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| from menpo_functions import * | |
| from deep_heatmaps_model_fusion_net import DeepHeatmapsModel | |
| import os | |
| import pickle | |
| # directory for saving predictions | |
| out_dir = '/Users/arik/Desktop/out/' | |
| if not os.path.exists(out_dir): | |
| os.mkdir(out_dir) | |
| # directory with conventional landmark detection datasets (for bounding box files) | |
| conv_dir = '/Users/arik/Dropbox/a_mac_thesis/face_heatmap_networks/conventional_landmark_detection_dataset/' | |
| # bounding box type for conventional landmark detection datasets (gt / init) | |
| bb_type='init' | |
| # directory with clm models for tuning step | |
| clm_path='pdm_clm_models/clm_models/g_t_all' | |
| # directory with pdm models for correction step | |
| pdm_path='pdm_clm_models/pdm_models/' | |
| # model path | |
| model_path = '/Users/arik/Dropbox/Thesis_dropbox/models/model_train_wiki/model/deep_heatmaps-60000' | |
| # directory containing test sets | |
| data_dir = '/Users/arik/Dropbox/a_mac_thesis/artistic_faces/artistic_face_dataset/' | |
| test_sets = ['all_AF'] # test sets to evaluate | |
| # data_dir = '/Users/arik/Desktop/Thesis_mac/semi_art_sets/semi_art_sets_wiki_train_2/' | |
| # test_sets = [ | |
| # 'challenging_set_aug_geom_texture', | |
| # 'common_set_aug_geom_texture', | |
| # 'test_set_aug_geom_texture', | |
| # 'full_set_aug_geom_texture' | |
| # ] | |
| # load heatmap model | |
| heatmap_model = DeepHeatmapsModel( | |
| mode='TEST', img_path=conv_dir, test_model_path=model_path, menpo_verbose=False, scale=1) | |
| bb_dir = os.path.join(conv_dir, 'Bounding_Boxes') | |
| # predict landmarks for input test sets | |
| for i,test_data in enumerate(test_sets): | |
| if i == 0: | |
| reuse=None | |
| else: | |
| reuse=True | |
| out_temp = os.path.join(out_dir, test_data) | |
| if not os.path.exists(out_temp): | |
| os.mkdir(out_temp) | |
| bb_dictionary = load_bb_dictionary(bb_dir, mode='TEST', test_data=test_data) | |
| img_list = load_menpo_image_list(img_dir=data_dir, train_crop_dir=data_dir, img_dir_ns=data_dir, mode='TEST', | |
| test_data=test_data, bb_type=bb_type, bb_dictionary=bb_dictionary) | |
| img_list = img_list[:10] | |
| print test_data + ':' + str(len(img_list)) + ' images' | |
| preds = heatmap_model.get_landmark_predictions(img_list=img_list, pdm_models_dir=pdm_path, clm_model_path=clm_path, | |
| reuse=reuse) | |
| init_lms = preds['E'] | |
| ppdm_lms = preds['ECp'] | |
| clm_lms = preds['ECpT'] | |
| ect_lms = preds['ECT'] | |
| ecptp_jaw_lms = preds['ECpTp_jaw'] | |
| ecptp_out_lms = preds['ECpTp_out'] | |
| filehandler = open(os.path.join(out_temp,'E_lms'),"wb") | |
| pickle.dump(init_lms,filehandler) | |
| filehandler.close() | |
| filehandler = open(os.path.join(out_temp,'ECp_lms'),"wb") | |
| pickle.dump(ppdm_lms,filehandler) | |
| filehandler.close() | |
| filehandler = open(os.path.join(out_temp,'ECpT_lms'),"wb") | |
| pickle.dump(clm_lms,filehandler) | |
| filehandler.close() | |
| filehandler = open(os.path.join(out_temp,'ECT_lms'),"wb") | |
| pickle.dump(ect_lms,filehandler) | |
| filehandler.close() | |
| filehandler = open(os.path.join(out_temp,'ECpTp_jaw_lms'),"wb") | |
| pickle.dump(ecptp_jaw_lms,filehandler) | |
| filehandler.close() | |
| filehandler = open(os.path.join(out_temp,'ECpTp_out_lms'),"wb") | |
| pickle.dump(ecptp_out_lms,filehandler) | |
| filehandler.close() | |
| print("\nDone!\n") |