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xview_to_coco.py
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xview_to_coco.py
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import random
from collections import defaultdict
from pathlib import Path
from typing import Dict, List
import fire
import numpy as np
from PIL import Image
from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage
from sahi.utils.file import load_json, save_json
from tqdm import tqdm
# fix the seed
random.seed(13)
def xview_to_coco(
train_images_dir,
train_geojson_path,
output_dir,
train_split_rate=0.75,
category_id_remapping=None,
):
"""
Converts visdrone-det annotations into coco annotation.
Args:
train_images_dir: str
'train_images' folder directory
train_geojson_path: str
'xView_train.geojson' file path
output_dir: str
Output folder directory
train_split_rate: bool
Train split ratio
category_id_remapping: dict
Used for selecting desired category ids and mapping them.
If not provided, xView mapping will be used.
format: str(id) to str(id)
"""
# init vars
category_id_to_name = {}
with open("xview/xview_class_labels.txt", encoding="utf8") as f:
lines = f.readlines()
for line in lines:
category_id = line.split(":")[0]
category_name = line.split(":")[1].replace("\n", "")
category_id_to_name[category_id] = category_name
if category_id_remapping is None:
category_id_remapping = load_json("xview/category_id_mapping.json")
category_id_remapping
# init coco object
coco = Coco()
# append categories
for category_id, category_name in category_id_to_name.items():
if category_id in category_id_remapping.keys():
remapped_category_id = category_id_remapping[category_id]
coco.add_category(
CocoCategory(id=int(remapped_category_id), name=category_name)
)
# parse xview data
coords, chips, classes, image_name_to_annotation_ind = get_labels(
train_geojson_path
)
image_name_list = get_ordered_image_name_list(image_name_to_annotation_ind)
# convert xView data to COCO format
for image_name in tqdm(image_name_list, "Converting xView data into COCO format"):
# create coco image object
width, height = Image.open(Path(train_images_dir) / image_name).size
coco_image = CocoImage(file_name=image_name, height=height, width=width)
annotation_ind_list = image_name_to_annotation_ind[image_name]
# iterate over image annotations
for annotation_ind in annotation_ind_list:
bbox = coords[annotation_ind].tolist()
category_id = str(int(classes[annotation_ind].item()))
coco_bbox = [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]
if category_id in category_id_remapping.keys():
category_name = category_id_to_name[category_id]
remapped_category_id = category_id_remapping[category_id]
else:
continue
# create coco annotation and append it to coco image
coco_annotation = CocoAnnotation(
bbox=coco_bbox,
category_id=int(remapped_category_id),
category_name=category_name,
)
if coco_annotation.area > 0:
coco_image.add_annotation(coco_annotation)
coco.add_image(coco_image)
result = coco.split_coco_as_train_val(train_split_rate=train_split_rate)
train_json_path = Path(output_dir) / "train.json"
val_json_path = Path(output_dir) / "val.json"
save_json(data=result["train_coco"].json, save_path=train_json_path)
save_json(data=result["val_coco"].json, save_path=val_json_path)
def get_ordered_image_name_list(image_name_to_annotation_ind: Dict):
image_name_list: List[str] = list(image_name_to_annotation_ind.keys())
def get_image_ind(image_name: str):
return int(image_name.split(".")[0])
image_name_list.sort(key=get_image_ind)
return image_name_list
def get_labels(fname):
"""
Gets label data from a geojson label file
Args:
fname: file path to an xView geojson label file
Output:
Returns three arrays: coords, chips, and classes corresponding to the
coordinates, file-names, and classes for each ground truth.
Modified from https://github.com/DIUx-xView.
"""
data = load_json(fname)
coords = np.zeros((len(data["features"]), 4))
chips = np.zeros((len(data["features"])), dtype="object")
classes = np.zeros((len(data["features"])))
image_name_to_annotation_ind = defaultdict(list)
for i in tqdm(range(len(data["features"])), "Parsing xView data"):
if data["features"][i]["properties"]["bounds_imcoords"] != []:
b_id = data["features"][i]["properties"]["image_id"]
# https://github.com/DIUx-xView/xView1_baseline/issues/3
if b_id == "1395.tif":
continue
val = np.array(
[
int(num)
for num in data["features"][i]["properties"][
"bounds_imcoords"
].split(",")
]
)
chips[i] = b_id
classes[i] = data["features"][i]["properties"]["type_id"]
image_name_to_annotation_ind[b_id].append(i)
if val.shape[0] != 4:
print("Issues at %d!" % i)
else:
coords[i] = val
else:
chips[i] = "None"
return coords, chips, classes, image_name_to_annotation_ind
if __name__ == "__main__":
fire.Fire(xview_to_coco)