File size: 2,670 Bytes
320b346 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | """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)
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