def get_knn_salience(labeled_imidx, ny, nx): ''' as name ''' knnsal_path = '../../data_viper/salience_all.mat' tmp = loadfile(knnsal_path) knn_gal = tmp['salience_all_gal'] # view a knn_prb = tmp['salience_all_prb'] # view b salmap_knn_sub = knn_gal[:, :, labeled_imidx].transpose((2, 0, 1)) salmap_knn_query = [mapresize(im, size=(ny, nx)) for im in salmap_knn_sub] salmap_knn_sub = knn_prb[:, :, labeled_imidx].transpose((2, 0, 1)) salmap_knn_gallery = [mapresize(im, size=(ny, nx)) for im in salmap_knn_sub] return np.asarray(salmap_knn_query), np.asarray(salmap_knn_gallery)
def get_calibrated_gtsal():
''' get calibrated groundtruth salience maps '''
# load viper dataset
viper = loadfile('../../data_viper/viper.pkl')
## load knn salience
knnsal_path = '../../data_viper/salience_all.mat'
tmp = loadfile(knnsal_path)
knn_gal = tmp['salience_all_gal'] # view a
knn_prb = tmp['salience_all_gal'] # view b
mapsz = viper.imgs[0].shape[:2]
labeled_imidx_path = '../../data_viper/labeled_imidx.mat'
tmp = loadfile(labeled_imidx_path)
labeled_imidx = tmp['labeled_imidx'].flatten()
salmap_knn_small = knn_gal[:, :, labeled_imidx].transpose((2, 0, 1))
salmap_knn_all = [mapresize(im, size=mapsz) for im in salmap_knn_small]
knn_arr = []
gt_arr = []
for seg, sal, msk in zip(viper.segmsks, viper.salmsks, salmap_knn_all):
idx = seg != 0
gt_arr = np.hstack((gt_arr, sal[idx]))
knn_arr = np.hstack((knn_arr, msk[idx]))
nBin_gt = 100
nBin_knn = 100
gt_hist = np.histogram(gt_arr, bins=nBin_gt, range=(0, 1))[0]/float(len(gt_arr))
knn_hist = np.histogram(knn_arr, bins=nBin_knn, range=(0, 1))[0]/float(len(knn_arr))
mapping = np.asarray(histmapping(gt_hist, knn_hist))
pl.figure(1)
pl.plot(mapping)
pl.savefig('mapping.jpg')
# mapping from knn to gt maps
# salmap_knn_mapped = [mapping[np.floor(sal*nBin_knn).astype(int)] for sal in salmap_knn_small]
salmap_gt_mapped = [mapping[np.floor(sal*nBin_gt).astype(int)] for sal in viper.salmsks]
return salmap_gt_mapped
def main():
''' pipeline for evaluating salience '''
# three types:
# 1) unsupervised, knnsal
# 2) groundtruth, gtsal
# 3) prediction, predsal
## load cnn salience with groundtruth
supsal_path = '../data_viper/model_feat/salmaps_comparison.pkl'
test_imids, salmap_gt, salmap_pred = loadfile(supsal_path)
mapsz = salmap_gt[0].shape
## load knn salience
knnsal_path = '../data_viper/salience_all.mat'
tmp = loadfile(knnsal_path)
knn_gal = tmp['salience_all_gal'] # view a
knn_prb = tmp['salience_all_gal'] # view b
labeled_imidx_path = '../data_viper/labeled_imidx.mat'
tmp = loadfile(labeled_imidx_path)
labeled_imidx = tmp['labeled_imidx'].flatten()
salmap_knn_small = knn_gal[:, :, labeled_imidx].transpose((2, 0, 1))
salmap_knn_all = [mapresize(im, size=mapsz) for im in salmap_knn_small]
# get rid of background for better illustration
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
salmap_knn = []
for seg, msk in zip(viper.segmsks, salmap_knn_all):
idx = seg == 0
msk[idx] = 0
salmap_knn.append(msk)
salmap_knn = np.asarray(salmap_knn)
# qualitative evaluation
save_path = '../data_viper/model_feat/'
for i in test_imids:
pl.figure(1)
pl.subplot(1, 4, 1) # show image
pl.imshow(viper.imgs[i])
pl.title('image')
pl.subplot(1, 4, 2) # show groundtruth salience
pl.imshow(salmap_gt[i]*255., cmap='hot', vmin=0, vmax=255)
pl.title('groundtruth')
pl.subplot(1, 4, 3) # show knn salience
pl.imshow(salmap_knn[i]*255., cmap='hot', vmin=0, vmax=255)
pl.title('KNN salience')
pl.xlabel('abserr={0:.2f}'.format(abs_error(salmap_knn[i].flatten(), salmap_gt[i].flatten())))
pl.subplot(1, 4, 4) # show CNN prediction salience
pl.imshow(salmap_pred[i]*255., cmap='hot', vmin=0, vmax=255)
pl.title('CNN salience')
pl.xlabel('abserr={0:.2f}'.format(abs_error(salmap_pred[i].flatten(), salmap_gt[i].flatten())))
pl.savefig(save_path + '{0:03d}.jpg'.format(i))
print save_path +'{0:03d}.jpg'.format(i) + ' saved!'
# quantitative evaluation
test_idx = np.unique(test_imids)
print 'mean abs error - KNN vs Gt: {0:.2f}'.format(abs_error(salmap_knn[test_idx], salmap_gt[test_idx]))
print 'mean abs error - CNN vs Gt: {0:.2f}'.format(abs_error(salmap_pred[test_idx], salmap_gt[test_idx]))
# pl.figure(2)
# test_idx = np.unique(test_imids)
# recall_knn, precision_knn = get_roc_curve(salmap_gt[test_idx], salmap_knn[test_idx])
# pl.plot(recall_knn, precision_knn, 'b', linewidth=2, label='knn vs. gt')
# recall_cnn, precision_cnn = get_roc_curve(salmap_gt[test_idx], salmap_pred[test_idx])
# pl.plot(recall_cnn, precision_cnn, 'r', linewidth=2, label='cnn vs. gt')
# pl.xlabel('recall')
# pl.ylabel('precision')
# pl.legend()
# pl.savefig(save_path+'roc.jpg')
# print 'ROC curve saved!'
os.system('xdg-open '+save_path)
def get_gt_salience(ny, nx): ''' get groundtruth salience ''' gtsal_path = '../../data_viper/salience_gt.pkl' salmap_gt_query = loadfile(gtsal_path) return np.asarray([mapresize(im, size=(ny, nx)) for im in salmap_gt_query])
def get_mask(segmsks, ny, nx): ''' get foreground masks ''' return np.asarray([mapresize(msk, size=(ny, nx))>0 for msk in segmsks])
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])
