另外还有一个问题，我又两个Openmv4 plusi，出现报错的这台插入SD卡后无法被电脑识别

在IDE上正常运行，脱机运行后出现了ERROR_log文件
报错内容是SDRAM failed testing!
代码如下

enable_lens_corr = False # turn on for straighter lines...

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

sensor.reset()
sensor.set_contrast(3)
sensor.set_pixformat(sensor.GRAYSCALE) # grayscale is faster
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
clock = time.clock()
uart=UART(3,9600)

# All line objects have a `theta()` method to get their rotation angle in degrees.
# You can filter lines based on their rotation angle.

min_degree = 0
max_degree = 179

rx1_l=[]
rx2_l=[]
ry1_l=[]
ry2_l=[]

rx1_r=[]
rx2_r=[]
ry1_r=[]
ry2_r=[]

# All lines also have `x1()`, `y1()`, `x2()`, and `y2()` methods to get their end-points
# and a `line()` method to get all the above as one 4 value tuple for `draw_line()`.

while(True):
    clock.tick()
    img = sensor.snapshot()
    if enable_lens_corr: img.lens_corr(0.7) # for 2.8mm lens...

    # `threshold` controls how many lines in the image are found. Only lines with
    # edge difference magnitude sums greater than `threshold` are detected...

    # More about `threshold` - each pixel in the image contributes a magnitude value
    # to a line. The sum of all contributions is the magintude for that line. Then
    # when lines are merged their magnitudes are added togheter. Note that `threshold`
    # filters out lines with low magnitudes before merging. To see the magnitude of
    # un-merged lines set `theta_margin` and `rho_margin` to 0...

    # `theta_margin` and `rho_margin` control merging similar lines. If two lines
    # theta and rho value differences are less than the margins then they are merged.

    for l in img.find_lines(threshold = 2800, theta_margin = 35, rho_margin = 35):
        if (min_degree <= l.theta()) and (l.theta() <= max_degree):
            img.draw_line(l.line(), color = (255, 0, 0))
            # print(l)
            #print("x1:",l.x1,"x2:",l.x2)
            #print(l.theta())
            #k=(l.y1()-l.y2())/(l.x1()-l.x2())
            if(l.x1()<125 and l.theta()<45):
                rx1_l.append(l.x1())
                rx2_l.append(l.x2())
                ry1_l.append(l.y1())
                ry2_l.append(l.y2())
                #print("left:",l.theta())
                #print(l.x1(),l.y1(),l.x2(),l.y2())
            if(l.x1()>125 and l.theta()>135):
                rx1_r.append(l.x1())
                rx2_r.append(l.x2())
                ry1_r.append(l.y1())
                ry2_r.append(l.y2())
                #print(l.x1(),l.y1(),l.x2(),l.y2())

            if rx1_l:
                if rx1_r:
                    A_1=ry2_l[-1]-ry1_l[-1]
                    B_1=rx1_l[-1]-rx2_l[-1]
                    C_1=rx1_l[-1]*(ry1_l[-1]-ry2_l[-1])+ry1_l[-1]*(rx2_l[-1]-rx1_l[-1])
                    A_2=ry2_r[-1]-ry1_r[-1]
                    B_2=rx1_r[-1]-rx2_r[-1]
                    C_2=rx1_r[-1]*(ry1_r[-1]-ry2_r[-1])+ry1_r[-1]*(rx2_r[-1]-rx1_r[-1])
                    cross_x=(B_1*C_2-B_2*C_1)/(B_2*A_1-B_1*A_2)
                    if(cross_x>0 and cross_x<320):
                        rcx=cross_x-125
                        print(cross_x-125)
                        rxy={"rx":rcx,"ry":0}
                        uart.write(ujson.dumps(rxy)+'\r\n')


    #print("FPS %f" % clock.fps())

# About negative rho values:
#
# A [theta+0:-rho] tuple is the same as [theta+180:+rho].

好，问题已解决，谢谢

@kidswong999 测试了，在检测特征点的时候还是会变暗

把原装模块换成MT9V034以后能检测到的特征点数量就少了很多
代码为教程代码，参数几乎没有改动，把threshold都设置到1了还是只能检测到几个点
检测特征点的时候屏幕会变暗一下（如图）
请问这个问题怎么解决？

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

# Reset sensor
sensor.reset()

# Sensor settings
sensor.set_contrast(3)
sensor.set_gainceiling(16)

sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.VGA)
#sensor.set_windowing((320, 240))

uart=UART(3,9600)
sensor.skip_frames(time = 10)
#sensor.set_auto_gain(True, value=100)

def draw_keypoints(img, kpts):
    if kpts:
        print(kpts)
        img.draw_keypoints(kpts)
        img = sensor.snapshot()
        time.sleep(1000)

kpts1 = None
 #NOTE: uncomment to load a keypoints descriptor from file
#kpts1 = image.load_descriptor("/parking_lot.orb")
img = sensor.snapshot()
#draw_keypoints(img, kpts1)

clock = time.clock()
while (True):
    clock.tick()
    img = sensor.snapshot()
    if (kpts1 == None):
        # NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.
        kpts1 = img.find_keypoints(normalize=True,max_keypoints=150, threshold=1, scale_factor=1.5)
        draw_keypoints(img, kpts1)
    else:
        # NOTE: When extracting keypoints to match the first descriptor, we use normalized=True to extract
        # keypoints from the first scale only, which will match one of the scales in the first descriptor.
        kpts2 = img.find_keypoints(max_keypoints=150, threshold=3, normalized=True)
        if (kpts2):
            match = image.match_descriptor(kpts1, kpts2, threshold=90)
            if (match.count()>10):
                # If we have at least n "good matches"
                # Draw bounding rectangle and cross.
                img.draw_rectangle(match.rect())
                img.draw_cross(match.cx(), match.cy(), size=10)
                lcx=match.cx()
                lcy=match.cy()
                lxy={"lx":lcx,"ly":lcy}
                uart.write(ujson.dumps(lxy)+'\r\n')
            print(kpts2, "matched:%d dt:%d"%(match.count(), match.theta()))
            # NOTE: uncomment if you want to draw the keypoints
            #img.draw_keypoints(kpts2, size=KEYPOINTS_SIZE, matched=True)

    # Draw FPS
    img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))

使用的是1.7mm的广角摄像头
需要识别如图的停车位
因为识别中平台会移动，所以没有使用模板匹配
使用过了矩形检测和特征点检测
矩形检测无法识别出矩形
特征点检测效果不佳
请问有没有更好的解决方案