def test_batch_generator(test_files, batch_size, img_width=224, img_height=224, model='vgg16'):
N = len(test_files)
batch_index = 0
while True:
current_index = (batch_index * batch_size) % N
if N >= (current_index + batch_size):
current_batch_size = batch_size
batch_index += 1
else:
current_batch_size = N - current_index
batch_index = 0
X_batch = np.zeros((current_batch_size, img_width, img_height, 3))
for i in range(current_index, current_index + current_batch_size):
img_path = test_files[i]
img = load_img(img_path, img_width)
X_batch[i - current_index] = img
X_batch = X_batch.astype(np.float16)
if model == 'vgg16':
X_batch = vgg16_preprocess_input(X_batch)
elif model == 'inceptv3':
X_batch = incept3_preprocess_input(X_batch)
yield X_batch
def generate_train_data(dataframe, nbr_classes, img_root_path, shuffle=True,
augment=False, img_width=224, img_height=224, model='vgg16'):
N = dataframe.shape[0]
if shuffle:
dataframe = sk_shuffle(dataframe)
X_train = np.zeros((N, img_width, img_height, 3))
Y_train = np.zeros((N, nbr_classes))
for index, row in dataframe.iterrows():
driver_id = row['subject']
classname = row['classname']
label = int(classname[-1])
img_name = row['img']
img_path = os.path.join(img_root_path, 'train', classname, img_name)
img = load_img(img_path, img_width)
X_train[index] = img
Y_train[index, label] = 1
X_train = X_train.astype(np.float16)
if model == 'vgg16':
X_train = vgg16_preprocess_input(X_train)
elif model == 'inceptv3':
X_train = incept3_preprocess_input(X_train)
return X_train, Y_train
def batch_generator(dataframe, nbr_classes, img_root_path, batch_size, shuffle=True, augment=False,
return_label=True, img_width=224, img_height=224, model='vgg16'):
N = dataframe.shape[0]
if shuffle:
dataframe = sk_shuffle(dataframe)
batch_index = 0
while True:
current_index = (batch_index * batch_size) % N
if N >= (current_index + batch_size):
current_batch_size = batch_size
batch_index += 1
else:
current_batch_size = N - current_index
batch_index = 0
if shuffle:
dataframe = sk_shuffle(dataframe)
X_batch = np.zeros((current_batch_size, img_width, img_height, 3))
Y_batch = np.zeros((current_batch_size, nbr_classes))
for i in range(current_index, current_index + current_batch_size):
row = dataframe.loc[i,:]
driver_id = row['subject']
classname = row['classname']
label = int(classname[-1])
img_name = row['img']
img_path = os.path.join(img_root_path, 'train', classname, img_name)
img = load_img(img_path, img_width)
X_batch[i - current_index] = img
if return_label:
Y_batch[i - current_index, label] = 1
if augment:
X_batch = X_batch.astype(np.uint8)
X_batch = seq.augment_images(X_batch)
X_batch = X_batch.astype(np.float16)
if model == 'vgg16':
X_batch = vgg16_preprocess_input(X_batch)
elif model == 'inceptv3':
X_batch = incept3_preprocess_input(X_batch)
if return_label:
yield (X_batch, Y_batch)
else:
yield X_batch
def batch_generator_triplet(dataframe, nbr_classes, img_root_path, batch_size=16, mode='train',
return_label=True, img_width=224, img_height=224, shuffle=True, augment=False, model='vgg16'):
N = dataframe.shape[0]
if shuffle:
dataframe = sk_shuffle(dataframe)
batch_index = 0
while True:
current_index = (batch_index * batch_size) % N
if N >= (current_index + batch_size):
current_batch_size = batch_size
batch_index += 1
else:
current_batch_size = N - current_index
batch_index = 0
if shuffle:
dataframe = sk_shuffle(dataframe)
X_anchor = np.zeros((current_batch_size, img_width, img_height, 3))
X_positive = np.zeros((current_batch_size, img_width, img_height, 3))
X_negative = np.zeros((current_batch_size, img_width, img_height, 3))
Y_class = np.zeros((current_batch_size, nbr_classes))
Y_pseudo_label = np.zeros((current_batch_size, 1))
for i in range(current_index, current_index + current_batch_size):
row = dataframe.loc[i, :]
driver_id = row['subject']
classname = row['classname']
label = int(classname[-1])
img_name = row['img']
anchor_img_path = os.path.join(img_root_path, 'train', classname, img_name)
anchor_img = load_img(anchor_img_path, img_width)
if mode == 'train':
same_driver_df = dataframe[dataframe['subject']==driver_id]
classname_list = same_driver_df['classname'].unique().tolist()
# find positive sample which has the same subject and the same classname but different img name
same_driver_same_class_df = same_driver_df[same_driver_df['classname']==classname]
same_driver_same_class_diff_img_df = same_driver_same_class_df[same_driver_same_class_df['img']!=img_name]
assert len(same_driver_same_class_diff_img_df) != 0, 'driver:{},classname:{},only has one img:{}'.format(driver_id,classname,img_name)
positive_row = same_driver_same_class_diff_img_df.sample(1)
same_driver_same_class_df = []
same_driver_same_class_diff_img_df = []
# find negative sample which has the same subject and the different classname also different img name (Hard negative)
classname_list.remove(classname)
assert classname_list != [], 'driver: {},only has one class: {}'.format(driver_id,classname)
other_classname = random.choice(classname_list)
hard_negative_df = same_driver_df[same_driver_df['classname']==other_classname]
negative_row = hard_negative_df.sample(1)
hard_negative_df = []
same_driver_df = []
positive_img_path = os.path.join(img_root_path, 'train', positive_row['classname'].values[0], positive_row['img'].values[0])
negative_img_path = os.path.join(img_root_path, 'train', negative_row['classname'].values[0], negative_row['img'].values[0])
positive_img = load_img(positive_img_path, img_width)
negative_img = load_img(negative_img_path, img_width)
X_anchor[i - current_index] = anchor_img
X_positive[i - current_index] = positive_img
X_negative[i - current_index] = negative_img
elif mode == 'val':
X_anchor[i - current_index] = anchor_img
X_positive[i - current_index] = anchor_img
X_negative[i - current_index] = anchor_img
if return_label:
Y_class[i - current_index, label] = 1
if augment:
X_anchor = X_anchor.astype(np.uint8)
X_anchor = seq.augment_images(X_anchor)
X_positive = X_positive.astype(np.uint8)
X_positive = seq.augment_images(X_positive)
X_negative = X_negative.astype(np.uint8)
X_negative = seq.augment_images(X_negative)
X_anchor = X_anchor.astype(np.float16)
X_positive = X_positive.astype(np.float16)
X_negative = X_negative.astype(np.float16)
if model == 'vgg16':
X_anchor = vgg16_preprocess_input(X_anchor)
X_positive = vgg16_preprocess_input(X_positive)
X_negative = vgg16_preprocess_input(X_negative)
elif model == 'inceptv3':
X_anchor = incept3_preprocess_input(X_anchor)
X_positive = incept3_preprocess_input(X_positive)
X_negative = incept3_preprocess_input(X_negative)
if return_label:
yield ([X_anchor, X_positive, X_negative], [Y_class, Y_pseudo_label])
else:
if mode == 'feature_extraction':
yield X_anchor
else:
yield [X_anchor, X_positive, X_negative]
def generate_train_data_triplet(dataframe, nbr_classes, img_root_path, shuffle=True, mode='train',
augment=False, img_width=224, img_height=224, model='vgg16'):
N = dataframe.shape[0]
if shuffle:
dataframe = sk_shuffle(dataframe)
X_anchor = np.zeros((N, img_width, img_height, 3))
X_positive = np.zeros((N, img_width, img_height, 3))
X_negative = np.zeros((N, img_width, img_height, 3))
Y_train = np.zeros((N, nbr_classes))
Y_pseudo_label = np.zeros((N, 1))
for index, row in dataframe.iterrows():
driver_id = row['subject']
classname = row['classname']
label = int(classname[-1])
img_name = row['img']
img_path = os.path.join(img_root_path, 'train', classname, img_name)
img = load_img(img_path, img_width)
if mode == 'train':
same_driver_df = dataframe[dataframe['subject']==driver_id]
classname_list = same_driver_df['classname'].unique().tolist()
# find positive sample which has the same subject and the same classname but different img name
same_driver_same_class_df = same_driver_df[same_driver_df['classname']==classname]
same_driver_same_class_diff_img_df = same_driver_same_class_df[same_driver_same_class_df['img']!=img_name]
assert len(same_driver_same_class_diff_img_df) != 0, 'driver:{},classname:{},only has one img:{}'.format(driver_id,classname,img_name)
positive_row = same_driver_same_class_diff_img_df.sample(1)
same_driver_same_class_df = []
same_driver_same_class_diff_img_df = []
# find negative sample which has the same subject and the different classname also different img name (Hard negative)
classname_list.remove(classname)
assert classname_list != [], 'driver: {},only has one class: {}'.format(driver_id,classname)
other_classname = random.choice(classname_list)
hard_negative_df = same_driver_df[same_driver_df['classname']==other_classname]
negative_row = hard_negative_df.sample(1)
hard_negative_df = []
same_driver_df = []
positive_img_path = os.path.join(img_root_path, 'train', positive_row['classname'].values[0], positive_row['img'].values[0])
negative_img_path = os.path.join(img_root_path, 'train', negative_row['classname'].values[0], negative_row['img'].values[0])
positive_img = load_img(positive_img_path, img_width)
negative_img = load_img(negative_img_path, img_width)
elif mode == 'valid':
positive_img = img
negative_img = img
X_anchor[index] = img
X_positive[index] = positive_img
X_negative[index] = negative_img
Y_train[index, label] = 1
X_anchor = X_anchor.astype(np.float16)
X_positive = X_positive.astype(np.float16)
X_negative = X_negative.astype(np.float16)
if model == 'vgg16':
X_anchor = vgg16_preprocess_input(X_anchor)
X_positive = vgg16_preprocess_input(X_positive)
X_negative = vgg16_preprocess_input(X_negative)
elif model == 'inceptv3':
X_anchor = incept3_preprocess_input(X_anchor)
X_positive = incept3_preprocess_input(X_positive)
X_negative = incept3_preprocess_input(X_negative)
return ([X_anchor,X_positive,X_positive], [Y_train,Y_pseudo_label])