4. 如何使用tf.free_from_fb()释放导入内存
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    • 如何使用tf.free_from_fb()释放导入内存

    • R

      请在这里粘贴代码

      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()

    clock.tick()

    Mask_Flag = False
    Count = 0
    Check_Num = 40

    for i in range(Check_Num):

        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)

            if("mask" == labels[i]):
                Count = Count + 1
                if(Count >= 20):
                    Mask_Flag = True
                    break

    tf.free_from_fb()
    print(clock.fps(), "fps", end="\n\n")

      使用net = tf.load("trained.tflite", load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))加载后想释放内存，看了函数库tf.free_from_fb()可以释放内存，但是再载入模型报错内存不足。手册上tf.free_from_fb()没有输入参数，我想知道如何使用这个函数

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    • 使用OpenMV4 H7 Plus 不会有问题，你用的是OpenMV 4？

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