
This SDK has been patched by Embedded Artists for the iMXRT1176 Developer's Kit.
The SDK was released on 2022-03-02 and is based on NXP's 2.11.0 SDK (SDK_2_11_0_MIMXRT1176xxxxx.zip).

This is what has been patched:
* LWIP projects - added reading of the MAC address from I2C EEPROM either on the 100Mbit adapter
  or on the uCOM board
* Added an I2C driver for the gpio expander (PCA6416) and code to use it
* Added an I2C driver for the PWM gpio expander (PCA9530) and code to use it
* SDRAM size has been corrected to 32Mb (including linker files, MPU and DCD)
* 1G Ethernet PHY has been changed from RTL8211F to AR8031DS
* SEMC projects - changed to correct settings for the SDRAM
* SEMC projects - changed algorithm for memory test and now test entire 32MB instead of only 4KB
* Examples using eLCDIF/LCDIFv2 have been updated to use PCA6416/PCA9530 for
  RST/PWR/BL signals
* BOARD_USER_BUTTON has been redirected to SW5/WAKEUP button on the uCOM Carrier Board
* USER_LED has been changed to the blue RGB LED using PCA6416
* Touch: I2C bus and GPIOs have been changed for RST/INT
* Camera pins
* Adjusted the USB interface number (it is different for host and device examples)
* Changed the Wi-Fi examples to use the Embedded Artists 1XK M.2 Module (EAR00385) as default
* Corrected the ethernet PHY addresses
* Changed CORE clock depending on speed grading of MCU (798MHz for Industrial, 996MHz for Commercial)
* Changed SEMC clock to be within maximum speed for SDRAM (now 148.5MHz, was 198MHz)
* 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).
* Converted the AzureRTOS examples to use the BOARD_NETWORK_USE_100M_ENET_PORT (same as all
  other networking examples) instead of using the old EXAMPLE_USE_1G_ENET_PORT

This has been added:
* HDMI support to most GUI examples. HDMI at 1024x768@60 is the default resolution but
  that can be changed per project in display_support.h/elcdif_support.h/lcdifv2_support.h.
* Added ADT example for TensorFlow Lite
* I2C probe example
* EDID reader example
* Wi-Fi (serial) examples for the CMWC1ZZABR-107-EVB (a.k.a ABR Module)

This has been removed:
* All projects for the expansion board AGM01

Important things to note:
* Read section "8 - Known Issues" in docs/MCUXpresso SDK Release Notes for MIMXRT1170-EVK.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:
* J29 (micro USB) is the default UART for the CM7 core and unless specified otherwise it is setup for 115200 8/N/1
* J30 (micro USB) is the default UART for the CM4 core and unless specified otherwise it is setup for 115200 8/N/1
* For 1Gbit Ethernet examples, use connector J25 on uCOM Carrier Board
* For 100Mbit Ethernet examples, use ethernet adapter connected between J12 on uCOM Carrier Board
  and J37 on the adapter. These four connections are also needed:
    1) uCOM Carrier Board, JP38:1 -> adapter JP37:1
    2) uCOM Carrier Board, JP38:2 -> adapter JP37:2
    3) uCOM Carrier Board, JP27:1 -> adapter JP39:2
    4) uCOM Carrier Board, JP27:2 -> adapter JP39:1
* The two CSI examples can use either an OV5640 camera in connector J23 or a camera in connector J24
* The EIQ examples that use a camera expects the camera in connector J24 (J23 might work for some
  of the examples but runs much slower)
* Some GUI examples are configured for the RK055AHD091 display which should be in connector "C" on
  the uCOM Carrier Board. Ignore the readme text about connecting extra 5V power.
* The default for GUI examples is to use an HDMI adapter in connector "C" on
  the uCOM Carrier Board. Ignore the readme text about connecting extra 5V power.


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  9.10.2
- Keil MDK  5.34
- GCC ARM Embedded  10.2.1
- MCUXpresso  11.5.0

Hardware requirements
=====================
- Mini/micro USB cable
- EVK-MIMXRT1170 board
- Personal computer
- RK055AHD091 display (optional)
- OV5640 camera (optional)

Board settings
==============
Connect the display to J48 (optional)
Connect the camera to J2 (optional)
Connect external 5V power to J43, set J38 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%)
----------------------------------------
