image.lens_corr([strength=1.8[, zoom=1.0]])
2.8mm的参数是1.8，那12mm镜头的参数默认值是多少？
此外，设置畸变矫正后会影响运算速度吗？

已解决，需要添加math.sqrt

import sensor, image, time, math

# Color Tracking Thresholds (L Min, L Max, A Min, A Max, B Min, B Max)
# The below thresholds track in general red/green things. You may wish to tune them...
#thresholds = [(30, 100, 15, 127, 15, 127), # generic_red_thresholds
#              (30, 100, -64, -8, -32, 32), # generic_green_thresholds
#              (0, 15, 0, 40, -80, -20)] # generic_blue_thresholds
# You may pass up to 16 thresholds above. However, it's not really possible to segment any
# scene with 16 thresholds before color thresholds start to overlap heavily.
thresholds = [(10, 65, 30, 80, 10, 70)]
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must be turned off for color tracking
sensor.set_auto_whitebal(False) # must be turned off for color tracking
clock = time.clock()

# Only blobs that with more pixels than "pixel_threshold" and more area than "area_threshold" are
# returned by "find_blobs" below. Change "pixels_threshold" and "area_threshold" if you change the
# camera resolution. Don't set "merge=True" becuase that will merge blobs which we don't want here.

def find_max(blobs):
    max_size=0
    for blob in blobs:
        if blob[2]*blob[3] > max_size:
            max_blob=blob
            max_size = blob[2]*blob[3]
    return max_blob

while(True):
    clock.tick()
    img = sensor.snapshot()
    blobs=img.find_blobs(thresholds, pixels_threshold=40, area_threshold=40)
    if(blobs):
     max_blob=find_max(blobs)
#    for blob in img.find_blobs(thresholds, pixels_threshold=200, area_threshold=200):
        # These values depend on the blob not being circular - otherwise they will be shaky.
#        if blob.elongation() > 0.5:
#            img.draw_edges(blob.min_corners(), color=(255,0,0))
#            img.draw_line(blob.major_axis_line(), color=(0,255,0))
#            img.draw_line(blob.minor_axis_line(), color=(0,0,255))
        # These values are stable all the time.
#        img.draw_rectangle(blob.rect())
#        img.draw_cross(blob.cx(), blob.cy())
        # Note - the blob rotation is unique to 0-180 only.
#        img.draw_keypoints([(blob.cx(), blob.cy(), int(math.degrees(blob.rotation())))], size=20)
     if (max_blob.elongation() > 0.5 and max_blob.elongation()<1):
          img.draw_edges(max_blob.min_corners(), color=(255,0,0))
          img.draw_line(max_blob.major_axis_line(), color=(0,255,0))
          img.draw_line(max_blob.minor_axis_line(), color=(0,0,255))
        # These values are stable all the time.
     if max_blob.elongation() > 0.5:
         img.draw_rectangle(max_blob.rect())
         img.draw_cross(max_blob.cx(), max_blob.cy())
        # Note - the blob rotation is unique to 0-180 only.
         img.draw_keypoints([(max_blob.cx(), max_blob.cy(), int(math.degrees(max_blob.rotation())))], size=20)
         a=max_blob.major_axis_line()
         b=max_blob.minor_axis_line()
         c=sqrt(4)
#         c=sqrt(((a[2]-a[0])^2) + ((a[3]-a[1])^2))
         print(c)

比如我只想要红色的三角形色块，其他红色都剔除，这种识别该调用什么函数？

@kidswong999 你好啊，我看到了他的英文注释，只是不理解他这个返回对象的长度是什么意思，是表示类似置信度这样一个概念吗？

@kidswong999 你好啊，我看到了他的英文注释，只是不理解他这个返回对象的长度是什么意思，是表示类似置信度这样一个概念吗？

在这儿用到