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import numpy as np | |
import matplotlib.pyplot as plt | |
import os.path | |
import json | |
import scipy | |
import argparse | |
import math | |
import pylab | |
from sklearn.preprocessing import normalize | |
caffe_root = '/SegNet/caffe-segnet/' # Change this to the absolute directoy to SegNet Caffe | |
import sys | |
sys.path.insert(0, caffe_root + 'python') | |
import caffe | |
# Import arguments | |
parser = argparse.ArgumentParser() | |
parser.add_argument('--model', type=str, required=True) | |
parser.add_argument('--weights', type=str, required=True) | |
parser.add_argument('--iter', type=int, required=True) | |
args = parser.parse_args() | |
caffe.set_mode_gpu() | |
net = caffe.Net(args.model, | |
args.weights, | |
caffe.TEST) | |
for i in range(0, args.iter): | |
net.forward() | |
image = net.blobs['data'].data | |
label = net.blobs['label'].data | |
predicted = net.blobs['prob'].data | |
image = np.squeeze(image[0,:,:,:]) | |
output = np.squeeze(predicted[0,:,:,:]) | |
ind = np.argmax(output, axis=0) | |
r = ind.copy() | |
g = ind.copy() | |
b = ind.copy() | |
r_gt = label.copy() | |
g_gt = label.copy() | |
b_gt = label.copy() | |
class0 = [0,0,0] | |
class1 = [255,0,0] | |
class2 = [0,0,255] | |
label_colours = np.array([class0, class1, class2]) | |
for l in range(0,3): | |
r[ind==l] = label_colours[l,0] | |
g[ind==l] = label_colours[l,1] | |
b[ind==l] = label_colours[l,2] | |
r_gt[label==l] = label_colours[l,0] | |
g_gt[label==l] = label_colours[l,1] | |
b_gt[label==l] = label_colours[l,2] | |
rgb = np.zeros((ind.shape[0], ind.shape[1], 3)) | |
rgb[:,:,0] = r | |
rgb[:,:,1] = g | |
rgb[:,:,2] = b | |
rgb_gt = np.zeros((ind.shape[0], ind.shape[1], 3)) | |
rgb_gt[:,:,0] = r_gt | |
rgb_gt[:,:,1] = g_gt | |
rgb_gt[:,:,2] = b_gt | |
image = image | |
image = np.transpose(image, (1,2,0)) | |
output = np.transpose(output, (1,2,0)) | |
image = image[:,:,(2,1,0)] | |
#scipy.misc.toimage(rgb, cmin=0.0, cmax=255).save(IMAGE_FILE+'_segnet.png') | |
plt.figure() | |
plt.imshow(image,vmin=0, vmax=1) | |
plt.figure() | |
plt.imshow(rgb_gt,vmin=0, vmax=1) | |
plt.figure() | |
plt.imshow(rgb,vmin=0, vmax=1) | |
plt.show() | |
print 'Success!' |
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