lesson 16 and most of 18 (#67)
* Adding content * Update en.json * Update README.md * Update TRANSLATIONS.md * Adding lesson tempolates * Fixing code files with each others code in * Update README.md * Adding lesson 16 * Adding virtual camera * Adding Wio Terminal camera capture * Adding wio terminal code * Adding SBC classification to lesson 16 * Adding challenge, review and assignment * Adding images and using new Azure icons * Update README.md * Update iot-reference-architecture.png * Adding structure for JulyOT links * Removing icons * Sketchnotes! * Create lesson-1.png * Starting on lesson 18 * Updated sketch * Adding virtual distance sensor * Adding Wio Terminal image classificationpull/69/head
Jim Bennett
3 years ago
committed by
GitHub
parent
8cf84b34e2
commit
eff54133b4
@ -0,0 +1,5 @@
|
||||
.pio
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.vscode/.browse.c_cpp.db*
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||||
.vscode/c_cpp_properties.json
|
||||
.vscode/launch.json
|
||||
.vscode/ipch
|
||||
@ -0,0 +1,7 @@
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{
|
||||
// See http://go.microsoft.com/fwlink/?LinkId=827846
|
||||
// for the documentation about the extensions.json format
|
||||
"recommendations": [
|
||||
"platformio.platformio-ide"
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||||
]
|
||||
}
|
||||
@ -0,0 +1,39 @@
|
||||
|
||||
This directory is intended for project header files.
|
||||
|
||||
A header file is a file containing C declarations and macro definitions
|
||||
to be shared between several project source files. You request the use of a
|
||||
header file in your project source file (C, C++, etc) located in `src` folder
|
||||
by including it, with the C preprocessing directive `#include'.
|
||||
|
||||
```src/main.c
|
||||
|
||||
#include "header.h"
|
||||
|
||||
int main (void)
|
||||
{
|
||||
...
|
||||
}
|
||||
```
|
||||
|
||||
Including a header file produces the same results as copying the header file
|
||||
into each source file that needs it. Such copying would be time-consuming
|
||||
and error-prone. With a header file, the related declarations appear
|
||||
in only one place. If they need to be changed, they can be changed in one
|
||||
place, and programs that include the header file will automatically use the
|
||||
new version when next recompiled. The header file eliminates the labor of
|
||||
finding and changing all the copies as well as the risk that a failure to
|
||||
find one copy will result in inconsistencies within a program.
|
||||
|
||||
In C, the usual convention is to give header files names that end with `.h'.
|
||||
It is most portable to use only letters, digits, dashes, and underscores in
|
||||
header file names, and at most one dot.
|
||||
|
||||
Read more about using header files in official GCC documentation:
|
||||
|
||||
* Include Syntax
|
||||
* Include Operation
|
||||
* Once-Only Headers
|
||||
* Computed Includes
|
||||
|
||||
https://gcc.gnu.org/onlinedocs/cpp/Header-Files.html
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@ -0,0 +1,46 @@
|
||||
|
||||
This directory is intended for project specific (private) libraries.
|
||||
PlatformIO will compile them to static libraries and link into executable file.
|
||||
|
||||
The source code of each library should be placed in a an own separate directory
|
||||
("lib/your_library_name/[here are source files]").
|
||||
|
||||
For example, see a structure of the following two libraries `Foo` and `Bar`:
|
||||
|
||||
|--lib
|
||||
| |
|
||||
| |--Bar
|
||||
| | |--docs
|
||||
| | |--examples
|
||||
| | |--src
|
||||
| | |- Bar.c
|
||||
| | |- Bar.h
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||||
| | |- library.json (optional, custom build options, etc) https://docs.platformio.org/page/librarymanager/config.html
|
||||
| |
|
||||
| |--Foo
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||||
| | |- Foo.c
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||||
| | |- Foo.h
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||||
| |
|
||||
| |- README --> THIS FILE
|
||||
|
|
||||
|- platformio.ini
|
||||
|--src
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|- main.c
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||||
|
||||
and a contents of `src/main.c`:
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||||
```
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||||
#include <Foo.h>
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#include <Bar.h>
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||||
|
||||
int main (void)
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||||
{
|
||||
...
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||||
}
|
||||
|
||||
```
|
||||
|
||||
PlatformIO Library Dependency Finder will find automatically dependent
|
||||
libraries scanning project source files.
|
||||
|
||||
More information about PlatformIO Library Dependency Finder
|
||||
- https://docs.platformio.org/page/librarymanager/ldf.html
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@ -0,0 +1,26 @@
|
||||
; PlatformIO Project Configuration File
|
||||
;
|
||||
; Build options: build flags, source filter
|
||||
; Upload options: custom upload port, speed and extra flags
|
||||
; Library options: dependencies, extra library storages
|
||||
; Advanced options: extra scripting
|
||||
;
|
||||
; Please visit documentation for the other options and examples
|
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; https://docs.platformio.org/page/projectconf.html
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[env:seeed_wio_terminal]
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platform = atmelsam
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board = seeed_wio_terminal
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framework = arduino
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lib_deps =
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seeed-studio/Seeed Arduino rpcWiFi
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seeed-studio/Seeed Arduino FS
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seeed-studio/Seeed Arduino SFUD
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seeed-studio/Seeed Arduino rpcUnified
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seeed-studio/Seeed_Arduino_mbedtls
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seeed-studio/Seeed Arduino RTC
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bblanchon/ArduinoJson @ 6.17.3
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build_flags =
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-w
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-DARDUCAM_SHIELD_V2
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-DOV2640_CAM
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@ -0,0 +1,160 @@
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#pragma once
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#include <ArduCAM.h>
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#include <Wire.h>
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class Camera
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{
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public:
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Camera(int format, int image_size) : _arducam(OV2640, PIN_SPI_SS)
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{
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_format = format;
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_image_size = image_size;
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}
|
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|
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bool init()
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{
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// Reset the CPLD
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_arducam.write_reg(0x07, 0x80);
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delay(100);
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|
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_arducam.write_reg(0x07, 0x00);
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delay(100);
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|
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// Check if the ArduCAM SPI bus is OK
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_arducam.write_reg(ARDUCHIP_TEST1, 0x55);
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if (_arducam.read_reg(ARDUCHIP_TEST1) != 0x55)
|
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{
|
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return false;
|
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}
|
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|
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// Change MCU mode
|
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_arducam.set_mode(MCU2LCD_MODE);
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uint8_t vid, pid;
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// Check if the camera module type is OV2640
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_arducam.wrSensorReg8_8(0xff, 0x01);
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_arducam.rdSensorReg8_8(OV2640_CHIPID_HIGH, &vid);
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_arducam.rdSensorReg8_8(OV2640_CHIPID_LOW, &pid);
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if ((vid != 0x26) && ((pid != 0x41) || (pid != 0x42)))
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{
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return false;
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}
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_arducam.set_format(_format);
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_arducam.InitCAM();
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_arducam.OV2640_set_JPEG_size(_image_size);
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_arducam.OV2640_set_Light_Mode(Auto);
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_arducam.OV2640_set_Special_effects(Normal);
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delay(1000);
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return true;
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}
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void startCapture()
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{
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_arducam.flush_fifo();
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_arducam.clear_fifo_flag();
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_arducam.start_capture();
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}
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bool captureReady()
|
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{
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return _arducam.get_bit(ARDUCHIP_TRIG, CAP_DONE_MASK);
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}
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bool readImageToBuffer(byte **buffer, uint32_t &buffer_length)
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{
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if (!captureReady()) return false;
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// Get the image file length
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uint32_t length = _arducam.read_fifo_length();
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buffer_length = length;
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|
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if (length >= MAX_FIFO_SIZE)
|
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{
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return false;
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}
|
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if (length == 0)
|
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{
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return false;
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}
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|
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// create the buffer
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byte *buf = new byte[length];
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uint8_t temp = 0, temp_last = 0;
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int i = 0;
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uint32_t buffer_pos = 0;
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bool is_header = false;
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|
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_arducam.CS_LOW();
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_arducam.set_fifo_burst();
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while (length--)
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{
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temp_last = temp;
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temp = SPI.transfer(0x00);
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//Read JPEG data from FIFO
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if ((temp == 0xD9) && (temp_last == 0xFF)) //If find the end ,break while,
|
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{
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buf[buffer_pos] = temp;
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buffer_pos++;
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i++;
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_arducam.CS_HIGH();
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}
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if (is_header == true)
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{
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//Write image data to buffer if not full
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if (i < 256)
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{
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buf[buffer_pos] = temp;
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buffer_pos++;
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i++;
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}
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else
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{
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_arducam.CS_HIGH();
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i = 0;
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buf[buffer_pos] = temp;
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buffer_pos++;
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i++;
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_arducam.CS_LOW();
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_arducam.set_fifo_burst();
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}
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}
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else if ((temp == 0xD8) & (temp_last == 0xFF))
|
||||
{
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is_header = true;
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buf[buffer_pos] = temp_last;
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buffer_pos++;
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i++;
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buf[buffer_pos] = temp;
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buffer_pos++;
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i++;
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}
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}
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_arducam.clear_fifo_flag();
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_arducam.set_format(_format);
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_arducam.InitCAM();
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_arducam.OV2640_set_JPEG_size(_image_size);
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// return the buffer
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*buffer = buf;
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}
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private:
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ArduCAM _arducam;
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int _format;
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int _image_size;
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};
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@ -0,0 +1,49 @@
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#pragma once
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#include <string>
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||||
using namespace std;
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||||
|
||||
// WiFi credentials
|
||||
const char *SSID = "<SSID>";
|
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const char *PASSWORD = "<PASSWORD>";
|
||||
|
||||
const char *PREDICTION_URL = "<PREDICTION_URL>";
|
||||
const char *PREDICTION_KEY = "<PREDICTION_KEY>";
|
||||
|
||||
// Microsoft Azure DigiCert Global Root G2 global certificate
|
||||
const char *CERTIFICATE =
|
||||
"-----BEGIN CERTIFICATE-----\r\n"
|
||||
"MIIF8zCCBNugAwIBAgIQAueRcfuAIek/4tmDg0xQwDANBgkqhkiG9w0BAQwFADBh\r\n"
|
||||
"MQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3\r\n"
|
||||
"d3cuZGlnaWNlcnQuY29tMSAwHgYDVQQDExdEaWdpQ2VydCBHbG9iYWwgUm9vdCBH\r\n"
|
||||
"MjAeFw0yMDA3MjkxMjMwMDBaFw0yNDA2MjcyMzU5NTlaMFkxCzAJBgNVBAYTAlVT\r\n"
|
||||
"MR4wHAYDVQQKExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xKjAoBgNVBAMTIU1pY3Jv\r\n"
|
||||
"c29mdCBBenVyZSBUTFMgSXNzdWluZyBDQSAwNjCCAiIwDQYJKoZIhvcNAQEBBQAD\r\n"
|
||||
"ggIPADCCAgoCggIBALVGARl56bx3KBUSGuPc4H5uoNFkFH4e7pvTCxRi4j/+z+Xb\r\n"
|
||||
"wjEz+5CipDOqjx9/jWjskL5dk7PaQkzItidsAAnDCW1leZBOIi68Lff1bjTeZgMY\r\n"
|
||||
"iwdRd3Y39b/lcGpiuP2d23W95YHkMMT8IlWosYIX0f4kYb62rphyfnAjYb/4Od99\r\n"
|
||||
"ThnhlAxGtfvSbXcBVIKCYfZgqRvV+5lReUnd1aNjRYVzPOoifgSx2fRyy1+pO1Uz\r\n"
|
||||
"aMMNnIOE71bVYW0A1hr19w7kOb0KkJXoALTDDj1ukUEDqQuBfBxReL5mXiu1O7WG\r\n"
|
||||
"0vltg0VZ/SZzctBsdBlx1BkmWYBW261KZgBivrql5ELTKKd8qgtHcLQA5fl6JB0Q\r\n"
|
||||
"gs5XDaWehN86Gps5JW8ArjGtjcWAIP+X8CQaWfaCnuRm6Bk/03PQWhgdi84qwA0s\r\n"
|
||||
"sRfFJwHUPTNSnE8EiGVk2frt0u8PG1pwSQsFuNJfcYIHEv1vOzP7uEOuDydsmCjh\r\n"
|
||||
"lxuoK2n5/2aVR3BMTu+p4+gl8alXoBycyLmj3J/PUgqD8SL5fTCUegGsdia/Sa60\r\n"
|
||||
"N2oV7vQ17wjMN+LXa2rjj/b4ZlZgXVojDmAjDwIRdDUujQu0RVsJqFLMzSIHpp2C\r\n"
|
||||
"Zp7mIoLrySay2YYBu7SiNwL95X6He2kS8eefBBHjzwW/9FxGqry57i71c2cDAgMB\r\n"
|
||||
"AAGjggGtMIIBqTAdBgNVHQ4EFgQU1cFnOsKjnfR3UltZEjgp5lVou6UwHwYDVR0j\r\n"
|
||||
"BBgwFoAUTiJUIBiV5uNu5g/6+rkS7QYXjzkwDgYDVR0PAQH/BAQDAgGGMB0GA1Ud\r\n"
|
||||
"JQQWMBQGCCsGAQUFBwMBBggrBgEFBQcDAjASBgNVHRMBAf8ECDAGAQH/AgEAMHYG\r\n"
|
||||
"CCsGAQUFBwEBBGowaDAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQu\r\n"
|
||||
"Y29tMEAGCCsGAQUFBzAChjRodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGln\r\n"
|
||||
"aUNlcnRHbG9iYWxSb290RzIuY3J0MHsGA1UdHwR0MHIwN6A1oDOGMWh0dHA6Ly9j\r\n"
|
||||
"cmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5jcmwwN6A1oDOG\r\n"
|
||||
"MWh0dHA6Ly9jcmw0LmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5j\r\n"
|
||||
"cmwwHQYDVR0gBBYwFDAIBgZngQwBAgEwCAYGZ4EMAQICMBAGCSsGAQQBgjcVAQQD\r\n"
|
||||
"AgEAMA0GCSqGSIb3DQEBDAUAA4IBAQB2oWc93fB8esci/8esixj++N22meiGDjgF\r\n"
|
||||
"+rA2LUK5IOQOgcUSTGKSqF9lYfAxPjrqPjDCUPHCURv+26ad5P/BYtXtbmtxJWu+\r\n"
|
||||
"cS5BhMDPPeG3oPZwXRHBJFAkY4O4AF7RIAAUW6EzDflUoDHKv83zOiPfYGcpHc9s\r\n"
|
||||
"kxAInCedk7QSgXvMARjjOqdakor21DTmNIUotxo8kHv5hwRlGhBJwps6fEVi1Bt0\r\n"
|
||||
"trpM/3wYxlr473WSPUFZPgP1j519kLpWOJ8z09wxay+Br29irPcBYv0GMXlHqThy\r\n"
|
||||
"8y4m/HyTQeI2IMvMrQnwqPpY+rLIXyviI2vLoI+4xKE4Rn38ZZ8m\r\n"
|
||||
"-----END CERTIFICATE-----\r\n";
|
||||
@ -0,0 +1,125 @@
|
||||
#include <Arduino.h>
|
||||
#include <ArduinoJson.h>
|
||||
#include <HTTPClient.h>
|
||||
#include <rpcWiFi.h>
|
||||
#include "SD/Seeed_SD.h"
|
||||
#include <Seeed_FS.h>
|
||||
#include <SPI.h>
|
||||
#include <WiFiClientSecure.h>
|
||||
|
||||
#include "config.h"
|
||||
#include "camera.h"
|
||||
|
||||
Camera camera = Camera(JPEG, OV2640_640x480);
|
||||
|
||||
WiFiClientSecure client;
|
||||
|
||||
void setupCamera()
|
||||
{
|
||||
pinMode(PIN_SPI_SS, OUTPUT);
|
||||
digitalWrite(PIN_SPI_SS, HIGH);
|
||||
|
||||
Wire.begin();
|
||||
SPI.begin();
|
||||
|
||||
if (!camera.init())
|
||||
{
|
||||
Serial.println("Error setting up the camera!");
|
||||
}
|
||||
}
|
||||
|
||||
void connectWiFi()
|
||||
{
|
||||
while (WiFi.status() != WL_CONNECTED)
|
||||
{
|
||||
Serial.println("Connecting to WiFi..");
|
||||
WiFi.begin(SSID, PASSWORD);
|
||||
delay(500);
|
||||
}
|
||||
|
||||
client.setCACert(CERTIFICATE);
|
||||
Serial.println("Connected!");
|
||||
}
|
||||
|
||||
void setup()
|
||||
{
|
||||
Serial.begin(9600);
|
||||
|
||||
while (!Serial)
|
||||
; // Wait for Serial to be ready
|
||||
|
||||
delay(1000);
|
||||
|
||||
connectWiFi();
|
||||
|
||||
setupCamera();
|
||||
|
||||
pinMode(WIO_KEY_C, INPUT_PULLUP);
|
||||
}
|
||||
|
||||
void classifyImage(byte *buffer, uint32_t length)
|
||||
{
|
||||
HTTPClient httpClient;
|
||||
httpClient.begin(client, PREDICTION_URL);
|
||||
httpClient.addHeader("Content-Type", "application/octet-stream");
|
||||
httpClient.addHeader("Prediction-Key", PREDICTION_KEY);
|
||||
|
||||
int httpResponseCode = httpClient.POST(buffer, length);
|
||||
|
||||
if (httpResponseCode == 200)
|
||||
{
|
||||
String result = httpClient.getString();
|
||||
|
||||
DynamicJsonDocument doc(1024);
|
||||
deserializeJson(doc, result.c_str());
|
||||
|
||||
JsonObject obj = doc.as<JsonObject>();
|
||||
JsonArray predictions = obj["predictions"].as<JsonArray>();
|
||||
|
||||
for(JsonVariant prediction : predictions)
|
||||
{
|
||||
String tag = prediction["tagName"].as<String>();
|
||||
float probability = prediction["probability"].as<float>();
|
||||
|
||||
char buff[32];
|
||||
sprintf(buff, "%s:\t%.2f%%", tag.c_str(), probability * 100.0);
|
||||
Serial.println(buff);
|
||||
}
|
||||
}
|
||||
|
||||
httpClient.end();
|
||||
}
|
||||
|
||||
void buttonPressed()
|
||||
{
|
||||
camera.startCapture();
|
||||
|
||||
while (!camera.captureReady())
|
||||
delay(100);
|
||||
|
||||
Serial.println("Image captured");
|
||||
|
||||
byte *buffer;
|
||||
uint32_t length;
|
||||
|
||||
if (camera.readImageToBuffer(&buffer, length))
|
||||
{
|
||||
Serial.print("Image read to buffer with length ");
|
||||
Serial.println(length);
|
||||
|
||||
classifyImage(buffer, length);
|
||||
|
||||
delete (buffer);
|
||||
}
|
||||
}
|
||||
|
||||
void loop()
|
||||
{
|
||||
if (digitalRead(WIO_KEY_C) == LOW)
|
||||
{
|
||||
buttonPressed();
|
||||
delay(2000);
|
||||
}
|
||||
|
||||
delay(200);
|
||||
}
|
||||
@ -0,0 +1,11 @@
|
||||
|
||||
This directory is intended for PlatformIO Unit Testing and project tests.
|
||||
|
||||
Unit Testing is a software testing method by which individual units of
|
||||
source code, sets of one or more MCU program modules together with associated
|
||||
control data, usage procedures, and operating procedures, are tested to
|
||||
determine whether they are fit for use. Unit testing finds problems early
|
||||
in the development cycle.
|
||||
|
||||
More information about PlatformIO Unit Testing:
|
||||
- https://docs.platformio.org/page/plus/unit-testing.html
|
||||
@ -1,3 +1,215 @@
|
||||
# Classify an image - Wio Terminal
|
||||
|
||||
Coming soon!
|
||||
In this part of the lesson, you will add send the image captured by the camera to the Custom Vision service to classify it.
|
||||
|
||||
## Classify an image
|
||||
|
||||
The Custom Vision service has a REST API you can call from the Wio Terminal use to classify images. THis REST API is accessed over an HTTPS connection - a secure HTTP connection.
|
||||
|
||||
When interacting with HTTPS endpoints, the client code needs to request the public key certificate from the server being accessed, and use that to encrypt the traffic it sends. Your web browser does this automatically, but microcontrollers do not. You will need to request this certificate manually and use it to create a secure connection to the REST API. These certificates don't change, so once you have a certificate, it can be hard coded in your application.
|
||||
|
||||
These certificates contain public keys, and don't need to be kept secure. You can use them in your source code and share them in public on places like GitHub.
|
||||
|
||||
### Task - set up a SSL client
|
||||
|
||||
1. Open the `fruit-quality-detector` app project if it's not already open
|
||||
|
||||
1. Open the `config.h` header file, and add the following:
|
||||
|
||||
```cpp
|
||||
const char *CERTIFICATE =
|
||||
"-----BEGIN CERTIFICATE-----\r\n"
|
||||
"MIIF8zCCBNugAwIBAgIQAueRcfuAIek/4tmDg0xQwDANBgkqhkiG9w0BAQwFADBh\r\n"
|
||||
"MQswCQYDVQQGEwJVUzEVMBMGA1UEChMMRGlnaUNlcnQgSW5jMRkwFwYDVQQLExB3\r\n"
|
||||
"d3cuZGlnaWNlcnQuY29tMSAwHgYDVQQDExdEaWdpQ2VydCBHbG9iYWwgUm9vdCBH\r\n"
|
||||
"MjAeFw0yMDA3MjkxMjMwMDBaFw0yNDA2MjcyMzU5NTlaMFkxCzAJBgNVBAYTAlVT\r\n"
|
||||
"MR4wHAYDVQQKExVNaWNyb3NvZnQgQ29ycG9yYXRpb24xKjAoBgNVBAMTIU1pY3Jv\r\n"
|
||||
"c29mdCBBenVyZSBUTFMgSXNzdWluZyBDQSAwNjCCAiIwDQYJKoZIhvcNAQEBBQAD\r\n"
|
||||
"ggIPADCCAgoCggIBALVGARl56bx3KBUSGuPc4H5uoNFkFH4e7pvTCxRi4j/+z+Xb\r\n"
|
||||
"wjEz+5CipDOqjx9/jWjskL5dk7PaQkzItidsAAnDCW1leZBOIi68Lff1bjTeZgMY\r\n"
|
||||
"iwdRd3Y39b/lcGpiuP2d23W95YHkMMT8IlWosYIX0f4kYb62rphyfnAjYb/4Od99\r\n"
|
||||
"ThnhlAxGtfvSbXcBVIKCYfZgqRvV+5lReUnd1aNjRYVzPOoifgSx2fRyy1+pO1Uz\r\n"
|
||||
"aMMNnIOE71bVYW0A1hr19w7kOb0KkJXoALTDDj1ukUEDqQuBfBxReL5mXiu1O7WG\r\n"
|
||||
"0vltg0VZ/SZzctBsdBlx1BkmWYBW261KZgBivrql5ELTKKd8qgtHcLQA5fl6JB0Q\r\n"
|
||||
"gs5XDaWehN86Gps5JW8ArjGtjcWAIP+X8CQaWfaCnuRm6Bk/03PQWhgdi84qwA0s\r\n"
|
||||
"sRfFJwHUPTNSnE8EiGVk2frt0u8PG1pwSQsFuNJfcYIHEv1vOzP7uEOuDydsmCjh\r\n"
|
||||
"lxuoK2n5/2aVR3BMTu+p4+gl8alXoBycyLmj3J/PUgqD8SL5fTCUegGsdia/Sa60\r\n"
|
||||
"N2oV7vQ17wjMN+LXa2rjj/b4ZlZgXVojDmAjDwIRdDUujQu0RVsJqFLMzSIHpp2C\r\n"
|
||||
"Zp7mIoLrySay2YYBu7SiNwL95X6He2kS8eefBBHjzwW/9FxGqry57i71c2cDAgMB\r\n"
|
||||
"AAGjggGtMIIBqTAdBgNVHQ4EFgQU1cFnOsKjnfR3UltZEjgp5lVou6UwHwYDVR0j\r\n"
|
||||
"BBgwFoAUTiJUIBiV5uNu5g/6+rkS7QYXjzkwDgYDVR0PAQH/BAQDAgGGMB0GA1Ud\r\n"
|
||||
"JQQWMBQGCCsGAQUFBwMBBggrBgEFBQcDAjASBgNVHRMBAf8ECDAGAQH/AgEAMHYG\r\n"
|
||||
"CCsGAQUFBwEBBGowaDAkBggrBgEFBQcwAYYYaHR0cDovL29jc3AuZGlnaWNlcnQu\r\n"
|
||||
"Y29tMEAGCCsGAQUFBzAChjRodHRwOi8vY2FjZXJ0cy5kaWdpY2VydC5jb20vRGln\r\n"
|
||||
"aUNlcnRHbG9iYWxSb290RzIuY3J0MHsGA1UdHwR0MHIwN6A1oDOGMWh0dHA6Ly9j\r\n"
|
||||
"cmwzLmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5jcmwwN6A1oDOG\r\n"
|
||||
"MWh0dHA6Ly9jcmw0LmRpZ2ljZXJ0LmNvbS9EaWdpQ2VydEdsb2JhbFJvb3RHMi5j\r\n"
|
||||
"cmwwHQYDVR0gBBYwFDAIBgZngQwBAgEwCAYGZ4EMAQICMBAGCSsGAQQBgjcVAQQD\r\n"
|
||||
"AgEAMA0GCSqGSIb3DQEBDAUAA4IBAQB2oWc93fB8esci/8esixj++N22meiGDjgF\r\n"
|
||||
"+rA2LUK5IOQOgcUSTGKSqF9lYfAxPjrqPjDCUPHCURv+26ad5P/BYtXtbmtxJWu+\r\n"
|
||||
"cS5BhMDPPeG3oPZwXRHBJFAkY4O4AF7RIAAUW6EzDflUoDHKv83zOiPfYGcpHc9s\r\n"
|
||||
"kxAInCedk7QSgXvMARjjOqdakor21DTmNIUotxo8kHv5hwRlGhBJwps6fEVi1Bt0\r\n"
|
||||
"trpM/3wYxlr473WSPUFZPgP1j519kLpWOJ8z09wxay+Br29irPcBYv0GMXlHqThy\r\n"
|
||||
"8y4m/HyTQeI2IMvMrQnwqPpY+rLIXyviI2vLoI+4xKE4Rn38ZZ8m\r\n"
|
||||
"-----END CERTIFICATE-----\r\n";
|
||||
```
|
||||
|
||||
This is the *Microsoft Azure DigiCert Global Root G2 certificate* - it's one of the certificates used by many Azure services globally.
|
||||
|
||||
> 💁 To see that this is the certificate to use, run the following command on macOS or Linux. If you are using Windows, you can run this command using the [Windows Subsystem for Linux (WSL)](https://docs.microsoft.com/windows/wsl/?WT.mc_id=academic-17441-jabenn):
|
||||
>
|
||||
> ```sh
|
||||
> openssl s_client -showcerts -verify 5 -connect api.cognitive.microsoft.com:443
|
||||
> ```
|
||||
>
|
||||
> The output will list the DigiCert Global Root G2 certificate.
|
||||
|
||||
1. Open `main.cpp` and add the following include directive:
|
||||
|
||||
```cpp
|
||||
#include <WiFiClientSecure.h>
|
||||
```
|
||||
|
||||
1. Below the include directives, declare an instance of `WifiClientSecure`:
|
||||
|
||||
```cpp
|
||||
WiFiClientSecure client;
|
||||
```
|
||||
|
||||
This class contains code to communicate with web endpoints over HTTPS.
|
||||
|
||||
1. In the `connectWiFi` method, set the WiFiClientSecure to use the DigiCert Global Root G2 certificate:
|
||||
|
||||
```cpp
|
||||
client.setCACert(CERTIFICATE);
|
||||
```
|
||||
|
||||
### Task - classify an image
|
||||
|
||||
1. Add the following as an additional line to the `lib_deps` list in the `platformio.ini` file:
|
||||
|
||||
```ini
|
||||
bblanchon/ArduinoJson @ 6.17.3
|
||||
```
|
||||
|
||||
This imports [ArduinoJson](https://arduinojson.org), an Arduino JSON library, and will be used to decode the JSON response from the REST API.
|
||||
|
||||
1. In `config.h`, add constants for the prediction URL and Key from the Custom Vision service:
|
||||
|
||||
```cpp
|
||||
const char *PREDICTION_URL = "<PREDICTION_URL>";
|
||||
const char *PREDICTION_KEY = "<PREDICTION_KEY>";
|
||||
```
|
||||
|
||||
Replace `<PREDICTION_URL>` with the prediction URL from Custom Vision. Replace `<PREDICTION_KEY>` with the prediction key.
|
||||
|
||||
1. In `main.cpp`, add an include directive for the ArduinoJson library:
|
||||
|
||||
```cpp
|
||||
#include <ArduinoJSON.h>
|
||||
```
|
||||
|
||||
1. Add the following function to `main.cpp`, above the `buttonPressed` function.
|
||||
|
||||
```cpp
|
||||
void classifyImage(byte *buffer, uint32_t length)
|
||||
{
|
||||
HTTPClient httpClient;
|
||||
httpClient.begin(client, PREDICTION_URL);
|
||||
httpClient.addHeader("Content-Type", "application/octet-stream");
|
||||
httpClient.addHeader("Prediction-Key", PREDICTION_KEY);
|
||||
|
||||
int httpResponseCode = httpClient.POST(buffer, length);
|
||||
|
||||
if (httpResponseCode == 200)
|
||||
{
|
||||
String result = httpClient.getString();
|
||||
|
||||
DynamicJsonDocument doc(1024);
|
||||
deserializeJson(doc, result.c_str());
|
||||
|
||||
JsonObject obj = doc.as<JsonObject>();
|
||||
JsonArray predictions = obj["predictions"].as<JsonArray>();
|
||||
|
||||
for(JsonVariant prediction : predictions)
|
||||
{
|
||||
String tag = prediction["tagName"].as<String>();
|
||||
float probability = prediction["probability"].as<float>();
|
||||
|
||||
char buff[32];
|
||||
sprintf(buff, "%s:\t%.2f%%", tag.c_str(), probability * 100.0);
|
||||
Serial.println(buff);
|
||||
}
|
||||
}
|
||||
|
||||
httpClient.end();
|
||||
}
|
||||
```
|
||||
|
||||
This code starts by declaring an `HTTPClient` - a class that contains methods to interact with REST APIs. It then connects the client to the prediction URL using the `WiFiClientSecure` instance that was set up with the Azure public key.
|
||||
|
||||
Once connected, it sends headers - information about the upcoming request that will be made against the REST API. The `Content-Type` header indicates the API call will send raw binary data, the `Prediction-Key` header passes the Custom Vision prediction key.
|
||||
|
||||
Next a POST request is made to the HTTP client, uploading a byte array. This will contain the JPEG image captured from the camera when this function is called.
|
||||
|
||||
> 💁 POST request are meant for sending data, and getting a response. There are other request types such as GET requests that retrieve data. GET requests are used by your web browser to load web pages.
|
||||
|
||||
The POST request returns a response status code. These are well-defined values, with 200 meaning **OK** - the POST request was successful.
|
||||
|
||||
> 💁 You can see all the response status codes in the [List of HTTP status codes page on Wikipedia](https://wikipedia.org/wiki/List_of_HTTP_status_codes)
|
||||
|
||||
If a 200 is returned, the result is read from the HTTP client. This is a text response from the REST API with the results of the prediction as a JSON document. The JSON is in the following format:
|
||||
|
||||
```jSON
|
||||
{
|
||||
"id":"45d614d3-7d6f-47e9-8fa2-04f237366a16",
|
||||
"project":"135607e5-efac-4855-8afb-c93af3380531",
|
||||
"iteration":"04f1c1fa-11ec-4e59-bb23-4c7aca353665",
|
||||
"created":"2021-06-10T17:58:58.959Z",
|
||||
"predictions":[
|
||||
{
|
||||
"probability":0.5582016,
|
||||
"tagId":"05a432ea-9718-4098-b14f-5f0688149d64",
|
||||
"tagName":"ripe"
|
||||
},
|
||||
{
|
||||
"probability":0.44179836,
|
||||
"tagId":"bb091037-16e5-418e-a9ea-31c6a2920f17",
|
||||
"tagName":"unripe"
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
The important part here is the `predictions` array. This contains the predictions, with one entry for each tag containing the tag name and the probability. The probabilities returned are floating point numbers from 0-1, with 0 being a 0% chance of matching the tag, and 1 being a 100% chance.
|
||||
|
||||
> 💁 Image classifiers will return the percentages for all tags that have been used. Each tag will have a probability that the image matches that tag.
|
||||
|
||||
This JSON is decoded, and the probabilities for each tag are sent to the serial monitor.
|
||||
|
||||
1. In the `buttonPressed` function, either replace the code that saves to the SD card with a call to `classifyImage`, or add it after the image is written, but **before** the buffer is deleted:
|
||||
|
||||
```cpp
|
||||
classifyImage(buffer, length);
|
||||
```
|
||||
|
||||
> 💁 If you replace the code that saves to the SD card you can clean up your code removing the `setupSDCard` and `saveToSDCard` functions.
|
||||
|
||||
1. Upload and run your code. Point the camera at some fruit and press the C button. You will see the output in the serial monitor:
|
||||
|
||||
```output
|
||||
Connecting to WiFi..
|
||||
Connected!
|
||||
Image captured
|
||||
Image read to buffer with length 8200
|
||||
ripe: 56.84%
|
||||
unripe: 43.16%
|
||||
```
|
||||
|
||||
You will be able to see the image that was taken, and these values in the **Predictions** tab in Custom Vision.
|
||||
|
||||
|
||||
|
||||
> 💁 You can find this code in the [code-classify/wio-terminal](code-classify/wio-terminal) folder.
|
||||
|
||||
😀 Your fruit quality classifier program was a success!
|
||||
|
||||
@ -1,9 +1,18 @@
|
||||
#
|
||||
# Build a fruit quality detector
|
||||
|
||||
## Instructions
|
||||
|
||||
Build the fruit quality detector!
|
||||
|
||||
Take everything you have learned so far and build the prototype fruit quality detector. Trigger image classification based off proximity using an AI model running on the edge, store the results of the classification in storage, and control an LED based off the ripeness of the fruit.
|
||||
|
||||
You should be able to piece this together using code you have previously written in all the lessons so far.
|
||||
|
||||
## Rubric
|
||||
|
||||
| Criteria | Exemplary | Adequate | Needs Improvement |
|
||||
| -------- | --------- | -------- | ----------------- |
|
||||
| | | | |
|
||||
| Configure all the services | Was able to set up an IoT Hub, Azure functions application and Azure storage | Was able to set up the IoT Hub, but not either the Azure functions app or Azure storage | Was unable to set up any internet IoT services |
|
||||
| Monitor proximity and send the data to IoT Hub if an object is closer than a pre-defined distance and trigger the camera via a command | Was able to measure distance and send a message to an IoT Hub when an object is close enough, and have a command send to trigger the camera | Was able to measure proximity and send to IoT Hub, but unable to get a command sent to the camera | Was unable to measure distance and send a message to IoT Hub, or trigger a command |
|
||||
| Capture an image, classify it and send the results to IoT Hub | Was able to capture an image, classify it using an edge device and send the results to IoT Hub | Was able to classify the image but not using an edge device, or was unable to send the results to IoT Hub | Was unable to classify an image |
|
||||
| Turn the LED on or off depending on the results of the classification using a command sent to a device | Was able to turn an LED on via a command if the fruit was unripe | Was able to send the command to the device but not control the LED | Was unable to send a command to control the LED |
|
||||
|
||||
@ -0,0 +1,11 @@
|
||||
import time
|
||||
from grove.i2c import Bus
|
||||
from rpi_vl53l0x.vl53l0x import VL53L0X
|
||||
|
||||
distance_sensor = VL53L0X(bus = Bus().bus)
|
||||
distance_sensor.begin()
|
||||
|
||||
while True:
|
||||
distance_sensor.wait_ready()
|
||||
print(f'Distance = {distance_sensor.get_distance()} mm')
|
||||
time.sleep(1)
|
||||
@ -0,0 +1,14 @@
|
||||
from counterfit_connection import CounterFitConnection
|
||||
CounterFitConnection.init('127.0.0.1', 5000)
|
||||
|
||||
import time
|
||||
|
||||
from counterfit_shims_rpi_vl53l0x.vl53l0x import VL53L0X
|
||||
|
||||
distance_sensor = VL53L0X()
|
||||
distance_sensor.begin()
|
||||
|
||||
while True:
|
||||
distance_sensor.wait_ready()
|
||||
print(f'Distance = {distance_sensor.get_distance()} mm')
|
||||
time.sleep(1)
|
||||
@ -0,0 +1,98 @@
|
||||
# Detect proximity - Raspberry Pi
|
||||
|
||||
In this part of the lesson, you will add a proximity sensor to your Raspberry Pi, and read distance from it.
|
||||
|
||||
## Hardware
|
||||
|
||||
The Raspberry Pi needs a proximity sensor.
|
||||
|
||||
The sensor you'll use is a [Grove Time of Flight distance sensor](https://www.seeedstudio.com/Grove-Time-of-Flight-Distance-Sensor-VL53L0X.html). This sensor uses a laser ranging module to detect distance. This sensor has a range of 10mm to 2000mm (1cm - 2m), and will report values in that range pretty accurately, with distances above 1000mm reported as 8109mm.
|
||||
|
||||
The laser rangefinder is on the back of the sensor, the opposite side to the Grove socket.
|
||||
|
||||
This is an I<sup>2</sup>C sensor.
|
||||
|
||||
### Connect the time of flight sensor
|
||||
|
||||
The Grove time of flight sensor can be connected to the Raspberry Pi.
|
||||
|
||||
#### Task - connect the time of flight sensor
|
||||
|
||||
Connect the time of flight sensor.
|
||||
|
||||
|
||||
|
||||
1. Insert one end of a Grove cable into the socket on the time of flight sensor. It will only go in one way round.
|
||||
|
||||
1. With the Raspberry Pi powered off, connect the other end of the Grove cable to one of the I<sup>2</sup>C sockets marked **I<sup>2</sup>C** on the Grove Base hat attached to the Pi. These sockets are on the bottom row, the opposite end to the GPI pins and next to the camera cable slot.
|
||||
|
||||
|
||||
|
||||
## Program the time of flight sensor
|
||||
|
||||
The Raspberry Pi can now be programmed to use the attached time of flight sensor.
|
||||
|
||||
### Task - program the time of flight sensor
|
||||
|
||||
Program the device.
|
||||
|
||||
1. Power up the Pi and wait for it to boot.
|
||||
|
||||
1. Open the `fruit-quality-detector` code in VS Code, either directly on the Pi, or connect via the Remote SSH extension.
|
||||
|
||||
1. Create a new file in this project called `distance-sensor.py`.
|
||||
|
||||
> 💁 An easy way to simulate multiple IoT devices is to do each in a different Python file, then run them at the same time.
|
||||
|
||||
1. Add the following code to this file:
|
||||
|
||||
```python
|
||||
import time
|
||||
|
||||
from grove.i2c import Bus
|
||||
from rpi_vl53l0x.vl53l0x import VL53L0X
|
||||
```
|
||||
|
||||
This imports the Grove I<sup>2</sup>C bus library, and a sensor library for the core sensor hardware built into the Grove time of flight sensor.
|
||||
|
||||
1. Below this, add the following code to access the sensor:
|
||||
|
||||
```python
|
||||
distance_sensor = VL53L0X(bus = Bus().bus)
|
||||
distance_sensor.begin()
|
||||
```
|
||||
|
||||
This code declares a distance sensor using the Grove I<sup>2</sup>C bus, then starts the sensor.
|
||||
|
||||
1. Finally, add an infinite loop to read distances:
|
||||
|
||||
```python
|
||||
while True:
|
||||
distance_sensor.wait_ready()
|
||||
print(f'Distance = {distance_sensor.get_distance()} mm')
|
||||
time.sleep(1)
|
||||
```
|
||||
|
||||
This code waits for a value to be ready to read from the sensor, then prints it to the console.
|
||||
|
||||
1. Run this code.
|
||||
|
||||
> 💁 Don't forget this file is called `distance-sensor.py`! Make sure to run this via Python, not `app.py`.
|
||||
|
||||
1. You will see distance measurements appear in the console. Position objects near the sensor and you will see the distance measurement:
|
||||
|
||||
```output
|
||||
pi@raspberrypi:~/fruit-quality-detector $ python3 distance_sensor.py
|
||||
Distance = 29 mm
|
||||
Distance = 28 mm
|
||||
Distance = 30 mm
|
||||
Distance = 151 mm
|
||||
```
|
||||
|
||||
The rangefinder is on the back of the sensor, so make sure you use hte correct side when measuring distance.
|
||||
|
||||
|
||||
|
||||
> 💁 You can find this code in the [code-proximity/pi](code-proximity/pi) folder.
|
||||
|
||||
😀 Your proximity sensor program was a success!
|
||||
@ -0,0 +1,107 @@
|
||||
# Detect proximity - Virtual IoT Hardware
|
||||
|
||||
In this part of the lesson, you will add a proximity sensor to your virtual IoT device, and read distance from it.
|
||||
|
||||
## Hardware
|
||||
|
||||
The virtual IoT device will use a simulated distance sensor.
|
||||
|
||||
In a physical IoT device you would use a sensor with a laser ranging module to detect distance.
|
||||
|
||||
### Add the distance sensor to CounterFit
|
||||
|
||||
To use a virtual distance sensor, you need to add one to the CounterFit app
|
||||
|
||||
#### Task - add the distance sensor to CounterFit
|
||||
|
||||
Add the distance sensor to the CounterFit app.
|
||||
|
||||
1. Open the `fruit-quality-detector` code in VS Code, and make sure the virtual environment is activated.
|
||||
|
||||
1. Install an additional Pip package to install a CounterFit shim that can talk to distance sensors by simulating the [rpi-vl53l0x Pip package](https://pypi.org/project/rpi-vl53l0x/), a Python package that interacts with [a VL53L0X time-of-flight distance sensor](https://wiki.seeedstudio.com/Grove-Time_of_Flight_Distance_Sensor-VL53L0X/). Make sure you are installing this from a terminal with the virtual environment activated.
|
||||
|
||||
```sh
|
||||
pip install counterfit-shims-rpi-vl53l0x
|
||||
```
|
||||
|
||||
1. Make sure the CounterFit web app is running
|
||||
|
||||
1. Create a distance sensor:
|
||||
|
||||
1. In the *Create sensor* box in the *Sensors* pane, drop down the *Sensor type* box and select *Distance*.
|
||||
|
||||
1. Leave the *Units* as `Millimeter`
|
||||
|
||||
1. This sensor is an I<sup>2</sup>C sensor, so set the address to `0x29`. If you used a physical VL53L0X sensor it would be hardcoded to this address.
|
||||
|
||||
1. Select the **Add** button to create the distance sensor
|
||||
|
||||
|
||||
|
||||
The distance sensor will be created and appear in the sensors list.
|
||||
|
||||
|
||||
|
||||
## Program the distance sensor
|
||||
|
||||
The virtual IoT device can now be programmed to use the simulated distance sensor.
|
||||
|
||||
### Task - program the time of flight sensor
|
||||
|
||||
1. Create a new file in the `fruit-quality-detector` project called `distance-sensor.py`.
|
||||
|
||||
> 💁 An easy way to simulate multiple IoT devices is to do each in a different Python file, then run them at the same time.
|
||||
|
||||
1. Start a connection to CounterFit with the following code:
|
||||
|
||||
```python
|
||||
from counterfit_connection import CounterFitConnection
|
||||
CounterFitConnection.init('127.0.0.1', 5000)
|
||||
```
|
||||
|
||||
1. Add the following code below this:
|
||||
|
||||
```python
|
||||
import time
|
||||
|
||||
from counterfit_shims_rpi_vl53l0x.vl53l0x import VL53L0X
|
||||
```
|
||||
|
||||
This imports the sensor library shim for the VL53L0X time of flight sensor.
|
||||
|
||||
1. Below this, add the following code to access the sensor:
|
||||
|
||||
```python
|
||||
distance_sensor = VL53L0X()
|
||||
distance_sensor.begin()
|
||||
```
|
||||
|
||||
This code declares a distance sensor, then starts the sensor.
|
||||
|
||||
1. Finally, add an infinite loop to read distances:
|
||||
|
||||
```python
|
||||
while True:
|
||||
distance_sensor.wait_ready()
|
||||
print(f'Distance = {distance_sensor.get_distance()} mm')
|
||||
time.sleep(1)
|
||||
```
|
||||
|
||||
This code waits for a value to be ready to read from the sensor, then prints it to the console.
|
||||
|
||||
1. Run this code.
|
||||
|
||||
> 💁 Don't forget this file is called `distance-sensor.py`! Make sure to run this via Python, not `app.py`.
|
||||
|
||||
1. You will see distance measurements appear in the console. Change the value in CounterFit to see this value change, or use random values.
|
||||
|
||||
```output
|
||||
(.venv) ➜ fruit-quality-detector python distance-sensor.py
|
||||
Distance = 37 mm
|
||||
Distance = 42 mm
|
||||
Distance = 29 mm
|
||||
```
|
||||
|
||||
> 💁 You can find this code in the [code-proximity/virtual-iot-device](code-proximity/virtual-iot-device) folder.
|
||||
|
||||
😀 Your proximity sensor program was a success!
|
||||
@ -0,0 +1,40 @@
|
||||
# Detect proximity - Wio Terminal
|
||||
|
||||
In this part of the lesson, you will add a proximity sensor to your Wio Terminal, and read distance from it.
|
||||
|
||||
## Hardware
|
||||
|
||||
The Wio Terminal needs a proximity sensor.
|
||||
|
||||
The sensor you'll use is a [Grove Time of Flight distance sensor](https://www.seeedstudio.com/Grove-Time-of-Flight-Distance-Sensor-VL53L0X.html). This sensor uses a laser ranging module to detect distance. This sensor has a range of 10mm to 2000mm (1cm - 2m), and will report values in that range pretty accurately, with distances above 1000mm reported as 8109mm.
|
||||
|
||||
The laser rangefinder is on the back of the sensor, the opposite side to the Grove socket.
|
||||
|
||||
This is an I<sup>2</sup>C sensor.
|
||||
|
||||
### Connect the time of flight sensor
|
||||
|
||||
The Grove time of flight sensor can be connected to the Wio Terminal.
|
||||
|
||||
#### Task - connect the time of flight sensor
|
||||
|
||||
Connect the time of flight sensor.
|
||||
|
||||
|
||||
|
||||
1. Insert one end of a Grove cable into the socket on the time of flight sensor. It will only go in one way round.
|
||||
|
||||
1. With the Wio Terminal disconnected from your computer or other power supply, connect the other end of the Grove cable to the left-hand side Grove socket on the Wio Terminal as you look at the screen. This is the socket closest to from the power button. This is a combined digital and I<sup>2</sup>C socket.
|
||||
|
||||
|
||||
|
||||
1. You can now connect the Wio Terminal to your computer.
|
||||
|
||||
## Program the time of flight sensor
|
||||
|
||||
The Wio Terminal can now be programmed to use the attached time of flight sensor.
|
||||
|
||||
### Task - program the time of flight sensor
|
||||
|
||||
1. Create a brand new Wio Terminal project using PlatformIO. Call this project `distance-sensor`. Add code in the `setup` function to configure the serial port.
|
||||
|
||||
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Reference in new issue