How to print filename and the bounding box in following format?

[nisnab]

I want to print the results in following format:
filename,class_name, [width1,1]x[height1,1]+[xmin1,1]+[ymin1,1]?

I created my dataset in the following way.
The custom datasets are made in this way.
`def create_dataset_dicts(df, classes):
dataset_dicts = []
for image_id, img_name in enumerate(df.filename.unique()):

record = {}

image_df = df[df.filename == img_name]

file_path = f'{IMAGES_PATH}/{img_name}'
record["file_name"] = file_path
record["image_id"] = image_id
record["height"] = int(image_df.iloc[0].height)
record["width"] = int(image_df.iloc[0].width)

objs = []
for _, row in image_df.iterrows():

  xmin = int(row.x_min)
  ymin = int(row.y_min)
  xmax = int(row.x_max)
  ymax = int(row.y_max)

  poly = [
      (xmin, ymin), (xmax, ymin), 
      (xmax, ymax), (xmin, ymax)
  ]
  poly = list(itertools.chain.from_iterable(poly))

  obj = {
    "bbox": [xmin, ymin, xmax, ymax],
    "bbox_mode": BoxMode.XYXY_ABS,
    "segmentation": [poly],
    "category_id": classes.index(row.class_name),
    "iscrowd": 0
  }
  objs.append(obj)

record["annotations"] = objs
dataset_dicts.append(record)

return dataset_dicts

for d in ["train", "val"]:
DatasetCatalog.register(d, lambda d=d: create_dataset_dicts(train_df if d == "train" else test_df, classes))
MetadataCatalog.get(d).set(thing_classes=classes)`

Here is the full link. [(https://github.com/Nisnab/solving-challenge-using-detectron2/blob/main/detectron2-coral.ipynb)
]

my results are displayed in the following format.
{"image_id": 0, "category_id": 1, "bbox": [2691.6044921875, 916.4785766601562, 217.886962890625, 179.80816650390625], "score": 0.8843753337860107, "segmentation": {"size": [3024, 4032], "counts": "Uddh7Q1]2]Ong2[1lVMFjh2Q3N2N2O1N2N2O001O2N2M3N1O1O1O1O001O00001O000O2O001O1O001O001O0000001O0000001O00000000O10000000000000000001O00O1000000O100000000O100000000000000000000000000000000000000000000000000000000001O000000000001O0000000001O000000000000001O000000000000001O000000000000000000000000000000000000000000000000001O0000000000000000000000000000000000000000O2O0000000000001O00001O001O1O1O1O001O0O101O00000O2O0O2O1N2O1N4L4L3M2N2O1N2N3L4M3L7Ib0YO[ZiW3"}}