camera_ultraface_view
Overview
This example shows how to use the library to create a use-case for face detection using camera as source.
The machine learning framework used for this example is TensorFlow Lite Micro. The face detection model used is quantized Ultraface slim model that detects multiple faces in an input image.
Toolchains supported
MCUXpresso, version 11.10.0
GCC Arm Embedded, version 13.2.Rel1
Hardware requirements
Refer to board.readme for hardware setup requirements.
Use-cases Description
HOW TO USE THE APPLICATION:
High-level description
+--------------------------------------------------------+
| |
| |
\ / |
+-------------+ +-------------+ +-------------+ +-------------+ +-------------+ |
| | | | | | | | | | |
Pipeline 0 | camera | --> | 2D convert | --> | labeled | --> | 2D convert | --> | Display | |
| | | |(color+flip) | | rectangle | | (rotation) | | | |
+-------------+ | +-------------+ +-------------+ +-------------+ +-------------+ |
| |
| +-------------+ +--------------+ +-------------+ |
| | | | | | | |
Pipeline 1 +---> | 2D convert | --> | ML Inference | --> | NULL sink | |
| | | | | | |
+-------------+ +--------------+ +-------------+ |
| |
| |
+-----------------+ | |
| Main app: | | |
| ML output | <----- ML Inference output callback -----+ |
| post processing | |
| | ------ labeled rectangle update ----------------------------------------------------------+
+-----------------+
Detailed description
Application creates two pipelines:
One pipeline that runs the camera preview.
Another pipeline that runs the ML inference on the image coming from the camera.
Pipeline 1 is split from pipeline 0
Pipeline 0 executes the processing of each element sequentially and CANNOT be preempted by another pipeline.
Pipeline 1 executes the processing of each element sequentially but CAN be preempted.
Pipelines elements description
Camera element is configured for a specific pixel format and resolution (board dependent)
Display element is configured for a specific pixel format and resolution (board dependent)
2D convert element on pipeline 0 is configured to perform:
color space conversion from the camera pixel format to the display pixel format
rotation depending on the display orientation compared to landscape mode (NB: Rotation should be performed after the labeled-rectangle to get labels in the right orientation).
2D convert element on pipeline 1 is configured to perform:
color space conversion from the camera pixel format to RGB888
cropping to maintain image aspect ratio
scaling to 240x320 as mandated by the face detection model
The labeled rectangle element draws a crop window from which the camera image is sent to the ML inference element. The labeled rectangle element also displays the label “face” of the detected face.
The ML inference element runs an inference on the image pre-processed by the 2D convert element.
The NULL sink element closes pipeline 1 (in MPP concept, only sink elements can close a pipeline).
At every inference, the ML inference element invokes a callback containing the inference outputs. These outputs are post-processed by the callback client component (in this case, the main task of the application)
Running the demo
EXPECTED OUTPUTS: The expected outputs of the example are:
For each detected face, a labeled rectangle should be displayed on the screen.
Logs below should be displayed on the debug console.
Logs for camera_ultraface_view example using TensorFlow Lite Micro model should look like this:
[MPP_VERSION_1.0.0]
Inference Engine: TensorFlow-Lite Micro
inference time 707 ms
ultraface : no face detected
inference time 707 ms
ultraface : no face detected
inference time 707 ms
ultraface : no face detected
inference time 706 ms
ultraface : box 0 label face score 99(%)
inference time 706 ms
ultraface : box 0 label face score 99(%)
inference time 706 ms
ultraface : box 0 label face score 99(%)
inference time 706 ms
ultraface : box 0 label face score 99(%)
inference time 710 ms
ultraface : box 0 label face score 99(%)
inference time 710 ms
ultraface : box 0 label face score 99(%)
inference time 710 ms
Important notes
TensorFLow Lite Micro is an optional engine for the ML Inference component of MPP. This project embeds NXP’s custom TensorFlow Lite Micro code by default. TensorFLow Lite allows short-listing the “Operations” used by a specific model in order to reduce the binary image footprint. This is done by implementing the function:
tflite::MicroOpResolver &MODEL_GetOpsResolver()
This example implements its own function MODEL_GetOpsResolver dedicated to Ultraface. User may provide its own implementation of MODEL_GetOpsResolver when using a different model.