for direct in tqdm(directories_inp):
fname_inp = path_inp + direct + '/' + name
fname_out = path + direct + '_face/' + name
smap = mpimg.imread(fname_inp)
if np.size(smap.shape) == 3:
smap = rgb2gray(smap)
if np.shape(smap) is not (369, 492):
smap = cv2.resize(smap, (492, 369), interpolation=cv2.INTER_AREA)
# normalize saliecies
smap = normalize_saliency_map(smap)
smap_face = normalize_saliency_map(smap_face)
smap_final_03 = normalize_saliency_map(smap * 0.7 + smap_face * 0.3)
smap_final_05 = normalize_saliency_map(smap * 0.5 + smap_face * 0.5)
smap_final_07 = normalize_saliency_map(smap * 0.3 + smap_face * 0.7)
smap_final = normalize_saliency_map(smap)
save_plot_without_frames(smap_final,
path + 'no_face/' + direct + '/' + name)
save_plot_without_frames(smap_final_03,
path + 'face_03/' + direct + '/' + name)
save_plot_without_frames(smap_final_05,
path + 'face_05/' + direct + '/' + name)
save_plot_without_frames(smap_final_07,
path + 'face_07/' + direct + '/' + name)
shutil.move(fname, './data/redo/done/')
img = mpimg.imread(fname)
pbar = tqdm(total=40)
# run basic models
smap_face, _ = IK.run(img, keys=[], faces=True)
pbar.update(10)
# run pysaliency models
smap_aim = aim.saliency_map(img)
pbar.update(10)
# normalize saliecies
smap_face = normalize_saliency_map(smap_face)
smap_aim = normalize_saliency_map(smap_aim)
pbar.update(10)
# add face to other (non-deep learnig) saliencies
smap_aim_face = normalize_saliency_map(smap_aim + smap_face)
# save plots
print('Saving plots')
save_plot_without_frames(smap_face, path + 'faces/' + name + '.jpg')
save_plot_without_frames(smap_aim, path + 'aim/' + name + '.jpg')
save_plot_without_frames(smap_aim_face, path + 'aim_face/' + name + '.jpg')
pbar.update(10)
shutil.move(fname, 'data/done')
pbar.close()
IK = IttiKoch(verbose=False)
location = 'test_models'
location_cache = 'model_caches'
for fname in tqdm(fnames):
print(fname)
name = fname[10:-4] # get just the name of the picture
img = mpimg.imread(fname)
pbar = tqdm(total=40)
# run basic models
smap_ik, _ = IK.run(img)
pbar.update(10)
smap_face, _ = IK.run(img, keys=[], faces=True)
pbar.update(10)
# run deep gaze models
# smap_dg, log_density_prediction = run_deep_gaze(img)
# pbar.update(10)
# smap_icf, log_density_prediction_icf = run_deep_gaze(img, model='ICF')
# pbar.update(10)
save_plot_without_frames(smap_ik, path + 'IK/' + name + '.jpg')
save_plot_without_frames(smap_face, path + 'faces/' + name + '.jpg')
# save_plot_without_frames(smap_dg, path + 'dg/' + name + '.jpg')
# save_plot_without_frames(smap_icf, path + 'icf/' + name + '.jpg')
pbar.close()
smap_gbvs = normalize_saliency_map(smap_gbvs)
smap_dg = normalize_saliency_map(smap_dg)
smap_icf = normalize_saliency_map(smap_icf)
# add face to other (non-deep learnig) saliencies
smap_ik_face = normalize_saliency_map(smap_ik + smap_face)
smap_aim_face = normalize_saliency_map(smap_aim + smap_face)
smap_sun_face = normalize_saliency_map(smap_sun + smap_face)
smap_cas_face = normalize_saliency_map(smap_cas + smap_face)
smap_covsal_face = normalize_saliency_map(smap_covsal + smap_face)
smap_gbvs_face = normalize_saliency_map(smap_gbvs + smap_face)
smap_icf_face = normalize_saliency_map(smap_icf + smap_face)
# save plots
print('Saving plots')
save_plot_without_frames(smap_ik, path + 'IK/' + name + '.jpg')
save_plot_without_frames(smap_face, path + 'faces/' + name + '.jpg')
save_plot_without_frames(smap_aim, path + 'aim/' + name + '.jpg')
save_plot_without_frames(smap_sun, path + 'sun/' + name + '.jpg')
save_plot_without_frames(smap_cas, path + 'cas/' + name + '.jpg')
save_plot_without_frames(smap_covsal, path + 'covsal/' + name + '.jpg')
save_plot_without_frames(smap_gbvs, path + 'gbvs/' + name + '.jpg')
save_plot_without_frames(smap_dg, path + 'dg/' + name + '.jpg')
save_plot_without_frames(smap_icf, path + 'icf/' + name + '.jpg')
save_plot_without_frames(smap_ik_face, path + 'ik_face/' + name + '.jpg')
save_plot_without_frames(smap_aim_face, path + 'aim_face/' + name + '.jpg')
save_plot_without_frames(smap_sun_face, path + 'sun_face/' + name + '.jpg')
save_plot_without_frames(smap_cas_face, path + 'cas_face/' + name + '.jpg')
save_plot_without_frames(smap_covsal_face,
path + 'covsal_face/' + name + '.jpg')
print(fnames)
# take care of directories
directories = ['dg', 'icf']
if not os.path.exists(path):
os.makedirs(path)
for direct in directories:
if not os.path.exists(path + direct):
print('creating directory ' + direct)
os.makedirs(path + direct)
for fname in tqdm(fnames):
print(fname)
name = fname[12:-4] # get just the name of the picture
img = mpimg.imread(fname)
pbar = tqdm(total=20)
# run deep gaze models
smap_dg, log_density_prediction = run_deep_gaze(img)
pbar.update(10)
smap_icf, log_density_prediction_icf = run_deep_gaze(img, model='ICF')
pbar.update(10)
save_plot_without_frames(smap_dg, path + 'dg/' + name + '.jpg')
save_plot_without_frames(smap_icf, path + 'icf/' + name + '.jpg')
shutil.move(fname, './data/dldl/done')
pbar.close()
os.makedirs(path)
if not os.path.exists('./data/test/done'):
os.makedirs('./data/test/done')
for direct in directories:
if not os.path.exists(path + direct):
print('creating directory ' + direct)
os.makedirs(path + direct)
# initiate our model
IK = IttiKoch(verbose=False)
for fname in tqdm(fnames):
name = fname[12:-4] # get just the name of the picture
print(name)
img = mpimg.imread(fname)
# run basic models
smap_ik, _ = IK.run(img, faces=False)
# normalize saliecies
smap_ik = normalize_saliency_map(smap_ik)
# save plots
print('Saving plots')
print(path + 'ik/' + name + '.jpg')
save_plot_without_frames(smap_ik, path + 'ik/' + name + '.jpg')
shutil.move(fname, 'data/test/done')