Add unit testing about yolov8

Author: SWHL

README.md

@@ -18,7 +18,7 @@
 
 ## Introduction
 
-A tool for object detection and image segmentation dataset format conversion. 
+A tool for object detection and image segmentation dataset format conversion.
 
 Supports conversion between labelme tool annotated data, labelImg tool annotated data, YOLO, PubLayNet and COCO data set formats.
 
@@ -34,6 +34,9 @@ E(labelme) --> B
 B --> F(labelImg)
 F --> G(PubLayNet)
 F --> J(YOLOv5)
+
+J --> H(YOLOv8)
+H --> J
 ```
 
 ## Installation

tests/test_yolov5_to_yolov8.py

@@ -0,0 +1,34 @@
+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: [email protected]
+import shutil
+import sys
+from pathlib import Path
+
+cur_dir = Path(__file__).resolve().parent
+root_dir = cur_dir.parent
+
+sys.path.append(str(root_dir))
+
+from label_convert.yolov5_to_yolov8 import YOLOV5ToYOLOV8
+
+test_file_dir = cur_dir / "test_files"
+
+data_dir_name = "yolov5_dataset"
+
+
+def test_normal():
+    data_dir = test_file_dir / data_dir_name
+    converter = YOLOV5ToYOLOV8(data_dir)
+    converter()
+
+    save_dir: Path = test_file_dir / f"{data_dir_name}_yolov8"
+    assert save_dir.exists()
+
+    train_img_dir = save_dir / "images" / "train"
+    assert train_img_dir.exists()
+
+    val_label_dir = save_dir / "labels" / "val"
+    assert val_label_dir.exists()
+
+    shutil.rmtree(save_dir)

tests/test_yolov8_to_yolov5.py

@@ -0,0 +1,40 @@
+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: [email protected]
+import shutil
+import sys
+from pathlib import Path
+
+cur_dir = Path(__file__).resolve().parent
+root_dir = cur_dir.parent
+
+sys.path.append(str(root_dir))
+
+from label_convert.yolov8_to_yolov5 import YOLOV8ToYOLOV5
+
+test_file_dir = cur_dir / "test_files"
+
+data_dir_name = "yolov8_dataset"
+
+
+def test_normal():
+    data_dir = test_file_dir / data_dir_name
+    converter = YOLOV8ToYOLOV5(data_dir)
+    converter()
+
+    save_dir: Path = test_file_dir / f"{data_dir_name}_yolov5"
+    assert save_dir.exists()
+
+    train_img_dir = save_dir / "images"
+    assert train_img_dir.exists()
+
+    val_label_dir = save_dir / "labels"
+    assert val_label_dir.exists()
+
+    train_txt_path = save_dir / "train.txt"
+    assert train_txt_path.exists()
+
+    classes_txt_path = save_dir / "classes.txt"
+    assert classes_txt_path.exists()
+
+    shutil.rmtree(save_dir)