def main():
K.set_image_dim_ordering('tf')
#sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
#os.getcwd()
current_path = os.getcwd()
# 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(current_path, 'test_video')
model_dir_path = os.path.join(current_path, 'models', 'p')
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)
#videos = scan_ucf_with_labels(data_dir_path)
#video_file_path_list = data_dir_path
#correct_count = 0
#count = 0
labels = []
videos = []
outlier = []
video_list = os.listdir(data_dir_path)
#video_list_10 = video_list[800:820]
count = 0
#for video in data_dir_path:
for video in video_list:
print(count)
video = os.path.join(data_dir_path, video)
#label = videos[vide o_file_path]
try:
predicted_label = predictor.predict(video)
videos = np.append(videos, video)
labels = np.append(labels, predicted_label)
except:
print(video)
outlier.append(video)
count += 1
#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)
return videos, labels, outlier
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 VGG16LSTMVideoClassifier
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf
print("PASS 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 = VGG16LSTMVideoClassifier()
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)
print("history = classifier.fit")
plot_and_save_history(history, VGG16LSTMVideoClassifier.model_name,report_dir_path + '/' + VGG16LSTMVideoClassifier.model_name + '-hi-dim-history.png')
plot_history_2win(history, VGG16LSTMVideoClassifier.model_name,report_dir_path + '/' + VGG16LSTMVideoClassifier.model_name + '-hi-dim-history2win.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 = '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(35)
# 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 = 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.png')
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 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__), 'very_large_data')
model_dir_path = os.path.join(os.path.dirname(__file__), 'models',
'UCF-101')
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)
#debug
# print([label for (label, label_index) in list(predictor.labels.items())[:3]]);
# print([label for (label, label_index) in predictor.labels.items()])
#debug over
videos = scan_ucf_with_labels(
data_dir_path,
[label for (label, label_index) in list(predictor.labels.items())[:3]])
video_file_path_list = np.array([file_path for file_path in videos.keys()])
np.random.shuffle(video_file_path_list)
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)
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)
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 VGG16LSTMVideoClassifier
from keras_video_classifier.library.utility.crime.UCF_Crime_loader import load_ucf, scan_ucf_with_labels
vgg16_include_top = False
data_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
model_dir_path = os.path.join(os.path.dirname(__file__), 'models',
'UCF-Anomaly-Detection-Dataset')
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)
videos = scan_ucf_with_labels(
data_dir_path,
[label for (label, label_index) in predictor.labels.items()])
video_file_path_list = np.array([file_path for file_path in videos.keys()])
np.random.shuffle(video_file_path_list)
correct_count = 0
count = 0
print("Begin Predict Using UCF-CRIME-Model: ")
for video_file_path in video_file_path_list:
label = videos[video_file_path]
predicted_label = predictor.predict(video_file_path)
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('correct_count: ' + str(correct_count) + ' count: ' + str(count))
print('accuracy: ', accuracy)
def main():
#K.set_image_dim_ordering('tf')
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 VGG16LSTMVideoClassifier
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 = VGG16LSTMVideoClassifier()
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,
test_size=0.1)
plot_and_save_history(
history, VGG16LSTMVideoClassifier.model_name, report_dir_path + '/' +
VGG16LSTMVideoClassifier.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.recurrent_networks import VGG16LSTMVideoClassifier
from keras_video_classifier.library.utility.ucf.UCF101_loader import load_ucf, scan_ucf_with_labels
from keras_video_classifier.library.utility.plot_utils import plot_confusion_matrix
from sklearn.metrics import confusion_matrix
vgg16_include_top = False
data_dir_path = os.path.join(os.path.dirname(__file__), 'very_large_data')
model_dir_path = os.path.join(os.path.dirname(__file__), 'models', 'UCF-101')
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)
### HARD_SPLIT = TRUE MEAN PREDICT IN UCF-101_Train folder ####
videos = scan_ucf_with_labels(data_dir_path, [label for (label, label_index) in predictor.labels.items()],hard_split=True)
#print("VIDEO KEY", videos)
#print("VALUE",videos.values())
all_label = videos.values()
#print("TYPE", type(all_label))
Total_label = []
[Total_label.append(x) for x in all_label if x not in Total_label]
print("Label: ",Total_label)
#print("Label shape",np.shape(Total_label))
with open("label.txt", "w") as output:
output.write(str(Total_label))
video_file_path_list = np.array([file_path for file_path in videos.keys()])
np.random.shuffle(video_file_path_list)
correct_count = 0
count = 0
label_predicted = []
label_true = []
for video_file_path in video_file_path_list:
label = videos[video_file_path]
predicted_label = predictor.predict(video_file_path)
label_predicted.append(predicted_label)
label_true.append(label)
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('correct_count: ' + str(correct_count) + ' count: ' + str(count))
print('accuracy: ', accuracy)
print("Label_Predicted",np.shape(label_predicted))
print("True Label",np.shape(label_true))
data_set_name = 'UCF-101'
report_dir_path = os.path.join(os.path.dirname(__file__), 'reports', data_set_name)
cm = confusion_matrix(label_true,label_predicted)
recall = np.diag(cm) / np.sum(cm, axis = 1)
precision = np.diag(cm) / np.sum(cm, axis = 0)
import pandas as pd
df = pd.DataFrame(cm)
df.to_csv("CM.csv")
df = pd.DataFrame(recall)
df.to_csv("ReCall.csv")
df = pd.DataFrame(precision)
df.to_csv("Precision.csv")
np.set_printoptions(precision=2)
plot_confusion_matrix(cm, Total_label,
title='Confusion matrix',normalize=True)
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))