
脱机运行人脸分辨红灯会双闪,然后再连上电脑运行提示文件损坏,这是为什么?
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严格按照教程一步步进行人脸分辨,在插上电脑的时候也可以正常运行,也安全的弹出了openmv,脱机运行的时候红灯会双闪,然后再连上电脑运行提示文件损坏,这是为什么?(已插入sd卡)
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如果涉及代码,需要报错提示与全部代码文本,请注意不要贴代码图片
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@kidswong999 与教程中的代码完全一致
# Snapshot Example # # Note: You will need an SD card to run this example. # # You can use your OpenMV Cam to save image files. import sensor, image, pyb RED_LED_PIN = 1 BLUE_LED_PIN = 3 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 = 2000) num = 1 #设置被拍摄者序号,第一个人的图片保存到s1文件夹,第二个人的图片保存到s2文件夹,以此类推。每次更换拍摄者时,修改num值。 n = 20 #设置每个人拍摄图片数量。 #连续拍摄n张照片,每间隔3s拍摄一次。 while(n): #红灯亮 pyb.LED(RED_LED_PIN).on() sensor.skip_frames(time = 3000) # Give the user time to get ready.等待3s,准备一下表情。 #红灯灭,蓝灯亮 pyb.LED(RED_LED_PIN).off() pyb.LED(BLUE_LED_PIN).on() #保存截取到的图片到SD卡 print(n) sensor.snapshot().save("singtown/s%s/%s.pgm" % (num, n) ) # or "example.bmp" (or others) n -= 1 pyb.LED(BLUE_LED_PIN).off() print("Done! Reset the camera to see the saved image.")
# 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 = 6 #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。