OpenMV 黑线寻线

实现效果

寻线输出偏转角度 效果

 算法解析

find_black_line_algrithm

将整个画面分割为若干的检测区域,并给每个区域设定一个权值。

很容易理解, 距离越远的,重要性越高/越小, 根据自己的需求来设定不同的权值

然后各自调用find_blobs 函数,找出各自最大的色块。

将每个色块的中心点的x坐标进行加权平均

然后根据中心公示, 利用arctan函数,求得要偏转的角度(估算)

Source Code

# 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.

算法不足分析

只适用于单条线, 如果是多条线,就不能识别

如何识别直角?

问题其实还有很多, 这个算法比较简单, 但是适用的情景比较局限。

Copyright 杭州云江科技有限公司 2017 all right reserved,powered by Gitbook该文件修订时间: 2019-04-06 01:21:24

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