Image Classification Using Parrot FPV Drones
This example shows you how to use the MATLAB® Support Package for Parrot® Drones to classify images captured by the drone's FPV camera.
The MATLAB® Support Package for Parrot® Drones enables you to control the Parrot drone and capture images from the first-person view (FPV) camera. The images captured by the drone's FPV camera can be classified using GoogLeNet, a pretrained deep convolutional neural network. GoogLeNet is trained on more than a million images from ImageNet database. It takes the image as input and provides a label for the object in the image.
Complete Getting Started with MATLAB® Support Package for Parrot® Drones.
To run this example, you need:
A fully charged Parrot FPV drone
A computer with a WiFi connection
Task 1 — Create a Connection to the Parrot Drone
parrotObj = parrot;
Task 2 — Create the GoogLeNet Neural Network Object
Create a GoogLeNet neural network object.
nnet = googlenet;
Task 3 — Activate FPV Camera
Start the drone flight and activate the FPV camera.
Create a connection to the drone's FPV camera.
camObj = camera(parrotObj, 'FPV');
Task 4 — Capture and Classify the Object in the Image
Move the drone forward for 2 seconds along the edges of a square path. Capture the image of an object. Classify the image while the drone moves forward.
1 Move the drone forward for the default duration of 0.5 seconds for each forward step, ensuring a nonblocking behavior. This enables the drone to capture the image and classify it while in motion.
2 Capture a single frame from the drone's FPV camera.
3 Resize the image and classify the object in image using the neural network.
4 Display the image with title as the label returned by the classify function.
5 Turn the drone by π/2 radians at each square vertex.
tOuter= tic; while(toc(tOuter)<=30 && parrotObj.BatteryLevel>20) tInner = tic; % Keep moving the drone for 2 seconds along each square path edge while(toc(tInner)<=2) moveforward(parrotObj); % Move the drone forward for default time of 0.5 seconds (nonblocking behavior) picture = snapshot(camObj); % Capture image from drone's FPV camera resizedPicture = imresize(picture,[224,224]); % Resize the picture label = classify(nnet,resizedPicture); % Classify the picture imshow(picture); % Show the picture title(char(label)); % Show the label drawnow; end turn(parrotObj,deg2rad(90)); % Turn the drone by pi/2 radians end
6 Execute steps 1–5 for 30 seconds.
For example, the drone classifies a monitor screen as captured by the FPV camera.
Task 5 — Land the Drone
Land the drone.
Task 6 — Clean Up
When finished, clear the connection to the Parrot drone, the FPV camera, and GoogLeNet.
clear parrotObj; clear camObj; clear nnet;