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')