vision.loadYOLOXObjectDetector

Load the pretrained yoloxObjectDetector object from the MAT file.

load("trainedYoloxObjectDetector.mat");

Create an entry-point function myYoloxObject.m that accepts an image as input. The entry-point function performs these operations:

function [bboxes,scores,labels] = myYoloxObject(imTest)
  persistent myDetector;
    if isempty(myDetector)
        myDetector = vision.loadYOLOXObjectDetector("trainedYoloxObjectDetector.mat");
    end
  [bboxes,scores,labels] = detect(myDetector,imTest);
end

Read a test image from a graphics file.

imTest = imread("highway.png");

Create a coder.config (MATLAB Coder) configuration object for MEX code generation and set the target language to C++. On the configuration object, set the DeepLearningConfig property to the target library "mkldnn", for MKL-DNN targets. For more information about the supported target libraries for C++ code generation in deep learning networks, see Generate Generic C/C++ Code for Deep Learning Networks (MATLAB Coder).

The codegen (MATLAB Coder) function must determine the size, class, and complexity of MATLAB^® function inputs. Use the -args option to specify the input test image imTest to the entry-point function. Use the -config option to pass the code configuration object myYoloxObject to codegen.

cfg = coder.config("mex");
cfg.TargetLang = "C++";
cfg.DeepLearningConfig = coder.DeepLearningConfig(TargetLibrary = "mkldnn");
codegen -args {imTest} -config cfg myYoloxObject -report;

The codegen command places all the generated files in the codegen folder. The folder contains the C code for the entry-point function myYoloxObject.c, as well as header and source files containing the C class definitions for the network, weight, and bias files.

Predict the bounding boxes, detection confidence scores, and object labels in the test image by calling the code generated mex file myYoloxObject_mex.

[predBboxes,predScores,predLabels] = myYoloxObject_mex(imTest)

predBboxes

predBboxes =

  3×4 single matrix

   97.9102  104.5984   39.9582   32.5561
   97.6418  104.9113   41.7848   32.5751
  146.5345   88.3480   82.4816   73.4598


predScores

predScores =

  3×1 single column vector

    0.4241
    0.7093
    0.9108

predLabels

predLabels = 

  3×1 categorical array

     car 
     truck 
     truck