PaddleDetection-release-2.6/tools/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 shutil
import xml.etree.ElementTree as ET

import numpy as np
import PIL.ImageDraw
from tqdm import tqdm
import cv2

label_to_num = {}
categories_list = []
labels_list = []


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 images_labelme(data, num):
    image = {}
    image['height'] = data['imageHeight']
    image['width'] = data['imageWidth']
    image['id'] = num + 1
    if '\\' in data['imagePath']:
        image['file_name'] = data['imagePath'].split('\\')[-1]
    else:
        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 = {}
    images_list = []
    annotations_list = []
    image_num = -1
    object_num = -1
    for img_file in os.listdir(img_path):
        img_label = os.path.splitext(img_file)[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)
                    p_type = shapes['shape_type']
                    if p_type == 'polygon':
                        points = shapes['points']
                        annotations_list.append(
                            annotations_polygon(data['imageHeight'], data[
                                'imageWidth'], points, label, image_num,
                                                object_num, label_to_num))

                    if p_type == 'rectangle':
                        (x1, y1), (x2, y2) = shapes['points']
                        x1, x2 = sorted([x1, x2])
                        y1, y2 = sorted([y1, y2])
                        points = [[x1, y1], [x2, y2], [x1, y2], [x2, y1]]
                        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 voc_get_label_anno(ann_dir_path, ann_ids_path, labels_path):
    with open(labels_path, 'r') as f:
        labels_str = f.read().split()
    labels_ids = list(range(1, len(labels_str) + 1))

    with open(ann_ids_path, 'r') as f:
        ann_ids = [lin.strip().split(' ')[-1] for lin in f.readlines()]

    ann_paths = []
    for aid in ann_ids:
        if aid.endswith('xml'):
            ann_path = os.path.join(ann_dir_path, aid)
        else:
            ann_path = os.path.join(ann_dir_path, aid + '.xml')
        ann_paths.append(ann_path)

    return dict(zip(labels_str, labels_ids)), ann_paths


def voc_get_image_info(annotation_root, im_id):
    filename = annotation_root.findtext('filename')
    assert filename is not None
    img_name = os.path.basename(filename)

    size = annotation_root.find('size')
    width = float(size.findtext('width'))
    height = float(size.findtext('height'))

    image_info = {
        'file_name': filename,
        'height': height,
        'width': width,
        'id': im_id
    }
    return image_info


def voc_get_coco_annotation(obj, label2id):
    label = obj.findtext('name')
    assert label in label2id, "label is not in label2id."
    category_id = label2id[label]
    bndbox = obj.find('bndbox')
    xmin = float(bndbox.findtext('xmin'))
    ymin = float(bndbox.findtext('ymin'))
    xmax = float(bndbox.findtext('xmax'))
    ymax = float(bndbox.findtext('ymax'))
    assert xmax > xmin and ymax > ymin, "Box size error."
    o_width = xmax - xmin
    o_height = ymax - ymin
    anno = {
        'area': o_width * o_height,
        'iscrowd': 0,
        'bbox': [xmin, ymin, o_width, o_height],
        'category_id': category_id,
        'ignore': 0,
    }
    return anno


def voc_xmls_to_cocojson(annotation_paths, label2id, output_dir, output_file):
    output_json_dict = {
        "images": [],
        "type": "instances",
        "annotations": [],
        "categories": []
    }
    bnd_id = 1  # bounding box start id
    im_id = 0
    print('Start converting !')
    for a_path in tqdm(annotation_paths):
        # Read annotation xml
        ann_tree = ET.parse(a_path)
        ann_root = ann_tree.getroot()

        img_info = voc_get_image_info(ann_root, im_id)
        output_json_dict['images'].append(img_info)

        for obj in ann_root.findall('object'):
            ann = voc_get_coco_annotation(obj=obj, label2id=label2id)
            ann.update({'image_id': im_id, 'id': bnd_id})
            output_json_dict['annotations'].append(ann)
            bnd_id = bnd_id + 1
        im_id += 1

    for label, label_id in label2id.items():
        category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
        output_json_dict['categories'].append(category_info)
    output_file = os.path.join(output_dir, output_file)
    with open(output_file, 'w') as f:
        output_json = json.dumps(output_json_dict)
        f.write(output_json)


def widerface_to_cocojson(root_path):
    train_gt_txt = os.path.join(root_path, "wider_face_split", "wider_face_train_bbx_gt.txt")
    val_gt_txt = os.path.join(root_path, "wider_face_split", "wider_face_val_bbx_gt.txt")
    train_img_dir = os.path.join(root_path, "WIDER_train", "images")
    val_img_dir = os.path.join(root_path, "WIDER_val", "images")
    assert train_gt_txt
    assert val_gt_txt
    assert train_img_dir
    assert val_img_dir
    save_path = os.path.join(root_path, "widerface_train.json")
    widerface_convert(train_gt_txt, train_img_dir, save_path)
    print("Wider Face train dataset converts sucess, the json path: {}".format(save_path))
    save_path = os.path.join(root_path, "widerface_val.json")
    widerface_convert(val_gt_txt, val_img_dir, save_path)
    print("Wider Face val dataset converts sucess, the json path: {}".format(save_path))


def widerface_convert(gt_txt, img_dir, save_path):
    output_json_dict = {
        "images": [],
        "type": "instances",
        "annotations": [],
        "categories": [{'supercategory': 'none', 'id': 0, 'name': "human_face"}]
    }
    bnd_id = 1  # bounding box start id
    im_id = 0
    print('Start converting !')
    with open(gt_txt) as fd:
        lines = fd.readlines()

    i = 0
    while i < len(lines):
        image_name = lines[i].strip()
        bbox_num = int(lines[i + 1].strip())
        i += 2
        img_info = get_widerface_image_info(img_dir, image_name, im_id)
        if img_info:
            output_json_dict["images"].append(img_info)
            for j in range(i, i + bbox_num):
                anno = get_widerface_ann_info(lines[j])
                anno.update({'image_id': im_id, 'id': bnd_id})
                output_json_dict['annotations'].append(anno)
                bnd_id += 1
        else:
            print("The image dose not exist: {}".format(os.path.join(img_dir, image_name)))
        bbox_num = 1 if bbox_num == 0 else bbox_num
        i += bbox_num
        im_id += 1
    with open(save_path, 'w') as f:
        output_json = json.dumps(output_json_dict)
        f.write(output_json)


def get_widerface_image_info(img_root, img_relative_path, img_id):
    image_info = {}
    save_path = os.path.join(img_root, img_relative_path)
    if os.path.exists(save_path):
        img = cv2.imread(save_path)
        image_info["file_name"] = os.path.join(os.path.basename(
            os.path.dirname(img_root)), os.path.basename(img_root),
            img_relative_path)
        image_info["height"] = img.shape[0]
        image_info["width"] = img.shape[1]
        image_info["id"] = img_id
    return image_info


def get_widerface_ann_info(info):
    info = [int(x) for x in info.strip().split()]
    anno = {
        'area': info[2] * info[3],
        'iscrowd': 0,
        'bbox': [info[0], info[1], info[2], info[3]],
        'category_id': 0,
        'ignore': 0,
        'blur': info[4],
        'expression': info[5],
        'illumination': info[6],
        'invalid': info[7],
        'occlusion': info[8],
        'pose': info[9]
    }
    return anno


def main():
    parser = argparse.ArgumentParser(
        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument(
        '--dataset_type',
        help='the type of dataset, can be `voc`, `widerface`, `labelme` or `cityscape`')
    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)
    parser.add_argument(
        '--voc_anno_dir',
        help='In Voc format dataset, path to annotation files directory.',
        type=str,
        default=None)
    parser.add_argument(
        '--voc_anno_list',
        help='In Voc format dataset, path to annotation files ids list.',
        type=str,
        default=None)
    parser.add_argument(
        '--voc_label_list',
        help='In Voc format dataset, path to label list. The content of each line is a category.',
        type=str,
        default=None)
    parser.add_argument(
        '--voc_out_name',
        type=str,
        default='voc.json',
        help='In Voc format dataset, path to output json file')
    parser.add_argument(
        '--widerface_root_dir',
        help='The root_path for wider face dataset, which contains `wider_face_split`, `WIDER_train` and `WIDER_val`.And the json file will save in this path',
        type=str,
        default=None)
    args = parser.parse_args()
    try:
        assert args.dataset_type in ['voc', 'labelme', 'cityscape', 'widerface']
    except AssertionError as e:
        print(
            'Now only support the voc, cityscape dataset and labelme dataset!!')
        os._exit(0)

    if args.dataset_type == 'voc':
        assert args.voc_anno_dir and args.voc_anno_list and args.voc_label_list
        label2id, ann_paths = voc_get_label_anno(
            args.voc_anno_dir, args.voc_anno_list, args.voc_label_list)
        voc_xmls_to_cocojson(
            annotation_paths=ann_paths,
            label2id=label2id,
            output_dir=args.output_dir,
            output_file=args.voc_out_name)
    elif args.dataset_type == "widerface":
        assert args.widerface_root_dir
        widerface_to_cocojson(args.widerface_root_dir)
    else:
        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 abs(args.train_proportion + args.val_proportion \
                    + args.test_proportion - 1.0) < 1e-5
        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)
            out_dir = args.output_dir + '/train'
            if not os.path.exists(out_dir):
                os.makedirs(out_dir)
        else:
            train_num = 0
        if args.val_proportion == 0.0:
            val_num = 0
            test_num = total_num - train_num
            out_dir = args.output_dir + '/test'
            if args.test_proportion != 0.0 and not os.path.exists(out_dir):
                os.makedirs(out_dir)
        else:
            val_num = int(total_num * args.val_proportion)
            test_num = total_num - train_num - val_num
            val_out_dir = args.output_dir + '/val'
            if not os.path.exists(val_out_dir):
                os.makedirs(val_out_dir)
            test_out_dir = args.output_dir + '/test'
            if args.test_proportion != 0.0 and not os.path.exists(test_out_dir):
                os.makedirs(test_out_dir)
        count = 1
        for img_name in os.listdir(args.image_input_dir):
            if count <= train_num:
                if osp.exists(args.output_dir + '/train/'):
                    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:
                    if osp.exists(args.output_dir + '/val/'):
                        shutil.copyfile(
                            osp.join(args.image_input_dir, img_name),
                            osp.join(args.output_dir + '/val/', img_name))
                else:
                    if osp.exists(args.output_dir + '/test/'):
                        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()