infer.py

import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
import caffe

# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
im = Image.open('data/testdata/000008.jpg')
in_ = np.array(im, dtype=np.float32)
in_ = in_[:,:,::-1]
in_ -= np.array((104.00698793,116.66876762,122.67891434))
in_ = in_.transpose((2,0,1))

# load net
net = caffe.Net('voc-fcn8s/deploy.prototxt', 'voc-fcn8s/fcn8s-heavy-pascal.caffemodel', caffe.TEST)
# shape for input (data blob is N x C x H x W), set data
net.blobs['data'].reshape(1, *in_.shape)
net.blobs['data'].data[...] = in_
# run net and take argmax for prediction
net.forward()
out = net.blobs['score'].data[0].argmax(axis=0)

#plt.imshow(out,cmap='gray');
plt.imshow(out);
plt.axis('off')
plt.savefig('results/voc-fcn8s/000008.png')
plt.show()