@kidswong999 是按照视频的步骤进行的

用给定的人脸分辨的例子，显示说断言失败

你好请问这个人脸识别代码为什么不能输出num的值，串行口显示没有输出值，万分感谢

import sensor, time, image, pyb

sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.B128X128) # or sensor.QQVGA (or others)
sensor.set_windowing((92,112))
sensor.skip_frames(10) # Let new settings take affect.
sensor.skip_frames(time = 5000) #等待5s
num = 0
def min(pmin, a, s):
    global num
    print(00000000)
    if a < pmin:
        pmin = a
        num = s
        
    return pmin
    sensor.reset() # Initialize the camera sensor.
    sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.GRAYSCALE
    sensor.set_framesize(sensor.B128X128) # or sensor.QQVGA (or others)
    sensor.set_windowing((92,112))
    sensor.skip_frames(10) # Let new settings take affect.
    sensor.skip_frames(time = 5000) #等待5s
    
    NUM_SUBJECTS = 2
    NUM_SUBJECTS_IMGS = 20
    img = sensor.snapshot()
    d0 = img.find_lbp((0, 0, img.width(), img.height()))
    img = None
    pmin = 999999
    num = 0
    for s in range(1, NUM_SUBJECTS + 1):
        dist = 0
        for i in range(2, NUM_SUBJECTS_IMGS + 1):
            img = image.Image("singtown/s%d/%d.pgm" % (s, i))
            d1 = img.find_lbp((0, 0, img.width(), img.height()))
            dist += image.match_descriptor(d0, d1)
        print("Average dist for subject %d: %d" % (s, dist / NUM_SUBJECTS_IMGS))
        pmin = min(pmin, dist / NUM_SUBJECTS_IMGS, s)
        print(pmin)
        if dist / NUM_SUBJECTS_IMGS == pmin:
            num = s
    print(num)

请问我现在想将openmv的图片截取一张传给onenet平台，openmv这边可以实现吗？如果可以的话，能否通过stm32来发送这个截取图片的命令

想通过串口给32发送mv识别二维码

的信息，但只想发送四个xywh坐标，不想发送其他的信息，但是改的代码却把qrcode函数下的九类信息全发到调试助手上了，请问怎样修改才能让只发送qrcodes函数下的一部分信息给别的单片机？拜托了！

import sensor, image, time
from pyb import UART
import json
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time = 2000)
sensor.set_auto_gain(False) # must turn this off to prevent image washout...
clock = time.clock()
uart = UART(3, 115200)

while(True):
    clock.tick()
    img = sensor.snapshot()
    img.lens_corr(1.8) # strength of 1.8 is good for the 2.8mm lens.
    for code in img.find_qrcodes():
        img.draw_rectangle(code.rect(), color = (255, 0, 0))
        output_str = json.dumps(code)
        print('you send:',output_str)
        uart.write(output_str+'\n')
    else:
        print('not found!')

这是根据例程人脸追踪改过来的，之前还可以用，但不知道为什么现在只能识别人体，然后就卡在识别那里，下方也不能输出数据，不能追踪了，

import sensor, time, image
sensor.reset()
sensor.set_contrast(3)
sensor.set_gainceiling(16)
sensor.set_framesize(sensor.VGA)
sensor.set_windowing((320, 240))
sensor.set_pixformat(sensor.RGB565)
# Skip a few frames to allow the sensor settle down
sensor.skip_frames(time = 2000)

# Load Haar Cascade
# By default this will use all stages, lower satges is faster but less accurate.
body_cascade = image.HaarCascade("haarcascade_fullbody.cascade", stages=25)
print(body_cascade)

# First set of keypoints
kpts1 = None

# Find a body!
while (kpts1 == None):
    img = sensor.snapshot()
    img.draw_string(0, 0, "Looking for a body...")
    # Find bodies
    objects = img.find_features(body_cascade, threshold=0.5, scale=1.25)
    if objects:
        # Expand the ROI by 31 pixels in every direction
        body = (objects[0][0]-31, objects[0][1]-31,objects[0][2]+31*2, objects[0][3]+31*2)
        # Extract keypoints using the detect face size as the ROI
        kpts1 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, roi=body)
        # Draw a rectangle around the first face
        img.draw_rectangle(objects[0])

# Draw keypoints
print(kpts1)
img.draw_keypoints(kpts1, size=24)
img = sensor.snapshot()
time.sleep(2000)

# FPS clock
clock = time.clock()

while (True):
    clock.tick()
    img = sensor.snapshot()
    # Extract keypoints from the whole frame
    kpts2 = img.find_keypoints(threshold=10, scale_factor=1.1, max_keypoints=100, normalized=True)

    if (kpts2):
        # Match the first set of keypoints with the second one
        c=image.match_descriptor(kpts1, kpts2, threshold=85)
        match = c[6] # C[6] contains the number of matches.
        if (match>5):
            img.draw_rectangle(c[2:6])
            img.draw_cross(c[0], c[1], size=10)
            print(kpts2, "matched:%d dt:%d"%(match, c[7]))

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

请问我如果要用opencv里的训练好的识别算法，我也已经将.xml文件用Python脚本转换成.cascade文件，在主代码中也改了，然后接下来应该怎样操作才能在摄像头上成功运行这个主函数，那个转化过来的.cascade识别算法文件应该放在哪里？