Hi Hrishi,
As per my understanding, you are referring to this https://www.mathworks.com/help/vision/ug/object-detection-using-yolo-v3-deep-learning.htmlexample and would like to know to how to create a variable like ‘vehicledata’.
The input data to any model, say YOLOv3 or YOLOv2, need not be in a specific data structure. How to input data is fed to a model depends on the pre-processing code you have before the model. In the case of the above example, we see that ‘vehicledata’ is a of class type or data type ‘table’. You can read more about ‘table’ type in this documentation page https://www.mathworks.com/help/matlab/ref/table.html.
As you noticed, each row of this table is a file path followed by a 1-by-4 matrix. The file path is the path to each image and the matrix contains details about the location of the bounding box. The 1-by-4 numeric vector of the form [xmin, ymin, width, height]. xmin and ymin specify the coordinates of the upper left corner of the rectangle. width and height specify the width and height of the rectangle.
If you would like to visually see this, please try out the below code after filling in the blank spaces with any 1 row of the table ‘vehicledata’.
tmp=imread(‘path\to\image\folder\image_name.jpg');
imshow(tmp);
h = drawrectangle('Position',[1-by-4 matrix for the corresponding row])
The images you labelled using Image Labeler App are exported as ‘groundTruth’ type. The ‘LabelData’ component of this type has this 1-by-4 matrix for rectangular labels. The ‘DataSource’ component has the file path to the image. Now you have both the values needed to populate the table like ‘vehicledata’. Please refer to the following documentation for more info on
- ‘groundTruth’ type https://www.mathworks.com/help/vision/ref/groundtruth.html
- Image Labeler App’s exported data https://www.mathworks.com/help/vision/ug/get-started-with-the-image-labeler.html#mw_72146e4f-e569-457d-91b9-9ac80caffb67
I hope this helps resolve the issue you are facing.
