import matplotlib.pyplot as plt
from train_config import train_images, val_images, test_images, train_gt, val_gt
from train_config import num_classes, train_batch_size, val_batch_size
from train_config import vgg_pretrained, epochs
# Put the paths to the datasets in lists, because that's what `BatchGenerator` requires as input.
train_image_dirs = [train_images]
train_ground_truth_dirs = [train_gt]
val_image_dirs = [val_images]
val_ground_truth_dirs = [val_gt]
train_dataset = BatchGenerator(image_dirs=train_image_dirs,
image_file_extension='png',
ground_truth_dirs=train_ground_truth_dirs,
image_name_split_separator='leftImg8bit',
ground_truth_suffix='gtFine_labelIds',
check_existence=True,
num_classes=num_classes)
val_dataset = BatchGenerator(image_dirs=val_image_dirs,
image_file_extension='png',
ground_truth_dirs=val_ground_truth_dirs,
image_name_split_separator='leftImg8bit',
ground_truth_suffix='gtFine_labelIds',
check_existence=True,
num_classes=num_classes)
num_train_images = train_dataset.get_num_files()
num_val_images = val_dataset.get_num_files()
df = pd.read_csv('dataset/csv/imdb_csv/imdb_age_regression_train_split_47950-70-10-20.csv')
# In[5]:
cols = list(df.columns[1:])
in_format = list(df.columns)
cols, in_format
# In[6]:
train_dataset = BatchGenerator(box_output_format=cols)
validation_dataset = BatchGenerator(box_output_format=cols)
train_dataset. parse_csv(labels_filename='dataset/csv/imdb_csv/imdb_age_regression_train_split_47950-70-10-20.csv',
images_dir='dataset/imdb-hand-crop',
input_format=in_format)
validation_dataset.parse_csv(labels_filename='dataset/csv/imdb_csv/imdb_age_regression_val_split_47950-70-10-20.csv',
images_dir='dataset/imdb-hand-crop',
input_format=in_format)
# In[7]:
img_height, img_width, img_depth = (224,224,3)
# ### ATENÇÃO: SELECIONAR OS PATHS PROS PESOS
# In[2]:
activations = ['relu', 'lrelu']
img_treats = ['image-treat-1', 'image-treat-2', 'image-treat-3']
nets = ['lenet', 'alexnet']
for img_treat in img_treats:
for net in nets:
for activation in activations:
# In[3]:
test_dataset = BatchGenerator(box_output_format=['class_id'])
test_dataset.parse_csv(
labels_filename=
'/home/nicoli/github/alexnet/dataset/csv/imdb_csv/imdb_age_regression_test_split_47950-70-10-20.csv',
images_dir=
'/home/nicoli/github/alexnet/dataset/imdb-hand-crop/',
input_format=['image_name', 'class_id'])
# In[4]:
print("Number of images in the dataset:",
test_dataset.get_n_samples())
# In[5]:
img_height, img_width, img_depth = (224, 224, 3)