神经计算棒使用步骤
1. GRAPH_PATH：graph文件路径；
2. IMAGE_PATH：要分类的图片的路径；
3. IMAGE_DIM：由选择的神经网络定义的图像尺寸； 例：GoogLeNet uses 224x224 pixels, AlexNet uses 227x227 pixels
4. IMAGE_STDDEV：由选择的神经网络定义的标准差（标度值） 例：GoogLeNet uses no scaling factor, InceptionV3 uses 128 (stddev = 1/128)
5. IMAGE_MEAN: 平均减法是深度学习中常用的一种技术，用于对数据进行中心处理
例：ILSVRC dataset, the mean is B = 102 Green = 117 Red = 123
使用NCS做图像分类的5个步骤：
从mvnc库中引入mvncapi模块
import mvnc.mvncapi as mvnc
Step 1:
将NCS插入应用处理器（Ubuntu笔记本电脑/台式机）USB端口时，它将自身列为USB设备。将调用API来查找枚举的NCS设备:
# Look for enumerated Intel Movidius NCS device(s); quit program if none found.
devices = mvnc.EnumerateDevices()
if len( devices ) == 0:
print( 'No devices found' )
quit()
如果插入了多个NCS，还需要选择一个NCS并打开：
# Get a handle to the first enumerated device and open it
device = mvnc.Device( devices[0] )
device.OpenDevice()
Step2：加载graph文件到NCS
# Read the graph file into a buffer
with open( GRAPH_PATH, mode='rb' ) as f:
blob = f.read()
# Load the graph buffer into the NCS
graph = device.AllocateGraph( blob )
Step3: 将单个图像加载到Intel Movidius NCS上以运行推理
图像预处理：
1. 调整图像大小/裁剪图像以匹配预先训练的网络定义的尺寸。
GoogLeNet uses 224x224 pixels, AlexNet uses 227x227 pixels.
2. 每个通道的平均值（蓝色，绿色和红色）从整个数据集中减去。这是深度学习中常用的一种技术，可以集中数据。
3. 将图像转换为半精度浮点数（fp16）数组，并使用LoadTensor函数调用将图像加载到NCS上。skimage库可以在一行代码中完成此操作。
# Read & resize image [Image size is defined during training]
img = print_img = skimage.io.imread( IMAGES_PATH )
img = skimage.transform.resize( img, IMAGE_DIM, preserve_range=True )
# Convert RGB to BGR [skimage reads image in RGB, but Caffe uses BGR]
img = img[:, :, ::-1]
# Mean subtraction & scaling [A common technique used to center the data]
img = img.astype( numpy.float32 )
img = ( img - IMAGE_MEAN ) * IMAGE_STDDEV
# Load the image as a half-precision floating point array
graph.LoadTensor( img.astype( numpy.float16 ), 'user object' )
Step4: 从NCS读取并打印推理结果
# Get the results from NCS
output, userobj = graph.GetResult()
# Print the results
print('\n------- predictions --------')
labels = numpy.loadtxt( LABELS_FILE_PATH, str, delimiter = '\t' )
order = output.argsort()[::-1][:6]
for i in range( 0, 5 ):
print ('prediction ' + str(i) + ' is ' + labels[order[i]])
# Display the image on which inference was performed
skimage.io.imshow( IMAGES_PATH )
skimage.io.show( )
Step 5: 卸载图形并关闭设备
为了避免内存泄漏和/或分段错误，我们应该关闭所有打开的文件或资源并释放所有使用的内存。
graph.DeallocateGraph()
device.CloseDevice()
原文： Build an Image Classifier in 5 steps