3 files changed, 0 insertions, 103 deletions
diff --git a/tests/test_character_predictor.py b/tests/test_character_predictor.py
deleted file mode 100644
index 01bda78..0000000
--- a/tests/test_character_predictor.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""Test for CharacterPredictor class."""
-import importlib
-import os
-from pathlib import Path
-import unittest
-
-from loguru import logger
-
-from text_recognizer.character_predictor import CharacterPredictor
-from text_recognizer.networks import MLP
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "emnist"
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-class TestCharacterPredictor(unittest.TestCase):
-    """Tests for the CharacterPredictor class."""
-
-    def test_filename(self) -> None:
-        """Test that CharacterPredictor correctly predicts on a single image, for serveral test images."""
-        network_fn_ = MLP
-        predictor = CharacterPredictor(network_fn=network_fn_)
-
-        for filename in SUPPORT_DIRNAME.glob("*.png"):
-            pred, conf = predictor.predict(str(filename))
-            logger.info(
-                f"Prediction: {pred} at confidence: {conf} for image with character {filename.stem}"
-            )
-            self.assertEqual(pred, filename.stem)
-            self.assertGreater(conf, 0.7)
diff --git a/tests/test_line_predictor.py b/tests/test_line_predictor.py
deleted file mode 100644
index eede4d4..0000000
--- a/tests/test_line_predictor.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Tests for LinePredictor."""
-import os
-from pathlib import Path
-import unittest
-
-
-import editdistance
-import numpy as np
-
-from text_recognizer.datasets import IamLinesDataset
-from text_recognizer.line_predictor import LinePredictor
-import text_recognizer.util as util
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support"
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-class TestEmnistLinePredictor(unittest.TestCase):
-    """Test LinePredictor class on the EmnistLines dataset."""
-
-    def test_filename(self) -> None:
-        """Test that LinePredictor correctly predicts on single images, for several test images."""
-        predictor = LinePredictor(
-            dataset="EmnistLineDataset", network_fn="CNNTransformer"
-        )
-
-        for filename in (SUPPORT_DIRNAME / "emnist_lines").glob("*.png"):
-            pred, conf = predictor.predict(str(filename))
-            true = str(filename.stem)
-            edit_distance = editdistance.eval(pred, true) / len(pred)
-            print(
-                f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}'
-            )
-            self.assertLess(edit_distance, 0.2)
diff --git a/tests/test_paragraph_text_recognizer.py b/tests/test_paragraph_text_recognizer.py
deleted file mode 100644
index 3e280b9..0000000
--- a/tests/test_paragraph_text_recognizer.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""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]
-            )