def test_transform_image_dataframe_to_matrix():
min_images_per_person = [5]
min_per_person = min_images_per_person[0]
multi_data = get_mounted_data(min_per_person, min_per_person)
#Y = multi_data[['name']]
#X = multi_data[['image_path']]
#print(Y.head(6))
#print(X.head(6))
data_x, data_y = transform_image_dataframe_to_matrix(
multi_data, 250, 250, 'lfw-dataset/lfw-deepfunneled/lfw-deepfunneled/')
def test_create_dataset_tfRecord():
min_images_per_person = [100]
min_per_person = min_images_per_person[0]
multi_data = get_mounted_data(min_per_person, min_per_person)
#Y = multi_data[['name']]
#X = multi_data[['image_path']]
#print(Y.head(6))
#print(X.head(6))
create_dataset_tfRecord(multi_data,
'lfw-dataset/lfw-deepfunneled/lfw-deepfunneled/')
def main():
min_images_per_person = [30]
models = ["DeepFace"]
num_folds = 5
batch_sizes = [60]
for min_per_person in min_images_per_person:
for batch in batch_sizes:
for model in models:
multi_data = get_mounted_data(min_per_person, min_per_person)
Y = multi_data[['name']]
X = multi_data[['image_path']]
CLASSES = Y.groupby('name').nunique().shape[0]
print("### run_k_fold ", " min_per_person ", min_per_person, " CLASSES ", CLASSES,
"model ", model, " batch_size ", batch)
run_k_fold(multi_data, X, Y, CLASSES, model, batch, num_folds)
tf.keras.backend.clear_session()
gc.collect()
def main():
epoch = 10
min_images_per_person = [130]#[30,25] # [25,20]
models = ["LeNet5"]#,"VGGFace"]#,"AlexNet","DeepFace","VGGFace"]
num_folds = 2
#aumentando o batch para 30 DeepFace conseguiu bons resultados, testar com outras
batch_sizes = [30]#[2,4,8,30]
for min_per_person in min_images_per_person:
for batch in batch_sizes:
for model in models:
multi_data = get_mounted_data(min_per_person, min_per_person)
Y = multi_data[['name']]
X = multi_data[['image_path']]
CLASSES = Y.groupby('name').nunique().shape[0]
# print("### run_hold_out "," epoch ", epoch, " min_per_person ", min_per_person," CLASSES ", CLASSES,"model ",model," batch_size ",batch)
# run_hold_out(multi_data, X, Y, CLASSES, epoch, model, batch)
print("### run_k_fold ", " epoch ", epoch, " min_per_person ", min_per_person, " CLASSES ", CLASSES, " model ", model, " batch_size ", batch)
run_k_fold(multi_data, X, Y, CLASSES, epoch, model, batch, num_folds)
def main():
epoch = 300
min_images_per_person = [30] #[30,25] # [25,20]
models = [
"AlexNet", "DeepFace"
] #["LeNet5","AlexNet","DeepFace"]#["LeNet5","AlexNet","DeepFace","VGGFace"]
num_folds = 5
batch_sizes = [30, 60] #[2,4,8,30]
for min_per_person in min_images_per_person:
for batch in batch_sizes:
for model in models:
multi_data = get_mounted_data(min_per_person, min_per_person)
Y = multi_data[['name']]
X = multi_data[['image_path']]
CLASSES = Y.groupby('name').nunique().shape[0]
print("### run_k_fold ", " epoch ", epoch, " min_per_person ",
min_per_person, " CLASSES ", CLASSES, "model ", model,
" batch_size ", batch)
run_k_fold(multi_data, X, Y, CLASSES, epoch, model, batch,
num_folds)
gc.collect()
def main():
min_images_per_person = [30]
models = ["DeepFace"]
num_folds = 5
batch_sizes = [60]
min_per_person = min_images_per_person[0]
multi_data = get_mounted_data(min_per_person, min_per_person)
Y = multi_data[['name']]
X = multi_data[['image_path']]
CLASSES = Y.groupby('name').nunique().shape[0]
nomes_classes = []
for i in pd.DataFrame(
Y.groupby('name')['name'].nunique().reset_index(
name="unique"))['name']:
nomes_classes.append(str(i))
print('CLASSES')
print(CLASSES)
#print(nomes_classes)
train, test = train_test_split(multi_data,
test_size=0.25,
random_state=42,
shuffle=True,
stratify=multi_data[['name']])
print('train')
print(train.shape)
print(train)
print(train.groupby('name').count().image_path)
print('test')
print(test.shape)
print(test)
print(test.groupby('name').count().image_path)
calfw_df = calfw_mount_data(nomes_classes)
print(calfw_df.shape)
calfw_Y = calfw_df[['name']]
calfw_X = calfw_df[['image_path']]
calfw_CLASSES = calfw_Y.groupby('name').nunique().shape[0]
#print(calfw_df.groupby('name')['name'].nunique().reset_index(name="unique")['name'])
print(calfw_Y)
print(calfw_X)
print(calfw_CLASSES)
ind_counts = calfw_df.groupby('name').count().image_path
print(ind_counts)
calfw_classes = []
for j in pd.DataFrame(
calfw_df.groupby('name')['name'].nunique().reset_index(
name="unique"))['name']:
calfw_classes.append(str(j))
print(calfw_classes)
print(set(nomes_classes) - set(calfw_classes))
print(set(calfw_classes) - set(nomes_classes))
for batch in batch_sizes:
for model in models:
print("### run_k_fold ", " min_per_person ", min_per_person,
" CLASSES ", CLASSES, "model ", model, " batch_size ", batch)
run_k_fold(calfw_df, X, Y, CLASSES, model, batch, num_folds,
nomes_classes)
tf.keras.backend.clear_session()
gc.collect()
