#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:

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


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