import os from pathlib import Path import fire from PIL import Image from sahi.utils.coco import Coco, CocoAnnotation, CocoCategory, CocoImage from sahi.utils.file import save_json from tqdm import tqdm import sys import xml.etree.ElementTree as ET NAME_TO_COCO_CATEGORY = { "A220": {"name": "A220", "supercategory": "A220"}, "A321": {"name": "A321", "supercategory": "A321"}, "A330": {"name": "A330", "supercategory": "A330"}, "A350": {"name": "A350", "supercategory": "A350"}, "ARJ21": {"name": "ARJ21", "supercategory": "ARJ21"}, "Baseball Field": {"name": "Baseball Field", "supercategory": "Baseball Field"}, "Basketball Court": {"name": "Basketball Court", "supercategory": "Basketball Court"}, "Boeing737": {"name": "Boeing737", "supercategory": "Boeing737"}, "Boeing747": {"name": "Boeing747", "supercategory": "Boeing747"}, "Boeing777": {"name": "Boeing777", "supercategory": "Boeing777"}, "Boeing787": {"name": "Boeing787", "supercategory": "Boeing787"}, "Bridge": {"name": "Bridge", "supercategory": "Bridge"}, "Bus": {"name": "Bus", "supercategory": "Bus"}, "C919": {"name": "C919", "supercategory": "C919"}, "Cargo Truck": {"name": "Cargo Truck", "supercategory": "Cargo Truck"}, "Dry Cargo Ship": {"name": "Dry Cargo Ship", "supercategory": "Dry Cargo Ship"}, "Dump Truck": {"name": "Dump Truck", "supercategory": "Dump Truck"}, "Engineering Ship": {"name": "Engineering Ship", "supercategory": "Engineering Ship"}, "Excavator": {"name": "Excavator", "supercategory": "Excavator"}, "Fishing Boat": {"name": "Fishing Boat", "supercategory": "Fishing Boat"}, "Football Field": {"name": "Football Field", "supercategory": "Football Field"}, "Intersection": {"name": "Intersection", "supercategory": "Intersection"}, "Liquid Cargo Ship": {"name": "Liquid Cargo Ship", "supercategory": "Liquid Cargo"}, "Motorboat": {"name": "Motorboat", "supercategory": "Motorboat"}, "other-airplane": {"name": "other-airplane", "supercategory": "other-airplane"}, "other-ship": {"name": "other-ship", "supercategory": "other-ship"}, "other-vehicle": {"name": "other-vehicle", "supercategory": "other-vehicle"}, "Passenger Ship": {"name": "Passenger Ship", "supercategory": "Passenger Ship"}, "Roundabout": {"name": "Roundabout", "supercategory": "Roundabout"}, "Small Car": {"name": "Small Car", "supercategory": "Small Car"}, "Tennis Court": {"name": "Tennis Court", "supercategory": "Tennis Court"}, "Tractor": {"name": "Tractor", "supercategory": "Tractor"}, "Trailer": {"name": "Trailer", "supercategory": "Trailer"}, "Truck Tractor": {"name": "Truck", "supercategory": "Truck"}, "Tugboat": {"name": "Tugboat", "supercategory": "Tugboat"}, "Van": {"name": "Van", "supercategory": "Van"}, "Warship": {"name": "Warship", "supercategory": "Warship"} } CATEGORY_ID_REMAPPING = { "A220": "0", "A321": "1", "A330": "2", "A350": "3", "ARJ21": "4", "Baseball Field": "5", "Basketball Court": "6", "Boeing737": "7", "Boeing747": "8", "Boeing777": "9", "Boeing787": "10", "Bridge": "11", "Bus": "12", "C919": "13", "Cargo Truck": "14", "Dry Cargo Ship": "15", "Dump Truck": "16", "Engineering Ship": "17", "Excavator": "18", "Fishing Boat": "19", "Football Field": "20", "Intersection": "21", "Liquid Cargo Ship": "22", "Motorboat": "23", "other-airplane": "24", "other-ship": "25", "other-vehicle": "26", "Passenger Ship": "27", "Roundabout": "28", "Small Car": "29", "Tennis Court": "30", "Tractor": "31", "Trailer": "32", "Truck Tractor": "33", "Tugboat": "34", "Van": "35", "Warship": "36" } def fair1m_to_coco( data_folder_dir, split_images_path, output_file_path, mode, category_id_remapping=None, ): """ Converts fair1m annotations into coco annotation. Args: data_folder_dir: str 'fair1m' folder directory split_images_path: str split_images_path txt file output_file_path: str Output file path mode: str mode original|car_other|car category_id_remapping: dict Used for selecting desired category ids and mapping them. If not provided, vedai mapping will be used. format: str(id) to str(id) """ # init paths/folders input_image_folder = str(Path(data_folder_dir) / "images") input_ann_folder = str(Path(data_folder_dir) / "labelXml") image_filepath_list = os.listdir(input_image_folder) if category_id_remapping is None: category_id_remapping = CATEGORY_ID_REMAPPING # init coco object coco = Coco() if mode == 'original': # 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_category = NAME_TO_COCO_CATEGORY[category_name] coco.add_category( CocoCategory( id=int(remapped_category_id), name=coco_category["name"], supercategory=coco_category["supercategory"], ) ) elif mode == 'car_other': print('"car_other" mode categories') coco.add_category(CocoCategory(id=0, name='Other')) coco.add_category(CocoCategory(id=1, name='Car')) for k,v in category_id_remapping.items(): if k in ['Small Car', 'Van']: category_id_remapping[k] =1 else: category_id_remapping[k]=0 elif mode == 'car': # car related categories: car, pickup, van print('"car" mode categories') coco.add_category(CocoCategory(id=0, name='Car')) category_id_remapping = {'Small Car': '0', 'Van': '0'} else: print('pick a defined mode: [original/car_other/car]') sys.exit() split_images_lst = [] with open(split_images_path, 'r') as f: for line in f: split_images_lst.append(line.strip().split('/')[1]) # convert fair1m annotations to coco for image_filename in tqdm(image_filepath_list): if image_filename in split_images_lst: # get image properties image_filepath = str(Path(input_image_folder) / image_filename) annotation_filename = image_filename.split(".tif")[0] + ".xml" annotation_filepath = str(Path(input_ann_folder) / annotation_filename) image = Image.open(image_filepath) cocoimage_filename = str(Path(image_filepath)).split(str(Path(data_folder_dir)))[1] if cocoimage_filename[0] == os.sep: cocoimage_filename = cocoimage_filename[1:] # create coco image object coco_image = CocoImage(file_name=cocoimage_filename, height=image.size[1], width=image.size[0]) # parse annotation file ### tree = ET.parse(annotation_filepath) xml_root = tree.getroot() objects = xml_root[3] for obj in objects: cat = obj[3][0].text points = obj[4] p1 = points[0].text p2 = points[1].text p3 = points[2].text p4 = points[3].text x_corners = [] y_corners = [] for p in points: broken = p.text.split(',') x_corners.append(float(broken[0])) y_corners.append(float(broken[1])) minx = min(x_corners) miny = min(y_corners) maxx = max(x_corners) maxy = max(y_corners) tbox = [minx, miny, maxx, maxy] # width = maxx-minx, height = maxy-miny width = tbox[2] - tbox[0] height = tbox[3] - tbox[1] bbox = [minx, miny, width, height] obj_area = width * height category_name = cat if category_name in category_id_remapping.keys(): remapped_category_id = category_id_remapping[category_name] else: continue # create coco annotation and append it to coco image coco_annotation = CocoAnnotation.from_coco_bbox( bbox=bbox, category_id=int(remapped_category_id), category_name=category_name, ) if mode == 'original': if coco_annotation.area > 0: coco_image.add_annotation(coco_annotation) elif mode == 'car_other': if 0 < coco_annotation.area < 400: coco_image.add_annotation(coco_annotation) elif mode == 'car': if 0 < coco_annotation.area < 400: coco_image.add_annotation(coco_annotation) coco.add_image(coco_image) save_path = output_file_path all_json_path = Path(save_path) / "{}.json".format(split_images_path.split('.')[0]) save_json(data=coco.json, save_path=all_json_path) if __name__ == "__main__": fire.Fire(fair1m_to_coco)