4. 我想用神经网络比较盒子的高度判断哪个在第二层哪个在第一层。试验了很多次一直报错，不知道错在哪。
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    • 我想用神经网络比较盒子的高度判断哪个在第二层哪个在第一层。试验了很多次一直报错，不知道错在哪。

    • X

      我想用神经网络比较盒子的高度判断哪个在第二层哪个在第一层。试验了很多次一直报错，不知道错在哪。

      # 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%s %d\ty%s %d' % (labels[i],center_x,labels[i], center_y))
            print('%d' % (i))
            if(i==1):
            a=center_y
            if(i==2):
            b=center_y
            if(a>b)
            print(12）
            if(b>a)
            print(21)
            
                    
            img.draw_circle((center_x, center_y, 12), color=colors[i], thickness=2)
        
    print(clock.fps(), "fps", end="\n\n")
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    • 什么叫做盒子的高度？

      FOMO只是检测对象，不能检测高度

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