"\n' |
| 'img = Image.open(hf_hub_download(repo, "data/imgs/0.png", repo_type="dataset"))\n' |
| 'pts = np.load(hf_hub_download(repo, "data/anns/points/0.npy", repo_type="dataset"))\n' |
| 'print(f"image {img.size}, {len(pts)} annotated berries")')) |
|
|
| cells.append(nbf.v4.new_markdown_cell("## Overlay the berry keypoints")) |
| cells.append(nbf.v4.new_code_cell( |
| 'import matplotlib.pyplot as plt\n\n' |
| 'fig, ax = plt.subplots(figsize=(6, 10))\n' |
| 'ax.imshow(img)\n' |
| 'ax.scatter(pts[:, 0], pts[:, 1], s=6, color="#FDE047", edgecolors="#B45309", linewidths=0.3)\n' |
| 'ax.set_title(f"{len(pts)} berries")\n' |
| 'ax.axis("off")\n' |
| 'plt.show()')) |
|
|
| cells.append(nbf.v4.new_markdown_cell("## Browse the dataset (streaming, no full download)")) |
| cells.append(nbf.v4.new_code_cell( |
| 'from datasets import load_dataset\n\n' |
| 'ds = load_dataset(repo, split="train", streaming=True)\n' |
| 'ex = next(iter(ds))\n' |
| 'display(ex["image"])\n' |
| 'print("berries:", ex["berry_count"], "clusters:", ex["cluster_count"])')) |
|
|
| cells.append(nbf.v4.new_markdown_cell( |
| "## Per-image metadata\nEvery image has computed stats in `data/metadata.csv`.")) |
| cells.append(nbf.v4.new_code_cell( |
| 'import pandas as pd\n\n' |
| 'meta = pd.read_csv(hf_hub_download(repo, "data/metadata.csv", repo_type="dataset"))\n' |
| 'meta.berry_count.hist(bins=50)\n' |
| 'meta.describe()')) |
|
|
| cells.append(nbf.v4.new_markdown_cell( |
| "## Next: count berries with a model\n" |
| "[GrapeSAM](https://github.com/tong-xz/GrapeSAM) trains and evaluates cluster " |
| "segmentation + berry counting models on ViViD-5k — full masks and COCO " |
| "annotations are in `data/anns/`.")) |
|
|
| nb["cells"] = cells |
| out = ROOT / "notebooks" / "demo.ipynb" |
| out.parent.mkdir(exist_ok=True) |
| nbf.write(nb, out) |
| print("wrote", out) |
|
|
