串口终端显示：{"width":24, "height":24, "n_stages":25, "n_features":2913, "n_rectangles":6383}
ktp cnt=0
input face

Traceback (most recent call last):
File "main.py", line 62, in
Exception: IDE interrupt

OpenMV v4.5.0; MicroPython v1.20-omv-r8; OPENMV4P-STM32H743
Type "help()" for more information.

"""
人脸识别例程
第一步先使用Haar Cascade找出人脸并记录该关键点
第二步就是不停的检测当前获取图片的关键点是否匹配
"""
# 导入相应的库
import sensor, image, time,os
import pyb

uart = pyb.UART(3, 115200, timeout_char = 1000)

# 初始化摄像头
sensor.reset()

# 设置相机图像的对比度为3
sensor.set_contrast(3)

# 设置相机的增益上限为16
sensor.set_gainceiling(16)

# 设置采集到照片的大小
sensor.set_framesize(sensor.VGA)

# 在VGA(640*480)下开个小窗口，相当于数码缩放
sensor.set_windowing((320, 240))

# 设置采集到照片的格式：灰色图像
sensor.set_pixformat(sensor.GRAYSCALE)

# 加载Haar Cascade 模型
# 默认使用25个步骤，减少步骤会加快速度但会影响识别成功率
face_cascade = image.HaarCascade("frontalface", stage = 25)
print(face_cascade)



# 特征数组
kpts=[None,None,None,None,None,None,None,None,None,None]
# 特征数组下标
kpts_index=0
# 录入人脸标志
input_face=False

#加载本地保存的人脸特征文件
for i in range(0,9):
    try:
        kpt_temp=image.load_descriptor("/face_feature/%s.orb"%(str(kpts_index)))
    except:
        break
    kpts[kpts_index]=kpt_temp
    kpts_index+=1

print('ktp cnt=%d'%(kpts_index))

while True:
    #录入人脸，由单片机发送录入命令开启
    while input_face:
        # 拍摄图片并返回img
        img = sensor.snapshot()
        img.draw_string(0, 0, "Looking for a face...")

        # 寻找人脸对象
        # threshold和scale_factor两个参数控制着识别的速度和准确性
        # threshold(0-1.0) 值越小检测速度越快，但误报率越高
        # scale_factor（大于1.0）数值越大速度越快，匹配度越差
        objects = img.find_features(face_cascade, threshold=1.0, scale_factor=0.9)

        if objects:
            # 将 ROI（x, y, w, h）往各个方向扩展31像素
            face = (objects[0][0]-31,
                    objects[0][1]-31,
                    objects[0][2]+31*2,
                    objects[0][3]+31*2)
            # 使用扩展后的ROI区域(人脸)学习关键点
            kpts1 = img.find_keypoints(threshold = 10,
                    scale_factor = 1.2,
                    max_keypoints = 150,
                    roi = face)

            if kpts1 == None:
                continue

            img.draw_keypoints(kpts1, size=24)

            img = sensor.snapshot()
            #将人脸保存到本地文件
            if kpts_index>=10:
                os.remove('/face_feature/0.orb')
                os.rename("/face_feature/1.orb","/face_feature/0.orb")
                os.rename("/face_feature/2.orb","/face_feature/1.orb")
                os.rename("/face_feature/3.orb","/face_feature/2.orb")
                os.rename("/face_feature/4.orb","/face_feature/3.orb")
                os.rename("/face_feature/5.orb","/face_feature/4.orb")
                os.rename("/face_feature/6.orb","/face_feature/5.orb")
                os.rename("/face_feature/7.orb","/face_feature/6.orb")
                os.rename("/face_feature/8.orb","/face_feature/7.orb")
                os.rename("/face_feature/9.orb","/face_feature/9.orb")
                kpts_index=9

            image.save_descriptor(kpts1, "/face_feature/%s.orb"%str(kpts_index))
            kpts[kpts_index] = kpts1
            kpts_index+=1


            input_face = False
            print('input over,kpts_index=',kpts_index)
            uart.write("input over\r\n") #P4（TX）和P5（RX）
            break


    #识别人脸
    img = sensor.snapshot()
    # 从图像中提取关键点
    kpts2 = img.find_keypoints(threshold = 10,
                                scale_factor = 1.1,
                                max_keypoints = 100,
                                normalized = True)
    if(kpts2):
        for i in range(0,kpts_index):
            kpts1 = kpts[i]
            # 跟kpts1匹配
            c = image.match_descriptor(kpts1, kpts2, threshold = 85)
            # c[6]为match值，值越大表示匹配程度越高
            match = c[6]
            if(match > 7):
                img.draw_rectangle(c[2:6])
                img.draw_cross(c[0], c[1], color = (255,0,0), size = 10)
                uart.write("recognition success,id="+str(i)+"\r\n") #P4（TX）和P5（RX）
                print('recognition success')
                print("id="+str(i)+"\r\n")

    cmd = uart.readline()
    if cmd != None:
        cmd=cmd.decode()
        if cmd=='input face\r\n':
            print('input face')
            input_face = True
            cmd = None

现在就是不知道该怎么办，无法储存图像，但是以前可以正常储存图像和正常进行人脸识别