via2coco format

# convert VIA-VGG annotations to MSCOCO JSON format
def load_annotations(json_path):

    with open(json_path) as f:
        annotations = json.load(f)

    return annotations

def via2coco(via_json, output_path):

    # load original via data
    loaded_json = load_annotations(via_json)

    # mscoco parts to be saved (example: not saving forehead, that's for mpii database)
    coco_parts = dict(nose=0, right_shoulder=1, right_elbow=2, right_wrist=3, left_shoulder=4, left_elbow=5,
                      left_wrist=6, right_hip=7, right_knee=8, right_ankle=9, left_hip=10, left_knee=11, left_ankle=12,
                      right_eye=13, left_eye=14, right_ear=15, left_ear=16)

    # create header info starting coco file
    # first: 'categories' data
    categories = []
    category = dict(supercategory='person', id=1, skeleton=[],
                    keypoints=['nose', 'right_shoulder', 'right_elbow', 'right_wrist', 'left_shoulder',
                               'left_elbow', 'left_wrist', 'right_hip', 'right_knee', 'right_ankle', 'left_hip',
                               'left_knee', 'left_ankle', 'right_eye', 'left_eye', 'right_ear', 'left_ear'],
                    name='baby')
    categories.append(category)

    # second: 'annotations' data
    annotations = []
    filenames = []
    area = 0
    via_annotations = loaded_json['_via_img_metadata']
    for an_item in via_annotations.values():
        filename = an_item['filename']
        filenames.append(filename)
        image_id = filename.split('_')
        image_id = image_id.pop()
        image_id = int(image_id[:-4])
        # generate keypoint vector
        keypoint_vector = np.zeros(len(coco_parts.keys())*3)  # x-y-visible, make it json serializable later
        bbox = []  # bounding baby box
        regions = an_item['regions']
        for a_region in regions:
            shape = a_region['shape_attributes']
            region = a_region['region_attributes']
            if region['id'] in coco_parts.keys():  # recognize mscoco part
                index = coco_parts[region['id']]*3
                keypoint_vector[index] = shape['cx']
                keypoint_vector[index + 1] = shape['cy']
                keypoint_vector[index + 2] = int(region['visible']) + 1  # visible in coco is 1 false 2 true
            if region['id'] == 'body':
                area = shape['width'] * shape['height']
                x0 = shape['x']
                w = shape['width']
                y0 = shape['y']
                h = shape['height']

                # save bbox in this weird cocotools notation
                bbox.append(int(x0))
                bbox.append(int(y0))
                bbox.append(int(w))
                bbox.append(int(h))
        # TODO: consolidate this parameters appart from keypoints
        annotation = dict(image_id=image_id, category_id=1, iscrowd=0, num_keypoints=len(keypoint_vector)/3,
                          id=image_id, segmentation=[], area=area, keypoints=keypoint_vector.tolist(), bbox=bbox)
        # TODO: id=image_id OR filename?
        annotations.append(annotation)

    # third: 'info' data
    info = dict(url='https://github.com/harrisonford', contributor='harrisonford', year=2019, description='alpha',
                date_created='2019', version=1.0)

    # fourth: 'images' data
    images = []
    for a_file in filenames:
        subname = a_file.split('_')
        file_id = subname[1]
        file_id = int(file_id[:-4])  # take ".png" out
        # TODO: We add a lot of dummy numbers to image dict (check wid-hei most importantly)
        an_image = dict(date_captured='secret_hehexd', id=file_id, coco_url='no-coco', height=0, width=0, license=0,
                        file_name=a_file, flickr_url='who_uses_flicker?')
        images.append(an_image)

    # fifth: 'licenses' data
    licenses = ['private']

    # put data in final dictionary
    data = dict(categories=categories, annotations=annotations, info=info, images=images, licenses=licenses)

    with open(output_path, 'w') as outfile:
        json.dump(data, outfile)

Hi, thanks for your code. What is difference between json_path and via_json and _via_img_metadata?
And how do you run this code?