Hi !

Once we have the a custom vision trained model instance, we can use it to recognize objects from the drone camera feed. Read my previous posts for descriptions on these.

Another interesting scenario, is to save local files for every detected object. In the following code, I’ll save 2 different files for every detected object

• A camera frame image, with a frame around the detected object
• A plain text file with the JSON information

In the sample code below, the save process is in the lines 122-129. And, not in a fancy way, the files have the same name to correlate them.

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

References

• Custom Vision Home
• Custom Vision Documentation

Hi !

In my previous post, I shared an example where I analyzed the camera feed using a Image Recognition model created using Custom Vision. Today I’ll expand the sample, and show in real time the detected MVPs logos with a frame in the drone camera feed.

Let’s take a look at the demo working in the following image.

In the top of the image, we can see the app console log, with the information received for each analyzed frame. When an image is detected, we can see the tag, the probability and the bounding box coordinates.

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"
    },

In order to position the frames in the correct location, I need to make some math using the current camera and image size and the returned bounding box values for, height, left, top and width. Lines 87-110.

resize_factor = 100

height = int(bb['height'] * resize_factor)
left = int(bb['left'] * resize_factor)
top = int(bb['top'] * resize_factor)
width = int(bb['width'] * resize_factor)

# adjust to size
camera_Width, 
height = int(height * camera_Heigth / 100)
left = int(left * camera_Width / 100)
top = int(top * camera_Heigth / 100)
width = int(width * camera_Width / 100)

# draw bounding boxes
start_point = (top, left)                 
end_point = (top + height, left + width) 
color = (255, 0, 0) 
thickness = 2                
cv2.rectangle(img, start_point, end_point, color, thickness)            

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

References

• Custom Vision Home
• Custom Vision Documentation