4. openmv想实现目标追踪
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    • openmv想实现目标追踪

    • M
       

      # 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

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