Remove synthetic training path and add fine-text OCR option
This commit is contained in:
OCR Team
@@ -2,7 +2,9 @@ from io import BytesIO
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import json
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from pathlib import Path
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import subprocess
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import sys
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import tempfile
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from types import SimpleNamespace
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import unittest
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from unittest.mock import patch
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@@ -10,6 +12,7 @@ from PIL import Image
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from starlette.testclient import TestClient
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from ocr_compare import server
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from ocr_compare import classic_worker
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from ocr_compare.fields import assign_fields
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from ocr_compare.vision_worker import _missing_neighbor_box, _retry_table, extract
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@@ -205,6 +208,49 @@ class LocalComparisonTests(unittest.TestCase):
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self.assertEqual(response.json()["results"]["classic"], classic)
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self.assertEqual(response.json()["results"]["vision"], vision)
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def test_fine_text_choice_reaches_classic_engine_only(self):
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classic = {"status": "error", "error": "classic_failed", "elapsed_ms": 0}
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vision = {"status": "error", "error": "vision_disabled", "elapsed_ms": 0}
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with patch.object(server, "_classic", return_value=classic) as run_classic, patch.object(
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server, "_vision", return_value=vision) as run_vision:
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with TestClient(server.app) as client:
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response = client.post("/api/compare", content=photo(), headers={
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"content-type": "image/jpeg", "x-ocr-detail": "fine"})
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invalid = client.post("/api/compare", content=photo(), headers={
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"content-type": "image/jpeg", "x-ocr-detail": "unknown"})
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self.assertEqual(response.status_code, 200)
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self.assertTrue(run_classic.call_args.kwargs["fine_text"])
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self.assertEqual(run_vision.call_count, 1)
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self.assertEqual(invalid.status_code, 422)
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self.assertEqual(invalid.json()["error"], "invalid_detail")
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def test_classic_worker_uses_requested_detector_resolution(self):
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with tempfile.TemporaryDirectory() as directory:
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root = Path(directory)
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for name in ("PP-OCRv5_mobile_det", "latin_PP-OCRv5_mobile_rec"):
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model = root / name
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model.mkdir()
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(model / "inference.pdiparams").touch()
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seen = []
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class FakePaddleOCR:
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def __init__(self, **kwargs):
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seen.append(kwargs)
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def predict(self, _image):
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return [SimpleNamespace(json={"res": {"rec_texts": ["B"],
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"rec_polys": [[[1, 1], [2, 1], [2, 2], [1, 2]]]}})]
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for options, expected in (([], 960), (["--fine-text"], 2048)):
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output = root / "result.json"
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with patch.object(classic_worker, "MODELS", root), patch.object(
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sys, "argv", ["classic_worker", str(root / "input.jpg"), str(output), *options]), patch.dict(
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sys.modules, {"paddleocr": SimpleNamespace(PaddleOCR=FakePaddleOCR)}):
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classic_worker.main()
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self.assertEqual(seen[-1]["text_det_limit_side_len"], expected)
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self.assertEqual(seen[-1]["text_det_limit_type"], "max")
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self.assertEqual(json.loads(output.read_text())["lines"][0]["text"], "B")
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def test_worker_launch_errors_are_reported_per_engine(self):
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with tempfile.TemporaryDirectory() as directory:
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model_home = Path(directory)
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@@ -1,117 +0,0 @@
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import json
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from pathlib import Path
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import tempfile
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import unittest
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from unittest.mock import patch
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import cv2
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import numpy as np
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from ocr_compare.synthetic import TARGET_CODES, _render, generate
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from ocr_compare.benchmark_synthetic import evaluate
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class SyntheticDatasetTests(unittest.TestCase):
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def test_document_level_split_and_exact_labels(self):
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with tempfile.TemporaryDirectory() as directory:
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output = Path(directory) / "dataset"
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manifest = generate(output, count=6, seed=31415)
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self.assertEqual(manifest["source"], "fully synthetic")
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self.assertEqual(manifest["train_documents"], 5)
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self.assertEqual(manifest["validation_documents"], 1)
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rows = [json.loads(line) for line in (output / "ground_truth.jsonl").read_text().splitlines()]
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self.assertEqual(len(rows), 6)
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self.assertEqual({row["id"] for row in rows if row["split"] == "val"},
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{"synthetic-00005"})
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self.assertEqual(len((output / "det_train.txt").read_text().splitlines()), 5)
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self.assertEqual(len((output / "det_val.txt").read_text().splitlines()), 1)
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crop_count = 0
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for row in rows:
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self.assertEqual(set(row["fields"]), set(TARGET_CODES))
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page = cv2.imread(str(output / row["image"]))
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self.assertIsNotNone(page)
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self.assertEqual(page.shape[:2], (row["height"], row["width"]))
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for item in row["lines"]:
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self.assertLessEqual(len(item["text"]), 25)
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self.assertTrue((output / item["crop"]).is_file())
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self.assertTrue(all(0 <= x < row["width"] and 0 <= y < row["height"]
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for x, y in item["polygon"]))
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crop_count += 1
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self.assertEqual(crop_count, manifest["recognition_crops"])
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for split in ("train", "val"):
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for entry in (output / f"rec_{split}.txt").read_text().splitlines():
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path, label = entry.split("\t", 1)
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self.assertTrue((output / path).is_file())
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self.assertEqual("synthetic-00005" in path, split == "val")
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self.assertTrue(label)
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def test_seed_repeats_pixels_and_values(self):
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first, lines_a, values_a, hardness_a = _render(12345, None)
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second, lines_b, values_b, hardness_b = _render(12345, None)
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self.assertTrue(np.array_equal(first, second))
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self.assertEqual(lines_a, lines_b)
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self.assertEqual(values_a, values_b)
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self.assertEqual(hardness_a, hardness_b)
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def test_requires_a_validation_document_and_never_overwrites(self):
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with tempfile.TemporaryDirectory() as directory:
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output = Path(directory) / "dataset"
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with self.assertRaises(ValueError):
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generate(output, count=1, seed=1)
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self.assertFalse(output.exists())
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generate(output, count=2, seed=1)
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with self.assertRaises(FileExistsError):
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generate(output, count=2, seed=2)
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def test_benchmark_separates_reading_from_assignment(self):
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with tempfile.TemporaryDirectory() as directory:
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output = Path(directory) / "dataset"
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generate(output, count=2, seed=31415)
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row = json.loads((output / "ground_truth.jsonl").read_text().splitlines()[-1])
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readings = []
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for item in row["lines"]:
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if item["part"] != "value" or item["code"] not in TARGET_CODES:
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continue
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xs, ys = zip(*item["polygon"])
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readings.append({"text": item["text"],
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"box": [min(xs), min(ys), max(xs), max(ys)]})
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fields = [{"code": code, "value": value} for code, value in row["fields"].items()
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if code not in ("B", "2.2")]
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fields.append({"code": "B", "value": "wrong"})
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result = {"status": "ok", "lines": readings, "fields": fields}
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with patch("ocr_compare.benchmark_synthetic.server._classic", return_value=result):
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counts = evaluate(output, limit=1)
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self.assertEqual(counts["value_lines"], 7)
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self.assertEqual(counts["value_text_read"], 7)
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self.assertEqual(counts["fields_exact"], 4)
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self.assertEqual(counts["fields_wrong"], 1)
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self.assertEqual(counts["fields_missing"], 1)
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def test_benchmark_rejects_non_synthetic_manifest(self):
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with tempfile.TemporaryDirectory() as directory:
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output = Path(directory) / "dataset"
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generate(output, count=2, seed=31415)
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manifest_path = output / "manifest.json"
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manifest = json.loads(manifest_path.read_text())
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manifest["training_on_real_documents"] = True
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manifest_path.write_text(json.dumps(manifest))
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with self.assertRaisesRegex(ValueError, "generated datasets only"):
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evaluate(output, limit=1)
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def test_benchmark_counts_failed_documents_in_denominator(self):
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with tempfile.TemporaryDirectory() as directory:
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output = Path(directory) / "dataset"
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generate(output, count=2, seed=31415)
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with patch("ocr_compare.benchmark_synthetic.server._classic",
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return_value={"status": "error"}):
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counts = evaluate(output, limit=1)
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self.assertEqual(counts["documents"], 1)
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self.assertEqual(counts["failed_documents"], 1)
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self.assertEqual(counts["value_lines"], 7)
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self.assertEqual(counts["value_text_read"], 0)
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self.assertEqual(counts["fields"], 6)
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self.assertEqual(counts["fields_missing"], 6)
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if __name__ == "__main__":
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unittest.main()
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