Buckets:
| """Render representative development error panels for manual review.""" | |
| import argparse | |
| from collections import defaultdict | |
| import json | |
| from pathlib import Path | |
| from PIL import Image, ImageDraw | |
| from load_dataset import dataset, load_sample | |
| from training.model_factory import ROOT | |
| from training.soda_a import inventory, materialize, tile_descriptors | |
| COLORS = {"target": "#25d366", "prediction": "#f44336", "highlight": "#ffd600"} | |
| def draw_polygon(draw, points, color, width=4): | |
| vertices = list(zip(points[::2], points[1::2])) | |
| if vertices: | |
| draw.line(vertices + vertices[:1], fill=color, width=width) | |
| def panel(image, targets, predictions, highlighted, title): | |
| canvas = Image.fromarray(image).convert("RGB") | |
| draw = ImageDraw.Draw(canvas) | |
| for item in targets: | |
| draw_polygon(draw, item["obb_polygon"], COLORS["target"], 3) | |
| for item in predictions: | |
| draw_polygon(draw, item["obb_polygon"], COLORS["prediction"], 2) | |
| draw_polygon(draw, highlighted["obb_polygon"], COLORS["highlight"], 5) | |
| canvas.thumbnail((384, 384)) | |
| result = Image.new("RGB", (400, 424), "black") | |
| result.paste(canvas, (8, 32)) | |
| ImageDraw.Draw(result).text((8, 8), title, fill="white") | |
| return result | |
| def records_by_id(records): | |
| return ({row["sample_id"]: row.get("annotations", []) for row in records["targets"]}, | |
| {row["sample_id"]: row.get("annotations", []) for row in records["predictions"]}) | |
| def sheet(items, path): | |
| columns = 3 | |
| rows = (len(items) + columns - 1) // columns | |
| output = Image.new("RGB", (columns * 400, rows * 424), "black") | |
| for index, item in enumerate(items): | |
| output.paste(item, ((index % columns) * 400, (index // columns) * 424)) | |
| path.parent.mkdir(parents=True, exist_ok=True) | |
| output.save(path) | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("locations", type=Path) | |
| parser.add_argument("soda_records", type=Path) | |
| parser.add_argument("dota_records", type=Path) | |
| parser.add_argument("output_dir", type=Path) | |
| args = parser.parse_args() | |
| if args.output_dir.exists(): | |
| raise FileExistsError("output directory already exists") | |
| locations = json.loads(args.locations.read_text()) | |
| soda_records = json.loads(args.soda_records.read_text()) | |
| dota_records = json.loads(args.dota_records.read_text())["candidate"] | |
| soda_targets, soda_predictions = records_by_id(soda_records) | |
| dota_targets, dota_predictions = records_by_id(dota_records) | |
| soda_descriptors = {item["id"]: item for row in inventory(splits=frozenset({"val"})) if row["split"] == "val" | |
| for item in tile_descriptors(row)} | |
| dota_data = dataset(ROOT / "datasets/corpus", "detection", "val", license_scope="all") | |
| dota_samples = {row["id"]: row for row in dota_data.samples} | |
| for source, target_rows, prediction_rows in (("soda_a_validation", soda_targets, soda_predictions), | |
| ("dota_selection", dota_targets, dota_predictions)): | |
| panels = [] | |
| for class_id, errors in locations[source].items(): | |
| for kind in ("false_positives", "missed_targets"): | |
| if not errors[kind]: | |
| continue | |
| item = errors[kind][0] | |
| sample_id = item["sample_id"] | |
| if source == "soda_a_validation": | |
| image = materialize(soda_descriptors[sample_id])["image"] | |
| else: | |
| image = load_sample(dota_data.root, dota_samples[sample_id], dota_data.expected_hashes)["image"] | |
| confidence = item.get("confidence") | |
| suffix = f" conf={confidence:.3f}" if confidence is not None else "" | |
| title = f"{errors['class']} {kind[:-1]}{suffix}\n{sample_id}" | |
| panels.append(panel(image, target_rows[sample_id], prediction_rows[sample_id], item, title)) | |
| sheet(panels, args.output_dir / f"{source}.jpg") | |
| print(json.dumps({"output_dir": str(args.output_dir)})) | |
| if __name__ == "__main__": | |
| main() | |
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