From 25b5d6983d51e0e791b96a76beb7e49f392cd9a8 Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Mon, 7 Dec 2020 22:54:04 +0100
Subject: Segmentation working!
---
.../tests/support/iam_paragraphs/a01-000u.jpg | Bin 0 -> 14890 bytes
.../tests/test_paragraph_text_recognizer.py | 37 +++++++++++++++++++++
2 files changed, 37 insertions(+)
create mode 100644 src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
create mode 100644 src/text_recognizer/tests/test_paragraph_text_recognizer.py
(limited to 'src/text_recognizer/tests')
diff --git a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg b/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
new file mode 100644
index 0000000..d9753b6
Binary files /dev/null and b/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg differ
diff --git a/src/text_recognizer/tests/test_paragraph_text_recognizer.py b/src/text_recognizer/tests/test_paragraph_text_recognizer.py
new file mode 100644
index 0000000..3e280b9
--- /dev/null
+++ b/src/text_recognizer/tests/test_paragraph_text_recognizer.py
@@ -0,0 +1,37 @@
+"""Test for ParagraphTextRecognizer class."""
+import os
+from pathlib import Path
+import unittest
+
+from text_recognizer.paragraph_text_recognizer import ParagraphTextRecognizor
+import text_recognizer.util as util
+
+
+SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "iam_paragraph"
+
+# Prevent using GPU.
+os.environ["CUDA_VISIBLE_DEVICES"] = ""
+
+
+class TestParagraphTextRecognizor(unittest.TestCase):
+ """Test that it can take non-square images of max dimension larger than 256px."""
+
+ def test_filename(self) -> None:
+ """Test model on support image."""
+ line_predictor_args = {
+ "dataset": "EmnistLineDataset",
+ "network_fn": "CNNTransformer",
+ }
+ line_detector_args = {"dataset": "EmnistLineDataset", "network_fn": "UNet"}
+ model = ParagraphTextRecognizor(
+ line_predictor_args=line_predictor_args,
+ line_detector_args=line_detector_args,
+ )
+ num_text_lines_by_name = {"a01-000u-cropped": 7}
+ for filename in (SUPPORT_DIRNAME).glob("*.jpg"):
+ full_image = util.read_image(str(filename), grayscale=True)
+ predicted_text, line_region_crops = model.predict(full_image)
+ print(predicted_text)
+ self.assertTrue(
+ len(line_region_crops), num_text_lines_by_name[filename.stem]
+ )
--
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