Using the dataset manipulator, we can augment the data of our datasets. Depending on our needs, we can generate additional images from our data using different operations. For this tutorial, you will use each of them.
In the previous tutorials, you added missing information to your dataset for object detection and classification. If you do not add these markings and use the manipulator, you will have to manually add each new object generated from the image. To proceed with the manipulation, click on the
Set the output file format to
PNG and enter the name
New Accessories for the new dataset that will be created as a result of the data augmentation.
OPERATIONS section, select the first type of operation,
To create 5 copies of the photo, each rotated by 20’, set the
Angle bar to 20, and enter 5 in the
Generate up to field. Set the
Crop threshold to 20%, this will remove the annotation of objects with visibility less than the indicated percentage.
Confirm the settings by clicking
OK. Create a new modification using
Add operation and select
Brightness and Contrast from the list.
Set the number of generated images in the
Generate up to field to 4. Set the
Brightness var to 60 and go to the
Contrast min to 0.5 and
Contrast max to 1.8. Confirm by clicking
OK. Finally, use the
Resize the images so that each is the same size. Set the
Height to 700px.
The operations are listed on an ongoing basis. Check whether the data entered has the values we require. If necessary, you can always edit an operation, add a new one, or change the order of the operations using the
up and down arrows. Finally, click
Start Manipulation and wait for the notification that will inform you that our server has finished generating the images.
The new dataset has been generated and contains
450 images, which is consistent with the settings and the fact that none of the images were deleted during the process. Using the initial
15 images, you generated
5 new rotated images from a single image
15 + 15 * 5 = 90. In the next operation, you created another
90 images with different contrast and brightness
90 + 90 * 4 = 450. You can now preview the resulting images.
As you can see, each of the newly created images also has annotations for object classification and detection, since you have previously categorized the initial image.