x2coco.py

#!/usr/bin/env python
# coding: utf-8
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import glob
import json
import os
import os.path as osp
import sys
import shutil

import numpy as np
import PIL.ImageDraw


class MyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.integer):
            return int(obj)
        elif isinstance(obj, np.floating):
            return float(obj)
        elif isinstance(obj, np.ndarray):
            return obj.tolist()
        else:
            return super(MyEncoder, self).default(obj)


def getbbox(self, points):
    polygons = points
    mask = self.polygons_to_mask([self.height, self.width], polygons)
    return self.mask2box(mask)


def images_labelme(data, num):
    image = {}
    image['height'] = data['imageHeight']
    image['width'] = data['imageWidth']
    image['id'] = num + 1
    image['file_name'] = data['imagePath'].split('/')[-1]
    return image


def images_cityscape(data, num, img_file):
    image = {}
    image['height'] = data['imgHeight']
    image['width'] = data['imgWidth']
    image['id'] = num + 1
    image['file_name'] = img_file
    return image


def categories(label, labels_list):
    category = {}
    category['supercategory'] = 'component'
    category['id'] = len(labels_list) + 1
    category['name'] = label
    return category


def annotations_rectangle(points, label, image_num, object_num, label_to_num):
    annotation = {}
    seg_points = np.asarray(points).copy()
    seg_points[1, :] = np.asarray(points)[2, :]
    seg_points[2, :] = np.asarray(points)[1, :]
    annotation['segmentation'] = [list(seg_points.flatten())]
    annotation['iscrowd'] = 0
    annotation['image_id'] = image_num + 1
    annotation['bbox'] = list(
        map(float, [
            points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
                1] - points[0][1]
        ]))
    annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
    annotation['category_id'] = label_to_num[label]
    annotation['id'] = object_num + 1
    return annotation


def annotations_polygon(height, width, points, label, image_num, object_num,
                        label_to_num):
    annotation = {}
    annotation['segmentation'] = [list(np.asarray(points).flatten())]
    annotation['iscrowd'] = 0
    annotation['image_id'] = image_num + 1
    annotation['bbox'] = list(map(float, get_bbox(height, width, points)))
    annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
    annotation['category_id'] = label_to_num[label]
    annotation['id'] = object_num + 1
    return annotation


def get_bbox(height, width, points):
    polygons = points
    mask = np.zeros([height, width], dtype=np.uint8)
    mask = PIL.Image.fromarray(mask)
    xy = list(map(tuple, polygons))
    PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
    mask = np.array(mask, dtype=bool)
    index = np.argwhere(mask == 1)
    rows = index[:, 0]
    clos = index[:, 1]
    left_top_r = np.min(rows)
    left_top_c = np.min(clos)
    right_bottom_r = np.max(rows)
    right_bottom_c = np.max(clos)
    return [
        left_top_c, left_top_r, right_bottom_c - left_top_c,
        right_bottom_r - left_top_r
    ]


def deal_json(ds_type, img_path, json_path):
    data_coco = {}
    label_to_num = {}
    images_list = []
    categories_list = []
    annotations_list = []
    labels_list = []
    image_num = -1
    object_num = -1
    for img_file in os.listdir(img_path):
        img_label = img_file.split('.')[0]
        if img_file.split('.')[
                -1] not in ['bmp', 'jpg', 'jpeg', 'png', 'JPEG', 'JPG', 'PNG']:
            continue
        label_file = osp.join(json_path, img_label + '.json')
        print('Generating dataset from:', label_file)
        image_num = image_num + 1
        with open(label_file) as f:
            data = json.load(f)
            if ds_type == 'labelme':
                images_list.append(images_labelme(data, image_num))
            elif ds_type == 'cityscape':
                images_list.append(images_cityscape(data, image_num, img_file))
            if ds_type == 'labelme':
                for shapes in data['shapes']:
                    object_num = object_num + 1
                    label = shapes['label']
                    if label not in labels_list:
                        categories_list.append(categories(label, labels_list))
                        labels_list.append(label)
                        label_to_num[label] = len(labels_list)
                    points = shapes['points']
                    p_type = shapes['shape_type']
                    if p_type == 'polygon':
                        annotations_list.append(
                            annotations_polygon(data['imageHeight'], data[
                                'imageWidth'], points, label, image_num,
                                                object_num, label_to_num))

                    if p_type == 'rectangle':
                        points.append([points[0][0], points[1][1]])
                        points.append([points[1][0], points[0][1]])
                        annotations_list.append(
                            annotations_rectangle(points, label, image_num,
                                                  object_num, label_to_num))
            elif ds_type == 'cityscape':
                for shapes in data['objects']:
                    object_num = object_num + 1
                    label = shapes['label']
                    if label not in labels_list:
                        categories_list.append(categories(label, labels_list))
                        labels_list.append(label)
                        label_to_num[label] = len(labels_list)
                    points = shapes['polygon']
                    annotations_list.append(
                        annotations_polygon(data['imgHeight'], data[
                            'imgWidth'], points, label, image_num, object_num,
                                            label_to_num))
    data_coco['images'] = images_list
    data_coco['categories'] = categories_list
    data_coco['annotations'] = annotations_list
    return data_coco


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument('--dataset_type', help='the type of dataset')
    parser.add_argument('--json_input_dir', help='input annotated directory')
    parser.add_argument('--image_input_dir', help='image directory')
    parser.add_argument(
        '--output_dir', help='output dataset directory', default='../../../')
    parser.add_argument(
        '--train_proportion',
        help='the proportion of train dataset',
        type=float,
        default=1.0)
    parser.add_argument(
        '--val_proportion',
        help='the proportion of validation dataset',
        type=float,
        default=0.0)
    parser.add_argument(
        '--test_proportion',
        help='the proportion of test dataset',
        type=float,
        default=0.0)
    args = parser.parse_args()
    try:
        assert args.dataset_type in ['labelme', 'cityscape']
    except AssertionError as e:
        print('Now only support the cityscape dataset and labelme dataset!!')
        os._exit(0)
    try:
        assert os.path.exists(args.json_input_dir)
    except AssertionError as e:
        print('The json folder does not exist!')
        os._exit(0)
    try:
        assert os.path.exists(args.image_input_dir)
    except AssertionError as e:
        print('The image folder does not exist!')
        os._exit(0)
    try:
        assert args.train_proportion + args.val_proportion + args.test_proportion == 1.0
    except AssertionError as e:
        print(
            'The sum of pqoportion of training, validation and test datase must be 1!'
        )
        os._exit(0)

    # Allocate the dataset.
    total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
    if args.train_proportion != 0:
        train_num = int(total_num * args.train_proportion)
        os.makedirs(args.output_dir + '/train')
    else:
        train_num = 0
    if args.val_proportion == 0.0:
        val_num = 0
        test_num = total_num - train_num
        if args.test_proportion != 0.0:
            os.makedirs(args.output_dir + '/test')
    else:
        val_num = int(total_num * args.val_proportion)
        test_num = total_num - train_num - val_num
        os.makedirs(args.output_dir + '/val')
        if args.test_proportion != 0.0:
            os.makedirs(args.output_dir + '/test')
    count = 1
    for img_name in os.listdir(args.image_input_dir):
        if count <= train_num:
            shutil.copyfile(
                osp.join(args.image_input_dir, img_name),
                osp.join(args.output_dir + '/train/', img_name))
        else:
            if count <= train_num + val_num:
                shutil.copyfile(
                    osp.join(args.image_input_dir, img_name),
                    osp.join(args.output_dir + '/val/', img_name))
            else:
                shutil.copyfile(
                    osp.join(args.image_input_dir, img_name),
                    osp.join(args.output_dir + '/test/', img_name))
        count = count + 1

    # Deal with the json files.
    if not os.path.exists(args.output_dir + '/annotations'):
        os.makedirs(args.output_dir + '/annotations')
    if args.train_proportion != 0:
        train_data_coco = deal_json(
            args.dataset_type, args.output_dir + '/train', args.json_input_dir)
        train_json_path = osp.join(args.output_dir + '/annotations',
                                   'instance_train.json')
        json.dump(
            train_data_coco,
            open(train_json_path, 'w'),
            indent=4,
            cls=MyEncoder)
    if args.val_proportion != 0:
        val_data_coco = deal_json(args.dataset_type,
                                  args.output_dir + '/val', 
                                  args.json_input_dir)
        val_json_path = osp.join(args.output_dir + '/annotations',
                                 'instance_val.json')
        json.dump(
            val_data_coco, open(val_json_path, 'w'), indent=4, cls=MyEncoder)
    if args.test_proportion != 0:
        test_data_coco = deal_json(args.dataset_type,
                                   args.output_dir + '/test',
                                   args.json_input_dir)
        test_json_path = osp.join(args.output_dir + '/annotations',
                                  'instance_test.json')
        json.dump(
            test_data_coco, open(test_json_path, 'w'), indent=4, cls=MyEncoder)


if __name__ == '__main__':
    main()