🔨 [Add] Converter to Transfrom JSON to txt format

utils/converter_json2txt.py

@@ -0,0 +1,84 @@
+import json
+import os
+from typing import Dict, List, Optional
+
+from tqdm import tqdm
+
+
+def discretize_categories(categories: List[Dict[str, int]]) -> Dict[int, int]:
+    """
+    Maps each unique 'id' in the list of category dictionaries to a sequential integer index.
+    Indices are assigned based on the sorted 'id' values.
+    """
+    sorted_categories = sorted(categories, key=lambda category: category["id"])
+    return {category["id"]: index for index, category in enumerate(sorted_categories)}
+
+
+def process_annotations(
+    image_annotations: Dict[int, List[Dict]],
+    image_info_dict: Dict[int, tuple],
+    output_dir: str,
+    id_to_idx: Optional[Dict[int, int]] = None,
+) -> None:
+    """
+    Process and save annotations to files, with option to remap category IDs.
+    """
+    for image_id, annotations in tqdm(image_annotations.items(), desc="Processing annotations"):
+        file_path = os.path.join(output_dir, f"{image_id:0>12}.txt")
+        with open(file_path, "w") as file:
+            for annotation in annotations:
+                process_annotation(annotation, image_info_dict[image_id], id_to_idx, file)
+
+
+def process_annotation(annotation: Dict, image_dims: tuple, id_to_idx: Optional[Dict[int, int]], file) -> None:
+    """
+    Convert a single annotation's segmentation and write it to the open file handle.
+    """
+    category_id = annotation["category_id"]
+    segmentation = (
+        annotation["segmentation"][0]
+        if annotation["segmentation"] and isinstance(annotation["segmentation"][0], list)
+        else None
+    )
+
+    if segmentation is None:
+        return
+
+    img_width, img_height = image_dims
+    normalized_segmentation = normalize_segmentation(segmentation, img_width, img_height)
+
+    if id_to_idx:
+        category_id = id_to_idx.get(category_id, category_id)
+
+    file.write(f"{category_id} {' '.join(normalized_segmentation)}\n")
+
+
+def normalize_segmentation(segmentation: List[float], img_width: int, img_height: int) -> List[str]:
+    """
+    Normalize and format segmentation coordinates.
+    """
+    return [f"{x/img_width:.6f}" if i % 2 == 0 else f"{x/img_height:.6f}" for i, x in enumerate(segmentation)]
+
+
+def convert_annotations(json_file: str, output_dir: str) -> None:
+    """
+    Load annotation data from a JSON file and process all annotations.
+    """
+    with open(json_file) as file:
+        data = json.load(file)
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    image_info_dict = {img["id"]: (img["width"], img["height"]) for img in data.get("images", [])}
+    id_to_idx = discretize_categories(data.get("categories", [])) if "categories" in data else None
+    image_annotations = {img_id: [] for img_id in image_info_dict}
+
+    for annotation in data.get("annotations", []):
+        if not annotation.get("iscrowd", False):
+            image_annotations[annotation["image_id"]].append(annotation)
+
+    process_annotations(image_annotations, image_info_dict, output_dir, id_to_idx)
+
+
+convert_annotations("./data/coco/annotations/instances_train2017.json", "./data/coco/labels/train2017/")
+convert_annotations("./data/coco/annotations/instances_val2017.json", "./data/coco/labels/val2017/")