4. 我们需要比9600更慢的传输速率
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    • 我们需要比9600更慢的传输速率

    • I

      回复: 如何加延时函数

      当openmv识别到色块时，需要让它一秒钟之内不发送数据，请问怎么解决，好像加上延迟函数它就跟睡死了，请问怎么解决呢？谢谢

      import sensor, image, pyb
import time, os, tf, math, uos, gc
from pyb import UART,LED

LED(3).on()

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(30)          # Let the camera adjust.

uart = UART(3, 115200)
uart.init(115200, bits=8, parity=None, stop=1)
def send_data(data1):
    global uart
    data = bytearray([0xb3,0xb3,data1,0x5b])
    uart.write(data)


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()
    row_data=0 # 0 正常行走，1 停止，2 等待

    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?])
        if labels[i] == 's':
            row_data = 1
            print('s')
            pyb.delay(1000)
            send_data(row_data)
        if labels[i] == 'w':
            row_data = 2
            print('w')
            pyb.delay(1000)
            send_data(row_data)
        else:
            print(0)
            send_data(row_data)
    #print(row_data)
      0

    • 你这个逻辑是有问题。每次识别可能会识别到多个目标点的。

      我改了一下，大概是这样的：

          img = sensor.snapshot()
    row_data = []
    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?])
        if labels[i] == 's':
            row_data.append(1)
            print('s')
            
        if labels[i] == 'w':
            row_data.append(2)
            print('w')
        else:
            print(0)
    for d in row_data:
        send_data(row_data)
    pyb.delay(1000)
      0