Abstract:

This video tutorial demonstrates how to implement image classification using TensorFlow Lite (TFLite) in a C++ application, leveraging the OpenCV library for image processing. The presenter guides viewers through setting up a cross-platform build environment using CMake, including configuring TFLite and OpenCV libraries. Key steps covered include loading a TFLite model, initializing and configuring the TFLite interpreter (setting precision and threads), loading and resizing an input image, performing inference, extracting and parsing prediction outputs (confidence scores and labels), and displaying the results. The example uses a MobileNet classification model, showcasing inference time measurement and result visualization, and emphasizes the cross-platform compatibility of the solution across Windows, Mac, and Linux.

Implementing TFLite Image Classification in C++

• 0:02 Introduction to TFLite C++ Image Classification: The video introduces how to perform image classification using TensorFlow Lite (TFLite) in C++.
• 0:09 Accessing the Code: All source code for the project is available on the presenter's GitHub page (karthik-ai/ta-flight).
• 0:22 Project Structure: The project comprises CMakeLists.txt for build configuration and main.cpp for the main program logic.
• 0:32 Prerequisites: Viewers are advised to watch previous videos for TFLite installation on Windows, Mac, and Linux systems. OpenCV is also required and can be installed via vcpkg or downloaded directly.
• 0:51 CMake Configuration (CMakeLists.txt):
  • Sets CMake version, project name, and C++ standard.
  • Configures OpenCV.
  • Includes TFLite directories, with pre-compiled libraries provided in the tf_flight_dist folder.
  • Creates an executable from main.cpp and links it with TFLite and OpenCV libraries.
  • Includes platform-specific linking logic for Windows, Linux, and Mac.
• 1:46 Main Program Structure (main.cpp):
  • Required header files are listed.
  • The application expects model file path, label file path, and input image file path as command-line arguments.
• 2:00 Loading the TFLite Model: A unique pointer tflite::FlatBufferModel is used to load the model from the specified file path.
• 2:14 Building and Configuring the Interpreter:
  • An interpreter is built using tflite::InterpreterBuilder to run TFLite models.
  • Tensors are allocated for the interpreter.
  • Float16 precision is set for FP32 calculations when possible.
  • The number of threads for the interpreter is configured.
• 2:51 Getting Input Tensor Dimensions: The height, width, and channels of the input tensor are extracted from the model (e.g., MobileNet).
• 3:08 Loading and Preprocessing Input Image:
  • OpenCV's imread function loads the image.
  • The image is resized to match the model's input size.
  • Image data is copied directly to the interpreter's input tensor using memcpy.
• 3:42 Performing Inference: The interpreter->Invoke() function executes the model. The chrono library is used to measure and store the inference time.
• 3:58 Extracting Output and Post-processing:
  • The interpreter's output tensor and its size are retrieved.
  • A vector top_result stores confidence scores and label indices.
  • A confidence threshold (0-1) can be set.
  • A helper function handles different output tensor types (int32, float32, unsigned int) to extract the top results.
• 4:58 Displaying Inference Time: The calculated inference time is added to the output image using OpenCV's putText function.
• 5:06 Loading Label Names: A load_labels function reads label names from a text file into a std::vector<std::string>, ensuring they are in the correct order.
• 5:47 Mapping Labels and Displaying Results:
  • The top_result vector is iterated (typically one result).
  • The confidence and index are extracted.
  • The index is mapped to the actual label name using the loaded label vector.
  • The label name and confidence score are added as text to the output image.
  • The final processed image is displayed using cv::imshow.
• 6:15 Building the Application: The tutorial demonstrates building the application from the terminal using CMake: mkdir build, cd build, cmake ... For Windows, a Visual Studio solution is generated; for Mac/Linux, a makefile is generated.
• 6:47 Running the Application: The compiled executable is run with arguments for the model, label, and input image files (e.g., tflite_classification.exe <model_path> <label_path> <image_path>). An example shows a prediction time of 248 milliseconds and a correct classification with a confidence score.
• 7:23 Custom Model Compatibility: The application is designed to be compatible with custom TFLite models.
• 7:23 Cross-Platform Support: The solution works seamlessly on Windows, Mac, and Linux.