def evaluate_endtoend_identification(training = False, num_folds = 10, fcn_filters = 128, augm = False, augm_type=AugmentationType.RND):
model_name = generate_model_name(representation_type=RepresentationType.EE, fcn_filters = fcn_filters, augm = augm, augm_type = augm_type)
if training == True:
df_idnet = pd.read_csv("input_csv_gait/idnet_raw.csv", header = None)
if augm == True:
df_idnet = get_augmented_dataset(df_idnet, augm_type)
df_idnet = normalize_rows( df_idnet, st.NormalizationType.ZSCORE)
train_model(df_idnet, model_name, fcn_filters = fcn_filters, representation_learning=True)
df1 = pd.read_csv("input_csv_gait/zju_session1_frames_raw.csv", header = None)
df2 = pd.read_csv("input_csv_gait/zju_session2_frames_raw.csv", header = None)
df1 = normalize_rows( df1, st.NormalizationType.ZSCORE)
df2 = normalize_rows( df2, st.NormalizationType.ZSCORE)
features1 = get_model_output_features( df1, model_name)
features2 = get_model_output_features( df2, model_name)
print("\nSession 1")
evaluate_identification_CV(features1, num_folds = num_folds)
print("\nSession 2")
evaluate_identification_CV(features2, num_folds = num_folds)
print("\nCross-Session")
def evaluate_endtoend_identification(training=False,
augm=False,
augm_type=AugmentationType.RND):
if augm == True:
if augm_type == AugmentationType.RND:
model_name = "gait_fcn_rnd.h5"
else:
model_name = "gait_fcn_cshift.h5"
else:
model_name = "gait_fcn.h5"
if training == True:
df_idnet = pd.read_csv("input_csv/idnet.csv", header=None)
if augm == True:
df_idnet = get_augmented_dataset(df_idnet, augm_type)
df_idnet = normalize_rows(df_idnet, st.NormalizationType.ZSCORE)
train_model(df_idnet, model_name)
df1 = pd.read_csv("input_csv/zju_session1_frames_raw.csv", header=None)
df2 = pd.read_csv("input_csv/zju_session2_frames_raw.csv", header=None)
df1 = normalize_rows(df1, st.NormalizationType.ZSCORE)
df2 = normalize_rows(df2, st.NormalizationType.ZSCORE)
features1 = get_model_output_features(df1, model_name)
features2 = get_model_output_features(df2, model_name)
evaluate_identification_CV(features1, num_folds=10)
evaluate_identification_CV(features2, num_folds=10)
evaluate_identification_Train_Test(features1, features2)
def evaluate_raw_identification():
df1 = pd.read_csv("input_csv/zju_session1_frames_raw.csv", header=None)
df2 = pd.read_csv("input_csv/zju_session2_frames_raw.csv", header=None)
df1 = normalize_rows(df1, st.NormalizationType.ZSCORE)
df2 = normalize_rows(df2, st.NormalizationType.ZSCORE)
evaluate_identification_CV(df1, num_folds=10)
evaluate_identification_CV(df2, num_folds=10)
evaluate_identification_Train_Test(df1, df2)
def evaluate_raw_identification(num_folds = 10):
df1 = pd.read_csv("input_csv_gait/zju_session1_frames_raw.csv", header = None)
df2 = pd.read_csv("input_csv_gait/zju_session2_frames_raw.csv", header = None)
df1 = normalize_rows( df1, st.NormalizationType.ZSCORE)
df2 = normalize_rows( df2, st.NormalizationType.ZSCORE)
print("\nSession 1")
evaluate_identification_CV(df1, num_folds = num_folds)
print("\nSession 2")
evaluate_identification_CV(df2, num_folds = num_folds)
print("\nCross-Session")
evaluate_identification_Train_Test(df1, df2)