def register():
    openpifpaf.DATAMODULES['apollo'] = ApolloKp

def initiate_json(self):
    """
    Initiate json file: one for training phase and another one for validation.
    """
    self.json_file["info"] = dict(url="https://github.com/vita-epfl/openpifpaf",
                                  date_created=time.strftime("%a, %d %b %Y %H:%M:%S +0000", time.localtime()),
                                  description="Conversion of ApolloCar3D dataset into MS-COCO format")
    self.json_file["categories"] = [dict(name='',  # Category name
                                         id=1,  # Id of category
                                         skeleton=[],  # Skeleton connections (check constants.py)
                                         supercategory='',  # Same as category if no supercategory
                                         keypoints=[])]  # Keypoint names
    self.json_file["images"] = []  # Empty for initialization
    self.json_file["annotations"] = []  # Empty for initialization


def process_image(json_file):
    """
    Update image field in json file
    """
    # ------------------
    # Add here your code
    # -------------------
    json_file["images"].append({
        'coco_url': "unknown",
        'file_name': '',  # Image name
        'id': 0,  # Image id
        'license': 1,  # License type
        'date_captured': "unknown",  
        'width': 0,  # Image width (pixels)
        'height': 0})  # Image height (pixels)


def process_annotation(json_file):
    """
    Process and include in the json file a single annotation (instance) from a given image
    """
    # ------------------
    # Add here your code
    # -------------------
    json_file["annotations"].append({
        'image_id': 0,  # Image id
        'category_id': 1,  # Id of the category (like car or person)
        'iscrowd': 0,  # 1 to mask crowd regions, 0 if the annotation is not a crowd annotation
        'id': 0,  # Id of the annotations
        'area': 0,  # Bounding box area of the annotation (width*height)
        'bbox': [],  # Bounding box  coordinates (x0, y0, width, heigth), where x0, y0 are the left corner
        'num_keypoints': 0,  # number of keypoints
        'keypoints': [],  # Flattened list of keypoints [x, y, visibility, x, y, visibility, .. ]
        'segmentation': []})  # To add a segmentation of the annotation, empty otherwise