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@kidswong999

AttributeError: 'module' object has no attribute'set_ whitebal'
和
AttributeError: 'lmage' object has no attribute'find markers'
的问题
这一次的问题很简单。。就是巡线的例程，死活运行不了，我寻思我应该都声明了啊
另附，例程地址：http://kaizhi-xu.com/post/openmvli-cheng-jiang-jie/openmvli-cheng-jiang-jie-53

# Line Following Example
#
# Making a line following robot requires a lot of effort. This example script
# shows how to do the computer 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 white line. Use [(0, 64)] for a tracking a black 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
weight_sum = 0 #权值和初始化
for r in ROIS: weight_sum += r[4]
#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(10) # Let new settings take affect.
#sensor.set_whitebal(False) # turn this off.
#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]) # r[0:4] is roi tuple.
        #找到视野中的线
        merged_blobs = img.find_markers(blobs) # merge overlapping blobs
        #将找到的图像区域合并成一个。

        #目标区域找到直线
        if merged_blobs:
            # Find the index of the blob with the most pixels.
            most_pixels = 0
            largest_blob = 0
            for i in range(len(merged_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 merged_blobs[i][4] > most_pixels:
                    most_pixels = merged_blobs[i][4] # [4] is 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.
            #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_rectangle(merged_blobs[largest_blob][0:4]) # rect
            img.draw_cross(merged_blobs[largest_blob][5], # cx
                           merged_blobs[largest_blob][6]) # cy

            # [5] of the blob is the x centroid - r[4] is the weight.
            centroid_sum += merged_blobs[largest_blob][5] * 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 = (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.