rvwk 发布的帖子

我们用了8种物品大小差距比较大，颜色也不宜。物品太小，摄像头识别不是很清楚，物品太大，摄像头看不全。如何提高识别精度。

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf
from pyb import Servo
import time
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.
s1 = Servo(1)
s2 = Servo(2)
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
n = 0
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 tf.classify(net, 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())
        #我知道写控制舵机的语句在这些，我分了11个类，我想知道当openmv识别到指定垃圾时，程序根据哪个变量来知道识别
        #的是哪种垃圾，因为我需要根据识别到的垃圾类型，来控制舵机转到相应的角度
        # 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")

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")

因为最新4.0.0的固件bug，我去烧了以往的固件版本，误烧了openmv2的挂件版本，导致现在电脑检测不到openmv的存在，同时也烧不进3.9.4的版本，烧3.9.4时一直卡在49%的进度，用的DFU模式烧录

@kidswong999 但这代码跟教程上一样啊，教程上能行，为啥我们这就不能行

下面这个代码也是一样，舵机就只转动一下

from pyb import Servo
import time


#180°的舵机
s1 = Servo(1) # servo on position 1 (P7)
s1.angle(0) # move to 0 degrees
time.sleep(1000)
while(1):
    s1.angle(90) # move to 90 degrees
    time.sleep(600)
    s1.angle(45) # move to 180 degrees
    time.sleep(0)

    s1.angle(-90) # move to 90 degrees
    time.sleep(600)
    s1.angle(-45) # move to -90 degrees
    time.sleep(600)
    s1.angle(0) # move to 0 degrees
    time.sleep(1000)
    #s1.angle(-60, 1500) # move to -60 degrees in 1500ms
    #s1.speed(50) # for continuous rotation servos

s1 = Servo(1)
s1.speed(50)
while(1):
    s1.angle(90)
    time.sleep(100)
    s1.angle(-60,1000)
    time.sleep(1000)
    S1.angle(0)