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  • 我们只解决官方正版的OpenMV的问题(STM32),其他的分支有很多兼容问题,我们无法解决。
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  • 必看:玩转星瞳论坛了解一下图片上传,代码格式等问题。
  • 为啥用openmv识别人脸的准确度那么低????



    • import sensor, image, time,pyb
      
      
      
      from pid import PID
      
      from pyb import Servo
      
      
      
      pan_servo=Servo(1)#p7
      
      tilt_servo=Servo(2)#tilt倾斜p8
      
      
      
      red_threshold  = (13, 49, 18, 61, 6, 47)#threshold  yuzhi
      
      
      
      #pan_pid = PID(p=0.07, i=0, imax=90) #脱机运行或者禁用图像传输,使用这个PID
      
      #tilt_pid = PID(p=0.05, i=0, imax=90) #脱机运行或者禁用图像传输,使用这个PID
      
      pan_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
      
      tilt_pid = PID(p=0.1, i=0, imax=90)#在线调试使用这个PID
      
      
      
      sensor.reset()
      
      # Sensor settings
      sensor.set_contrast(1)
      sensor.set_gainceiling(16)
      # HQVGA and GRAYSCALE are the best for face tracking.
      sensor.set_framesize(sensor.HQVGA)
      sensor.set_pixformat(sensor.GRAYSCALE)
      sensor.set_vflip(True)#镜像翻转
      sensor.skip_frames(10)
      sensor.set_auto_whitebal(False)
      clock = time.clock() # Tracks FPS.
      
      
      face_cascade = image.HaarCascade("frontalface", stages=25)
      
      def find_max(blobs):
      
          max_size=0
      
          for blob in blobs:
      
              if blob[2]*blob[3] > max_size:
      
                  max_blob=blob
      
                  max_size = blob[2]*blob[3]
      
          return max_blob
      
      
      
      
      
      while(True):
      
          clock.tick() # Track elapsed milliseconds between snapshots().
      
          img = sensor.snapshot() # Take a picture and return the image.
      
      
      
          objects = img.find_features(face_cascade, threshold=0.75, scale=1.35)
          if  objects:
      
              max_blob = find_max( objects)
      
              pan_error = max_blob[0]+max_blob[2]/2-img.width()/2
      
              tilt_error = max_blob[1]+max_blob[3]/2-img.height()/2
      
      
      
              print("pan_error: ", pan_error)
      
      
      
              img.draw_rectangle(max_blob) # rect
              img.draw_cross(int(max_blob[0]+max_blob[2]/2),int(max_blob[1]+max_blob[3]/2))
      
      
      
      
      
              pan_output=pan_pid.get_pid(pan_error,1)/2
      
              tilt_output=tilt_pid.get_pid(tilt_error,1)
      
              print("pan_output",pan_output)
      
              pan_servo.angle(pan_servo.angle()+pan_output)
      
              tilt_servo.angle(tilt_servo.angle()-tilt_output)
              #sensor.skip_frames(time = 5000)
      
      
      
      
      
      
      
              NUM_SUBJECTS = 2#图像库中不同人数,一共6人
      
              NUM_SUBJECTS_IMGS = 50 #每人有20张样本图片
      
              # 拍摄当前人脸。image.save(path[, roi[, quality=50]])
      
      
              img = sensor.snapshot()#.save("singtown/snapshot-%d.pgm" % pyb.rng())
      
              img = img.draw_rectangle(max_blob).save("singtown/snapshot-%d.pgm" % pyb.rng(),[max_blob[0],max_blob[1],max_blob[2],max_blob[3]])
      
              #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
      
              imge = None
      
              pmin = 999999
      
              num=0
      
      
      
              def min(pmin, a, x):
      
                  global num
      
                  if a<pmin:
      
                      pmin=a
      
                      num=x
      
      
                  return pmin
      
      
      
              for s in range(1, NUM_SUBJECTS+1):
      
                  dist = 0
      
                  for i in range(2, NUM_SUBJECTS_IMGS+1):
      
                      imge = image.Image("singtown/s%d/%d.pgm"%(s, i))
      
      
                      d1 = imge.find_lbp((0, 0, imge.width(), imge.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。
              if pmin>31062:
                   img.draw_string(int(max_blob[0]+max_blob[2]/2),int(max_blob[1]+max_blob[3]/2),"stranger",[0,0,0],scale=2, mono_space=False).save("singtown/stranger-%d.jpg" % pyb.rng())
                  # break
              elif  num ==1:
                      img.draw_string(int(max_blob[0]+max_blob[2]/2),int(max_blob[1]+max_blob[3]/2),"man",[0,0,0],scale=2, mono_space=False).save("singtown/1-%d.jpg" % pyb.rng())
              elif num==2:
                      img.draw_string(int(max_blob[0]+max_blob[2]/2),int(max_blob[1]+max_blob[3]/2),"woman",[0,0,0],scale=2, mono_space=False).save("singtown/2-%d.jpg" % pyb.rng())
             # elif num==3:
              #        img.draw_string(int(max_blob[0]+max_blob[2]/2),int(max_blob[1]+max_blob[3]/2),"ghx",[0,0,0],scale=2, mono_space=False).save("singtown/mb-%d.jpg" % pyb.rng())
                 # print(pmin)
      
              print(num) # num为当前最匹配的人的编号。
             # image.draw_string(x, y, text[, color[, scale=1[, x_spacing=0[, y_spacing=0[, mono_space=True]]]]])