3d_object_detection_server.py

import re
import json
from bottle import request, response, post, route, run
from waymo_open_dataset import dataset_pb2 as open_dataset

def main():
    @route('/<:re:.*>', method='OPTIONS')
    def cors():
        response.headers['Access-Control-Allow-Origin'] = '*'
        response.headers['Access-Control-Allow-Methods'] = 'GET, POST, DELETE, PUT, OPTIONS'
        response.headers['Access-Control-Allow-Headers'] = HEADERS

    @post('/predict')
    def predict():
        segment_path = request.json['recordPath']
        frame_index = request.json['frameIndex']

        dataset = tf.data.TFRecordDataset([segment_path])
        dataset = dataset.skip(frame_index)
        dataset = dataset.take(1)

        for data in dataset:
            frame = open_dataset.Frame()
            frame.ParseFromString(bytearray(data.numpy()))
            break

        #  Your 3D Object Detection magic goes here.
        #  Boxes should be list of a list with the 7 parameters
        #  center_x, center_y, center_z, width, length, height, heading
        boxes = ... 
        
        return json.dumps(boxes)

    run(host='localhost', port=9000)

if __name__ == '__main__':
    main()