void RLInferenceModel::sensorCB()

in inference_pkg/src/intel_inference_eng.cpp [247:308]
50 lines of code
11 McCabe index (conditional complexity)

    void RLInferenceModel::sensorCB(const deepracer_interfaces_pkg::msg::EvoSensorMsg::SharedPtr msg) {
        if(!doInference_) {
            return;
        }
        try {
            for(size_t i = 0; i < inputNamesArr_.size(); ++i) {
                auto inputPtr = inferRequest_.GetBlob(inputNamesArr_[i])->buffer().as<InferenceEngine::PrecisionTrait<InferenceEngine::Precision::FP32>::value_type *>();

                // Object that will hold the data sent to the inference engine post processed.
                cv::Mat retData;
                if (inputNamesArr_[i].find(STEREO) != std::string::npos)
                {
                    loadStereoImg<cv::Vec2b, float>(inputPtr, retData, imgProcess_, msg->images, paramsArr_[i]);
                }
                else if (inputNamesArr_[i].find(FRONT) != std::string::npos
                          || inputNamesArr_[i].find(LEFT) != std::string::npos
                          || inputNamesArr_[i].find(OBS) != std::string::npos) {
                    load1DImg<uchar, float>(inputPtr, retData, imgProcess_, msg->images.front(), paramsArr_[i]);
                }
                else if (inputNamesArr_[i].find(LIDAR) != std::string::npos){
                    loadLidarData(inputPtr, msg->lidar_data);
                }
                else {
                    RCLCPP_ERROR(inferenceNode->get_logger(), "Invalid input head");
                    return;
                }
                imgProcess_->reset();
            }
            // Do inference
            inferRequest_.Infer();

            auto output = inferRequest_.GetBlob(outputName_);
            // Package the results and publish to all subscribers.
            
            auto outputDims = output->getTensorDesc().getDims();
            auto outputData = output->buffer().as<InferenceEngine::PrecisionTrait<InferenceEngine::Precision::FP32>::value_type*>();

            auto inferMsg = deepracer_interfaces_pkg::msg::InferResultsArray();
            for (size_t i = 0; i < msg->images.size(); ++i) {
                // Send the image data over with the results
                inferMsg.images.push_back(msg->images[i]) ;
            }

            for (size_t label = 0; label < outputDims[1]; ++label) {
                auto inferData = deepracer_interfaces_pkg::msg::InferResults();
                inferData.class_label = label;
                inferData.class_prob = outputData[label];
                // Set bounding box data to -1 to indicate to subscribers that this model offers no
                // localization information.
                inferData.x_min = -1.0;
                inferData.y_min = -1.0;
                inferData.x_max = -1.0;
                inferData.y_max = -1.0;
                inferMsg.results.push_back(inferData);
            }
            // Send results to all subscribers.
            resultPub_->publish(inferMsg);
        }
        catch (const std::exception &ex) {
            RCLCPP_ERROR(inferenceNode->get_logger(), "Inference failed %s", ex.what());
        }
    }