jupyter notebook test

import os

import numpy as np

import scipy.io as sio

import Image

caffe_root = '../../../../'

import sys

sys.path.insert(0, caffe_root + 'python')

import caffe

data_root = '../../../../data/A_resize/'

test_dir = data_root+'test_data/images/'

gt_dir=data_root+'test_data/ground_truth/'

pathdir=os.listdir(test_dir)

error_all=0

error=np.zeros((1,len(pathdir)),dtype=np.float64)

pred=np.zeros((1,len(pathdir)),dtype=np.float64)

gt=np.zeros((1,len(pathdir)),dtype=np.float64)

i=0

pa=[]

for allpath in pathdir:

  img_file_name =allpath

  pa.append(allpath)

  img = Image.open(test_dir+img_file_name)

  img = np.array(img, dtype=np.float32)

  if len(img.shape)==2 :

    img2=np.empty((img.shape[0],img.shape[1],3),dtype=img.dtype)

    img2[:,:,0]=img

    img2[:,:,1]=img

    img2[:,:,2]=img

    img=img2

  img = img[:,:,::-1]

  img = img.transpose((2,0,1))

  caffe.set_mode_gpu()

  caffe.set_device(0)

  model_root = './'

  net = caffe.Net(model_root+'deploy.prototxt','./network_iter_4320000.caffemodel', caffe.TEST)

  # shape for input (data blob is N x C x H x W), set data

  net.blobs['data'].reshape(1, *img.shape)

  net.blobs['data'].data[...] = img

  # run net and take argmax for prediction

  net.forward()

  pred[0,i]=np.sum(net.blobs['score'].data[0][0,:,:])

  label_name='GT_'+img_file_name[:-3]+'mat'

  label_path=os.path.join(gt_dir,label_name)

  label=sio.loadmat(label_path)

  gt[0,i]=label['image_info'][0,0]['number']

  error[0,i]=abs(gt[0,i]-pred[0,i])

  error_all=error_all+error[0,i]

  i=i+1