？？？？应该怎么去找

哪里可以下载3的固件包

MemoryError:快帧缓冲区堆栈内存不足请降低运行此算法的分辨率图像以绕过此问题！

@kidswong999 有没有什么解决办法呢

@kidswong999 就是openmv4原本的神经网络模块用不了新的神经网络没法用就没有神经网络功能吗？

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")

错误翻译：MemoryError:快帧缓冲区堆栈内存不足请降低运行此算法的分辨率图像以绕过此问题！

@kidswong999 好的十分感谢

@kidswong999 这次没出之前的错误告诉我堆栈内存不足这个应该怎么解决

@kidswong999 我就是按照它的步骤放在u盘里面

Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf

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 = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

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 tf.classify(net, 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")