• 免费好用的星瞳AI云服务上线!简单标注,云端训练,支持OpenMV H7和OpenMV H7 Plus。可以替代edge impulse。 https://forum.singtown.com/topic/9519
  • 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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  • 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。
  • 出现“module object has no attribute "load"是啥原因,按照视频教程弄得



    • # 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.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 = 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")
      
      

      0_1725435952299_微信图片.jpg



    • 运行这个代码:

      
      import sensor
      import time
      import ml
      from ml.utils import NMS
      import math
      import image
      
      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.
      
      min_confidence = 0.4
      threshold_list = [(math.ceil(min_confidence * 255), 255)]
      
      
      print(model)
      
      model = ml.Model("trained.tflite", load_to_fb=True)
      labels = [line.rstrip('\n') for line in open("labels.txt")]
      
      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),
      ]
      
      
      # FOMO outputs an image per class where each pixel in the image is the centroid of the trained
      # object. So, we will get those output images and then run find_blobs() on them to extract the
      # centroids. We will also run get_stats() on the detected blobs to determine their score.
      # The Non-Max-Supression (NMS) object then filters out overlapping detections and maps their
      # position in the output image back to the original input image. The function then returns a
      # list per class which each contain a list of (rect, score) tuples representing the detected
      # objects.
      def fomo_post_process(model, inputs, outputs):
          n, oh, ow, oc = model.output_shape[0]
          nms = NMS(ow, oh, inputs[0].roi)
          for i in range(oc):
              img = image.Image(outputs[0][0, :, :, i] * 255)
              blobs = img.find_blobs(
                  threshold_list, x_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
                  )
                  nms.add_bounding_box(x, y, x + w, y + h, score, i)
          return nms.get_bounding_boxes()
      
      
      clock = time.clock()
      while True:
          clock.tick()
      
          img = sensor.snapshot()
      
          for i, detection_list in enumerate(model.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")