#!/usr/bin/env python # Copyright (c) 2017-present, Facebook, Inc. # # 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. ############################################################################## # This file is copy from https://github.com/facebookresearch/Detectron/tree/master/tools from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import argparse import h5py import json import os import scipy.misc import sys import cityscapesscripts.evaluation.instances2dict_with_polygons as cs def parse_args(): parser = argparse.ArgumentParser(description='Convert dataset') parser.add_argument( '--dataset', help="cocostuff, cityscapes", default=None, type=str) parser.add_argument( '--outdir', help="output dir for json files", default=None, type=str) parser.add_argument( '--datadir', help="data dir for annotations to be converted", default=None, type=str) if len(sys.argv) == 1: parser.print_help() sys.exit(1) return parser.parse_args() def poly_to_box(poly): """Convert a polygon into a tight bounding box.""" x0 = min(min(p[::2]) for p in poly) x1 = max(max(p[::2]) for p in poly) y0 = min(min(p[1::2]) for p in poly) y1 = max(max(p[1::2]) for p in poly) box_from_poly = [x0, y0, x1, y1] return box_from_poly def xyxy_to_xywh(xyxy_box): xmin, ymin, xmax, ymax = xyxy_box TO_REMOVE = 1 xywh_box = (xmin, ymin, xmax - xmin + TO_REMOVE, ymax - ymin + TO_REMOVE) return xywh_box def convert_coco_stuff_mat(data_dir, out_dir): """Convert to png and save json with path. This currently only contains the segmentation labels for objects+stuff in cocostuff - if we need to combine with other labels from original COCO that will be a TODO.""" sets = ['train', 'val'] categories = [] json_name = 'coco_stuff_%s.json' ann_dict = {} for data_set in sets: file_list = os.path.join(data_dir, '%s.txt') images = [] with open(file_list % data_set) as f: for img_id, img_name in enumerate(f): img_name = img_name.replace('coco', 'COCO').strip('\n') image = {} mat_file = os.path.join( data_dir, 'annotations/%s.mat' % img_name) data = h5py.File(mat_file, 'r') labelMap = data.get('S') if len(categories) == 0: labelNames = data.get('names') for idx, n in enumerate(labelNames): categories.append( {"id": idx, "name": ''.join(chr(i) for i in data[ n[0]])}) ann_dict['categories'] = categories scipy.misc.imsave( os.path.join(data_dir, img_name + '.png'), labelMap) image['width'] = labelMap.shape[0] image['height'] = labelMap.shape[1] image['file_name'] = img_name image['seg_file_name'] = img_name image['id'] = img_id images.append(image) ann_dict['images'] = images print("Num images: %s" % len(images)) with open(os.path.join(out_dir, json_name % data_set), 'wb') as outfile: outfile.write(json.dumps(ann_dict)) # for Cityscapes def getLabelID(self, instID): if (instID < 1000): return instID else: return int(instID / 1000) def convert_cityscapes_instance_only( data_dir, out_dir): """Convert from cityscapes format to COCO instance seg format - polygons""" sets = [ 'gtFine_val', 'gtFine_train', 'gtFine_test', # 'gtCoarse_train', # 'gtCoarse_val', # 'gtCoarse_train_extra' ] ann_dirs = [ 'gtFine_trainvaltest/gtFine/val', 'gtFine_trainvaltest/gtFine/train', 'gtFine_trainvaltest/gtFine/test', # 'gtCoarse/train', # 'gtCoarse/train_extra', # 'gtCoarse/val' ] json_name = 'instancesonly_filtered_%s.json' ends_in = '%s_polygons.json' img_id = 0 ann_id = 0 cat_id = 1 category_dict = {} category_instancesonly = [ 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', ] for data_set, ann_dir in zip(sets, ann_dirs): print('Starting %s' % data_set) ann_dict = {} images = [] annotations = [] ann_dir = os.path.join(data_dir, ann_dir) for root, _, files in os.walk(ann_dir): for filename in files: if filename.endswith(ends_in % data_set.split('_')[0]): if len(images) % 50 == 0: print("Processed %s images, %s annotations" % ( len(images), len(annotations))) json_ann = json.load(open(os.path.join(root, filename))) image = {} image['id'] = img_id img_id += 1 image['width'] = json_ann['imgWidth'] image['height'] = json_ann['imgHeight'] image['file_name'] = filename[:-len( ends_in % data_set.split('_')[0])] + 'leftImg8bit.png' image['seg_file_name'] = filename[:-len( ends_in % data_set.split('_')[0])] + \ '%s_instanceIds.png' % data_set.split('_')[0] images.append(image) fullname = os.path.join(root, image['seg_file_name']) objects = cs.instances2dict_with_polygons( [fullname], verbose=False)[fullname] for object_cls in objects: if object_cls not in category_instancesonly: continue # skip non-instance categories for obj in objects[object_cls]: if obj['contours'] == []: print('Warning: empty contours.') continue # skip non-instance categories len_p = [len(p) for p in obj['contours']] if min(len_p) <= 4: print('Warning: invalid contours.') continue # skip non-instance categories ann = {} ann['id'] = ann_id ann_id += 1 ann['image_id'] = image['id'] ann['segmentation'] = obj['contours'] if object_cls not in category_dict: category_dict[object_cls] = cat_id cat_id += 1 ann['category_id'] = category_dict[object_cls] ann['iscrowd'] = 0 ann['area'] = obj['pixelCount'] xyxy_box = poly_to_box(ann['segmentation']) xywh_box = xyxy_to_xywh(xyxy_box) ann['bbox'] = xywh_box annotations.append(ann) ann_dict['images'] = images categories = [{"id": category_dict[name], "name": name} for name in category_dict] ann_dict['categories'] = categories ann_dict['annotations'] = annotations print("Num categories: %s" % len(categories)) print("Num images: %s" % len(images)) print("Num annotations: %s" % len(annotations)) with open(os.path.join(out_dir, json_name % data_set), 'w') as outfile: outfile.write(json.dumps(ann_dict)) if __name__ == '__main__': args = parse_args() if args.dataset == "cityscapes_instance_only": convert_cityscapes_instance_only(args.datadir, args.outdir) elif args.dataset == "cocostuff": convert_coco_stuff_mat(args.datadir, args.outdir) else: print("Dataset not supported: %s" % args.dataset)