def main():
salmap_gt_mapped = get_calibrated_gtsal()
gtsal_path = '../../data_viper/salience_gt.pkl'
savefile(gtsal_path, salmap_gt_mapped)
pl.figure(1)
pl.imshow(immontage(salmap_gt_mapped, [6, 17]))
pl.savefig('tmp1.jpg')
os.system('open tmp1.jpg')
def main():
''' pipeline for testing and evaluation '''
# preset parameters
save_path = '../data_viper/model/'
# load data
imL = 48
bs = 100
datafile_viper = '../data_viper/viper_class.pkl'
viper = loadfile(datafile_viper)
# load model
modelfile_viper = '../data_viper/model/model.pkl'
model = loadfile(modelfile_viper)
# evaluation and testing
# test_x = viper.test_x.get_value(borrow=True)
test_x = np.asarray(viper.test_ims)
test_y = viper.test_y.get_value(borrow=True)
n_test = test_x.shape[0]
n_batches_test = np.int(1.0*n_test/bs)
n_test = n_batches_test * bs
test_ypred = [model.test(i)[-1] for i in range(n_batches_test)]
test_ypred = np.asarray(test_ypred).flatten()
test_ims = test_x[:n_test].reshape((n_test, imL, imL, -1))
# assign predicted scores to images
h, w = viper.imgs[0].shape[:2]
mh, mw = len(np.unique(viper.yy)), len(np.unique(viper.xx))
msk0 = np.zeros(mh*mw)
msks = [msk0.copy() for im in viper.imgs]
showlist = []
for i in range(n_test):
imgid = viper.test_imgids[i]
patid = viper.test_ctrids[i]
score = test_ypred[i]
msks[imgid][patid] = score
# resize predicted salience map to match image size
msks_rs = [imresize(msk.reshape((mh, mw)), size=(h, w))/255. for msk in msks]
# save salience map for comparison
test_imids = np.asarray(np.unique(viper.test_imgids))
salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids])
salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids])
savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred])
# quantize to show different test patches
kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10)
kmeans.fit(test_ypred.reshape(n_test, 1))
# save to result folder
for i in range(10):
idx = kmeans.labels_== i
if any(idx):
im = immontage(list(test_ims[idx]))
imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), im)
print 'testing finished'
os.system('xdg-open '+save_path)
def main():
''' pipeline for supervised salience training '''
if os.path.isdir('../data_viper/'):
datapath = '../data_viper/'
else:
datapath = '../data/'
save_path = '../data_viper/model_feat/'
DATA_OPT = 'feat' # feature type
TRAIN_OPT = 'SVR' # training model option
TRAIN = True # wheather re-train the model
# prepare training data for supervised salience training
#=======================================================
datafile_viper = datapath + 'viper.pkl'
if not os.path.isfile(datafile_viper):
viper = DataMan_viper_small()
viper.make_data()
savefile(datafile_viper, viper)
else:
viper = loadfile(datafile_viper)
viper = preprocess_data(viper, DATA_OPT)
# training
# ==============
modelfile = datapath + 'model_feat/model.pkl'
if TRAIN:
tic = time.clock()
model = train_model(viper, TRAIN_OPT)
toc = time.clock()
print 'Elapsed training time: {0:.2f} min'.format((toc-tic)/60.)
savefile(modelfile, model)
os.system('ls -lh ' + modelfile)
else:
model = loadfile(modelfile)
## validation
#=========================================
print 'validating trained model'
nValid = 5000
valididx = np.random.permutation(viper.valid_feat.shape[0])[:nValid]
# valid_ypred = model.predict(viper.valid_feat[valididx])
valid_ypred = predict(model, viper.valid_feat[valididx], viper.yy[viper.valid_ctrids][valididx], viper.imH)
#- quantize patches based on testing scores
kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10, verbose=1)
kmeans.fit(valid_ypred.reshape(nValid, 1))
#- crop patches for testing image
valid_patset = np.asarray(viper.get_patchset('valid'))[valididx]
#- save to result folder
os.system('rm '+save_path+'*.jpg')
for i in range(10):
idx = kmeans.labels_== i
if any(idx):
pats = immontage(list(valid_patset[idx]))
imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), pats)
print 'patchset {} saved'.format(i)
### testing
#===============
print 'testing'
# test_ypred = model.predict(viper.test_feat)
test_ypred = predict(model, viper.test_feat, viper.yy_test[viper.test_ctrids], viper.imH)
## assign predicted scores to images
h, w = viper.imgs[0].shape[:2]
mh, mw = len(np.unique(viper.yy_test)), len(np.unique(viper.xx_test))
msk0 = np.zeros(mh*mw, dtype=np.float32)
msks = [msk0.copy() for im in viper.imgs]
showlist = []
n_test = len(test_ypred)
for i in range(n_test):
imgid = viper.test_imgids[i]
patid = viper.test_ctrids[i]
score = test_ypred[i]
msks[imgid][patid] = score
# resize predicted salience map to match image size
msks_rs = [mapresize(msk.reshape((mw, mh)).T, size=(h, w)) for msk in msks]
# msks_rs = msks
# save salience map for comparison
test_imids = np.asarray(np.unique(viper.test_imgids))
salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids])
salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids])
savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred])
def test_knn():
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
viper = downsample_data(viper)
# from sklearn.neighbors import KNeighborsRegressor
# model = KNeighborsRegressor(n_neighbors=5, weights='uniform', metric='euclidean')
# model.fit(viper.train_feat, viper.train_y)
from sklearn.neighbors import KDTree
# divide into stripes
nStripe = 10
y_max = viper.yy.max()
y_min = viper.yy.min()
y_len = np.int((y_max - y_min)/10.)
y_centers = np.round(np.linspace(y_min+y_len, y_max-y_len, nStripe))
k = 5
y_ctr = y_centers[k]
stripe_idx = np.where((viper.yy[viper.train_ctrids] >= y_ctr-y_len) & (viper.yy[viper.train_ctrids] < y_ctr+y_len))[0]
model = KDTree(viper.train_feat[stripe_idx, :288], metric='euclidean')
train_patset = viper.get_patchset('train')
test_patset = viper.get_patchset('test')
test_ids = np.where((viper.yy[viper.test_ctrids] >= y_ctr-y_len) & (viper.yy[viper.test_ctrids] < y_ctr+y_len))[0]
np.random.shuffle(test_ids)
for i in test_ids:
get_testrect = lambda i: [viper.xx[viper.test_ctrids[i]] - viper.patL/2,
viper.yy[viper.test_ctrids[i]] - viper.patL/2,
viper.patL, viper.patL]
get_trainrect = lambda i: [viper.xx[viper.train_ctrids[i]] - viper.patL/2,
viper.yy[viper.train_ctrids[i]] - viper.patL/2,
viper.patL, viper.patL]
gray2color = lambda grayim: np.dstack((grayim, grayim, grayim))
imlist = []
patlist = []
maplist = []
patlist.append(imresize(test_patset[i], size=(100, 100)))
imlist.append(drawrect(viper.imgs[viper.test_imgids[i]], get_testrect(i)))
maplist.append(viper.salmsks[viper.test_imgids[i]])
dist, ind = model.query(viper.test_feat[i, :288], k=30, return_distance=True)
print viper.test_y[i]
hist = np.histogram(viper.train_y[stripe_idx[ind[0]]])
print hist[0]
print hist[1]
print dist
for id in stripe_idx[ind[0]]:
patlist.append(imresize(train_patset[id], size=(100, 100)))
imlist.append(drawrect(viper.imgs[viper.train_imgids[id]], get_trainrect(id)))
maplist.append(viper.salmsks[viper.train_imgids[id]])
pats = immontage(patlist)
imgs = immontage(imlist)
maps = immontage(maplist)
imsave('tmp1.jpg', pats)
imsave('tmp2.jpg', imgs)
imsave('tmp3.jpg', maps)
raw_input()
os.system('xdg-open tmp1.jpg')