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    swik

    @swik

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    swik 发布的帖子

    • RE: 为什么我按照视频里的训练,放进openmv中会显示这个

      @swik 还有代码应该怎么搞😢

      发布在 OpenMV Cam
      S
      swik
    • RE: 为什么我按照视频里的训练,放进openmv中会显示这个

      我还想问问,识别图像中,是不是只有神经网络模型的才能显示概率的,模板匹配没有对吗。

      发布在 OpenMV Cam
      S
      swik
    • RE: 为什么我按照视频里的训练,放进openmv中会显示这个

      0_1746629890838_0c2378f7-8072-4ba1-933d-4f2488508a6a-QQ_1746629880349.png 这个为什么不支持啊🤔

      发布在 OpenMV Cam
      S
      swik
    • 为什么我按照视频里的训练,放进openmv中会显示这个

      0_1746626700853_15eb7933-ca20-4ea9-a4a1-cede2754f8bd-QQ_1746626695313.png !

      # Edge Impulse - OpenMV FOMO Object Detection Example
      #
      # This work is licensed under the MIT license.
      # Copyright (c) 2013-2024 OpenMV LLC. All rights reserved.
      # https://github.com/openmv/openmv/blob/master/LICENSE
      
      import sensor, image, time, ml, math, uos, gc
      
      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.
      
      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 = ml.Model("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),
      ]
      
      threshold_list = [(math.ceil(min_confidence * 255), 255)]
      
      def fomo_post_process(model, inputs, outputs):
          ob, oh, ow, oc = model.output_shape[0]
      
          x_scale = inputs[0].roi[2] / ow
          y_scale = inputs[0].roi[3] / oh
      
          scale = min(x_scale, y_scale)
      
          x_offset = ((inputs[0].roi[2] - (ow * scale)) / 2) + inputs[0].roi[0]
          y_offset = ((inputs[0].roi[3] - (ow * scale)) / 2) + inputs[0].roi[1]
      
          l = [[] for i in range(oc)]
      
          for i in range(oc):
              img = image.Image(outputs[0][0, :, :, i] * 255)
              blobs = img.find_blobs(
                  threshold_list, x_stride=1, y_stride=1, area_threshold=1, pixels_threshold=1
              )
              for b in blobs:
                  rect = b.rect()
                  x, y, w, h = rect
                  score = (
                      img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0
                  )
                  x = int((x * scale) + x_offset)
                  y = int((y * scale) + y_offset)
                  w = int(w * scale)
                  h = int(h * scale)
                  l[i].append((x, y, w, h, score))
          return l
      
      clock = time.clock()
      while(True):
          clock.tick()
      
          img = sensor.snapshot()
      
          for i, detection_list in enumerate(net.predict([img], callback=fomo_post_process)):
              if i == 0: continue  # background class
              if len(detection_list) == 0: continue  # no detections for this class?
          
              print("********** %s **********" % labels[i])
              for x, y, w, h, score in detection_list:
                  center_x = math.floor(x + (w / 2))
                  center_y = math.floor(y + (h / 2))
                  print(f"x {center_x}\ty {center_y}\tscore {score}")
                  img.draw_circle((center_x, center_y, 12), color=colors[i])
      
          print(clock.fps(), "fps", end="\n\n")
      
      发布在 OpenMV Cam
      S
      swik