#!/usr/bin/env python
caffe_root = '../../' # this file should be run from {caffe_root}/examples (otherwise change this line)
import sys, os
sys.path.insert(0, caffe_root + 'python')
import caffe
dirname = os.path.abspath('./style-train-log')
if not os.path.exists(dirname): os.mkdir(dirname)
caffe.set_logdir(sys.argv[0], dirname)
#default is cpu mode
#caffe.set_device(0)
#caffe.set_mode_gpu()
#caffe.set_mode_cpu()
import numpy as np
from pylab import *
#%matplotlib inline
import tempfile
# Helper function for deprocessing preprocessed images, e.g., for display.
def deprocess_net_image(image):
image = image.copy() # don't modify destructively
image = image[::-1] # BGR -> RGB
image = image.transpose(1, 2, 0) # CHW -> HWC
image += [123, 117, 104] # (approximately) undo mean subtraction
# clamp values in [0, 255]
import numpy as np
from PIL import Image
caffe_root = '../../'
import sys, os
sys.path.insert(0, caffe_root + 'python')
import caffe
caffe.set_logdir(sys.argv[0], os.path.abspath('./log'))
# load image, switch to BGR, subtract mean, and make dims C x H x W for Caffe
#im = Image.open('pascal/VOC2010/JPEGImages/2007_000129.jpg')
im = Image.open('data/pascal/VOC2007/JPEGImages/000129.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)
#print str(out)
class_num = {}
