def main():
K.set_image_dim_ordering('tf')
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from keras_video_classifier.library.recurrent_networks import VGG16BidirectionalLSTMVideoClassifier
from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf
data_set_name = 'AM'
input_dir_path = os.path.join(os.path.dirname(__file__), 'AM_data')
output_dir_path = os.path.join(os.path.dirname(__file__), 'models',
data_set_name)
report_dir_path = os.path.join(os.path.dirname(__file__), 'reports',
data_set_name)
np.random.seed(42)
# this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
#load_ucf(input_dir_path)
classifier = VGG16BidirectionalLSTMVideoClassifier()
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
vgg16_include_top=False,
data_set_name=data_set_name,
from_picture=True)
plot_and_save_history(
history, VGG16BidirectionalLSTMVideoClassifier.model_name,
report_dir_path + '/' +
VGG16BidirectionalLSTMVideoClassifier.model_name +
'-hi-dim-history.png')
def main():
K.set_image_dim_ordering('tf')
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from keras_video_classifier.library.utility.plot_utils import plot_and_save_history, plot_history_2win
from keras_video_classifier.library.recurrent_networks import ResnetLSTMVideoClassifier
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf
data_set_name = 'UCF-101'
input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
output_dir_path = os.path.join(os.path.dirname(__file__), 'models',
data_set_name)
report_dir_path = os.path.join(os.path.dirname(__file__), 'reports',
data_set_name)
np.random.seed(42)
# this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
load_ucf(input_dir_path)
classifier = ResnetLSTMVideoClassifier()
print("pass load model")
print(type(classifier))
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
resnet_include_top=False,
data_set_name=data_set_name)
plot_and_save_history(
history, ResnetLSTMVideoClassifier.model_name, report_dir_path + '/' +
ResnetLSTMVideoClassifier.model_name + '-hi-dim-history.png')
plot_history_2win(
history, ResnetLSTMVideoClassifier.model_name, report_dir_path + '/' +
ResnetLSTMVideoClassifier.model_name + '-hi-dim-history2win.png')
def main():
sys.path.append(patch_path('..'))
from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
from keras_video_classifier.library.convolutional import CnnVideoClassifier
data_set_name = 'videos'
input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
output_dir_path = os.path.join(os.path.dirname(__file__), 'models',
data_set_name)
report_dir_path = os.path.join(os.path.dirname(__file__), 'reports',
data_set_name)
np.random.seed(42)
# this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
#load_ucf(input_dir_path)
classifier = CnnVideoClassifier()
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
data_set_name=data_set_name,
max_frames=10)
plot_and_save_history(
history, CnnVideoClassifier.model_name,
report_dir_path + '/' + CnnVideoClassifier.model_name + '-history.png')
def main():
K.set_image_dim_ordering('tf')
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf
data_set_name = 'UCF-101'
input_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
output_dir_path = os.path.join(os.path.dirname(__file__), 'models',
data_set_name)
report_dir_path = os.path.join(os.path.dirname(__file__), 'reports',
data_set_name)
np.random.seed(42)
print('loading dataset')
# this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
load_ucf(input_dir_path)
print('training classifier')
classifier = VGG16LSTMVideoClassifier()
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
data_set_name=data_set_name)
plot_and_save_history(
history, VGG16LSTMVideoClassifier.model_name, report_dir_path + '/' +
VGG16LSTMVideoClassifier.model_name + '-history.png')
prnit('Training completed')
def main():
data_set_name = 'UCF-101'
input_dir_path = patch_path('very_large_data')
output_dir_path = patch_path('models/' + data_set_name)
report_dir_path = patch_path('reports/' + data_set_name)
np.random.seed(42)
# this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
load_ucf(input_dir_path)
classifier = CnnVideoClassifier()
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
data_set_name=data_set_name,
max_frames=10)
plot_and_save_history(
history, CnnVideoClassifier.model_name,
report_dir_path + '/' + CnnVideoClassifier.model_name + '-history.png')
def main():
K.set_image_data_format('channels_last')
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
from keras_video_classifier.library.recurrent_networks import ResnetLSTMVideoClassifier
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf
#from keras_video_classifier.library.utility.crime.UCF_Crime_loader import load_ucf
data_set_name = 'UCF-Anomaly-Detection-Dataset'
input_dir_path = os.path.join(os.path.dirname(__file__),
'/content/drive/My Drive')
output_dir_path = os.path.join(os.path.dirname(__file__),
'/content/drive/My Drive/models',
data_set_name)
report_dir_path = os.path.join(os.path.dirname(__file__),
'/content/drive/My Drive/reports',
data_set_name)
np.random.seed(42)
# this line downloads the video files of UCF-101 dataset if they are not available in the very_large_data folder
load_ucf(input_dir_path)
classifier = ResnetLSTMVideoClassifier()
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
resnet_include_top=False,
data_set_name=data_set_name,
test_size=0.1)
plot_and_save_history(
history, ResnetLSTMVideoClassifier.model_name, report_dir_path + '/' +
ResnetLSTMVideoClassifier.model_name + '-hi-dim-history.png')
plot_history_2win(
history, ResnetLSTMVideoClassifier.model_name, report_dir_path + '/' +
ResnetLSTMVideoClassifier.model_name + '-hi-dim-history2win.png')
def main():
testsetCount = 5
accuracies = [0, 0, 0, 0, 0]
for testid in range(0, testsetCount):
print(testid)
K.set_image_dim_ordering('tf')
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from keras_video_classifier.library.utility.plot_utils import plot_and_save_history
from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
#from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf
data_set_name = 'AM_pics_' + str(testid)
input_dir_path = os.path.join(os.path.dirname(__file__), 'AM_data')
output_dir_path = os.path.join(os.path.dirname(__file__), 'models',
data_set_name)
report_dir_path = os.path.join(os.path.dirname(__file__), 'reports',
data_set_name)
print("input_dir_path", input_dir_path)
print("output_dir_path", output_dir_path)
print("report_dir_path", report_dir_path)
np.random.seed(35)
classifier = VGG16LSTMVideoClassifier()
history = classifier.fit(data_dir_path=input_dir_path,
model_dir_path=output_dir_path,
data_set_name=data_set_name,
from_picture=True)
plot_and_save_history(
history, VGG16LSTMVideoClassifier.model_name,
report_dir_path + '/' + VGG16LSTMVideoClassifier.model_name +
'-history_' + str(testid) + '.png')
K.set_image_dim_ordering('tf')
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
print("predicting now !!!!!")
data_set_name = 'AM_pics_' + str(testid)
from keras_video_classifier.library.recurrent_networks import VGG16LSTMVideoClassifier
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf, scan_ucf_with_labels
vgg16_include_top = True
data_dir_path = os.path.join(os.path.dirname(__file__), 'AM_data')
model_dir_path = os.path.join(os.path.dirname(__file__), 'models',
data_set_name)
config_file_path = VGG16LSTMVideoClassifier.get_config_file_path(
model_dir_path, vgg16_include_top=vgg16_include_top)
weight_file_path = VGG16LSTMVideoClassifier.get_weight_file_path(
model_dir_path, vgg16_include_top=vgg16_include_top)
np.random.seed(42)
# load_ucf(data_dir_path)
predictor = VGG16LSTMVideoClassifier()
predictor.load_model(config_file_path, weight_file_path)
print("Reading weights from :", weight_file_path)
print("Reading Config from :", config_file_path)
videos = scan_ucf_with_labels(
data_dir_path,
[label
for (label, label_index) in predictor.labels.items()], testid)
video_file_path_list = np.array(
[file_path for file_path in videos.keys()])
np.random.shuffle(video_file_path_list)
print(videos)
correct_count = 0
count = 0
for video_file_path in video_file_path_list:
label = videos[video_file_path]
predicted_label = predictor.predict(video_file_path,
from_picture=True)
print('predicted: ' + predicted_label + ' actual: ' + label)
correct_count = correct_count + 1 if label == predicted_label else correct_count
count += 1
accuracy = correct_count / count
print('accuracy: ', accuracy)
accuracies[testid] = accuracy
print(accuracies)
print(sum(accuracies) / len(accuracies))
