#Coding4Fun – How to control your #drone with 20 lines of code! (15/N)

Buy Me A Coffee

Hi !

Let’s use Custom Vision to analyze the images from our drone camera. In this scenario, I created a custom model to recognize MVP awards from my MVP wall. I know, that’s bragging, but I like it.

Disclaimer: There are plenty of documentation and tutorials about Custom Vision. I won’t go deep on the steps about how to create a model. See references.

For my next scenario, I would assume that

  • You have created a model in Custom Vision
  • You have published the Custom Vision model, and have a HTTP endpoint
  • Or the model is exported as a docker image, and it’s running in a docker container. And we have a HTTP endpoint.

The code is similar to the one we used before. OpenCV to hookup the camera, commands to take off and land. Let me remark a couple of important lines in this code:

  • There are a couple of new references, mostly used for the process of the JSON response from the Custom Vision model.
  • Lines 146-155. Get the frame from the drone camera and save a local file. Apply a specific format to the file name, for demo purposes.
  • Lines 157-163. Make a HTTP POST call to analyze the saved file. Convert the result to a JSON object (room for improvement here), and analyze the JSON response.
  • Lines 70-85. Analyzed the JSON response from the Custom Vision model. Sort the results by probability and filter the results using a threshold (75). Return a string with the detected object.
  • Lines 165-178. Calculate and display FPS and detected objects.

A sample JSON return string start like this one:

  "created": "2020-04-08T17:22:02.179359",
  "id": "",
  "iteration": "",
  "predictions": [
      "boundingBox": {
        "height": 0.1979116,
        "left": 0.3235259,
        "top": 0.05847502,
        "width": 0.20438321
      "probability": 0.89171505,
      "tagId": 0,
      "tagName": "MVP"
      "boundingBox": {
        "height": 0.2091526,
        "left": 0.65271178,
        "top": 0.0433814,
        "width": 0.17669522
      "probability": 0.70330358,
      "tagId": 0,
      "tagName": "MVP"

So let’s go to the full code:

# Bruno Capuano
# open camera with openCV
# analyze camera frame with local docker custom vision project
# display recognized objects in output log
import socket
import time
import threading
import cv2
import urllib
import json
import requests
import os
from flask import Flask, request, jsonify
def receiveData():
global response
while True:
response, _ = clientSocket.recvfrom(1024)
def readStates():
global battery
while True:
response_state, _ = stateSocket.recvfrom(256)
if response_state != 'ok':
response_state = response_state.decode('ASCII')
list = response_state.replace(';', ':').split(':')
battery = int(list[21])
def sendCommand(command):
global response
timestamp = int(time.time() * 1000)
clientSocket.sendto(command.encode('utf-8'), address)
while response is None:
if (time.time() * 1000) timestamp > 5 * 1000:
return False
return response
def sendReadCommand(command):
response = sendCommand(command)
response = str(response)
return response
def sendControlCommand(command):
response = None
for i in range(0, 5):
response = sendCommand(command)
if response == 'OK' or response == 'ok':
return True
return False
# ———————————————–
# Local calls
# ———————————————–
probabilityThreshold = 50
def getPredictionsSorted(jsonPrediction):
jsonObj = json.loads(jsonPrediction)
preds = jsonObj['predictions']
sorted_preds = sorted(preds, key=lambda x: x['probability'], reverse=True)
strSortedPreds = ""
if (sorted_preds):
for pred in sorted_preds:
# tag name and prob * 100
tagName = str(pred['tagName'])
probability = pred['probability'] * 100
# apply threshold
if (probability >= probabilityThreshold):
strSortedPreds = strSortedPreds + tagName + ": " + str(probability) + "\n"
return strSortedPreds
# instantiate flask app and push a context
app = Flask(__name__)
# ———————————————–
# Main program
# ———————————————–
# connection info
UDP_IP = ''
UDP_PORT = 8889
last_received_command = time.time()
address = (UDP_IP, UDP_PORT)
response = None
response_state = None
clientSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
clientSocket.bind(('', UDP_PORT))
stateSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
stateSocket.bind(('', STATE_UDP_PORT))
# start threads
recThread = threading.Thread(target=receiveData)
recThread.daemon = True
stateThread = threading.Thread(target=readStates)
stateThread.daemon = True
# connect to drone
response = sendControlCommand("command")
print(f'command response: {response}')
response = sendControlCommand("streamon")
print(f'streamon response: {response}')
# drone information
battery = 0
# open UDP
print(f'opening UDP video feed, wait 2 seconds ')
videoUDP = 'udp://'
cap = cv2.VideoCapture(videoUDP)
# open
i = 0
while True:
i = i + 1
start_time = time.time()
print(f'battery: {battery} % – i: {i}')
ret, frame = cap.read()
img = cv2.resize(frame, (640, 480))
# save image to disk and open it
imgNumber = str(i).zfill(5)
frameImageFileName = str(f'image{imgNumber}.png')
if os.path.exists(frameImageFileName):
cv2.imwrite(frameImageFileName, img)
with open(frameImageFileName, 'rb') as f:
img_data = f.read()
# analyze file in local container
api_url = ""
r = requests.post(api_url, data=img_data)
with app.app_context():
jsonResults = jsonify(r.json())
jsonStr = jsonResults.get_data(as_text=True)
predSorted = getPredictionsSorted(jsonStr)
fpsInfo = ""
if (time.time() start_time ) > 0:
fpsInfo = "FPS: " + str(1.0 / (time.time() start_time)) + "\n——————-\n" # FPS = 1 / time to process loop
# display FPS and Predictions, split text into lines, thanks OpenCV putText()
frameInfo = fpsInfo + predSorted
j = 0
for j, line in enumerate(frameInfo.split('\n')):
cv2.putText(img, line, (10, 10 * j), cv2.FONT_HERSHEY_SIMPLEX, 0.4, (255, 255, 255), 1)
cv2.imshow('@elbruno – DJI Tello Camera', img)
except Exception as e:
print(f'exc: {e}')
if cv2.waitKey(1) & 0xFF == ord('q'):
response = sendControlCommand("streamoff")
print(f'streamon response: {response}')

And if you want to see this up and running, it’s much better to see this in a video (start at ):

The complete source code can be found here https://github.com/elbruno/events/tree/master/2020%2004%2018%20Global%20AI%20On%20Tour%20MTY%20Drone%20AI%20Mex

Happy coding!


El Bruno

More posts in my blog ElBruno.com.

More info in https://beacons.ai/elbruno


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