#uncoding:utf-8

set up Python environment: numpy for numerical routines, and matplotlib for plotting

import numpy as np
import matplotlib.pyplot as plt

display plots in this notebook

#%matplotlib inline

set display defaults

plt.rcParams['figure.figsize'] = (10, 10) # large images
plt.rcParams['image.interpolation'] = 'nearest' # don't interpolate: show square pixels
plt.rcParams['image.cmap'] = 'gray' # use

The caffe module needs to be on the Python path;

we'll add it here explicitly.

import sys
caffe_root = '/home/sea/caffe/' # this file should be run from {caffe_root}/examples (otherwise change this line)
sys.path.insert(0, caffe_root + 'python')

import caffe

If you get "No module named _caffe", either you have no

import os
if os.path.isfile(caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'):
print 'CaffeNet found.'
else:
print 'Downloading pre-trained CaffeNet model…'
#!../scripts/download_model_binary.py ../models/bvlc_reference_caffenet

caffe.set_mode_cpu()

model_def = caffe_root + 'models/bvlc_reference_caffenet/deploy.prototxt'
model_weights = caffe_root + 'models/bvlc_reference_caffenet/bvlc_reference_caffenet.caffemodel'
print "定义网络结构："
net = caffe.Net(model_def, # defines the structure of the model
model_weights, # contains the trained weights
caffe.TEST) # use test mode (e.g., don't perform dropout)

print "加载平均图："

load the mean ImageNet image (as distributed with Caffe) for subtraction

mu = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
mu = mu.mean(1).mean(1) # average over pixels to obtain the mean (BGR) pixel values
print 'mean-subtracted values:', zip('BGR', mu)

print "初始化转换输入数据格式转换器："

create transformer for the input called 'data'

transformer = caffe.io.Transformer({
'data': net.blobs['data'].data.shape})

print "设置输入数据格式转换器参数："
transformer.set_transpose('data', (2,0,1)) # move image channels to outermost dimension
transformer.set_mean('data', mu) # subtract the dataset-mean value in each channel
transformer.set_raw_scale('data', 255) # rescale from [0, 1] to [0, 255]
transformer.set_channel_swap('data', (2,1,0)) # swap channels from RGB to BGR

print "设置输入数据格式："

set the size of the input (we can skip this if we're happy

with the default; we can also change it later, e.g., for different batch sizes)

net.blobs['data'].reshape(50, # batch size
3, # 3-channel (BGR) images
227, 227) # image size is 227x227

print "加载猫："
image = caffe.io.load_image(caffe_root + 'examples/images/cat.jpg')
transformed_image = transformer.preprocess('data', image)
plt.imshow(image)
plt.show()

print "将猫加载到内存："

copy the image data into the memory allocated for the net

net.blobs['data'].data[…] = transformed_image

perform classification

output = net.forward()
output_prob = output['prob'][0] # the output probability vector for the first image in the batch
print 'predicted class is:', output_prob.argmax()

print "加载图像集合标签："

load ImageNet labels

labels_file = caffe_root + 'data/ilsvrc12/synset_words.txt'
if not os.path.exists(labels_file):
print "/data/ilsvrc12/get……sh"
#!../data/ilsvrc12/get_ilsvrc_aux.sh

labels = np.loadtxt(labels_file, str, delimiter='\t')
print 'output label:', labels[output_prob.argmax()]

sort top five predictions from softmax output

top_inds = output_prob.argsort()[::-1][:5] # reverse sort and take five largest items

print "打印分类结果：概率和标签："
print 'probabilities and labels:'
zip(output_prob[top_inds], labels[top_inds])

#%timeit net.forward()

print "切换到gpu模式："
caffe.set_device(0) # if we have multiple GPUs, pick the first one
caffe.set_mode_gpu()
net.forward() # run once before timing to set up memory
#%timeit net.forward()

for each layer, show the output shape

for layer_name, blob in net.blobs.iteritems():
print layer_name + '\t' + str(blob.data.shape)

for layer_name, param in net.params.iteritems():
print layer_name + '\t' + str(param[0].data.shape), str(param[1].data.shape)

print "定义可视化直方图的函数："
def vis_square(data):
"""Take an array of shape (n, height, width) or (n, height, width, 3)
and visualize each (height, width) thing in a grid of size approx. sqrt(n) by sqrt(n)"""
# normalize data for display
data = (data - data.min()) / (data.max() - data.min())

# force the number of filters to be square  
n = int(np.ceil(np.sqrt(data.shape\[0\])))  
padding = (((0, n \*\* 2 - data.shape\[0\]),  
    (0, 1), (0, 1))                 # add some space between filters  
    + ((0, 0),) \* (data.ndim - 3))  # don't pad the last dimension (if there is one)  
data = np.pad(data, padding, mode='constant', constant\_values=1)  # pad with ones (white)

# tile the filters into an image  
data = data.reshape((n, n) + data.shape\[1:\]).transpose((0, 2, 1, 3) + tuple(range(4, data.ndim + 1)))  
data = data.reshape((n \* data.shape\[1\], n \* data.shape\[3\]) + data.shape\[4:\])

plt.imshow(data); plt.axis('off')  

plt.show()

print "显示：直方图--conv1"

the parameters are a list of [weights, biases]

filters = net.params['conv1'][0].data
vis_square(filters.transpose(0, 2, 3, 1))

plt.show()

print "显示：直方图：conv5"
feat = net.blobs['conv1'].data[0, :36]
vis_square(feat)

plt.show()

print "显示：直方图, pool5"
feat = net.blobs['pool5'].data[0]
vis_square(feat)

plt.show()

print "显示：hist -fc6 "
feat = net.blobs['fc6'].data[0]
plt.subplot(2, 1, 1)
plt.plot(feat.flat)
plt.subplot(2, 1, 2)
_ = plt.hist(feat.flat[feat.flat > 0], bins=100)

plt.show()

print "显示：t--prob"
t = net.blobs['prob'].data[0]
plt.figure(figsize=(15, 3))

plt.plot(feat.flat)

plt.show()

download an image

#my_image_url = "…" # paste your URL here

for example:

my_image_url = "https://upload.wikimedia.org/wikipedia/commons/b/be/Orang_Utan%2C_Semenggok_Forest_Reserve%2C_Sarawak%2C_Borneo%2C_Malaysia.JPG"

#!wget -O image.jpg $my_image_url

print "加载图像"

transform it and copy it into the net

#image = caffe.io.load_image('/home/sea/shareVm/images/monkey/2.jpg')
image=caffe.io.load_image('/home/sea/Downloads/555eae4532988a6dc175031eed969fc0.jpg')
net.blobs['data'].data[…] = transformer.preprocess('data', image)

perform classification

net.forward()

obtain the output probabilities

output_prob = net.blobs['prob'].data[0]

print "output_prob = ", output_prob

sort top five predictions from softmax output

top_inds = output_prob.argsort()[::-1][:5]
print "top_inds = ", top_inds

print "显示：图像"
plt.imshow(image)
plt.show()

print "打印分类结果："
print 'probabilities and labels:'
zd = zip(output_prob[top_inds], labels[top_inds])
print "结果： ", zd
for e in zd:
print e

#copy-------------------------------------------------------------------------------

output_prob = output['prob'][0] # the output probability vector for the first image in the batch
print 'predicted class is:', output_prob.argmax()
indd = output_prob.argmax()
top_inds = indd
print "加载图像集合标签："
print (output_prob[top_inds], labels[top_inds])