if __name__ == '__main__':
dataset = 'ucf'
if len(sys.argv) > 0:
dataset = sys.argv[0]
cwd = os.getcwd()
data_dir = os.path.join(cwd, 'data')
if 'hmdb' in dataset.lower():
list_dir = os.path.join(data_dir, 'hmdb51_test_train_splits')
else:
list_dir = os.path.join(data_dir, 'ucfTrainTestlist')
weights_dir = os.path.join(cwd, 'models')
# fine tune resnet50
# train_data = os.path.join(list_dir, 'trainlist.txt')
# test_data = os.path.join(list_dir, 'testlist.txt')
video_dir = os.path.join(data_dir, 'UCF-Preprocessed-OF')
train_data, test_data, class_index = get_data_list(list_dir, video_dir)
input_shape = (10, 216, 216, 3)
weights_dir = os.path.join(weights_dir, 'finetuned_resnet_RGB_65.h5')
model = finetuned_resnet(include_top=True, weights_dir=weights_dir)
fit_model(model, train_data, test_data, weights_dir, input_shape)
# train CNN using optical flow as input
# weights_dir = os.path.join(weights_dir, 'temporal_cnn_42.h5')
# train_data, test_data, class_index = get_data_list(list_dir, video_dir)
# video_dir = os.path.join(data_dir, 'OF_data')
# input_shape = (216, 216, 18)
# model = temporal_CNN(input_shape, N_CLASSES, weights_dir, include_top=True)
# fit_model(model, train_data, test_data, weights_dir, input_shape, optical_flow=True)
import os
from models.finetuned_resnet import finetuned_resnet
from utils.model_processing import model_processing
N_CLASSES = 101
IMSIZE = (216, 216, 3)
if __name__ == '__main__':
src_dir = 'E:/ActionRecognition_rnn/data/UCF-Preprocessed'
dest_dir = 'E:/ActionRecognition_rnn/data/CNN_Predicted'
weights_dir = 'E:/ActionRecognition_rnn/models'
finetuned_resnet_weights = os.path.join(weights_dir, 'finetuned_resnet.h5')
model = finetuned_resnet(include_top=False, weights_dir=finetuned_resnet_weights)
TIMESEQ_LEN = 10
model_processing(model, src_dir, dest_dir, TIMESEQ_LEN)