pyni 发布的帖子

@kidswong999 明白你的意思了，但积分是指识别出来的置信度嘛，我应该如何获取呢

使用fomo模型后，如果有多个目标，如何使openmv先检测任何 “一个” 目标!
我直接使用了detection数组的第0位，但目标点容易晃动（也有可能是模型不准确，背景产生的影响），如下图所示

原生成代码为

请问有无更好的方法，谢谢

在openmv ide中，使用img = sensor.snapshot()函数可以截图保存当前照片作为对象。想请问在使用img = sensor.snapshot()函数后，我能否通过通过某些语句去获得这张照片的所有像素点数据，如rgb565等数据。我们的目的想通过获取整张图片的所有像素数据显示在某个lcd屏幕上而不是openmv自身所带的lcd，希望可以帮忙解答，谢谢

@kidswong999 找到了，create impulse学习时将迁移学习变为object detection

@kidswong999 刚刚去看了一眼edge impulse 上好像还没有这个模型0.0，是还没更新吗

openmv可以通过edge impulse训练模型，识别出塑料瓶，但能够将其在屏幕中用方框标出来并返回像素坐标吗

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, 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.QQVGA)      # 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

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:
    print(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) + ')')

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()

    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img, min_scale=1.0, scale_mul=0.8, x_overlap=0.5, y_overlap=0.5):
        print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        img.draw_rectangle(obj.rect())
        # This combines the labels and confidence values into a list of tuples
        predictions_list = list(zip(labels, obj.output()))

        for i in range(len(predictions_list)):
            print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))

    print(clock.fps(), "fps")