fgqg 发布的帖子

@kidswong999 我去试了一下，tf的库都不行，然后我准备用network神经网络的，但是IDE却提示ImportError: no module named 'nn'，没有nn的库，这是什么情况？

# TensorFlow Lite Person Dection Example
#
# Google's Person Detection Model detects if a person is in view.
#
# In this example we slide the detector window over the image and get a list
# of activations. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.

import sensor, image, time, os, tf

sensor.reset()                         # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # 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.

# Load the built-in person detection network (the network is in your OpenMV Cam's firmware).
net = tf.load('./mobilenet_small_quant.tflite')
labels = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]

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

    img = sensor.snapshot()

    # net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
    # specified). A classification score output vector will be generated for each location. At each scale the
    # detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
    # If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
    # the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
    # sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
    # down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
    # Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...

    # default settings just do one detection... change them to search the image...
    for obj in net.classify(img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
        print("**********\nDetections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
        for i in range(len(obj.output())):
            print("%s = %f" % (labels[i], obj.output()[i]))
        img.draw_rectangle(obj.rect())
        img.draw_string(obj.x()+3, obj.y()-1, labels[obj.output().index(max(obj.output()))], mono_space = False)
    print(clock.fps(), "fps")

报错信息：MemoryError: memory allocation failed, allocating 156856 bytes MicroPython: v1.15-r57 OpenMV: v4.1.1 HAL: v1.2.8 BOARD: OPENMV3-STM32F765