Integration

The Challenge

The task that you are working on will be to enable your robot to identify a fedora on the floor. Your robot should drive towards the fedora and stop shortly before it.

The basic flow for your app will be:

retrieving an image
sending it to the object detection service
analyzing the results
- detected class
- confidence score
- coordinates of the bounding boxes in your camera image
working with the result to determine where the robot should move to next

MVP (Minimal Viable Product)

To speed things up we will give you a solution as an MVP.

Change your code to this :

# Drop your code here
turn_counter = 0
while thread_event.is_set():
    objects = take_picture_and_detect_objects()
    coordinates = find_highest_score(objects)

    if coordinates and coordinates.confidence_score > 0.5:
        print(f'''Object with highest score -> [
            confidence score: {coordinates.confidence_score},
            x upper left corner: {coordinates.x_upper_left},
            y upper left corner: {coordinates.y_upper_left},
            x lower right corner: {coordinates.x_lower_right},
            y lower right corner: {coordinates.y_lower_right},
            object class: {coordinates.object_class} ]''')

        move_x = (coordinates.x_upper_left + coordinates.x_lower_right) / 2
        print(f'move_x: {move_x}')
        if move_x < 320:
            turn_left(10)
        else:
            turn_right(10)

        delta = coordinates.x_lower_right - coordinates.x_upper_left
        print(f'delta: {delta}')
        if delta < 350:
            move_forward(10)
        else:
            print('Done - arrived at the object')
            return

    else:
        if turn_counter < 360:
            turn_left(10)
            turn_counter = turn_counter + 10
        else:
            print('Done - no object found')
            return

print('Done')

Now place a Fedora somewhere near your robot and see if it can find it.

Dev & AI Collaboration

Have look at this code. Perhaps there is something you can optimize?

Think about these points :

How can I align the robot towards the fedora with the coordinate information
How do I know when to stop the robot
How can I optimize the "think" and "react" phases
How can I handle corner cases
Perhaps the model should be tuned increase detection results?

Pushing your Code

Currently your code only resides in your Workspace, which is called the "Inner Loop" in Development terms. To publish and build you will need to push your code to your Git repository.

To be able to push you need to set up your git email

Open a new Terminal by click on the + Icon in the bottom right and choosing New Terminal

enter

git config --global user.email "pilot@robot.to"
git config --global user.name "pilot"

To push your code:

In the Dev Spaces menu on the left click on the Git icon
Your modified files will be shown
Select the relevant files (app.py and config.py)
Right Click and select Stage Changes
Enter a comment in the top field
At the blue Commit button, click on the downwards bracket at the very right and choose Commit & Push
On the top a prompt dialog will open to enter your Gitea username and password
- username:
  team-1
- password :
  secret-password
  Now you code has been pushed to your repo. You can check in Gitea if you want.

Next Steps

Your deliverable will be Container Images for the app as well as the object detection services. The next chapter explains how to build these. Make sure you test the CI/CD build in time.