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  • 帖子需要目的,你要做什么?
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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。
  • 运行时卡顿帧率只有几这么解决?



    • from pyb import UART,LED            ###串口要用
      import json
      import ustruct                      ###串口要用
      
      LED_R = pyb.LED(1) # Red LED = 1, Green LED = 2, Blue LED = 3, IR LEDs = 4.
      LED_G = pyb.LED(2)
      LED_B = pyb.LED(3)
      
      LED_R.off()
      LED_G.off()
      LED_B.off()
      
      uart = UART(1, 115200)                       # init with given baudrate
      uart.init(115200, bits=8,parity=None, stop=1) # init with given parameters
      
      def mask_face():
          net = None
          labels = None
          try:
              # load the model, alloc the model file on the heap if we have at least 64K free after loading
              net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))
          except Exception as e:
              print(e)
              raise Exception('Failed to load "trained.tflite", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
      
          try:
              labels = [line.rstrip('\n') for line in open("labels.txt")]
          except Exception as e:
              raise Exception('Failed to load "labels.txt", did you copy the .tflite and labels.txt file onto the mass-storage device? (' + str(e) + ')')
          clock = time.clock()
      
          clock.tick()
          img = sensor.snapshot()
          # default settings just do one detection... change them to search the image...
          for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
              print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
              img.draw_rectangle(obj.rect())
              # This combines the labels and confidence values into a list of tuples
              predictions_list = list(zip(labels, obj.output()))
              for i in range(len(predictions_list)):
                  print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
          uart.write("mask:%.2f" % predictions_list[1][1])
          print(clock.fps(), "fps")
          LED_G.on()
      
      def min(pmin, a, s):
          global num
          if a<pmin:
              pmin=a
              num=s
          return pmin
      
      def people_shibie():
          #SUB = "s1"
          NUM_SUBJECTS = 2 #图像库中不同人数,一共2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
          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。
          uart.write("face:%d" % num)
      
      def main():
          mode = 0
          omode = 1
          while(True):
      
              if mode == 0:
                  if omode == 1:
                      omode = 0
                      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
                  people_shibie()
              else:
                  if omode == 0:
                      omode = 1
                      sensor.reset()                         # Reset and initialize the sensor.
                      sensor.set_pixformat(sensor.RGB565)    # Set pixel format to RGB565 (or GRAYSCALE)
                      sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
                      sensor.set_windowing((240, 240))       # Set 240x240 window.
                      sensor.skip_frames(time=2000)          # Let the camera adjust.
                  mask_face()
              if uart.read() != None:
                  print("Change")
                  mode = not mode
      
      main()
      
      
      


    • 我感觉这个帧率是正常的。



    • 但是运行起来看图像太卡了



    • 脱机运行比在电脑上看着帧率要高