初始化

json2yolo.py

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+
+import json
+import os
+from PIL import Image
+from tqdm import tqdm
+
+def process_annotations(json_file, images_folder, labels_folder):
+ # 读取JSON文件
+ with open(json_file, 'r') as file:
+ data = json.load(file)
+
+ annotations = data['annotations']
+ ignore_count = 0
+
+ for annotation in tqdm(annotations):
+ category_id = annotation['category_id']
+ image_id = annotation['image_id']
+
+ # 跳过ignore标签
+ if category_id == 0:
+ ignore_count += 1
+ continue
+
+ # 获取图片尺寸
+ file_path = os.path.join(images_folder, str(image_id) + '.jpg')
+ try:
+ with Image.open(file_path) as img:
+ width, height = img.size
+ except FileNotFoundError:
+ continue
+
+ # 计算归一化的坐标
+ bbox = annotation['bbox']
+ x_center = (bbox[0] + bbox[2] / 2) / width
+ y_center = (bbox[1] + bbox[3] / 2) / height
+ w_norm = bbox[2] / width
+ h_norm = bbox[3] / height
+
+ # 写入标签文件，标签编号减1
+ label = category_id - 1
+ label_content = f"{label} {x_center} {y_center} {w_norm} {h_norm}"
+
+ label_file_path = os.path.join(labels_folder, str(image_id) + '.txt')
+ with open(label_file_path, 'a') as file:
+ file.write(label_content + '\n')
+
+ return ignore_count
+
+def create_labels_folder():
+ # 创建labels文件夹及其子文件夹
+ labels_folder = 'labels'
+ train_labels_folder = os.path.join(labels_folder, 'train')
+ val_labels_folder = os.path.join(labels_folder, 'val')
+ os.makedirs(train_labels_folder, exist_ok=True)
+ os.makedirs(val_labels_folder, exist_ok=True)
+ return train_labels_folder, val_labels_folder
+
+def main():
+ train_labels_folder, val_labels_folder = create_labels_folder()
+
+ # 处理训练和验证数据
+ ignore_count_train = process_annotations('annotations/instances_train.json', 'images/train', train_labels_folder)
+ ignore_count_val = process_annotations('annotations/instances_val.json', 'images/val', val_labels_folder)
+
+ print(f"完成处理。忽略的标签数量：训练集 = {ignore_count_train}, 验证集 = {ignore_count_val}")
+
+if __name__ == "__main__":
+ main()

yolo2json.py

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+import glob
+import json
+import os
+from PIL import Image
+
+def yolo_to_coco_for_subset(yolo_images_folder, yolo_labels_folder, categories):
+ # Initialize COCO dataset structure for the subset
+ coco_format = {
+ "images": [],
+ "annotations": [],
+ "categories": []
+ }
+
+ # Add category information
+ for i, category in enumerate(categories):
+ coco_format["categories"].append({
+ "id": i + 1,
+ "name": category,
+ "supercategory": "none"
+ })
+
+ image_id = 0
+ annotation_id = 0
+
+ for image_file in glob.glob(f"{yolo_images_folder}/*.jpg"):
+ # Read image to get width and height
+ with Image.open(image_file) as img:
+ width, height = img.size
+
+ # Add image information with size
+ coco_format["images"].append({
+ "id": image_id + 1,
+ "file_name": os.path.basename(image_file),
+ "width": width,
+ "height": height
+ })
+
+ # Corresponding annotation file
+ yolo_annotation_file = os.path.join(yolo_labels_folder, os.path.basename(image_file).replace(".jpg", ".txt"))
+ if os.path.exists(yolo_annotation_file):
+ with open(yolo_annotation_file, "r") as file:
+ for line in file:
+ category_id, x_center, y_center, bbox_width, bbox_height = map(float, line.split())
+
+ # Convert YOLO format to COCO format
+ x_min = (x_center - bbox_width / 2) * width
+ y_min = (y_center - bbox_height / 2) * height
+ coco_bbox_width = bbox_width * width
+ coco_bbox_height = bbox_height * height
+
+ # Add annotation information
+ coco_format["annotations"].append({
+ "id": annotation_id + 1,
+ "image_id": image_id + 1,
+ "category_id": int(category_id) + 1,
+ "bbox": [x_min, y_min, coco_bbox_width, coco_bbox_height],
+ "area": coco_bbox_width * coco_bbox_height,
+ "segmentation": [], # Optional
+ "iscrowd": 0
+ })
+ annotation_id += 1
+ image_id += 1
+
+ return coco_format
+
+def save_coco_format(coco_format, output_file):
+ with open(output_file, "w") as file:
+ json.dump(coco_format, file, indent=4)
+
+# Example usage
+yolo_base_folder = "/public/home/lvshuhang/pea_od/yolov8-main/mydata"
+categories = ["pea"]
+
+# Convert train set
+train_coco_format = yolo_to_coco_for_subset(
+ os.path.join(yolo_base_folder, "images/train"),
+ os.path.join(yolo_base_folder, "labels/train"),
+ categories
+)
+save_coco_format(train_coco_format, "train_coco_format.json")
+
+# Convert val set
+val_coco_format = yolo_to_coco_for_subset(
+ os.path.join(yolo_base_folder, "images/val"),
+ os.path.join(yolo_base_folder, "labels/val"),
+ categories
+)
+save_coco_format(val_coco_format, "val_coco_format.json")