Image Processing with TensorFlow Lite C++: Fundamentals, Implementation Examples, and Performance Tips

Image processing and artificial intelligence are becoming increasingly common on low-power devices. At this point, TensorFlow Lite (TFLite) is an ideal tool for developers who want to perform high-performance AI operations with C++ in embedded systems or desktop applications. In this article, we explain step by step how TFLite is integrated with C++ APIs, how it is used in image processing projects, and how you can optimize performance.

You can also see an example of object recognition with TensorFlow Lite at the following link: https://www.ekasunucu.com/bilgi/tensorflow-lite-c-ile-object-detection-nesne-tanima-ve-coco-label-kullanimi-baslangictan-optimizasyona-kadar-rehber

Getting Started with TensorFlow Lite C++ API

Required Files:

.tflite model file (e.g., mobilenet_v1.tflite)
Label file (for COCO: labelmap.txt)
TensorFlow Lite C++ libraries (libtensorflow-lite.a, header files)

Compilation Environment:

Linux + GCC / CMake
Alternative: Android NDK (for embedded systems)

Basic Code Structure

Model Loading:

#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include "tensorflow/lite/model.h"

std::unique_ptr model = tflite::FlatBufferModel::BuildFromFile("model.tflite");
tflite::ops::builtin::BuiltinOpResolver resolver;
tflite::InterpreterBuilder(*model, resolver)(&interpreter);
interpreter->AllocateTensors();

Input Data Preparation:

float* input = interpreter->typed_input_tensor(0);
// 224x224x3 normalized pixel data is loaded here (e.g., with OpenCV)

Running the Model:

interpreter->Invoke();

Output Retrieval:

float* output = interpreter->typed_output_tensor(0);
// output data: class_id, score, bbox

OpenCV Integration for Image Processing

cv::Mat img = cv::imread("image.jpg");
cv::resize(img, img, cv::Size(224, 224));
img.convertTo(img, CV_32FC3, 1.0f / 255.0f);
memcpy(input, img.data, sizeof(float) * 224 * 224 * 3);

Example Application: Object Detection

Model Used: SSD MobileNet v1 (COCO trained)
Input: image.jpg
Output: detected class, location, and confidence score

COCO Label Reading:

std::vector labels = LoadLabels("labelmap.txt");
std::cout << "Detected class: " << labels[class_id] << std::endl;

Performance Improvement Methods

Method	Description
Using a quantized model	Faster, smaller model
Delegate usage	XNNPACK, GPU, EdgeTPU supported acceleration
Thread settings	CPU throughput is increased with settings such as `interpreter->SetNumThreads(4);`
Reducing input size	Faster inference with smaller input sizes such as 160 instead of 224

Other Applications

Project	Description
Face Recognition	FaceNet or BlazeFace model integration
Hand Gesture Recognition	Gesture detection with MediaPipe models
Traffic Sign Recognition	Real-time analysis with road camera + TFLite
Barcode Scanning	Barcode classification via image

✅ Conclusion

TensorFlow Lite is a powerful and optimized solution for lightweight artificial intelligence projects in embedded systems or desktop applications with C++. It easily integrates with OpenCV in image processing operations and is successfully used in projects such as real-time object recognition. For detailed application examples:

Object Detection with TensorFlow Lite C++: COCO Label and Optimization Guide