| """Render dataset-card statistics figures into assets/.""" |
| import sys |
| from pathlib import Path |
|
|
| import matplotlib |
|
|
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| import numpy as np |
| import pandas as pd |
| from tqdm import tqdm |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parent)) |
| from vivid_utils import STYLE, apply_style |
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| ASSETS = ROOT / "assets" |
| S = STYLE |
|
|
|
|
| def new_fig(): |
| fig, ax = plt.subplots(figsize=S["figsize"], dpi=S["dpi"], facecolor="white") |
| ax.set_facecolor("white") |
| apply_style(ax) |
| return fig, ax |
|
|
|
|
| def save(fig, name): |
| fig.tight_layout() |
| fig.savefig(ASSETS / name, dpi=S["dpi"], facecolor="white", bbox_inches="tight") |
| plt.close(fig) |
| print("wrote", ASSETS / name) |
|
|
|
|
| def plot_hist(series, xlabel, title, name, callout=None): |
| fig, ax = new_fig() |
| ax.hist(series, bins=50, color=S["accent"], edgecolor="white", linewidth=0.4) |
| mean, med = series.mean(), series.median() |
| ax.axvline(mean, color=S["text"], linewidth=1.2, linestyle="--") |
| ax.axvline(med, color=S["secondary"], linewidth=1.2, linestyle=":") |
| ax.text(0.98, 0.95, f"mean {mean:.0f} · median {med:.0f}", |
| transform=ax.transAxes, ha="right", va="top", fontsize=10, color=S["text"]) |
| if callout: |
| ax.text(0.98, 0.86, callout, transform=ax.transAxes, ha="right", va="top", |
| fontsize=11, fontweight="bold", color=S["accent"]) |
| ax.text(0.98, 0.78, "x-axis clipped at 99th percentile", transform=ax.transAxes, |
| ha="right", va="top", fontsize=8, color=S["secondary"]) |
| ax.set_xlabel(xlabel) |
| ax.set_ylabel("images") |
| ax.set_title(title, fontsize=12, loc="left") |
| ax.set_xlim(0, series.quantile(0.99)) |
| save(fig, name) |
|
|
|
|
| def main(): |
| ASSETS.mkdir(exist_ok=True) |
| meta = pd.read_csv(ROOT / "data" / "metadata.csv") |
| clusters = pd.read_csv(ROOT / "scripts" / "cache" / "clusters.csv") |
|
|
| total_berries = meta.berry_count.sum() |
| plot_hist(meta.berry_count, "berry keypoints per image", |
| "Berries per image", "berries_per_image.png", |
| callout=f"{total_berries:,} berries total") |
|
|
| fig, ax = new_fig() |
| counts = meta.cluster_count.value_counts().sort_index() |
| ax.bar(counts.index, counts.values, color=S["accent"], edgecolor="white", linewidth=0.4) |
| ax.set_xlabel("grape clusters per image") |
| ax.set_ylabel("images") |
| ax.set_title(f"Clusters per image · {meta.cluster_count.sum():,} total", |
| fontsize=12, loc="left") |
| save(fig, "clusters_per_image.png") |
|
|
| fig, ax = new_fig() |
| ax.scatter(meta.cluster_count, meta.berry_count, s=8, alpha=0.25, |
| color=S["accent"], edgecolors="none") |
| ax.set_xlabel("clusters per image") |
| ax.set_ylabel("berries per image") |
| ax.set_title("Berries vs clusters per image", fontsize=12, loc="left") |
| save(fig, "berries_vs_clusters.png") |
|
|
| fig, ax = new_fig() |
| ax.hist(clusters.rel_area * 100, bins=60, color=S["accent"], |
| edgecolor="white", linewidth=0.4) |
| ax.set_xlabel("cluster bbox area (% of image area)") |
| ax.set_ylabel("cluster instances") |
| ax.set_title("Cluster size distribution", fontsize=12, loc="left") |
| save(fig, "cluster_size_distribution.png") |
|
|
| dims = meta.set_index("file_name")[["width", "height"]] |
| xs, ys = [], [] |
| for p in tqdm(sorted((ROOT / "data" / "anns" / "points").glob("*.npy")), desc="points"): |
| pts = np.load(p) |
| if len(pts) == 0: |
| continue |
| matches = meta[meta.file_name.str.startswith(p.stem + ".")] |
| if len(matches) != 1: |
| sys.exit(f"ABORT: {len(matches)} metadata rows match points file {p.name}") |
| w, h = dims.loc[matches.iloc[0].file_name] |
| |
| xs.append(pts[:, 0] / w) |
| ys.append(pts[:, 1] / h) |
| xs, ys = np.concatenate(xs), np.concatenate(ys) |
| assert xs.max() <= 1.05 and ys.max() <= 1.05 |
|
|
| fig, ax = plt.subplots(figsize=(6, 6), dpi=S["dpi"], facecolor="white") |
| hb = ax.hist2d(xs, ys, bins=100, cmap="magma") |
| ax.invert_yaxis() |
| ax.set_xlabel("normalized x", color=S["text"]) |
| ax.set_ylabel("normalized y", color=S["text"]) |
| ax.set_title(f"Spatial density of {len(xs):,} berry centroids", |
| fontsize=12, loc="left", color=S["text"]) |
| ax.tick_params(colors=S["text"], labelsize=9) |
| fig.colorbar(hb[3], ax=ax, shrink=0.85) |
| save(fig, "spatial_density.png") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
