|
|
瞎逛github发现的：https://github.com/01org/Intel-Pattern-Matching-Technology
intel对curie上的机器学习功能的叫法是“模式匹配引擎”，这个库也叫curie模型匹配引擎库
该库中自动的机器学习识别手写字母的示例：
[mw_shl_code=cpp,true]/*
* This example demonstrates using the pattern matching engine (CuriePME)
* to classify streams of accelerometer data from CurieIMU.
*
* First, the sketch will prompt you to draw some letters in the air (just
* imagine you are writing on an invisible whiteboard, using your board as the
* pen), and the IMU data from these motions is used as training data for the
* PME. Once training is finished, you can keep drawing letters and the PME
* will try to guess which letter you are drawing.
*
* This example requires a button to be connected to digital pin 4
* https://www.arduino.cc/en/Tutorial/Button
*
* NOTE: For best results, draw big letters, at least 1-2 feet tall.
*
* Copyright (c) 2016 Intel Corporation. All rights reserved.
* See license notice at end of file.
*/
#include "CurieIMU.h"
#include "CuriePME.h"
/* This controls how many times a letter must be drawn during training.
* Any higher than 4, and you may not have enough neurons for all 26 letters
* of the alphabet. Lower than 4 means less work for you to train a letter,
* but the PME may have a harder time classifying that letter. */
const unsigned int trainingReps = 4;
/* Increase this to 'A-Z' if you like-- it just takes a lot longer to train */
const unsigned char trainingStart = 'A';
const unsigned char trainingEnd = 'F';
/* The input pin used to signal when a letter is being drawn- you'll
* need to make sure a button is attached to this pin */
const unsigned int buttonPin = 4;
/* Sample rate for accelerometer */
const unsigned int sampleRateHZ = 200;
/* No. of bytes that one neuron can hold */
const unsigned int vectorNumBytes = 128;
/* Number of processed samples (1 sample == accel x, y, z)
* that can fit inside a neuron */
const unsigned int samplesPerVector = (vectorNumBytes / 3);
/* This value is used to convert ASCII characters A-Z
* into decimal values 1-26, and back again. */
const unsigned int upperStart = 0x40;
const unsigned int sensorBufSize = 2048;
const int IMULow = -32768;
const int IMUHigh = 32767;
void setup()
{
Serial.begin(9600);
while(!Serial);
pinMode(buttonPin, INPUT);
/* Start the IMU (Intertial Measurement Unit) */
CurieIMU.begin();
/* Start the PME (Pattern Matching Engine) */
CuriePME.begin();
CurieIMU.setAccelerometerRate(sampleRateHZ);
CurieIMU.setAccelerometerRange(2);
trainLetters();
Serial.println("Training complete. Now, draw some letters (remember to ");
Serial.println("hold the button) and see if the PME can classify them.");
}
void loop ()
{
byte vector[vectorNumBytes];
unsigned int category;
char letter;
/* Record IMU data while button is being held, and
* convert it to a suitable vector */
readVectorFromIMU(vector);
/* Use the PME to classify the vector, i.e. return a category
* from 1-26, representing a letter from A-Z */
category = CuriePME.classify(vector, vectorNumBytes);
if (category == CuriePME.noMatch) {
Serial.println("Don't recognise that one-- try again.");
} else {
letter = category + upperStart;
Serial.println(letter);
}
}
/* Simple "moving average" filter, removes low noise and other small
* anomalies, with the effect of smoothing out the data stream. */
byte getAverageSample(byte samples[], unsigned int num, unsigned int pos,
unsigned int step)
{
unsigned int ret;
unsigned int size = step * 2;
if (pos < (step * 3) || pos > (num * 3) - (step * 3)) {
ret = samples[pos];
} else {
ret = 0;
pos -= (step * 3);
for (unsigned int i = 0; i < size; ++i) {
ret += samples[pos - (3 * i)];
}
ret /= size;
}
return (byte)ret;
}
/* We need to compress the stream of raw accelerometer data into 128 bytes, so
* it will fit into a neuron, while preserving as much of the original pattern
* as possible. Assuming there will typically be 1-2 seconds worth of
* accelerometer data at 200Hz, we will need to throw away over 90% of it to
* meet that goal!
*
* This is done in 2 ways:
*
* 1. Each sample consists of 3 signed 16-bit values (one each for X, Y and Z).
* Map each 16 bit value to a range of 0-255 and pack it into a byte,
* cutting sample size in half.
*
* 2. Undersample. If we are sampling at 200Hz and the button is held for 1.2
* seconds, then we'll have ~240 samples. Since we know now that each
* sample, once compressed, will occupy 3 of our neuron's 128 bytes
* (see #1), then we know we can only fit 42 of those 240 samples into a
* single neuron (128 / 3 = 42.666). So if we take (for example) every 5th
* sample until we have 42, then we should cover most of the sample window
* and have some semblance of the original pattern. */
void undersample(byte samples[], int numSamples, byte vector[])
{
unsigned int vi = 0;
unsigned int si = 0;
unsigned int step = numSamples / samplesPerVector;
unsigned int remainder = numSamples - (step * samplesPerVector);
/* Centre sample window */
samples += (remainder / 2) * 3;
for (unsigned int i = 0; i < samplesPerVector; ++i) {
for (unsigned int j = 0; j < 3; ++j) {
vector[vi + j] = getAverageSample(samples, numSamples, si + j, step);
}
si += (step * 3);
vi += 3;
}
}
void readVectorFromIMU(byte vector[])
{
byte accel[sensorBufSize];
int raw[3];
unsigned int samples = 0;
unsigned int i = 0;
/* Wait until button is pressed */
while (digitalRead(buttonPin) == LOW);
/* While button is being held... */
while (digitalRead(buttonPin) == HIGH) {
if (CurieIMU.accelDataReady()) {
CurieIMU.readAccelerometer(raw[0], raw[1], raw[2]);
/* Map raw values to 0-255 */
accel = (byte) map(raw[0], IMULow, IMUHigh, 0, 255);
accel[i + 1] = (byte) map(raw[1], IMULow, IMUHigh, 0, 255);
accel[i + 2] = (byte) map(raw[2], IMULow, IMUHigh, 0, 255);
i += 3;
++samples;
/* If there's not enough room left in the buffers
* for the next read, then we're done */
if (i + 3 > sensorBufSize) {
break;
}
}
}
undersample(accel, samples, vector);
}
void trainLetter(char letter, unsigned int repeat)
{
unsigned int i = 0;
while (i < repeat) {
byte vector[vectorNumBytes];
if (i) Serial.println("And again...");
readVectorFromIMU(vector);
CuriePME.learn(vector, vectorNumBytes, letter - upperStart);
Serial.println("Got it!");
delay(1000);
++i;
}
}
void trainLetters()
{
for (char i = trainingStart; i <= trainingEnd; ++i) {
Serial.print("Hold down the button and draw the letter '");
Serial.print(String(i) + "' in the air. Release the button as soon ");
Serial.println("as you are done.");
trainLetter(i, trainingReps);
Serial.println("OK, finished with this letter.");
delay(2000);
}
}
/*
* This library is free software; you can redistribute it and/or
* modify it under the terms of the GNU Lesser General Public
* License as published by the Free Software Foundation; either
* version 2.1 of the License, or (at your option) any later version.
*
* This library is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public
* License along with this library; if not, write to the Free Software
* Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
*/[/mw_shl_code]
|
|
[复制链接]
发表于 2016-12-28 09:49
楼主