dx1g 发布的帖子

你好，请问神经网络训练出来可以识别其它东西了吗

现有的神经网络识别数字、字母的方法，其运行时间大概多长

请问如何在openmv M4的lenet.network网络模型自己训练lenet模型使用。

请问openmv中使用神经网络能够识别H吗？

openmv网络连接，wifi图传的wifi模块是什么，可以不用它的wifi拓展版吗？

为什么openmv插上储存卡后脱机就不能运行了？拔了储存卡就可以运行这是为什么？

事情里面的办法都试了，都不行。无法升级固件

openmv可以在上电后，过段时间由认为控制它启动吗？

如果我将threshold的增大了，检测到的圆是少了，但同时找圆的时间也长了，如果我摄像头稍微晃动，就找不到圆了，但我需要一直有圆的坐标值返回

对着电脑照为什会将我的屏幕识别成圆？

openmv不能连接电脑，亮绿灯，但不能与IDE连接

openmv
图片中的x_margin、y_margin、r_margin这三个参数是什么意思，控制所检测的圆的合并是什么意思？麻烦指导下，谢谢！

openmv在使用找圆例程执行找圆操作时，摄像头放到圆的上方但却找不到圆，圆的坐标值没有数据返回，这是什么原因？

OPENMV具有手势识别的功能吗？

# Black Grayscale Line Following Example
#
# Making a line following robot requires a lot of effort. This example script
# shows how to do the machine vision part of the line following robot. You
# can use the output from this script to drive a differential drive robot to
# follow a line. This script just generates a single turn value that tells
# your robot to go left or right.
#
# For this script to work properly you should point the camera at a line at a
# 45 or so degree angle. Please make sure that only the line is within the
# camera's field of view.

import sensor, image, time, math

# 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(30) # 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 = 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 += blobs[largest_blob].cx() * r[4] # r[4] is the roi weight.
            #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 = (centroid_sum / weight_sum) # Determine center of line.
    #中间公式

    # Convert the center_pos to a deflection angle. We're using a non-linear
    # operation so that the response gets stronger the farther off the line we
    # are. Non-linear operations are good to use on the output of algorithms
    # like this to cause a response "trigger".
    deflection_angle = 0
    #机器人应该转的角度

    # The 80 is from half the X res, the 60 is from half the Y res. The
    # equation below is just computing the angle of a triangle where the
    # opposite side of the triangle is the deviation of the center position
    # from the center and the adjacent side is half the Y res. This limits
    # the angle output to around -45 to 45. (It's not quite -45 and 45).
    deflection_angle = -math.atan((center_pos-80)/60)
    #角度计算.80 60 分别为图像宽和高的一半，图像大小为QQVGA 160x120.
    #注意计算得到的是弧度值

    # Convert angle in radians to degrees.
    deflection_angle = math.degrees(deflection_angle)
    #将计算结果的弧度值转化为角度值

    # Now you have an angle telling you how much to turn the robot by which
    # incorporates the part of the line nearest to the robot and parts of
    # the line farther away from the robot for a better prediction.
    print("Turn Angle: %f" % deflection_angle)
    #将结果打印在terminal中

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

代码中的角度值deflection_angle表示什么，（center_pos-80)/60，分母为60，如果视野中是直线，但center_pos偏差值很大，那么也会返回一个很大的角度，这样不就不对了吗

@xsco

@bm6h 可以改变你的roi区间，即“感兴趣区域”