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Save caiofcm/0b93b0084669a1287633d9ebf32f3833 to your computer and use it in GitHub Desktop.
| ## | |
| # Author: Caio Marcellos | |
| # Email: caiocuritiba@gmail.com | |
| ## | |
| import os | |
| import numpy as np | |
| import json | |
| import glob | |
| from datetime import datetime | |
| from pathlib import Path | |
| import argparse | |
| import sys | |
| """ | |
| Converting from suvervisely to COCO Format (only detection (bbox) tested in this version) | |
| Example of Usage from commandline: | |
| `py supervisely2coco.py meta.json './ds/ann/' formatted2.json ` | |
| """ | |
| def convert_supervisely_to_coco(meta_path, | |
| ann_base_dir = './ds/ann/', save_as=None, | |
| only_img_name=False | |
| ): | |
| """ | |
| - ann_base_dir: directory for annotation files | |
| - Annotation files are expected to be <image-filename>.json | |
| - save_as: if defined (not None) is a path to save the COCO generated json format | |
| - bbox outputted as BoxMode.XYWH_ABS | |
| TODO: | |
| - tags: e.g train, val | |
| - Segmentation, for now just converting the bbox (for detection) | |
| """ | |
| ann_fnames, ann_jsons = get_all_ann_file(ann_base_dir) | |
| map_category = get_categories_from_meta(meta_path) | |
| catg_repr = [{ | |
| "id": v, | |
| "name": k, | |
| "supercategory": "type" | |
| } for k,v in map_category.items()] | |
| out_cnv_imgs = [ | |
| convert_single_image(id_img, ann_fnames[id_img], ann_jsons[id_img], | |
| map_category, ann_base_dir, only_img_name) | |
| for id_img in range(len(ann_fnames)) | |
| ] | |
| images_repr = [o[0] for o in out_cnv_imgs] | |
| ann_repr = [o[1] for o in out_cnv_imgs] | |
| # Flatten annotation (len(images) to len(all-annotations)) | |
| ann_repr_flatten = [inner for lst in ann_repr for inner in lst] | |
| # Adjust Annotations ID: | |
| for i, ann in enumerate(ann_repr_flatten): | |
| ann['id'] = i | |
| coco_fmt = { | |
| "info": { | |
| "year": datetime.now().strftime('%Y'), | |
| "version": "1", | |
| "description": "", | |
| "contributor": "converted from supervisely2coco - caiofcm", | |
| "url": "", | |
| "date_created": datetime.now().strftime("%m/%d/%Y, %H:%M:%S") | |
| }, | |
| "images": images_repr, | |
| "annotations": ann_repr_flatten, | |
| "licenses": [ | |
| { | |
| "id": 1, | |
| "name": "Unknown", | |
| "url": "" | |
| } | |
| ], | |
| "categories": catg_repr | |
| } | |
| if save_as: | |
| with open(save_as, 'w') as fp: | |
| json.dump(coco_fmt, fp, cls=NpEncoder) | |
| return coco_fmt | |
| class NpEncoder(json.JSONEncoder): | |
| def default(self, obj): #pylint: disable=method-hidden | |
| 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(NpEncoder, self).default(obj) | |
| def convert_single_image(idimg, fname_img, json_suprv, map_category, imgs_base_dir, only_img_name=False, start_annotation_id=0): | |
| # output in mode BoxMode.XYWH_ABS | |
| image_base = { | |
| "id": idimg, | |
| "width": json_suprv['size']['width'], | |
| "height": json_suprv['size']['height'], | |
| "file_name": fname_img if not only_img_name else Path(fname_img).name, | |
| "license": 1, | |
| "date_captured": "" | |
| } | |
| objects = [obj for obj in json_suprv['objects'] if obj['classTitle'] != 'bg'] | |
| obj_exteriors = [ | |
| np.array(obj['points']['exterior']) | |
| for obj in objects | |
| ] | |
| bboxes = [ | |
| [ | |
| extr.min(axis=0)[0], | |
| extr.min(axis=0)[1], | |
| extr.max(axis=0)[0] - extr.min(axis=0)[0], | |
| extr.max(axis=0)[1] - extr.min(axis=0)[1], | |
| ] | |
| for extr in obj_exteriors | |
| ] | |
| ann = [ | |
| { | |
| "id": start_annotation_id + i, | |
| "image_id": idimg, | |
| "segmentation": [], | |
| "area": bbox[2]*bbox[3], | |
| "bbox": bbox, | |
| "category_id": map_category[obj['classTitle']], | |
| "iscrowd": 0 | |
| } | |
| for i, (obj, bbox) in enumerate(zip(objects, bboxes)) | |
| ] | |
| return image_base, ann | |
| def get_all_ann_file(base_dir): | |
| all_ann_files = glob.glob(os.path.join(base_dir, "*.json")) | |
| all_fname_img = [fname[:-5] for fname in all_ann_files] | |
| all_json_ann = [] | |
| for json_path in all_ann_files: | |
| with open(json_path) as fs: | |
| json_suprv = json.load(fs) | |
| all_json_ann += [json_suprv] | |
| return all_fname_img, all_json_ann | |
| def get_categories_from_meta(meta_json_path): | |
| with open(meta_json_path) as fs: | |
| json_meta = json.load(fs) | |
| classes = [clss['title'] for clss in json_meta['classes'] if clss['title'] != 'bg'] | |
| mapCategories = {c: i for i, c in enumerate(classes)} | |
| return mapCategories | |
| ###Test | |
| def case_dev(): | |
| coco_fmt = convert_supervisely_to_coco('./meta.json', save_as='formatted_coco.json', only_img_name=True) | |
| pass | |
| def main(): | |
| parser = argparse.ArgumentParser(description=""" | |
| Supervisely2Coco: | |
| Converting from suvervisely to COCO Format (only detection (bbox) tested in this version) | |
| Example of Usage from commandline: | |
| `py supervisely2coco.py meta.json './ds/ann/' formatted2.json ` | |
| """) | |
| parser.add_argument( | |
| "-v", | |
| "--version", | |
| help="display version information", | |
| action="version", | |
| version="Supervisely2Coco {}, Python {}".format('0.0.1', sys.version), | |
| ) | |
| parser.add_argument("meta", type=str, help="Meta JSON File") | |
| parser.add_argument("ann_base_dir", type=str, help="Annotations base directory (usually downloaded in './ds/ann/' )") | |
| parser.add_argument("output", type=str, help="Output Coco JSON File") | |
| parser.add_argument('-n', '--only-image-name', action='store_true', | |
| help="Save only the image name (not the full path)") | |
| args = parser.parse_args() | |
| meta = args.meta | |
| ann_base_dir = args.ann_base_dir | |
| save_as = args.output | |
| flag_only_name = args.only_image_name | |
| print('Converting from meta={}; annotations in [{}] to output={}'.format(meta, ann_base_dir, save_as)) | |
| coco_fmt = convert_supervisely_to_coco(meta, ann_base_dir=ann_base_dir, save_as=save_as, only_img_name=flag_only_name) | |
| print('Done.') | |
| if __name__ == "__main__": | |
| main() | |
| pass |
How was the command that you evoked the script ? I did not use this script in a while, but I remember a subtle issue that the path of the folder should ends with the backslash / (`'./ds/ann/')
well that is not the problem actually did u get a json file for each image as output on superviser.ly ? my output is a json file for each image and the json file does not contain the image name as an attribute.
an example of my ann directory
what is in one of the json files
{
"description": "",
"tags": [],
"size": {
"height": 150,
"width": 150
},
"objects": [
{
"id": 458095953,
"classId": 1528218,
"description": "",
"geometryType": "polygon",
"labelerLogin": "shepherd25",
"createdAt": "2020-06-06T13:58:24.814Z",
"updatedAt": "2020-06-06T13:58:54.280Z",
"tags": [],
"classTitle": "Handgun",
"points": {
"exterior": [
[
36,
1
],
[
34,
6
],
[
31,
8
],
[
34,
14
],
[
25,
91
],
[
28,
99
],
[
28,
105
],
[
43,
110
],
[
68,
110
],
[
70,
99
],
[
70,
99
],
[
76,
98
],
[
94,
19
],
[
102,
12
],
[
99,
0
]
],
"interior": []
}
}
]
}
My meta-file
{
"classes": [
{
"title": "Handgun",
"shape": "polygon",
"color": "#854FE8",
"geometry_config": {}
}
],
"tags": []
}
what I get after applying your code on the directory
python sutoco.py D:\Projects\Aidc\Mask_RCNN\datasets\newtr\Handgun_train\meta.json 'D:\Projects\Aidc\Mask_RCNN\datasets\newtr\Handgun_train\ds\ann\' out.json
{"info": {"year": "2020", "version": "1", "description": "", "contributor": "converted from supervisely2coco - caiofcm", "url": "", "date_created": "06/18/2020, 18:10:47"}, "images": [], "annotations": [], "licenses": [{"id": 1, "name": "Unknown", "url": ""}], "categories": [{"id": 0, "name": "Handgun", "supercategory": "type"}]}
well that is not the problem actually did u get a json file for each image as output on superviser.ly ? my output is a json file for each image and the json file does not contain the image name as an attribute.
an example of my ann directory
what is in one of the json files{ "description": "", "tags": [], "size": { "height": 150, "width": 150 }, "objects": [ { "id": 458095953, "classId": 1528218, "description": "", "geometryType": "polygon", "labelerLogin": "shepherd25", "createdAt": "2020-06-06T13:58:24.814Z", "updatedAt": "2020-06-06T13:58:54.280Z", "tags": [], "classTitle": "Handgun", "points": { "exterior": [ [ 36, 1 ], [ 34, 6 ], [ 31, 8 ], [ 34, 14 ], [ 25, 91 ], [ 28, 99 ], [ 28, 105 ], [ 43, 110 ], [ 68, 110 ], [ 70, 99 ], [ 70, 99 ], [ 76, 98 ], [ 94, 19 ], [ 102, 12 ], [ 99, 0 ] ], "interior": [] } } ] }My meta-file
{ "classes": [ { "title": "Handgun", "shape": "polygon", "color": "#854FE8", "geometry_config": {} } ], "tags": [] }what I get after applying your code on the directory
python sutoco.py D:\Projects\Aidc\Mask_RCNN\datasets\newtr\Handgun_train\meta.json 'D:\Projects\Aidc\Mask_RCNN\datasets\newtr\Handgun_train\ds\ann\' out.json
{"info": {"year": "2020", "version": "1", "description": "", "contributor": "converted from supervisely2coco - caiofcm", "url": "", "date_created": "06/18/2020, 18:10:47"}, "images": [], "annotations": [], "licenses": [{"id": 1, "name": "Unknown", "url": ""}], "categories": [{"id": 0, "name": "Handgun", "supercategory": "type"}]}
I am also facing the same issue, Can u help me to resolve?
Hi there i tried this on my dataset but I only got a single attribute name which happens to be the class of the object.