def create_ctf_file(seq_root, exp_root, output_file, is_training=True):
databases = ["RAVDESS", "VIDTIMIT", "SAVEE"]
nSeq = 0
fout = open(output_file, "w")
for i, db in enumerate(databases):
# each database
db_dir = seq_root + "/" + db
actors = get_items(db_dir)
if is_training:
if db == "RAVDESS":
actors = actors[:20]
elif db == "SAVEE":
actors = actors[:3]
elif db == "VIDTIMIT":
actors = actors[:40]
else:
raise IOError("folder does not exist!")
else:
if db == "RAVDESS":
actors = actors[20:]
elif db == "SAVEE":
actors = actors[3:]
elif db == "VIDTIMIT":
actors = actors[40:]
else:
raise IOError("folder does not exist!")
for actor in actors:
seq_dir = db_dir + "_feat/" + actor
exp_dir = exp_root + "/" + db + "/" + actor
items = get_items(seq_dir)
for seq in items:
print(seq_dir + "/" + seq)
seq_path = seq_dir + "/" + seq + "/dbspectrogram.csv"
exp_path = exp_dir + "/" + seq + ".npy"
if plb.Path(seq_path).exists() and plb.Path(exp_path).exists():
# load data
audio = np.loadtxt(seq_path,
dtype=np.float32,
delimiter=',')
exp = np.load(exp_path)
if exp.shape[0] != audio.shape[0]:
print("length not matched")
continue
# write
write_seq(nSeq, audio, exp, fout)
nSeq += 1
else:
print("file not exist")
fout.close()
def test_one_seq():
# directory to store output video. It will be created if it doesn't exist
save_dir = "../speech_dir/output_folder"
model_file = "../speech_dir/model_audio2exp_2019-03-24-21-03/model_audio2exp_2019-03-24-21-03.dnn"
# video directory holding separate frames of the video. Each image should be square.
video_direct = "../FrontalFaceData"
# spectrogram sequence is stored in a .csv file
audio_dir = "../speech_dir/RAVDESS_feat"
# AU labels are stored in an .npy file
exp_dir = "../ExpLabels"
make_dirs(video_direct,save_dir)
video_dir=plb.Path(video_direct)
for actor in video_dir.iterdir():
for frame_list in actor.iterdir():
try:
video_path=plb.Path(video_direct + "/" + actor.name + "/" + frame_list.name)
audio_path = plb.Path(audio_dir + "/" + actor.name + "/" + frame_list.name + "/dbspectrogram.csv")
save_path=save_dir + "/" + actor.name + "/" + frame_list.name
exp_path=plb.Path(exp_dir + "/" + actor.name + "/" + frame_list.stem + ".npy")
video_path=str(video_path)
except FileNotFoundError:
print("doesn't exit")
print(actor.name ,frame_list.name)
video_list = get_items(video_path, "full") # set to None if video_dir does not exist
model = C.load_model(model_file)
visualize_one_audio_seq(model, video_list, audio_path, exp_path, save_path)
def test_one_seq():
# directory to store output video. It will be created if it doesn't exist
save_dir = "../speech_dir/test_output_single"
model_file = "../speech_dir/model_audio2exp_2019-03-24-21-03/model_audio2exp_2019-03-24-21-03.dnn"
# video directory holding separate frames of the video. Each image should be square.
video_dir = "../FrontalFaceData"
# spectrogram sequence is stored in a .csv file
audio_file = "../speech_dir/RAVDESS_feat/Actor_21/01-01-07-02-01-01-21/dbspectrogram.csv"
# AU labels are stored in an .npy file
exp_file = "../ExpLabels/RAVDESS/Actor_21/01-01-07-02-01-01-21.npy"
video_list = get_items(video_dir,
"full") # set to None if video_dir does not exist
model = C.load_model(model_file)
visualize_one_audio_seq(model, video_list, audio_file, exp_file, save_dir)
def test_one_seq(visualizer):
""" Test one sequence """
''' Load model '''
# model_file = "../Model/model_audio2exp_2020-02-25-13-02/model_audio2exp_2020-02-25-13-02.dnn"
# model_file = "../Model/model_audio2exp_2020-02-25-15-07/model_audio2exp_2020-02-25-15-07.dnn" # GRU
model_file = "../Model/model_audio2exp_2020-02-26-10-49/model_audio2exp_2020-02-26-10-49.dnn" # LSTM
model = C.load_model(model_file)
''' Set input and output dir '''
current_time = get_current_time_string()
modality_list = ["01"]
vocal_channel_list = ["01"]
emotion_list = ["01", "02", "03", "04", "05", "06", "07", "08"]
emotion_intensity_list = ["01", "02"]
statement_list = ["01", "02"]
repetition_list = ["01", "02"]
actor_list = ["01", "02", "03", "04", "05", "06", "07", "08", "09", "10",
"11", "12", "13", "14", "15", "16", "17", "18", "19", "20",
"21", "22", "23", "24"]
actor = "05"
for emotion in emotion_list:
for emotion_intensity in emotion_intensity_list:
for statement in statement_list:
for repetition in repetition_list:
if emotion == "01" and emotion_intensity == "02":
continue
start = time.time()
sample = f"01-01-{emotion}-{emotion_intensity}-{statement}-{repetition}-{actor}"
print(f"[Start] sample: {sample}")
save_dir = f"../Test_output/LSTM_{sample}"
# video directory holding separate frames of the video. Each image should be square.
video_dir = f"../Data/RAVDESS/Video_Frame/Actor_{actor}/{sample}"
# spectrogram sequence is stored in a .csv file
audio_file = f"../Data/RAVDESS/features/Actor_{actor}/{sample}/dbspectrogram.csv"
# AU labels are stored in an .npy file
exp_file = f"../Data/ExpLabels/RAVDESS/Actor_{actor}/{sample}.npy"
video_list = get_items(video_dir, "full") # set to None if video_dir does not exist
visualize_one_audio_seq(model, video_list, audio_file, exp_file, visualizer, save_dir)
duration = time.time() - start
print(f"Time: {duration}")
# filename = "01-01-01-01-01-01-01"
# # directory to store output video. It will be created if it doesn't exist
# save_dir = f"../Test_output/GRU_{filename}"
# # video directory holding separate frames of the video. Each image should be square.
# video_dir = f"../Data/RAVDESS/Video_Frame/Actor_01/{filename}"
# # spectrogram sequence is stored in a .csv file
# audio_file = f"../Data/RAVDESS/features/Actor_01/{filename}/dbspectrogram.csv"
# # AU labels are stored in an .npy file
# exp_file = f"../Data/ExpLabels/RAVDESS/Actor_01/{filename}.npy"
# # single test
# save_dir = "../Data/test_output_single"
# # model_file = "../Model/model_audio2exp_2020-02-25-13-02/model_audio2exp_2020-02-25-13-02.dnn"
# model_file = "../Model/model_audio2exp_2020-02-25-15-07/model_audio2exp_2020-02-25-15-07.dnn"
# video_dir = "../Data/RAVDESS/Video_Frame/Actor_01/01-01-01-01-01-01-01"
# audio_file = "../Data/RAVDESS/features/Actor_01/01-01-01-01-01-01-01/dbspectrogram.csv"
# exp_file = "../Data/ExpLabels/RAVDESS/Actor_01/01-01-01-01-01-01-01.npy"
# video_list = get_items(video_dir, "full") # set to None if video_dir does not exist
# model = C.load_model(model_file)
#
# visualize_one_audio_seq(model, video_list, audio_file, exp_file, visualizer, save_dir)
print("Eval finished!")