5pqn 发布的帖子

那能问下这些都弄好了之后怎么改写底层代码吗

额，的确是直接复制粘贴的
还有能问下这些都弄完了之后怎么改写底层代码吗

在安装必要软件的时候最后一步出现这个怎么解决？

编译的时候最后出现了问题，是什么问题啊？

是在模拟机上安装pip吗

是我前面漏打代码了，还是要在这个模拟机中另外装

openmv的
在编译固件里的安装必须使用的软件的时候
在Ubuntu的终端打sudo pip install numpy pyserial==2.7 pyusb==1.0.0b2 Pillow函数的时候显示sudo:command not found
这怎么办

@kidswong999 那我可以通过将底层代码中一部分拿出来编成c工程文件再自己改写后烧进单片机里吗

我是OPENMV2 m4的摄像头，想问下有底层代码开放的吗，还有能自己改写底层代码烧入吗？因为我有些方法用不到，想自己写一些算法

我的是openmv2 m4的
在进行示例的人脸识别的时候精度很低，基本都识别错误，而且我只有3个人来辨别
是因为openmv2精度就是很低还是其他原因

# Face recognition with LBP descriptors.
# See Timo Ahonen's "Face Recognition with Local Binary Patterns".
#
# Before running the example:
# 1) Download the AT&T faces database http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip
# 2) Exract and copy the orl_faces directory to the SD card root.


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



#SUB = "s1"
NUM_SUBJECTS = 3 #图像库中不同人数，一共6人
NUM_SUBJECTS_IMGS = 20 #每人有20张样本图片

# 拍摄当前人脸。
img = sensor.snapshot()
#img = image.Image("singtown/%s/1.pgm"%(SUB))
d0 = img.find_lbp((0, 0, img.width(), img.height()))
#d0为当前人脸的lbp特征
img = None
pmin = 999999
num=0

def min(pmin, a, s):
    global num
    if a<pmin:
        pmin=a
        num=s
    return pmin

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()))
        #d1为第s文件夹中的第i张图片的lbp特征
        dist += image.match_descriptor(d0, d1)#计算d0 d1即样本图像与被检测人脸的特征差异度。
    print("Average dist for subject %d: %d"%(s, dist/NUM_SUBJECTS_IMGS))
    pmin = min(pmin, dist/NUM_SUBJECTS_IMGS, s)#特征差异度越小，被检测人脸与此样本更相似更匹配。
    print(pmin)

print(num) # num为当前最匹配的人的编号。

照片是拍了三组的，背景也是在全白背景下的

OSError: Image is not PGM!
我是用openmv2 m4的板子
运行分辨人脸的时候出现这个错误OSError: Image is not PGM!
我是先拍了两套人脸的图片想试一下
也把人数改成了2了

# Face recognition with LBP descriptors.
# See Timo Ahonen's "Face Recognition with Local Binary Patterns".
#
# Before running the example:
# 1) Download the AT&T faces database http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/att_faces.zip
# 2) Exract and copy the orl_faces directory to the SD card root.


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



#SUB = "s1"
NUM_SUBJECTS = 2 #图像库中不同人数，一共6人
NUM_SUBJECTS_IMGS = 20 #每人有20张样本图片

# 拍摄当前人脸。
img = sensor.snapshot()
#img = image.Image("singtown/%s/1.pgm"%(SUB))
d0 = img.find_lbp((0, 0, img.width(), img.height()))
#d0为当前人脸的lbp特征
img = None
pmin = 999999
num=0

def min(pmin, a, s):
    global num
    if a<pmin:
        pmin=a
        num=s
    return pmin

for s in range(1, NUM_SUBJECTS+1):
    dist = 0
    for i in range(1, NUM_SUBJECTS_IMGS+1):
        img = image.Image("singtown/s%d/%d.pgm"%(s, i))
        d1 = img.find_lbp((0, 0, img.width(), img.height()))
        #d1为第s文件夹中的第i张图片的lbp特征
        dist += image.match_descriptor(d0, d1)#计算d0 d1即样本图像与被检测人脸的特征差异度。
    print("Average dist for subject %d: %d"%(s, dist/NUM_SUBJECTS_IMGS))
    pmin = min(pmin, dist/NUM_SUBJECTS_IMGS, s)#特征差异度越小，被检测人脸与此样本更相似更匹配。
    print(pmin)

print(num) # num为当前最匹配的人的编号。

img = image.Image("singtown/s%d/%d.pgm"%(s, i))这句话出现了OSError: Image is not PGM!错误

@kidswong999 emmmmmm可以额外问下那我还可以自己写底层函数来做形状识别的吗

MemoryError: Out of fast Frame Buffer Stack Memory! Please reduce the resolution of the image you are running this algorithm on to bypass this issue!

在试用模板示例的时候遇到MemoryError: Out of fast Frame Buffer Stack Memory! Please reduce the resolution of the image you are running this algorithm on to bypass this issue!问题是为什么啊
我也加了sd卡了，是M2ram不够大吗

while (True):
    clock.tick()
    img = sensor.snapshot()

    # find_template(template, threshold, [roi, step, search])
    # ROI: The region of interest tuple (x, y, w, h).
    # Step: The loop step used (y+=step, x+=step) use a bigger step to make it faster.
    # Search is either image.SEARCH_EX for exhaustive search or image.SEARCH_DS for diamond search
    #
    # Note1: ROI has to be smaller than the image and bigger than the template.
    # Note2: In diamond search, step and ROI are both ignored.
    r = img.find_template(template, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
    if r:
        img.draw_rectangle(r)

    print(clock.fps())

中 r = img.find_template(template, 0.70, step=4, search=SEARCH_EX) #, roi=(10, 0, 60, 60))
出问题

是M3才有吗，还是说要自己写底层函数

出现OSError: This function is unavailable on your OpenMV Cam问题
在运行寻找rects示例代码时出错了

while(True):
    clock.tick()
    img = sensor.snapshot()

    # `threshold` below should be set to a high enough value to filter out noise
    # rectangles detected in the image which have low edge magnitudes. Rectangles
    # have larger edge magnitudes the larger and more contrasty they are...

    for r in img.find_rects(threshold = 10000):
        img.draw_rectangle(r.rect(), color = (255, 0, 0))
        for p in r.corners(): img.draw_circle(p[0], p[1], 5, color = (0, 255, 0))
        print(r)

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

中 for r in img.find_rects(threshold = 10000):显示出错