pan_output=pan_pid.get_pid(pan_error,1)
pan_servo.angle(int(pan_servo.angle()+pan_output))
print(pan_output)
print(int(pan_servo.angle()+pan_output))
angle=int(pan_servo.angle()+pan_output)
uart.write(angle)
如果我想用串口通信穿这个角度的话应该怎么办我这样写他会报错：“TypeError: object with buffer protocol required”
如果我知道一个物体的x，y坐标，如何控制云台追踪，可以只用控制底部的舵机，应该怎么改代码
pan_error = max_blob.cx()-img.width()/2
tilt_error = max_blob.cy()-img.height()/2
print("pan_error: ", pan_error)
img.draw_rectangle(max_blob.rect()) # rect
img.draw_cross(max_blob.cx(), max_blob.cy()) # cx, cy
pan_output=pan_pid.get_pid(pan_error,1)/2
tilt_output=tilt_pid.get_pid(tilt_error,1)
pan_servo.angle(pan_servo.angle()+pan_output)
tilt_servo.angle(tilt_servo.angle()-tilt_output)
while(True):
clock.tick()
img = sensor.snapshot()
# detect() returns all objects found in the image (splitted out per class already)
# we skip class index 0, as that is the background, and then draw circles of the center
# of our objects
result=net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])#检测目标
print(result)
if (state==1):
for i, detection_list in enumerate(result):
if (i == 0): continue # background class
if (len(detection_list) == 0): continue # no detections for this class?
#print("********** %s **********" % labels[i])
#print(i)
if (i==2):#识别草莓
for d in detection_list:
[x, y, w, h] = d.rect()
center_x = math.floor(x + (w / 2))#中心坐标的计算
center_y = math.floor(y + (h / 2))
img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
pan_error = center_x
tilt_error = center_y
if(center_x>0 and center_y>0)
pan_output=pan_pid.get_pid(pan_error,1)/2
tilt_output=tilt_pid.get_pid(tilt_error,1)
pan_servo.angle(pan_servo.angle()+pan_output)
tilt_servo.angle(tilt_servo.angle()-tilt_output)
else ：#识别南瓜
for d in detection_list:
[x, y, w, h] = d.rect()
center_x = math.floor(x + (w / 2))
center_y = math.floor(y + (h / 2))
#print('x %d\ty %d' % (center_x, center_y))
img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
if(center_x>0 and center_y>0)
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)
我这里会报错然后我想问一下我这里哪里错了，还有就是我这种代码思路正确吗，报错的时if（i==2）哪里，然后我先把if（i==2）注释掉之后， pan_output=pan_pid.get_pid(pan_error,1)/2这里也报错了
# Edge Impulse - OpenMV Object Detection Example
import sensor, image, time, os, tf, math, uos, gc
from pid import PID
from pyb import Servo
from pyb import UART
import json
pan_servo=Servo(1) #p7
tilt_servo=Servo(2) #p8
#设置对应的脉宽，分别对应为：允许的最小脉宽：500、允许的最大脉冲：2500、中心/零位置对应的脉宽：500
pan_servo.calibration(500,2500,500)
tilt_servo.calibration(500,2500,500)
red_threshold = (11, 100, 6, 127, -128, 127)
#pid包的函数，设置舵机的pid参数，一般只需要调整p,如果云台抖动的很厉害（速度过快），可以将p调小
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() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
sensor.set_vflip(True)
net = None
labels = None
min_confidence = 0.5
try:
# load the model, alloc the model file on the heap if we have at least 64K free after loading
net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
try:
labels = [line.rstrip('\n') for line in open("labels.txt")]
except Exception as e:
raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
(255, 0, 0),
( 0, 255, 0),
(255, 255, 0),
( 0, 0, 255),
(255, 0, 255),
( 0, 255, 255),
(255, 255, 255),
]
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# detect() returns all objects found in the image (splitted out per class already)
# we skip class index 0, as that is the background, and then draw circles of the center
# of our objects
for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
if (i == 0): continue # background class
if (len(detection_list) == 0): continue # no detections for this class?
print("********** %s **********" % labels[i])
for d in detection_list:
[x, y, w, h] = d.rect()
center_x = math.floor(x + (w / 2))
center_y = math.floor(y + (h / 2))
print('x %d\ty %d' % (center_x, center_y))
img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
pan_error=center_x
tilt_error=center_y
pan_output=pan_pid.get_pid(pan_error,1)/2
tilt_output=tilt_pid.get_pid(tilt_error,1)
pan_servo.angle(pan_servo.angle()+pan_output)
tilt_servo.angle(tilt_servo.angle()-tilt_output)
print(clock.fps(), "fps", end="\n\n")
请问一下这个能控制云台进行追踪吗，我上电之后发现舵机会没有规律乱动，请问一下该怎么改正
我使用edge impluse训练出来的目标检测，如果还要控制云台的话，是不是直接在生成的ei_object_detection.py中编写其他部分模块的代码，就相当于把这个.py文件当成main.py来用