jetson-tello 1.0.0

Tello drone integration with Jetson

jetson-tello

Utility code for using the NVIDIA Jetson and tello-asyncio to interact with the Tello EDU drone.

The primary function so far is to pipe video frame data from the drone through to neural networks running on the Jetson, typically for object or face detection.

Created for my autonomous drone project, drone-braain.

Package jetson-tello on PyPi.

Full documentation on Read the Docs.

Prerequisites

There are two prerequisites that require manual installation:

• NVIDIA's jetson-inference project, following these instructions to build from source and install.

• My fork of h264decoder. This is identical to the original repo apart from building with the slightly old version of CMake (3.10) available on the Jetson.

Example code

The face_and_object_detection.py example demonstrates feeding video frames to object and face detection neural nets

#!/usr/bin/env python3

import asyncio
import jetson.inference
from jetson_tello import h264_frame_to_cuda, FrameDecodeError
from tello_asyncio import Tello

face_detector = jetson.inference.detectNet("facenet", threshold=0.5)
object_detector = jetson.inference.detectNet("ssd-mobilenet-v2", threshold=0.5)


async def process_frame(frame):
    try:
        cuda, width, height = h264_frame_to_cuda(frame)

        face_detections = face_detector.Detect(cuda)
        object_detections = object_detector.Detect(cuda)

        print('faces:')
        for d in face_detections:
            print(d)

        print('objects:')
        for d in object_detections:
            print(d)

    except FrameDecodeError:
        pass    

async def main():
    global next_frame

    drone = Tello()

    await drone.wifi_wait_for_network()
    await drone.connect()
    await drone.start_video()

    async def fly():
        await drone.takeoff()

    async def process_video():
        async for frame in drone.video_stream:
            await process_frame(frame)

    try:
        await asyncio.wait([fly(), process_video()])
    finally:
        await drone.stop_video()
        await drone.disconnect()

loop = asyncio.get_event_loop()
loop.run_until_complete(main())

Which typically outputs a stream of results like this (along with a fair amount of spam from the h.264 decoder):

    faces:
    <detectNet.Detection object>
       -- ClassID: 0
       -- Confidence: 0.809878
       -- Left:    434.667
       -- Top:     0
       -- Right:   702.267
       -- Bottom:  302.5
       -- Width:   267.6
       -- Height:  302.5
       -- Area:    80949
       -- Center:  (568.467, 151.25)
    objects:
    <detectNet.Detection object>
       -- ClassID: 7
       -- Confidence: 0.500977
       -- Left:    0
       -- Top:     7.30054
       -- Right:   959
       -- Bottom:  719.04
       -- Width:   959
       -- Height:  711.74
       -- Area:    682559
       -- Center:  (479.5, 363.171)

Version history

** 1.0.0 **

• Decode h.264 video frame data from the drone and load into NumPy arrays and CUDA memory
• Face and object detection neural net examples
• Common Objects in Context (COCO) classes