4. 用FOMO算法识别出来目标后想用它得到的中心坐标控制云台，但是云台只向下转动到最低就没反应了
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    • 用FOMO算法识别出来目标后想用它得到的中心坐标控制云台，但是云台只向下转动到最低就没反应了

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      用FOMO算法识别出来目标后想用它得到的中心坐标控制云台，但是云台只向下转动到最低就没反应了

      # Edge Impulse - OpenMV Object Detection Example

import sensor, image, time, os, tf, math, uos, gc, tv

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)
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.


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()

tv.init(triple_buffer=False) # 初始化tv
tv.channel(8) # 用于无线图传扩展板

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)])
    print(result)
    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])
        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-img.width()
            tilt_error = center_y-img.height()

            print(pan_error , pan_error)

            pan_output=pan_pid.get_pid(pan_error,1)
            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)

    tv.display(sensor.snapshot()) # 拍照并显示图像


    print(clock.fps(), "fps", end="\n\n")
  #  print('x %d\ty %d' % (math.floor(x + (w / 2)),  math.floor(y + (h / 2)))
      0