Ultralytics Code Refactor https://ultralytics.com/actions

README.md

@@ -3,7 +3,7 @@
 
 # 🚀 Introduction
 
-Welcome to the [COCO2YOLO](https://github.com/ultralytics/COCO2YOLO) repository! This toolkit is designed to help you convert datasets in JSON format, following the COCO (Common Objects in Context) standards, into YOLO (You Only Look Once) format, which is widely recognized for its efficiency in real-time object detection tasks.
+Welcome to the [COCO2YOLO](https://github.com/ultralytics/JSON2YOLO) repository! This toolkit is designed to help you convert datasets in JSON format, following the COCO (Common Objects in Context) standards, into YOLO (You Only Look Once) format, which is widely recognized for its efficiency in real-time object detection tasks.
 
 This process is essential for machine learning practitioners looking to train object detection models using the Darknet framework. Our code is flexible and can be utilized across various platforms including Linux, MacOS, and Windows.
 
@@ -25,7 +25,7 @@ If you find our tool useful for your research or development, please consider ci
 
 # 🤝 Contribute
 
-We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge 🙏 and thank you to all of our contributors!
+We welcome contributions from the community! Whether you're fixing bugs, adding new features, or improving documentation, your input is invaluable. Take a look at our [Contributing Guide](https://docs.ultralytics.com/help/contributing) to get started. Also, we'd love to hear about your experience with Ultralytics products. Please consider filling out our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A huge 🙏 and thank you to all of our contributors!
 
 <!-- Ultralytics contributors -->
 
@@ -36,12 +36,12 @@ We welcome contributions from the community! Whether you're fixing bugs, adding
 
 Ultralytics is excited to offer two different licensing options to meet your needs:
 
-- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/licenses/) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.
-- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://ultralytics.com/license).
+- **AGPL-3.0 License**: Perfect for students and hobbyists, this [OSI-approved](https://opensource.org/license) open-source license encourages collaborative learning and knowledge sharing. Please refer to the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for detailed terms.
+- **Enterprise License**: Ideal for commercial use, this license allows for the integration of Ultralytics software and AI models into commercial products without the open-source requirements of AGPL-3.0. For use cases that involve commercial applications, please contact us via [Ultralytics Licensing](https://www.ultralytics.com/license).
 
 # 📬 Contact Us
 
-For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/JSON2YOLO/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://ultralytics.com/discord)!
+For bug reports, feature requests, and contributions, head to [GitHub Issues](https://github.com/ultralytics/JSON2YOLO/issues). For questions and discussions about this project and other Ultralytics endeavors, join us on [Discord](https://discord.com/invite/ultralytics)!
 
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 <div align="center">

general_json2yolo.py

@@ -33,13 +33,13 @@ def convert_infolks_json(name, files, img_path):
 
         # filename
         with open(name + ".txt", "a") as file:
-            file.write("%s\n" % f)
+            file.write(f"{f}\n")
 
     # Write *.names file
     names = sorted(np.unique(cat))
     # names.pop(names.index('Missing product'))  # remove
     with open(name + ".names", "a") as file:
-        [file.write("%s\n" % a) for a in names]
+        [file.write(f"{a}\n") for a in names]
 
     # Write labels file
     for i, x in enumerate(tqdm(data, desc="Annotations")):
@@ -58,7 +58,7 @@ def convert_infolks_json(name, files, img_path):
                 box[[1, 3]] /= wh[i][1]  # normalize y by height
                 box = [box[[0, 2]].mean(), box[[1, 3]].mean(), box[2] - box[0], box[3] - box[1]]  # xywh
                 if (box[2] > 0.0) and (box[3] > 0.0):  # if w > 0 and h > 0
-                    file.write("%g %.6f %.6f %.6f %.6f\n" % (category_id, *box))
+                    file.write("{:g} {:.6f} {:.6f} {:.6f} {:.6f}\n".format(category_id, *box))
 
     # Split data into train, test, and validate files
     split_files(name, file_name)
@@ -89,7 +89,7 @@ def convert_vott_json(name, files, img_path):
     # Write *.names file
     names = sorted(pd.unique(cat))
     with open(name + ".names", "a") as file:
-        [file.write("%s\n" % a) for a in names]
+        [file.write(f"{a}\n") for a in names]
 
     # Write labels file
     n1, n2 = 0, 0
@@ -107,7 +107,7 @@ def convert_vott_json(name, files, img_path):
 
                 # append filename to list
                 with open(name + ".txt", "a") as file:
-                    file.write("%s\n" % f)
+                    file.write(f"{f}\n")
 
                 # write labelsfile
                 label_name = Path(f).stem + ".txt"
@@ -123,11 +123,11 @@ def convert_vott_json(name, files, img_path):
                         box = [box[0] + box[2] / 2, box[1] + box[3] / 2, box[2], box[3]]  # xywh
 
                         if (box[2] > 0.0) and (box[3] > 0.0):  # if w > 0 and h > 0
-                            file.write("%g %.6f %.6f %.6f %.6f\n" % (category_id, *box))
+                            file.write("{:g} {:.6f} {:.6f} {:.6f} {:.6f}\n".format(category_id, *box))
         else:
             missing_images.append(x["asset"]["name"])
 
-    print("Attempted %g json imports, found %g images, imported %g annotations successfully" % (i, n1, n2))
+    print(f"Attempted {i:g} json imports, found {n1:g} images, imported {n2:g} annotations successfully")
     if len(missing_images):
         print("WARNING, missing images:", missing_images)
 
@@ -203,7 +203,7 @@ def convert_ath_json(json_dir):  # dir contains json annotations and images
                                 ]  # xywh (left-top to center x-y)
 
                                 if box[2] > 0.0 and box[3] > 0.0:  # if w > 0 and h > 0
-                                    file.write("%g %.6f %.6f %.6f %.6f\n" % (category_id, *box))
+                                    file.write("{:g} {:.6f} {:.6f} {:.6f} {:.6f}\n".format(category_id, *box))
                                     n3 += 1
                                     nlabels += 1
 
@@ -224,7 +224,7 @@ def convert_ath_json(json_dir):  # dir contains json annotations and images
                         ifile = dir + "images/" + Path(f).name
                         if cv2.imwrite(ifile, img):  # if success append image to list
                             with open(dir + "data.txt", "a") as file:
-                                file.write("%s\n" % ifile)
+                                file.write(f"{ifile}\n")
                             n2 += 1  # correct images
 
                     except Exception:
@@ -236,16 +236,15 @@ def convert_ath_json(json_dir):  # dir contains json annotations and images
 
     nm = len(missing_images)  # number missing
     print(
-        "\nFound %g JSONs with %g labels over %g images. Found %g images, labelled %g images successfully"
-        % (len(jsons), n3, n1, n1 - nm, n2)
+        f"\nFound {len(jsons):g} JSONs with {n3:g} labels over {n1:g} images. Found {n1 - nm:g} images, labelled {n2:g} images successfully"
     )
     if len(missing_images):
         print("WARNING, missing images:", missing_images)
 
     # Write *.names file
     names = ["knife"]  # preserves sort order
     with open(dir + "data.names", "w") as f:
-        [f.write("%s\n" % a) for a in names]
+        [f.write(f"{a}\n") for a in names]
 
     # Split data into train, test, and validate files
     split_rows_simple(dir + "data.txt")
@@ -266,15 +265,15 @@ def convert_coco_json(json_dir="../coco/annotations/", use_segments=False, cls91
             data = json.load(f)
 
         # Create image dict
-        images = {"%g" % x["id"]: x for x in data["images"]}
+        images = {"{:g}".format(x["id"]): x for x in data["images"]}
         # Create image-annotations dict
         imgToAnns = defaultdict(list)
         for ann in data["annotations"]:
             imgToAnns[ann["image_id"]].append(ann)
 
         # Write labels file
         for img_id, anns in tqdm(imgToAnns.items(), desc=f"Annotations {json_file}"):
-            img = images["%g" % img_id]
+            img = images[f"{img_id:g}"]
             h, w, f = img["height"], img["width"], img["file_name"]
 
             bboxes = []

utils.py

@@ -55,7 +55,7 @@ def split_files(out_path, file_name, prefix_path=""):  # split training data
         if item.any():
             with open(f"{out_path}_{key}.txt", "a") as file:
                 for i in item:
-                    file.write("%s%s\n" % (prefix_path, file_name[i]))
+                    file.write(f"{prefix_path}{file_name[i]}\n")
 
 
 def split_indices(x, train=0.9, test=0.1, validate=0.0, shuffle=True):  # split training data
@@ -84,7 +84,7 @@ def make_dirs(dir="new_dir/"):
 def write_data_data(fname="data.data", nc=80):
     """Writes a Darknet-style .data file with dataset and training configuration."""
     lines = [
-        "classes = %g\n" % nc,
+        f"classes = {nc:g}\n",
         "train =../out/data_train.txt\n",
         "valid =../out/data_test.txt\n",
         "names =../out/data.names\n",
@@ -153,7 +153,7 @@ def flatten_recursive_folders(path="../../Downloads/data/sm4/"):  # from utils i
             stem, suffix = f.stem, f.suffix
             if suffix.lower()[1:] in img_formats:
                 n += 1
-                stem_new = "%g_" % n + stem
+                stem_new = f"{n:g}_" + stem
                 image_new = nidir / (stem_new + suffix)  # converts all formats to *.jpg
                 json_new = njdir / f"{stem_new}.json"
 
@@ -164,7 +164,7 @@ def flatten_recursive_folders(path="../../Downloads/data/sm4/"):  # from utils i
                 os.system(f"cp '{image}' '{image_new}'")
                 # cv2.imwrite(str(image_new), cv2.imread(str(image)))
 
-    print("Flattening complete: %g jsons and images" % n)
+    print(f"Flattening complete: {n:g} jsons and images")
 
 
 def coco91_to_coco80_class():  # converts 80-index (val2014) to 91-index (paper)