OpenMV：20追踪其他物体的云台

首先获得目标物体的x,y坐标，然后通过目标物体的xy坐标来控制我们云台的两个舵机的pid运动

无论追踪什么物体，都是通过物体的x,y坐标来控制云台的运动，对于云台的舵机来说，它只知道传给它的是x,y坐标，并不知道OpenMV传给它的是小球的xy坐标还是人脸的xy坐标

所以我们只需要修改main.py中的函数即可

追踪人脸的云台

搜索函数：objects = img.find_features(face_cascade,threshold ,scale)

记得要载入haar算子，并且寻找小球的函数find_blob()和寻找人脸的函数find_feartures()的返回值是不一样的！

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)

pan_servo.calibration(500,2500,500)
tilt_servo.calibration(500,2500,500)

red_threshold  = (13, 49, 18, 61, 6, 47)

pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)  # 插入：设置对比度
sensor.set_gainceiling(16)  # 插入：设置增益

# 由于云台上的OpenMV是反着装的，而检测人脸的haar算子是正着看的，所以就必须把照片倒过来才能检测成功
    # 我们不用那么麻烦：直接设置图像为镜像模式，是垂直方向的翻转即可
sensor.set_vflip(True)

sensor.set_pixformat(sensor.GRAYSCALE) # 人脸识别最好采用灰度图模式
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.


# 加载人脸的haar算子
face_cascade = image.HaarCascade("frontalface" , stages = 25)

def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob


while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # 截取一张图片
    
    # 截取一张图片后，对我们的图像进行find_features()
    # objects是返回的人脸矩形框(x,y,w,h)
    objects = img.find_features(face_cascade,threshold = 0.75,scale = 1.35)
    
    
    if objects: # 如果识别到人脸——>找到视野中最大的人脸
        max_object = find_max(objects)
        pan_error = max_object[0] +  max_object[2]/2 - img.width()/2
        tilt_error =  max_object[1] +  max_object[3]/2  - img.height()/2

        print("pan_error: ", pan_error)

        img.draw_rectangle( max_object.rect()) # rect
        img.draw_cross(int( max_object[0] +  max_object[2]/2),int( max_object[1] +  max_object[3]/2)) # cx, cy

        pan_output=pan_pid.get_pid(pan_error,1)/2
        tilt_output=tilt_pid.get_pid(tilt_error,1)
        print("pan_output",pan_output)
        pan_servo.angle(pan_servo.angle()+pan_output)
        tilt_servo.angle(tilt_servo.angle()-tilt_output)

追踪AprilTags的云台

搜索函数： objects = img.find_apriltags()

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)

pan_servo.calibration(500,2500,500)
tilt_servo.calibration(500,2500,500)

red_threshold  = (13, 49, 18, 61, 6, 47)

pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)  # 插入：设置对比度
sensor.set_gainceiling(16)  # 插入：设置增益

sensor.set_pixformat(sensor.GRAYSCALE) # 人脸识别最好采用灰度图模式
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.


# 加载人脸的haar算子
face_cascade = image.HaarCascade("frontalface" , stages = 25)

def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob


while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # 截取一张图片
    
    # 截取一张图片后，对我们的图像进行find_apriltags()
    # 函数的返回值是一个AprilTag的对象列表
    objects = img.find_apriltags()
    
    
    if objects: # 如果识别到人脸——>找到视野中最大的人脸
        max_object = find_max(objects)
        pan_error = max_object[0] +  max_object[2]/2 - img.width()/2
        tilt_error =  max_object[1] +  max_object[3]/2  - img.height()/2

        print("pan_error: ", pan_error)

        img.draw_rectangle( max_object.rect()) # rect
        img.draw_cross(int( max_object[0] +  max_object[2]/2),int( max_object[1] +  max_object[3]/2)) # cx, cy

        pan_output=pan_pid.get_pid(pan_error,1)/2
        tilt_output=tilt_pid.get_pid(tilt_error,1)
        print("pan_output",pan_output)
        pan_servo.angle(pan_servo.angle()+pan_output)
        tilt_servo.angle(tilt_servo.angle()-tilt_output)

追踪圆形的云台

搜索函数：objects = img.find_circles()

注意要用到畸变矫正，加在我们的sensor.snapshot()之后img = sensor.snapshot().lens_corr(1,8)

对于圆来说，比较大小就不用if blob[2]*blob[3] > max_size:了，而是直接比较半径cirle[2]即可

import sensor, image, time

from pid import PID
from pyb import Servo

pan_servo=Servo(1)
tilt_servo=Servo(2)

pan_servo.calibration(500,2500,500)
tilt_servo.calibration(500,2500,500)

red_threshold  = (13, 49, 18, 61, 6, 47)

pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输，使用这个PID
#pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
#tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID

sensor.reset() # Initialize the camera sensor.
sensor.set_contrast(1)  # 插入：设置对比度
sensor.set_gainceiling(16)  # 插入：设置增益

sensor.set_pixformat(sensor.GRAYSCALE) # 人脸识别最好采用灰度图模式
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(10) # Let new settings take affect.
sensor.set_auto_whitebal(False) # turn this off.
clock = time.clock() # Tracks FPS.




def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[2] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[2]
    return max_blob


while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot().lens_corr(1,8)  # 截取一张图片的同时进行畸变矫正 
    
    # 截取一张图片后，对我们的图像进行find_apriltags()
    # 函数的返回值是一个圆的圆心在摄像头里的xy坐标以及半径(x,y,r)
    objects = img.find_circles(threshold = 3500,x_margin = 10,y_margin = 10,r_margin = 10,r_min = 2,r_max = 100,r_step = 2)
    
    
    if objects: # 如果识别到人脸——>找到视野中最大的人脸
        max_object = find_max(objects)
        pan_error = max_object[0]  - img.width()/2    # 圆心的x坐标即为object[0]
        tilt_error =  max_object[1]   - img.height()/2    # 圆心的y坐标即为object[1]

        print("pan_error: ", pan_error)

        img.draw_rectangle( max_object.rect()) # rect
        # 直接改为画圆而非矩形
        img.draw_circle(max_object[0],max_object[1],max_object[2],color = (255,0,0))
        img.draw_cross(int(max_object[0]),int(max_object[1]) # 在圆形(x,y)处画十字

        pan_output=pan_pid.get_pid(pan_error,1)/2
        tilt_output=tilt_pid.get_pid(tilt_error,1)
        print("pan_output",pan_output)
        pan_servo.angle(pan_servo.angle()+pan_output)
        tilt_servo.angle(tilt_servo.angle()-tilt_output)