4. uncaught exception in Timer(2) interrupt handler，这种问题怎么解决呢
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    • uncaught exception in Timer(2) interrupt handler，这种问题怎么解决呢

    • 袁
       

      import sensor, image, time
import pyb
from pyb import Timer, UART

uart = UART(3, 115200, timeout_char = 1000)
uart.init(115200, bits = 8, parity = None, stop = 1 )

uart_buf = bytearray([0x55,0,0,0,0,0])

def tick(timer):
    print("回调")
    uart.write(uart_buf)
    print("回调结束")

senddata = Timer(2, freq = 100)
senddata.callback(tick)


# Tracks a black line. Use [(128, 255)] for a tracking a white line.
GRAYSCALE_THRESHOLD = [(0, 64)]
#设置阈值，如果是黑线，GRAYSCALE_THRESHOLD = [(0, 64)]；
#如果是白线，GRAYSCALE_THRESHOLD = [(128，255)]


# Each roi is (x, y, w, h). The line detection algorithm will try to find the
# centroid of the largest blob in each roi. The x position of the centroids
# will then be averaged with different weights where the most weight is assigned
# to the roi near the bottom of the image and less to the next roi and so on.
ROIS = [ # [ROI, weight]
        (0, 100, 160, 20, 0.7), # You'll need to tweak the weights for you app
        (0, 050, 160, 20, 0.3), # depending on how your robot is setup.
        (0, 000, 160, 20, 0.1)
       ]
#roi代表三个取样区域，（x,y,w,h,weight）,代表左上顶点（x,y）宽高分别为w和h的矩形，
#weight为当前矩形的权值。注意本例程采用的QQVGA图像大小为160x120，roi即把图像横分成三个矩形。
#三个矩形的阈值要根据实际情况进行调整，离机器人视野最近的矩形权值要最大，
#如上图的最下方的矩形，即(0, 100, 160, 20, 0.7)

# Compute the weight divisor (we're computing this so you don't have to make weights add to 1).
weight_sum = 0 #权值和初始化
for r in ROIS: weight_sum += r[4] # r[4] is the roi weight.
#计算权值和。遍历上面的三个矩形，r[4]即每个矩形的权值。

# Camera setup...
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # use grayscale.
sensor.set_framesize(sensor.QQVGA) # use QQVGA for speed.
sensor.skip_frames(20) # Let new settings take affect.
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
#关闭白平衡
clock = time.clock() # Tracks FPS.

while(True):
    clock.tick() # Track elapsed milliseconds between snapshots().
    img = sensor.snapshot() # Take a picture and return the image.

    centroid_sum_x = 0
    centroid_sum_y = 0
    #利用颜色识别分别寻找三个矩形区域内的线段
    for r in ROIS:
        blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=True)
        # r[0:4] is roi tuple.
        #找到视野中的线,merge=true,将找到的图像区域合并成一个

        #目标区域找到直线
        if blobs:
            # Find the index of the blob with the most pixels.
            most_pixels = 0
            largest_blob = 0
            for i in range(len(blobs)):
            #目标区域找到的颜色块（线段块）可能不止一个，找到最大的一个，作为本区域内的目标直线
                if blobs[i].pixels() > most_pixels:
                    most_pixels = blobs[i].pixels()
                    #merged_blobs[i][4]是这个颜色块的像素总数，如果此颜色块像素总数大于
                    #most_pixels，则把本区域作为像素总数最大的颜色块。更新most_pixels和largest_blob
                    largest_blob = i

            # Draw a rect around the blob.
            img.draw_rectangle(blobs[largest_blob].rect())
            #img.draw_rectangle((0,0,30, 30))
            #将此区域的像素数最大的颜色块画矩形和十字形标记出来
            img.draw_cross(blobs[largest_blob].cx(), blobs[largest_blob].cy())

            centroid_sum_x += blobs[largest_blob].cx() * r[4] # r[4] is the roi weight.
            centroid_sum_y += blobs[largest_blob].cy() * r[4]
            #计算centroid_sum，centroid_sum等于每个区域的最大颜色块的中心点的x坐标值乘本区域的权值
            
    center_pos_x = int(centroid_sum_x / weight_sum) # Determine center of line.
    center_pos_y = int(centroid_sum_y / weight_sum)
    
    #print(center_pos_x, center_pos_x)
   # print(center_pos_x>>8, center_pos_x, center_pos_y>>8, center_pos_y)
    #中间公式

    #print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while
    # connected to your computer. The FPS should increase once disconnected.

    uart_buf = ([0x55, center_pos_x>>8, center_pos_x, center_pos_y>>8, center_pos_y,0])
      0

    • 来自星瞳实验室APP: MicroPython教程7-IO中断与回调函数 https://singtown.com/learn/582/

      你的中断服务程序里面有问题，变量需要加global修饰

      0