而且换了6.6V电源给openmv供电还是不行

二维云台我先接了一个舵机，P7，VC，GND，使用代码，舵机不动，请问是什么问题，应该如何解决？
连接两个舵机怎么接线？可以用绿色拓展版直接插引脚吗？图二所示!

# 舵机控制例子
#
# 这个例子展示了如何使用OpenMV来控制舵机

import time
from pyb import Servo

s1 = Servo(1) # P7
s2 = Servo(2) # P8
s3 = Servo(3) # P9

while(True):
    for i in range(1000):
        s1.pulse_width(1000 + i)
        s2.pulse_width(1999 - i)
        s3.pulse_width(1000 + i)
        time.sleep_ms(10)
    for i in range(1000):
        s1.pulse_width(1999 - i)
        s2.pulse_width(1000 + i)
        s3.pulse_width(1999 - i)
        time.sleep_ms(10)

可是我只识别了两种颜色啊

# Edge Impulse - OpenMV Object Detection Example

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

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.

#导入文件
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), #red
    (  0, 255,   0), #green
    (255, 255,   0), #yellow
    (  0,   0, 255), #blue
    (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])
        qty=len(detection_list)
        print ("qty=%d"%qty)
        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)

    print(clock.fps(), "fps", end="\n\n")