Support yolov5 to yolov8

Author: SWHL

label_convert/yolov5_to_coco.py

@@ -80,7 +80,7 @@ def _init_json(self):
         ]
 
     def append_bg_img(self, img_list):
-        bg_dir = self.data_dir / "background_images"
+        bg_dir = self.data_dir / "non_labels"
         if bg_dir.exists():
             bg_img_list = list(bg_dir.iterdir())
             for bg_img_path in bg_img_list:

label_convert/yolov5_to_yolov8.py

@@ -0,0 +1,98 @@
+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: [email protected]
+import argparse
+import shutil
+from pathlib import Path
+from typing import Tuple, Union
+
+from tqdm import tqdm
+
+ValueType = Union[str, Path, None]
+
+
+class YOLOV5ToYOLOV8:
+    def __init__(self, data_dir: ValueType = None, save_dir: ValueType = None):
+        if data_dir is None:
+            raise ValueError("data_dir must not be None")
+        self.data_dir = Path(data_dir)
+        self.verify_exists(self.data_dir)
+
+        self.img_dir = self.data_dir / "images"
+        self.verify_exists(self.img_dir)
+
+        self.label_dir = self.data_dir / "labels"
+        self.verify_exists(self.label_dir)
+
+        if save_dir is None:
+            save_dir = self.data_dir.parent / f"{Path(self.data_dir).name}_yolov8"
+        self.save_dir = save_dir
+        self.mkdir(self.save_dir)
+
+        self.save_img_dir = save_dir / "images"
+        self.mkdir(self.save_img_dir)
+
+        self.save_label_dir = save_dir / "labels"
+        self.mkdir(self.save_label_dir)
+
+    def __call__(self, mode_list: Tuple[str] = ("train", "val")):
+        if not mode_list:
+            raise ValueError("mode_list is empty!!")
+
+        for mode in tqdm(mode_list):
+            txt_path = self.data_dir / f"{mode}.txt"
+            self.verify_exists(txt_path)
+            img_list = self.read_txt(txt_path)
+
+            save_mode_img_dir = self.save_img_dir / mode
+            self.mkdir(save_mode_img_dir)
+
+            save_mode_label_dir = self.save_label_dir / mode
+            self.mkdir(save_mode_label_dir)
+
+            # copy images to new img dir
+            for img_path in img_list:
+                img_full_path = self.data_dir / img_path
+                shutil.copy(img_full_path, save_mode_img_dir)
+
+                label_path = self.label_dir / Path(img_path).with_suffix(".txt").name
+                shutil.copy(label_path, save_mode_label_dir)
+
+        print(f"Successfully convert, detail in {self.save_dir}")
+
+    @staticmethod
+    def read_txt(txt_path):
+        with open(str(txt_path), "r", encoding="utf-8") as f:
+            data = list(map(lambda x: x.rstrip("\n"), f))
+        return data
+
+    @staticmethod
+    def mkdir(dir_path):
+        Path(dir_path).mkdir(parents=True, exist_ok=True)
+
+    @staticmethod
+    def verify_exists(file_path: Union[Path, str]):
+        if not Path(file_path).exists():
+            raise FileNotFoundError(f"The {file_path} is not exists!!!")
+
+
+def main():
+    parser = argparse.ArgumentParser("Datasets converter from YOLOV5 to COCO")
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default="tests/test_files/yolov5_dataset",
+        help="Dataset root path",
+    )
+    parser.add_argument("--save_dir", type=str, default=None)
+    parser.add_argument(
+        "--mode_list", type=str, default="train,val", help="generate which mode"
+    )
+    args = parser.parse_args()
+
+    converter = YOLOV5ToYOLOV8(args.data_dir, args.save_dir)
+    converter(mode_list=args.mode_list.split(","))
+
+
+if __name__ == "__main__":
+    main()

label_convert/yolov8_to_yolov5.py

@@ -0,0 +1,99 @@
+# -*- encoding: utf-8 -*-
+# @Author: SWHL
+# @Contact: [email protected]
+import argparse
+import shutil
+from pathlib import Path
+from typing import Tuple, Union
+
+from tqdm import tqdm
+
+ValueType = Union[str, Path, None]
+
+
+class YOLOV8ToYOLOV5:
+    def __init__(self, data_dir: ValueType = None, save_dir: ValueType = None):
+        if data_dir is None:
+            raise ValueError("data_dir must not be None")
+        self.data_dir = Path(data_dir)
+        self.verify_exists(self.data_dir)
+
+        self.img_dir = self.data_dir / "images"
+        self.verify_exists(self.img_dir)
+
+        self.label_dir = self.data_dir / "labels"
+        self.verify_exists(self.label_dir)
+
+        if save_dir is None:
+            save_dir = self.data_dir.parent / f"{Path(self.data_dir).name}_yolov8"
+        self.save_dir = save_dir
+        self.mkdir(self.save_dir)
+
+        self.save_img_dir = save_dir / "images"
+        self.mkdir(self.save_img_dir)
+
+        self.save_label_dir = save_dir / "labels"
+        self.mkdir(self.save_label_dir)
+
+    def __call__(self, mode_list: Tuple[str] = ("train", "val")):
+        if not mode_list:
+            raise ValueError("mode_list is empty!!")
+
+        for mode in tqdm(mode_list):
+            txt_path = self.data_dir / f"{mode}.txt"
+            self.verify_exists(txt_path)
+            img_list = self.read_txt(txt_path)
+
+            save_mode_img_dir = self.save_img_dir / mode
+            self.mkdir(save_mode_img_dir)
+
+            save_mode_label_dir = self.save_label_dir / mode
+            self.mkdir(save_mode_label_dir)
+
+            # copy images to new img dir
+            for img_path in img_list:
+                img_full_path = self.data_dir / img_path
+                shutil.copy(img_full_path, save_mode_img_dir)
+
+                label_path = self.label_dir / Path(img_path).with_suffix(".txt").name
+                shutil.copy(label_path, save_mode_label_dir)
+
+        print(f"Successfully convert, detail in {self.save_dir}")
+
+    @staticmethod
+    def read_txt(txt_path):
+        with open(str(txt_path), "r", encoding="utf-8") as f:
+            data = list(map(lambda x: x.rstrip("\n"), f))
+        return data
+
+    @staticmethod
+    def mkdir(dir_path):
+        Path(dir_path).mkdir(parents=True, exist_ok=True)
+
+    @staticmethod
+    def verify_exists(file_path: Union[Path, str]):
+        if not Path(file_path).exists():
+            raise FileNotFoundError(f"The {file_path} is not exists!!!")
+
+
+def main():
+    parser = argparse.ArgumentParser("Datasets converter from YOLOV5 to COCO")
+    parser.add_argument(
+        "--data_dir",
+        type=str,
+        default="tests/test_files/yolov5_dataset",
+        help="Dataset root path",
+    )
+    parser.add_argument("--save_dir", type=str, default=None)
+    parser.add_argument(
+        "--mode_list", type=str, default="train,val", help="generate which mode"
+    )
+    parser.add_argument("--yaml_path", type=str, default=None)
+    args = parser.parse_args()
+
+    converter = YOLOV8ToYOLOV5(args.data_dir, args.save_dir)
+    converter(mode_list=args.mode_list.split(","))
+
+
+if __name__ == "__main__":
+    main()

tests/test_files/yolov8_dataset/images/train/0dcddf72-0cd7-4577-b59b-ed191863a13d_png.rf.7ed0e14fcc5dde85d883c6d41187908b.jpg

@@ -0,0 +1,4 @@
+0 0.3716715984375 0.41050295937500003 0.401257396875 0.3272928984375 0.41605029531250004 0.22744082968749998 0.35872780937500004 0.1756656796875 0.2921597640625 0.17011834374999998 0.238535503125 0.14977810625000001 0.20894970468750002 0.12019230781250001 0.1923076921875 0.11279585781250001 0.15162721875000001 0.11279585781250001 0.12019230781250001 0.123890534375 0.0924556203125 0.1386834328125 0.05917159843749999 0.17751479375 0.0480769234375 0.21819526718749999 0.0573224859375 0.3254437859375 0.1109467453125 0.390162721875 0.21449704062500002 0.43269230781249995 0.3716715984375 0.41050295937500003
+0 0.06841715937500001 0.8265532546875001 0.15902366875 0.867233728125 0.2625739640625 0.8468934906250001 0.3291420125 0.7618343203125 0.35318047343750003 0.669378696875 0.31989645 0.5898668625 0.2644230765625 0.571375740625 0.22928994062500002 0.5824704140625 0.1830621296875 0.552884615625 0.12019230781250001 0.560281065625 0.0314349109375 0.6065088750000001 0 0.7100591703125 0.0388313609375 0.78772189375 0.06841715937500001 0.8265532546875001
+0 0.5732248515625 0.519600590625 0.6508875749999999 0.528846153125 0.680473371875 0.501109465625 0.6841715984375 0.48076923125000004 0.7193047328125 0.4419378703125 0.7451923078125 0.40495562031250004 0.77847633125 0.3476331375 0.7766272187500001 0.330991125 0.76553254375 0.2736686390625 0.7451923078125 0.21449704062500002 0.713757396875 0.1756656796875 0.687869821875 0.1553254453125 0.6675295859375 0.13313609375 0.6471893484375 0.1183431953125 0.5954142015625 0.1183431953125 0.560281065625 0.123890534375 0.5159023671875 0.1664201171875 0.5094897671874999 0.2353555578125 0.5214497046875 0.286612425 0.48076923125000004 0.3254437859375 0.45673076874999996 0.377218934375 0.4678254453125 0.44563609375 0.510355028125 0.5122041421875 0.5732248515625 0.519600590625
+0 0.5954142015625 0.8949704140625 0.6268491125 0.8857248515625 0.689718934375 0.8487426031249999 0.7636834328125001 0.7377958578125 0.7525887578125 0.63239645 0.6915680484375 0.5510355031249999 0.6157544374999999 0.538091715625 0.545488165625 0.5436390531249999 0.4974112421875 0.560281065625 0.43823964374999996 0.6527366875 0.44748520625 0.7193047328125 0.4881656796875 0.78402366875 0.5232988171875 0.841346153125 0.5954142015625 0.8949704140625

tests/test_files/yolov8_dataset/images/train/images(3).jpg

@@ -0,0 +1,5 @@
+0 0.274554 0.205357 0.388393 0.383929
+0 0.754464 0.207589 0.375000 0.379464
+0 0.504464 0.511161 0.339286 0.343750
+0 0.243304 0.792411 0.379464 0.379464
+0 0.774554 0.796875 0.370536 0.370536

tests/test_files/yolov8_dataset/images/val/8ae4af51-4376-4d42-b6bd-54d17c8c5e02_png.rf.2db43284642a5e92dfe56830435549a1.jpg

@@ -0,0 +1,6 @@
+0 0.0166420125 0.6231508875 0.040680473437499996 0.6582840234375 0.07211538437500001 0.6860207093749999 0.12204142031250001 0.6860207093749999 0.16457100468750002 0.6471893484375 0.12573964375 0.5898668625 0.079511834375 0.5399408296875 0.055473371875 0.5491863890625 0.012943785937499999 0.5843195265625 0.0166420125 0.6231508875
+0 0.46597633125 0.7433431953125 0.48076923125000004 0.739644971875 0.49371301718750005 0.693417159375 0.4585798828125 0.6490384609375 0.399408284375 0.6120562140625 0.3698224859375 0.6009615390625 0.3254437859375 0.5861686390624999 0.26627219062499996 0.6009615390625 0.2644230765625 0.6213017734374999 0.2644230765625 0.625 0.2699704140625 0.6749260359375 0.3291420125 0.713757396875 0.393860946875 0.7359467453125 0.46597633125 0.7433431953125
+0 0.704511834375 0.6860207093749999 0.7451923078125 0.636094675 0.7267011828125 0.588017753125 0.6860207093749999 0.526997040625 0.63239645 0.5510355031249999 0.571375740625 0.5972633125 0.5732248515625 0.6434911249999999 0.5935650890625 0.687869821875 0.661982246875 0.721153846875 0.704511834375 0.6860207093749999
+0 0.27181952656249997 0.41420118281249996 0.31989645 0.393860946875 0.3365384609375 0.3439349109375 0.3346893484375 0.301405325 0.3439349109375 0.28476331250000003 0.380917159375 0.24963017812500002 0.393860946875 0.19600591875 0.366124259375 0.1312869828125 0.30880177343750004 0.1017011828125 0.26627219062499996 0.1109467453125 0.20525147812500003 0.1090976328125 0.16272189374999999 0.13498520625 0.12019230781250001 0.16087278124999999 0.099852071875 0.257026628125 0.1368343203125 0.36427514843750003 0.17936390625 0.403106509375 0.22559171562500002 0.41605029531250004 0.27181952656249997 0.41420118281249996
+0 0.6009615390625 0.2810650890625 0.6268491125 0.24963017812500002 0.6342455625000001 0.22559171562500002 0.6545857984375 0.20525147812500003 0.6397928984375 0.1571745546875 0.61760355 0.11649408125 0.5639792906250001 0.105399409375 0.5048076921875 0.1553254453125 0.49926035625000004 0.188609465625 0.501109465625 0.247781065625 0.5343934906250001 0.2773668625 0.6009615390625 0.2810650890625
+0 0.9837278093749999 0.1997041421875 0.9948224859374999 0.1553254453125 0.9615384609375001 0.107248521875 0.9079142015624999 0.073964496875 0.8653846156249999 0.070266271875 0.79326923125 0.1183431953125 0.80251479375 0.16087278124999999 0.8450443796875 0.197855028125 0.919008875 0.20894970468750002 0.9837278093749999 0.1997041421875