openmv如果要做激光扫描三维重建测量物体尺寸要怎么实现

能帮修复整一个完整代码吗

import sensor, image, time
from pyb import Pin, LED

# 初始化相机、LED灯和引脚
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
led = LED(3)
pin1 = Pin('P1', Pin.OUT_PP)

# 预设颜色阈值
green_threshold = (72, 100, -1, 82, -11, 101)
red_threshold = (0, 50, 20, 80, -10, 40)

while True:
    
    # 获取图像并处理
    img = sensor.snapshot()
    
    # 进行颜色分割
    green_mask = img.color_threshold(green_threshold)
    red_mask = img.color_threshold(red_threshold)
    
    # 对颜色分割进行形态学操作
    green_mask.erode(1)
    green_mask.dilate(2)
    red_mask.erode(1)
    red_mask.dilate(2)
    
    # 对颜色分割进行轮廓检测
    green_blobs = green_mask.find_blobs(min_area=500)
    red_blobs = red_mask.find_blobs(min_area=500)
    
    # 数量统计到count变量中
    count = len(green_blobs) + len(red_blobs)
    
    # 判断数量是否大于等于2，输出电平和点亮LED蓝灯
    if count >= 2:
        pin1.low()
        led.on()
    else:
        pin1.high()
        led.off()

为什么这段代码里面green_mask = img.color_threshold(green_threshold)段会报错找不到原因

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, 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.

net = None
labels = None

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:
    print(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) + ')')

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        img.draw_rectangle(obj.rect())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))

        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

    print(clock.fps(), "fps")

@kidswong999 在 怎么在这个代码里添加一个线段数量值然后达到线段数量比如识别5条线段后输出到一个引脚作低电平信号输出 中说：

enable_lens_corr = False

import sensor, image, time

sensor.reset()
sensor.set_pixformat(sensor.RGB565) # 灰度更快
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()

pin1 = Pin('P1', Pin.OUT_PP, Pin.PULL_NONE)
pin1.value(0)

while(True):
clock.tick()
img = sensor.snapshot()
if enable_lens_corr: img.lens_corr(1.8) # for 2.8mm lens...
segments = img.find_line_segments(merge_distance = 0, max_theta_diff = 5)
result_segments = []

for l in segments:
    if l.length() > 50:
        result_segments.append(l)

if len(result_segments) > 5:
    pin1.value(1)
else:
    pin1.value(0)
for l in result_segments:
    img.draw_line(l.line(), color = (255, 0, 0))
    # print(l)

print("FPS %f" % clock.fps())

现在是可以过滤掉短的了 ，然后有一个就是 这个有办法过滤掉，好的线段的跳动吗？跳动变动，输出信号不稳定，

@kidswong999 在 怎么在这个代码里添加一个线段数量值然后达到线段数量比如识别5条线段后输出到一个引脚作低电平信号输出 中说：

result_segments = []

for l in segments:
if l.length() > 50:
result_segments.append(l)

这段代码放上去过滤不了 短的线 没效果

@kidswong999 在吗 在吗过滤短的线条

@kidswong999 你好！在吗？怎么过滤掉那个比较短的线。或者说只识别边缘的线段就可以。可以这样设置吗

@kidswong999 哦 放进去了 就是还有很多出现很短的线 要怎么屏蔽过滤

@kidswong999 非常感谢！要怎么插入到那段代码上呢，刚贴进去，报错