“MicroMV 人脸识别”的版本间的差异

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=='''基本原理'''==
 
=='''基本原理'''==
 
*MicroMV捕捉人脸获取人脸坐标
 
*MicroMV捕捉人脸获取人脸坐标
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*通过pid.py算法计算出舵机将要转动的角度，并通过串口发送给microduino
 
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*microduino接收数据后控制舵机
 
  
 
=='''MicroMV的代码准备'''==
 
=='''MicroMV的代码准备'''==
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把pid.py复制到MicroMV生成的U盘空间中<br />
 
−
pid.py:
 
 
<source lang="py">
 
<source lang="py">
−
from pyb import millis
+
# Face Detection Example
−
from math import pi, isnan
+
#
−
class PID:
+
# This example shows off the built-in face detection feature of the OpenMV Cam.
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    _kp = _ki = _kd = _integrator = _imax = 0
+
#
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    _last_error = _last_derivative = _last_t = 0
+
# Face detection works by using the Haar Cascade feature detector on an image. A
−
    _RC = 1/(2 * pi * 20)
+
# Haar Cascade is a series of simple area contrasts checks. For the built-in
−
    def __init__(self, p=0, i=0, d=0, imax=0):
+
# frontalface detector there are 25 stages of checks with each stage having
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        self._kp = float(p)
+
# hundreds of checks a piece. Haar Cascades run fast because later stages are
−
        self._ki = float(i)
+
# only evaluated if previous stages pass. Additionally, your OpenMV Cam uses
−
        self._kd = float(d)
+
# a data structure called the integral image to quickly execute each area
−
        self._imax = abs(imax)
+
# contrast check in constant time (the reason for feature detection being
−
        self._last_derivative = float('nan')
+
# grayscale only is because of the space requirment for the integral image).
  
−
    def get_pid(self, error, scaler):
 
−
        tnow = millis()
 
−
        dt = tnow - self._last_t
 
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        output = 0
 
−
        if self._last_t == 0 or dt > 1000:
 
−
            dt = 0
 
−
            self.reset_I()
 
−
        self._last_t = tnow
 
−
        delta_time = float(dt) / float(1000)
 
−
        output += error * self._kp
 
−
        if abs(self._kd) > 0 and dt > 0:
 
−
            if isnan(self._last_derivative):
 
−
                derivative = 0
 
−
                self._last_derivative = 0
 
−
            else:
 
−
                derivative = (error - self._last_error) / delta_time
 
−
            derivative = self._last_derivative + \
 
−
                                    ((delta_time / (self._RC + delta_time)) * \
 
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                                        (derivative - self._last_derivative))
 
−
            self._last_error = error
 
−
            self._last_derivative = derivative
 
−
            output += self._kd * derivative
 
−
        output *= scaler
 
−
        if abs(self._ki) > 0 and dt > 0:
 
−
            self._integrator += (error * self._ki) * scaler * delta_time
 
−
            if self._integrator < -self._imax: self._integrator = -self._imax
 
−
            elif self._integrator > self._imax: self._integrator = self._imax
 
−
            output += self._integrator
 
−
        return output
 
−
    def reset_I(self):
 
−
        self._integrator = 0
 
−
        self._last_derivative = float('nan')
 
−
</source>
 
−
把face_detection1024.py替换到MicroMV生成的U盘空间中的main.py做为主程序<br />
 
−
face_detection1024.py:
 
−
<source lang="py">
 
 
import sensor, time, image
 
import sensor, time, image
−
from pyb import UART
+
 
−
from pid import PID
 
−
from pyb import LED
 
−
import json
 
−
red_led  = LED(1)
 
−
green_led = LED(2)
 
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blue_led  = LED(3)
 
−
ir_led    = LED(4)
 
−
pan_pid = PID(p=0.42, i=0.01, imax=90)
 
−
tilt_pid = PID(p=0.22, i=0.005, imax=90)
 
−
pan_servo_pos=90
 
−
tilt_servo_pos=90
 
−
pan_center = 60
 
−
tilt_center = 40
 
 
# Reset sensor
 
# Reset sensor
 
sensor.reset()
 
sensor.reset()
 +
 
# Sensor settings
 
# Sensor settings
 
sensor.set_contrast(1)
 
sensor.set_contrast(1)
 
sensor.set_gainceiling(16)
 
sensor.set_gainceiling(16)
 
# HQVGA and GRAYSCALE are the best for face tracking.
 
# HQVGA and GRAYSCALE are the best for face tracking.
−
sensor.set_framesize(sensor.HQQVGA)
+
sensor.set_framesize(sensor.HQVGA)
 
sensor.set_pixformat(sensor.GRAYSCALE)
 
sensor.set_pixformat(sensor.GRAYSCALE)
−
sensor.set_brightness(-3)
+
 
 
# Load Haar Cascade
 
# Load Haar Cascade
 
# By default this will use all stages, lower satges is faster but less accurate.
 
# By default this will use all stages, lower satges is faster but less accurate.
 
face_cascade = image.HaarCascade("frontalface", stages=25)
 
face_cascade = image.HaarCascade("frontalface", stages=25)
 
print(face_cascade)
 
print(face_cascade)
 +
 
# FPS clock
 
# FPS clock
 
clock = time.clock()
 
clock = time.clock()
−
uart = UART(4, 115200)
+
 
 
while (True):
 
while (True):
 
     clock.tick()
 
     clock.tick()
 +
 
     # Capture snapshot
 
     # Capture snapshot
 
     img = sensor.snapshot()
 
     img = sensor.snapshot()
 +
 
     # Find objects.
 
     # Find objects.
 
     # Note: Lower scale factor scales-down the image more and detects smaller objects.
 
     # Note: Lower scale factor scales-down the image more and detects smaller objects.
 
     # Higher threshold results in a higher detection rate, with more false positives.
 
     # Higher threshold results in a higher detection rate, with more false positives.
 
     objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
 
     objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)
 +
 
     # Draw objects
 
     # Draw objects
 
     for r in objects:
 
     for r in objects:
−
        red_led.on()
 
 
         img.draw_rectangle(r)
 
         img.draw_rectangle(r)
−
        pan_center = int(r[0]+r[2]/2)
 
−
        tilt_center = int(r[1]+r[3]/2)
 
−
        img.draw_cross(pan_center, tilt_center)
 
−
        pan_error = pan_center-img.width()/2
 
−
        tilt_error = tilt_center-img.height()/2
 
−
        pan_output=pan_pid.get_pid(pan_error,1)/3
 
−
        tilt_output=tilt_pid.get_pid(tilt_error,1)
 
−
        pan_servo_pos=int(pan_servo_pos-pan_output)
 
−
        tilt_servo_pos=int(tilt_servo_pos+tilt_output)
 
−
        if pan_servo_pos>=170:
 
−
          pan_servo_pos=170
 
−
        if pan_servo_pos<=10:
 
−
          pan_servo_pos=10
 
−
        if tilt_servo_pos>=160:
 
−
          tilt_servo_pos=160
 
−
        if tilt_servo_pos<=30:
 
−
          tilt_servo_pos=30
 
−
        output_str="[%d,%d,%d]" % (pan_servo_pos,tilt_servo_pos,r[2])
 
−
        print('you send:',output_str)
 
−
        uart.write(output_str+'\n')
 
−
        red_led.off()
 
−
</source>
 
−
IDE运行效果为<br/>
 
−
[[File:microMVFaceDetector1.png||600px|left]]<br style="clear: left"/>
 
−
屏幕中有人脸就会用方框注明
 
  
−
=='''Microduino的代码准备'''==
+
    # Print FPS.
−
<source lang="py">
+
    # Note: Actual FPS is higher, streaming the FB makes it slower.
−
#include <Servo.h>
+
     print(clock.fps())
−
#define servoPin1  A2
 
−
#define servoPin2  A3
 
−
Servo myservo1;
 
−
Servo myservo2;
 
−
String msg = "";
 
−
int16_t data0, data1, data2, data3;
 
−
void setup() {
 
−
  pinMode(A0, INPUT);
 
−
  Serial.begin(115200);
 
−
  myservo1.attach(servoPin1);
 
−
  myservo2.attach(servoPin2);
 
−
  myservo1.write(90);
 
−
  myservo2.write(90);
 
−
}
 
−
void loop() {
 
−
  if (Serial.available() > 0) {
 
−
    msg = Serial.readStringUntil('\n');
 
−
     sscanf(msg.c_str(), "[%d,%d,%d]", &data0, &data1, &data2);
 
−
    Serial.print(data0);
 
−
    Serial.print(" ,");
 
−
    Serial.print(data1);
 
−
    Serial.print(" ,");
 
−
    Serial.println(data2);
 
−
    myservo1.write(data0);
 
−
    myservo2.write(data1);
 
−
    delay(1);
 
−
  }
 
−
}
 
 
</source>
 
</source>
 +
效果如下：
 +
[[File:microMVGettingStart8.png||600px|center]]

2018年5月16日 (三) 05:25的版本

基本原理

  • MicroMV捕捉人脸获取人脸坐标

MicroMV的代码准备

# Face Detection Example
#
# This example shows off the built-in face detection feature of the OpenMV Cam.
#
# Face detection works by using the Haar Cascade feature detector on an image. A
# Haar Cascade is a series of simple area contrasts checks. For the built-in
# frontalface detector there are 25 stages of checks with each stage having
# hundreds of checks a piece. Haar Cascades run fast because later stages are
# only evaluated if previous stages pass. Additionally, your OpenMV Cam uses
# a data structure called the integral image to quickly execute each area
# contrast check in constant time (the reason for feature detection being
# grayscale only is because of the space requirment for the integral image).

import sensor, time, image

# Reset sensor
sensor.reset()

# Sensor settings
sensor.set_contrast(1)
sensor.set_gainceiling(16)
# HQVGA and GRAYSCALE are the best for face tracking.
sensor.set_framesize(sensor.HQVGA)
sensor.set_pixformat(sensor.GRAYSCALE)

# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
face_cascade = image.HaarCascade("frontalface", stages=25)
print(face_cascade)

# FPS clock
clock = time.clock()

while (True):
    clock.tick()

    # Capture snapshot
    img = sensor.snapshot()

    # Find objects.
    # Note: Lower scale factor scales-down the image more and detects smaller objects.
    # Higher threshold results in a higher detection rate, with more false positives.
    objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.25)

    # Draw objects
    for r in objects:
        img.draw_rectangle(r)

    # Print FPS.
    # Note: Actual FPS is higher, streaming the FB makes it slower.
    print(clock.fps())

效果如下：

MicroMVGettingStart8.png
取自“http:///https//wiki.microduino.cn/index.php?title=MicroMV_人脸识别&oldid=25826”