
This SDK has been patched by Embedded Artists for the iMXRT1062 Developer's Kit.
The SDK was released on 2022-11-18 and is based on NXP's 2.12.1 SDK (SDK_2_12_1_MIMXRT1062xxxxA.zip).

This is what has been patched:
* Set CPU speed according to Commercial/Industrial CPU
* Correction of the VDD_SOC_IN voltage.
* Flash settings (speed, algorithm, size, driver) to work with the 4MB OctalSPI ATXP032
* LWIP projects - added reading of the MAC address from the onboard I2C EEPROM
* Added an I2C driver for the gpio expander (PCA6416) and code to use it
* Modified pin muxing
* SEMC projects - changed algorithm for memory test and now test entire 32MB instead of only 4KB
* Adjusted the USB interface number for USB Host examples (it is different for host and device examples)
* Added a software_reset() function in board.c/.h to issue a JEDEC reset before NVIC_SystemReset()
* Changed the Wi-Fi examples to use the Embedded Artists 1XK M.2 Module (EAR00385) as default
* Many of the projects have been updated to use a more complete pin_mux.c file where all
  necessary pins have been initialized. The SDK examples used to only configure the pins
  that they use (and often not every pin) and most of the time the configuration was only
  for MUX:ing and not the PAD settings (pull up/down/none, drive strength and slew).
* Embedded Wizard project 'ew_gui_smart_thermostat' was incorrectly setup for EVKB
* Changed the default display to RK043FN02H as it is the one mounted on the Developer's Kits

This has been added:
* New WDOG examples that work
* I2C probe example
* Example to show the use of software_reset()

This has been removed:
* All projects for the EVK - only keeping EVKB which is then patched
* The original WDOG and RTWDOG examples as those were not working

Important things to note:
* Read section "8 - Known Issues" in docs/MCUXpresso SDK Release Notes for EVK-MIMXRT1060.pdf
  to see known issues with the current version of the SDK.
* For Iperf examples, set compiler optimization to -O3 or similar to improve performance.
* If the hardware seems unresponsive and the debugger cannot connect/flash/erase the current program
  then the most likely cause is the running program preventing the access. To stop the currently
  running program and regain control:
  1) Press and hold down the ISP_ENABLE button (SW1)
  2) Press and hold down the RESET button (SW3)
  3) Let go of the RESET button
  4) Wait an extra second or two
  5) Release the ISP_ENABLE button
  6) The hardware is now in a mode where programming/erasing it should work

Connectors:
* J22 (micro USB) is the default UART and unless specified otherwise it is setup for 115200 8/N/1


!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
THIS PROJECT IS NOT DIRECTLY COMPATIBLE WITH THE HARDWARE AND WILL NOT WORK.

This example for the EVKB requires requires a camera but that is not
supported on the iMX OEM Carrier Board.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!



Everything below this line is the original content of the readme file.
=======================================================================



Overview
========
TensorFlow Lite model based implementation of object detector based on
TensorFlow Lite example [2] adjusted to run on MCUs.

A 3-channel color image is set as an input to a quantized Mobilenet
convolutional neural network model [1] that classifies the input image into
one of 1000 output classes.

Firstly a static stopwatch image is set as input regardless camera is connected or not.
Secondly runtime image processing from camera in the case camera and display
is connected. Camera data are displayed on LCD.

HOW TO USE THE APPLICATION:
To classify an image, place an image in front of camera so that it fits in the
white rectangle in the middle of the display.
Note semihosting implementation causes slower or discontinuous video experience. 
Select UART in 'Project Options' during project import for using external debug console 
via UART (virtual COM port).

[1] https://www.tensorflow.org/lite/models
[2] https://github.com/tensorflow/tensorflow/tree/r2.3/tensorflow/lite/examples/label_image

Files:
  main.cpp - example main function
  image_data.h - image file converted to a C language array of RGB values
    using Python with the OpenCV and Numpy packages:
    import cv2
    import numpy as np
    img = cv2.imread('stopwatch.bmp')
    img = cv2.resize(img, (128, 128))
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    with open('image_data.h', 'w') as fout:
      print('#define STATIC_IMAGE_NAME "stopwatch"', file=fout)
      print('static const uint8_t image_data[] = {', file=fout)
      img.tofile(fout, ', ', '0x%02X')
      print('};\n', file=fout)
  labels.h - names of object classes
  mobilenet_v1_0.25_128_quant_int8.tflite - pre-trained TensorFlow Lite model quantized
    using TF Lite converter (for more details see the eIQ TensorFlow Lite User's Guide, which
    can be downloaded with the MCUXpresso SDK package)
    (source: http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_0.25_128.tgz)
  stopwatch.bmp - image file of the object to recognize
    (source: https://commons.wikimedia.org/wiki/File:Stopwatch2.jpg)
  timer.c - timer source code
  image/* - image capture and pre-processing code
  model/get_top_n.cpp - top results retrieval
  model/model_data.h - model data from the .tflite file
    converted to a C language array using the xxd tool (distributed
    with the Vim editor at www.vim.org)
  model/model.cpp - model initialization and inference code
  model/model_mobilenet_ops.cpp - model operations registration
  model/output_postproc.cpp - model output processing
  video/* - camera and display handling


Toolchain supported
===================
- IAR embedded Workbench  9.30.1
- Keil MDK  5.37
- GCC ARM Embedded  10.3.1
- MCUXpresso  11.6.0

Hardware requirements
=====================
- Mini/micro USB cable
- EVK-MIMXRT1060 board
- Personal computer
- MT9M114 camera (optional)
- RK043FN02H-CT display (optional)

Board settings
==============
Connect the camera to J35 (optional)
Connect the display to A1-A40 and B1-B6 (optional)
Connect external 5V power supply to J2, set J1 to 1-2

Prepare the Demo
================
1. Connect a USB cable between the host PC and the OpenSDA USB port on the target board. 
2. Open a serial terminal with the following settings:
   - 115200 baud rate
   - 8 data bits
   - No parity
   - One stop bit
   - No flow control
3. Download the program to the target board.
4. Either press the reset button on your board or launch the debugger in your IDE to begin running the demo.

Running the demo
================
The log below shows the output of the demo in the terminal window (compiled with ARM GCC):

Label image object recognition example using a TensorFlow Lite Micro model.
Detection threshold: 23%
Expected category: stopwatch
Model: mobilenet_v1_0.25_128_quant_int8

Static data processing:
----------------------------------------
     Inference time: 88 ms
     Detected:  stopwatch (87%)
----------------------------------------


Camera data processing:
Data for inference are ready
----------------------------------------
     Inference time: 88 ms
     Detected: No label detected (0%)
----------------------------------------

Data for inference are ready
----------------------------------------
     Inference time: 88 ms
     Detected:     jaguar (92%)
----------------------------------------

Data for inference are ready
----------------------------------------
     Inference time: 88 ms
     Detected:  pineapple (97%)
----------------------------------------
