forked from PulseFocusPlatform/PulseFocusPlatform
449 lines
16 KiB
Python
449 lines
16 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import glob
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import json
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import os
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import os.path as osp
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import shutil
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import xml.etree.ElementTree as ET
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from tqdm import tqdm
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import numpy as np
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import PIL.ImageDraw
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label_to_num = {}
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categories_list = []
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labels_list = []
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class MyEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, np.integer):
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return int(obj)
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elif isinstance(obj, np.floating):
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return float(obj)
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elif isinstance(obj, np.ndarray):
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return obj.tolist()
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else:
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return super(MyEncoder, self).default(obj)
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def images_labelme(data, num):
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image = {}
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image['height'] = data['imageHeight']
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image['width'] = data['imageWidth']
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image['id'] = num + 1
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if '\\' in data['imagePath']:
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image['file_name'] = data['imagePath'].split('\\')[-1]
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else:
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image['file_name'] = data['imagePath'].split('/')[-1]
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return image
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def images_cityscape(data, num, img_file):
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image = {}
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image['height'] = data['imgHeight']
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image['width'] = data['imgWidth']
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image['id'] = num + 1
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image['file_name'] = img_file
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return image
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def categories(label, labels_list):
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category = {}
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category['supercategory'] = 'component'
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category['id'] = len(labels_list) + 1
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category['name'] = label
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return category
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def annotations_rectangle(points, label, image_num, object_num, label_to_num):
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annotation = {}
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seg_points = np.asarray(points).copy()
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seg_points[1, :] = np.asarray(points)[2, :]
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seg_points[2, :] = np.asarray(points)[1, :]
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annotation['segmentation'] = [list(seg_points.flatten())]
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annotation['iscrowd'] = 0
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annotation['image_id'] = image_num + 1
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annotation['bbox'] = list(
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map(float, [
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points[0][0], points[0][1], points[1][0] - points[0][0], points[1][
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1] - points[0][1]
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]))
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annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
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annotation['category_id'] = label_to_num[label]
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annotation['id'] = object_num + 1
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return annotation
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def annotations_polygon(height, width, points, label, image_num, object_num,
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label_to_num):
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annotation = {}
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annotation['segmentation'] = [list(np.asarray(points).flatten())]
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annotation['iscrowd'] = 0
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annotation['image_id'] = image_num + 1
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annotation['bbox'] = list(map(float, get_bbox(height, width, points)))
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annotation['area'] = annotation['bbox'][2] * annotation['bbox'][3]
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annotation['category_id'] = label_to_num[label]
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annotation['id'] = object_num + 1
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return annotation
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def get_bbox(height, width, points):
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polygons = points
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mask = np.zeros([height, width], dtype=np.uint8)
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mask = PIL.Image.fromarray(mask)
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xy = list(map(tuple, polygons))
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PIL.ImageDraw.Draw(mask).polygon(xy=xy, outline=1, fill=1)
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mask = np.array(mask, dtype=bool)
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index = np.argwhere(mask == 1)
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rows = index[:, 0]
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clos = index[:, 1]
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left_top_r = np.min(rows)
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left_top_c = np.min(clos)
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right_bottom_r = np.max(rows)
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right_bottom_c = np.max(clos)
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return [
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left_top_c, left_top_r, right_bottom_c - left_top_c,
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right_bottom_r - left_top_r
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]
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def deal_json(ds_type, img_path, json_path):
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data_coco = {}
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images_list = []
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annotations_list = []
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image_num = -1
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object_num = -1
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for img_file in os.listdir(img_path):
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img_label = os.path.splitext(img_file)[0]
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if img_file.split('.')[
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-1] not in ['bmp', 'jpg', 'jpeg', 'png', 'JPEG', 'JPG', 'PNG']:
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continue
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label_file = osp.join(json_path, img_label + '.json')
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print('Generating dataset from:', label_file)
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image_num = image_num + 1
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with open(label_file) as f:
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data = json.load(f)
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if ds_type == 'labelme':
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images_list.append(images_labelme(data, image_num))
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elif ds_type == 'cityscape':
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images_list.append(images_cityscape(data, image_num, img_file))
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if ds_type == 'labelme':
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for shapes in data['shapes']:
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object_num = object_num + 1
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label = shapes['label']
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if label not in labels_list:
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categories_list.append(categories(label, labels_list))
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labels_list.append(label)
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label_to_num[label] = len(labels_list)
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p_type = shapes['shape_type']
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if p_type == 'polygon':
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points = shapes['points']
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annotations_list.append(
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annotations_polygon(data['imageHeight'], data[
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'imageWidth'], points, label, image_num,
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object_num, label_to_num))
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if p_type == 'rectangle':
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(x1, y1), (x2, y2) = shapes['points']
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x1, x2 = sorted([x1, x2])
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y1, y2 = sorted([y1, y2])
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points = [[x1, y1], [x2, y2], [x1, y2], [x2, y1]]
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annotations_list.append(
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annotations_rectangle(points, label, image_num,
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object_num, label_to_num))
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elif ds_type == 'cityscape':
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for shapes in data['objects']:
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object_num = object_num + 1
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label = shapes['label']
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if label not in labels_list:
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categories_list.append(categories(label, labels_list))
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labels_list.append(label)
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label_to_num[label] = len(labels_list)
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points = shapes['polygon']
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annotations_list.append(
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annotations_polygon(data['imgHeight'], data[
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'imgWidth'], points, label, image_num, object_num,
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label_to_num))
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data_coco['images'] = images_list
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data_coco['categories'] = categories_list
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data_coco['annotations'] = annotations_list
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return data_coco
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def voc_get_label_anno(ann_dir_path, ann_ids_path, labels_path):
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with open(labels_path, 'r') as f:
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labels_str = f.read().split()
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labels_ids = list(range(1, len(labels_str) + 1))
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with open(ann_ids_path, 'r') as f:
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ann_ids = [lin.strip().split(' ')[-1] for lin in f.readlines()]
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ann_paths = []
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for aid in ann_ids:
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if aid.endswith('xml'):
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ann_path = os.path.join(ann_dir_path, aid)
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else:
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ann_path = os.path.join(ann_dir_path, aid + '.xml')
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ann_paths.append(ann_path)
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return dict(zip(labels_str, labels_ids)), ann_paths
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def voc_get_image_info(annotation_root, im_id):
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filename = annotation_root.findtext('filename')
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assert filename is not None
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img_name = os.path.basename(filename)
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size = annotation_root.find('size')
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width = float(size.findtext('width'))
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height = float(size.findtext('height'))
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image_info = {
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'file_name': filename,
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'height': height,
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'width': width,
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'id': im_id
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}
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return image_info
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def voc_get_coco_annotation(obj, label2id):
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label = obj.findtext('name')
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assert label in label2id, "label is not in label2id."
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category_id = label2id[label]
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bndbox = obj.find('bndbox')
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xmin = float(bndbox.findtext('xmin'))
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ymin = float(bndbox.findtext('ymin'))
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xmax = float(bndbox.findtext('xmax'))
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ymax = float(bndbox.findtext('ymax'))
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assert xmax > xmin and ymax > ymin, "Box size error."
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o_width = xmax - xmin
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o_height = ymax - ymin
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anno = {
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'area': o_width * o_height,
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'iscrowd': 0,
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'bbox': [xmin, ymin, o_width, o_height],
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'category_id': category_id,
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'ignore': 0,
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}
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return anno
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def voc_xmls_to_cocojson(annotation_paths, label2id, output_dir, output_file):
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output_json_dict = {
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"images": [],
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"type": "instances",
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"annotations": [],
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"categories": []
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}
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bnd_id = 1 # bounding box start id
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im_id = 0
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print('Start converting !')
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for a_path in tqdm(annotation_paths):
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# Read annotation xml
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ann_tree = ET.parse(a_path)
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ann_root = ann_tree.getroot()
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img_info = voc_get_image_info(ann_root, im_id)
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output_json_dict['images'].append(img_info)
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for obj in ann_root.findall('object'):
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ann = voc_get_coco_annotation(obj=obj, label2id=label2id)
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ann.update({'image_id': im_id, 'id': bnd_id})
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output_json_dict['annotations'].append(ann)
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bnd_id = bnd_id + 1
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im_id += 1
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for label, label_id in label2id.items():
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category_info = {'supercategory': 'none', 'id': label_id, 'name': label}
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output_json_dict['categories'].append(category_info)
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output_file = os.path.join(output_dir, output_file)
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with open(output_file, 'w') as f:
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output_json = json.dumps(output_json_dict)
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f.write(output_json)
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def main():
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter)
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parser.add_argument(
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'--dataset_type',
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help='the type of dataset, can be `voc`, `labelme` or `cityscape`')
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parser.add_argument('--json_input_dir', help='input annotated directory')
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parser.add_argument('--image_input_dir', help='image directory')
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parser.add_argument(
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'--output_dir', help='output dataset directory', default='./')
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parser.add_argument(
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'--train_proportion',
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help='the proportion of train dataset',
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type=float,
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default=1.0)
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parser.add_argument(
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'--val_proportion',
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help='the proportion of validation dataset',
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type=float,
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default=0.0)
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parser.add_argument(
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'--test_proportion',
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help='the proportion of test dataset',
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type=float,
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default=0.0)
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parser.add_argument(
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'--voc_anno_dir',
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help='In Voc format dataset, path to annotation files directory.',
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type=str,
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default=None)
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parser.add_argument(
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'--voc_anno_list',
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help='In Voc format dataset, path to annotation files ids list.',
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type=str,
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default=None)
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parser.add_argument(
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'--voc_label_list',
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help='In Voc format dataset, path to label list. The content of each line is a category.',
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type=str,
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default=None)
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parser.add_argument(
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'--voc_out_name',
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type=str,
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default='voc.json',
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help='In Voc format dataset, path to output json file')
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args = parser.parse_args()
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try:
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assert args.dataset_type in ['voc', 'labelme', 'cityscape']
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except AssertionError as e:
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print(
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'Now only support the voc, cityscape dataset and labelme dataset!!')
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os._exit(0)
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if args.dataset_type == 'voc':
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assert args.voc_anno_dir and args.voc_anno_list and args.voc_label_list
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label2id, ann_paths = voc_get_label_anno(
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args.voc_anno_dir, args.voc_anno_list, args.voc_label_list)
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voc_xmls_to_cocojson(
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annotation_paths=ann_paths,
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label2id=label2id,
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output_dir=args.output_dir,
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output_file=args.voc_out_name)
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else:
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try:
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assert os.path.exists(args.json_input_dir)
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except AssertionError as e:
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print('The json folder does not exist!')
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os._exit(0)
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try:
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assert os.path.exists(args.image_input_dir)
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except AssertionError as e:
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print('The image folder does not exist!')
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os._exit(0)
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try:
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assert abs(args.train_proportion + args.val_proportion \
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+ args.test_proportion - 1.0) < 1e-5
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except AssertionError as e:
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print(
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'The sum of pqoportion of training, validation and test datase must be 1!'
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)
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os._exit(0)
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# Allocate the dataset.
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total_num = len(glob.glob(osp.join(args.json_input_dir, '*.json')))
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if args.train_proportion != 0:
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train_num = int(total_num * args.train_proportion)
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out_dir = args.output_dir + '/train'
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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else:
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train_num = 0
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if args.val_proportion == 0.0:
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val_num = 0
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test_num = total_num - train_num
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out_dir = args.output_dir + '/test'
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if args.test_proportion != 0.0 and not os.path.exists(out_dir):
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os.makedirs(out_dir)
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else:
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val_num = int(total_num * args.val_proportion)
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test_num = total_num - train_num - val_num
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val_out_dir = args.output_dir + '/val'
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if not os.path.exists(val_out_dir):
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os.makedirs(val_out_dir)
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test_out_dir = args.output_dir + '/test'
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if args.test_proportion != 0.0 and not os.path.exists(test_out_dir):
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os.makedirs(test_out_dir)
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count = 1
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for img_name in os.listdir(args.image_input_dir):
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if count <= train_num:
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if osp.exists(args.output_dir + '/train/'):
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shutil.copyfile(
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osp.join(args.image_input_dir, img_name),
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osp.join(args.output_dir + '/train/', img_name))
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else:
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if count <= train_num + val_num:
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if osp.exists(args.output_dir + '/val/'):
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shutil.copyfile(
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osp.join(args.image_input_dir, img_name),
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osp.join(args.output_dir + '/val/', img_name))
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else:
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if osp.exists(args.output_dir + '/test/'):
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shutil.copyfile(
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osp.join(args.image_input_dir, img_name),
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osp.join(args.output_dir + '/test/', img_name))
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count = count + 1
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# Deal with the json files.
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if not os.path.exists(args.output_dir + '/annotations'):
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os.makedirs(args.output_dir + '/annotations')
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if args.train_proportion != 0:
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train_data_coco = deal_json(args.dataset_type,
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args.output_dir + '/train',
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args.json_input_dir)
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train_json_path = osp.join(args.output_dir + '/annotations',
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'instance_train.json')
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json.dump(
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train_data_coco,
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open(train_json_path, 'w'),
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indent=4,
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cls=MyEncoder)
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if args.val_proportion != 0:
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val_data_coco = deal_json(args.dataset_type,
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args.output_dir + '/val',
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args.json_input_dir)
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val_json_path = osp.join(args.output_dir + '/annotations',
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'instance_val.json')
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json.dump(
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val_data_coco,
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open(val_json_path, 'w'),
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indent=4,
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cls=MyEncoder)
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if args.test_proportion != 0:
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test_data_coco = deal_json(args.dataset_type,
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args.output_dir + '/test',
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args.json_input_dir)
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test_json_path = osp.join(args.output_dir + '/annotations',
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'instance_test.json')
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json.dump(
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test_data_coco,
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open(test_json_path, 'w'),
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indent=4,
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cls=MyEncoder)
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if __name__ == '__main__':
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main()
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