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  • 神经网络训练目标检测数字1到8,要把镜头放到离数字很近时才会检测到1个数字是怎么回事



    • # Edge Impulse - OpenMV Object Detection Example
      
      import sensor, image, time, os, tf, math, uos, gc
      
      sensor.reset()                         # Reset and initialize the sensor.
      sensor.set_pixformat(sensor.GRAYSCALE)    # Set pixel format to RGB565 (or GRAYSCALE)
      sensor.set_framesize(sensor.QVGA)      # Set frame size to QVGA (320x240)
      sensor.skip_frames(time=2000)          # Let the camera adjust.
      
      net = None
      labels = None
      min_confidence = 0.5
      
      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:
          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) + ')')
      
      colors = [ # Add more colors if you are detecting more than 7 types of classes at once.
          (255,   0,   0),
          (  0, 255,   0),
          (255, 255,   0),
          (  0,   0, 255),
          (255,   0, 255),
          (  0, 255, 255),
          (255, 255, 255),
      ]
      
      clock = time.clock()
      while(True):
          clock.tick()
      
          img = sensor.snapshot()
      
          # detect() returns all objects found in the image (splitted out per class already)
          # we skip class index 0, as that is the background, and then draw circles of the center
          # of our objects
      
          for i, detection_list in enumerate(net.detect(img, thresholds=[(math.ceil(min_confidence * 255), 255)])):
              if (i == 0): continue # background class
              if (len(detection_list) == 0): continue # no detections for this class?
      
              print("********** %s **********" % labels[i])
              for d in detection_list:
                  [x, y, w, h] = d.rect()
                  center_x = math.floor(x + (w / 2))
                  center_y = math.floor(y + (h / 2))
                  print('x %d\ty %d' % (center_x, center_y))
                  img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
      
          print(clock.fps(), "fps", end="\n\n")
      


    • 我用神经网络训练目标检测数字1到8,但是图像只能识别一个数字,出现多个数字的时候,就都识别不了,要把镜头放到离数字很近时才会检测到1个数字



    • 在训练的时候我给照片贴标签时,我的框就尽可能的完全框住数字,框内不会有其他杂物



    • 你这个是识别准确度的问题,多加图片,每个类别加到1000张,可以有比较好的效果。