# recog_sq.append(label) # target_sq.append(target) recog_sq[str(k)].append(label) target_sq[str(k)].append(target) k += 1 start_To = start_To + S_enc_frame for m in range(len(recog_sq.keys())): target = target_sq[str(m)][1:] recog = recog_sq[str(m)][1:] f_name = vid.split('/')[-1].split('.')[0] path = args.results_save_path + "/obs" + str(int( args.S_enc)) + "-pred" + str(m) write_predictions(path, f_name, recog) path = args.results_save_path + "/obs" + str(int( args.S_enc)) + "-pred" + str(m) + "_target" write_predictions(path, f_name + "_target", target) # report = classification_report(target, recog,output_dict=True) # print ("Accuracy: " + str(round(accuracy_score(target,recog) * 100,2))) # print ("Precision " + str(round(report['macro avg']['precision']*100,2))) # print ("Recall " + str(round(report['macro avg']['recall']*100,2))) # print ("f1-score " + str( round(report['macro avg']['f1-score']*100,2))) # n_T=np.zeros(len(actions_dict.keys())) # n_F=np.zeros(len(actions_dict.keys())) # for i in range(len(target)): # if target[i]==recog[i]: # n_T[actions_dict[target[i]]]+=1 # Se la classe per questo frame e stata riconosciuta aggiugno 1 a True # else: # n_F[actions_dict[target[i]]]+=1 # Se la calss di questo frame e errata aggiungo uno a False
for pred_p in pred_percentages: pred_len = int(pred_p*vid_len) output_len = pred_len + len(observed_content) label_seq, length_seq = get_label_length_seq(observed_content) with tf.Session() as sess: label_seq, length_seq = model.predict(sess, model_restore_path, pred_len, label_seq, length_seq, actions_dict, T) recognition = [] for i in range(len(label_seq)): recognition = np.concatenate((recognition, [label_seq[i]]*int(length_seq[i]))) recognition = recognition[:output_len] #write results to file f_name = vid.split('/')[-1].split('.')[0] path=args.results_save_path+"/obs"+str(obs_p)+"-pred"+str(pred_p) write_predictions(path, f_name, recognition) elif args.model == "cnn": model = ModelCNN(args.nRows, nClasses) for vid in list_of_videos: f_name = vid.split('/')[-1].split('.')[0] observed_content=[] vid_len = 0 if args.input_type == "gt": file_ptr = open(vid, 'r') content = file_ptr.read().split('\n')[:-1] vid_len = len(content) for obs_p in obs_percentages: if args.input_type == "decoded":