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 CATEGORY_ID_TO_NAME = { "1": "car", "2":"truck", "3": "pickup", "4": "tractor", "5": "camping", "6": "boat", "7": "motorcycle", "9": "bus", "10": "van", "11": "other", "12": "small car", "13": "large car", "31": "plane", "23": "board" } CATEGORY_ID_REMAPPING = { "1": "0", "2": "1", "4": "2", "5": "3", "7": "4", "9": "5", "10": "6", "11": "7", "31": "8", "23": "9" } NAME_TO_COCO_CATEGORY = { "car": {"name": "car", "supercategory": "car"}, "truck": {"name": "truck", "supercategory": "truck"}, "tractor": {"name": "tractor", "supercategory": "tractor"}, "camping": {"name": "camping", "supercategory": "camping"}, "motorcycle": {"name": "motorcycle", "supercategory": "motorcycle"}, "bus": {"name": "bus", "supercategory": "bus"}, "van": {"name": "van", "supercategory": "van"}, "other": {"name": "other", "supercategory": "other"}, "plane": {"name": "plane", "supercategory": "plane"}, "board": {"name": "board", "supercategory": "board"} } def vedai_to_coco( data_folder_dir, split_images_path, output_dir, mode, category_id_remapping=None, ): """ Converts vedai annotations into coco annotation. Args: data_folder_dir: str 'vedai' 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) / "Vehicules512") input_ann_folder = str(Path(data_folder_dir) / "Annotations512") image_filepath_list = os.listdir(input_image_folder) # Path(output_dir).parents[0].mkdir(parents=True, exist_ok=True) if category_id_remapping is None: category_id_remapping = CATEGORY_ID_REMAPPING # init coco object coco = Coco() # append categories if mode == 'original': 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 ['1','10']: 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 = {'1': '0','10': '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 vedai annotations to coco for image_name in tqdm(image_filepath_list): # get image properties if image_name in split_images_lst: image_filepath = str(Path(input_image_folder) / image_name) annotation_filename = image_name.split("_co.png")[0] + ".txt" 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 file = open(annotation_filepath, "r") lines = file.readlines() for line in lines: # parse annotation bboxes new_line = line.strip("\n").split(" ") y_corners = [int(x) for x in new_line[-4:]] x_corners = [int(x) for x in new_line[-8:-4]] minx_miny = (min(x_corners), min(y_corners)) maxx_maxy = (max(x_corners), max(y_corners)) # i_bbox = [minx_miny[0], minx_miny[1], maxx_maxy[0], maxx_maxy[1]] # width = maxx-minx, height = maxy-miny width = i_bbox[2] - i_bbox[0] height = i_bbox[3] - i_bbox[1] #top left point minx = min(x_corners) miny = min(y_corners) bbox = [minx, miny, width, height] # parse category id and name category_id = new_line[3] 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.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 coco_annotation.area < 400: coco_image.add_annotation(coco_annotation) elif mode == 'car': if coco_annotation.area < 400: coco_image.add_annotation(coco_annotation) coco.add_image(coco_image) save_path = output_dir 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(vedai_to_coco)