static_image_mobilenet_view#
Overview#
This example shows how to use the library to create a use-case for image classification using static image as source.
The machine learning frameworks used is TensorFlow Lite Micro. The image classification model used is quantized Mobilenet convolutional neural network model [1] optimized for RT700 NPU that classifies the input image into one of 1000 output classes.
[1] https://www.tensorflow.org/lite/models
Toolchains supported#
Refer to build instructions:
Hardware requirements#
Refer to board_readme.md for hardware setup requirements.
Use-cases Description#
HOW TO USE THE APPLICATION:
High-level description#
+---------------------------------------------------------+
| |
| |
\ / |
+-------------+ +-------------+ +-------------+ +-------------+ +-------------+ |
| static | | | | | | | | | |
Pipeline 0 | image | --> | 2D convert | --> | labeled | --> | 2D convert | --> | Display | |
| | | |(color+scale)| | rectangle | | | | | |
+-------------+ | +-------------+ +-------------+ +-------------+ +-------------+ |
| |
| +-------------+ +--------------+ +-------------+ |
| | | | | | | |
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 static image preview.
Another pipeline that runs the ML inference on the static image.
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#
Static image 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 RGB888 format to the display pixel format
rotation depending on the display orientation compared to landscape mode (NB: Rotation should be performed
Scaling from 128x128 to the display resolution after the labeled-rectangle to get labels in the right orientation).
2D convert element on pipeline 1 is configured to perform:
cropping to maintain image aspect ratio
scaling to 128x128 as mandated by the image classification model
The labeled rectangle element draws a crop window from which the static image is sent to the ML inference element. The labeled rectangle element also displays the label of the object detected.
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:
Detected label should be displayed on the screen
Logs below should be displayed on the debug console
Logs for static_image_mobilenet_view example using TensorFlow Lite Micro model should look like this:
[MPP_VERSION_3.0.0]
Inference Engine: TensorFlow-Lite Micro
Element stats --------------------------
mobilenet : exec_time 18 (ms)
inference time 5 (ms)
mobilenet : stopwatch (81%)
Element stats --------------------------
mobilenet : exec_time 17 (ms)
inference time 5 (ms)
mobilenet : stopwatch (81%)
Element stats --------------------------
mobilenet : exec_time 18 (ms)
inference time 5 (ms)
mobilenet : stopwatch (79%)
Element stats --------------------------
mobilenet : exec_time 17 (ms)
inference time 5 (ms)
mobilenet : stopwatch (79%)
Element stats --------------------------
mobilenet : exec_time 17 (ms)
inference time 5 (ms)
mobilenet : stopwatch (81%)
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()
If this example uses TensorFLow Lite Micro, it implements its own function MODEL_GetOpsResolver dedicated to Mobilenet. User may provide its own implementation of MODEL_GetOpsResolver when using a different model.