| """HOWC hierarchical perception - HuggingFace Space (Gradio). |
| |
| Upload a road image; a detector proposes boxes and each is classified by |
| hierarchical taxonomic abstraction: the most specific safe level, or an explicit |
| UNKNOWN, never a confident wrong leaf. Models download lazily on first run. |
| |
| To deploy: this file plus requirements.txt and README.md, together with the |
| `hpercept/` package and `taxonomy.yaml` at the Space root (copy them in, or |
| `pip install` the package). See README.md. |
| """ |
| from __future__ import annotations |
|
|
| import numpy as np |
| import gradio as gr |
|
|
| from hpercept.abstraction import AbstractionConfig |
| from hpercept.pipeline import get_pipeline |
| from hpercept.viz import annotate, taxonomy_html |
|
|
| PIPE = get_pipeline() |
| TAX = PIPE.taxonomy |
|
|
|
|
| def analyze(image: np.ndarray, det_conf: float, commit_mass: float, floor: bool): |
| if image is None: |
| return None, "Upload an image first.", [], taxonomy_html(TAX, None), gr.update(choices=[]) |
| cfg = AbstractionConfig(commit_mass=float(commit_mass), enforce_floor=bool(floor)) |
| scene = PIPE.run(image, mode="clip", det_conf=float(det_conf), cfg=cfg) |
| annotated = annotate(image, scene) |
| rows = [[i, p.classification.label, p.classification.outcome.value, |
| f"{p.classification.confidence:.2f}", p.box.coco_name] |
| for i, p in enumerate(scene.predictions)] |
| c = scene.counts() |
| summary = (f"**{len(scene.predictions)} detections** - " |
| f"🟢 {c['identified']} identified · 🟠 {c['abstracted']} abstracted · " |
| f"🔴 {c['unknown']} unknown · ⬜ {c['rejected']} rejected") |
| choices = [f"{i}: {p.classification.label}" for i, p in enumerate(scene.predictions)] |
| first = scene.predictions[0] if scene.predictions else None |
| analyze.scene = scene |
| return annotated, summary, rows, taxonomy_html(TAX, first), gr.update( |
| choices=choices, value=(choices[0] if choices else None)) |
|
|
|
|
| def highlight(sel: str): |
| scene = getattr(analyze, "scene", None) |
| if not scene or not sel: |
| return taxonomy_html(TAX, None) |
| i = min(int(sel.split(":")[0]), len(scene.predictions) - 1) |
| return taxonomy_html(TAX, scene.predictions[i]) |
|
|
|
|
| with gr.Blocks(title="HOWC Hierarchical Perception") as demo: |
| gr.Markdown( |
| "# HOWC : hierarchical perception for novel road objects\n" |
| "Objects that fit no flat class are **abstracted up a taxonomy** to the " |
| "most specific *safe* level (bounded by a 🛡 floor), or flagged as an " |
| "explicit **UNKNOWN OBSTACLE** - never dropped, never a confident wrong " |
| "label. Paper: doi:10.5281/zenodo.21593472") |
| with gr.Row(): |
| with gr.Column(): |
| img = gr.Image(label="Road image", type="numpy") |
| det = gr.Slider(0.05, 0.9, value=0.25, step=0.05, label="Detection confidence") |
| cm = gr.Slider(0.3, 0.9, value=0.40, step=0.05, |
| label="Commit mass (↑ = abstracts sooner)") |
| fl = gr.Checkbox(value=True, label="Safety floor 🛡 (anti-paranoia)") |
| btn = gr.Button("Analyze", variant="primary") |
| with gr.Column(): |
| out = gr.Image(label="Annotated", type="numpy") |
| summ = gr.Markdown() |
| tbl = gr.Dataframe(headers=["#", "label", "outcome", "conf", "yolo"], |
| label="Detections") |
| pick = gr.Dropdown(label="Highlight in taxonomy", choices=[]) |
| tree = gr.HTML(taxonomy_html(TAX, None)) |
| btn.click(analyze, [img, det, cm, fl], [out, summ, tbl, tree, pick]) |
| pick.change(highlight, pick, tree) |
|
|
| if __name__ == "__main__": |
| import sys |
| |
| |
| demo.launch(share="--share" in sys.argv) |
|
|
