def main():
overall_start = time.time()
exec_time = time.strftime('%Y-%m-%d %H:%M')
# Load model paths
caffe_path = path_params.caffe_path
pycaffe_path = path_params.pycaffe_path
finetune_path = path_params.finetune_path
placesCNN_path = path_params.placesCNN_path
googlenet_path = path_params.googlenet_path
yolo_path = path_params.yolo_path
# Load required file paths
fpickle = path_params.fpickle
features_file = path_params.features_file
# Start video processing
clip_path = sys.argv[1] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/', 1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/', 1)[1] ## video.mp4
clip = clip_name.rsplit('.', 1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
[shot_boundaries,
py_times] = shotdetect.shotdetect(clip_dir, new_clip_path)
py_images = fileops.get_pyframeslist(clip_dir, clip_name)
[all_images,
all_timestamps] = fileops.rename_frames(clip_dir, keyframe_times,
keyframes_list, py_times,
py_images)
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, all_images,
new_clip_path)
for image in all_images:
os.remove(image)
else:
image_files = all_images
os.remove(clip_dir + clip_name)
print "Video preprocessing done...\n"
[person_count, obj_loc_set] = yolo.yolo(pycaffe_path, yolo_path,
image_files)
print "Retrieved yolo labels...\n"
## Run a model and get labels for keyframe
[fc7, scene_type_list, places_labels,
scene_attributes_list] = placesCNN.placesCNN(pycaffe_path, placesCNN_path,
image_files)
fileops.save_features(clip_dir + features_file, fc7)
print "Extracted fc7 features...\n"
with open(fpickle, 'r') as pickle_file:
myclassifier = cPickle.load(pickle_file)
test_data = dataset.testset(clip_dir, features_file)
classifier_label_list = classifier.classifier_predict(
myclassifier, test_data)
print "(SVM) Classified frames...\n"
os.remove(clip_dir + features_file)
[finetune_output,
finetune_labels] = finetune.mynet(pycaffe_path, finetune_path,
image_files)
print "(Finetuned net) Classified frames...\n"
[googlenet_cat,
googlenet_labels] = googlenet.googlenet(pycaffe_path, googlenet_path,
image_files)
print "Retrieved imagenet labels...\n"
format_output.output_labels(
exec_time, rel_clip_path + output_filename, output_filename,
all_timestamps, image_files, shot_boundaries, classifier_label_list,
finetune_output, finetune_labels, googlenet_cat, googlenet_labels,
scene_type_list, places_labels, scene_attributes_list, person_count,
obj_loc_set)
shutil.rmtree(clip_dir)
print "Processing complete!\n"
sht_to_json.sht_to_json(rel_clip_path + output_filename + '.sht')
print "JSON lines ready!\n"
overall_end = time.time()
print "Total time taken: %.2f" % (overall_end - overall_start)
def main():
overall_start = time.time()
# Load model paths
caffe_path = path_params.caffe_path
pycaffe_path = path_params.pycaffe_path
finetune_path = path_params.finetune_path
placesCNN_path = path_params.placesCNN_path
googlenet_path = path_params.googlenet_path
yolo_path = path_params.yolo_path
# Load required file paths
fpickle = path_params.fpickle
features_file = path_params.features_file
if sys.argv[1] == "snaps":
# clip_dir = sys.argv[2]
# source = sorted(os.listdir(clip_dir))
# keyframes_list = []
# for file in source:
# if file.endswith(".jpg"):
# image = clip_dir + os.path.basename(file)
# print file
# keyframes_list.append(image)
# keyframes_list.sort(key = fileops.natural_sorting)
# new_clip_path = clip_dir + 'temp.mp4'
# if features_file == 'cropped_places_fc7.csv':
# image_files = cropframes.cropframes(clip_dir, keyframes_list, new_clip_path)
clip_path = sys.argv[2] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/', 1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/', 1)[1] ## video.mp4
clip = clip_name.rsplit('.', 1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, keyframes_list,
new_clip_path)
print "Video preprocessing done...\n"
[person_count, obj_loc_set] = yolo.yolo(pycaffe_path, yolo_path,
image_files)
print "Retrieved yolo labels...\n"
# shutil.rmtree(clip_dir)
print "Processing complete!\n"
# ## Run a model and get labels for keyframe
# [fc7, scene_type_list, places_labels, scene_attributes_list] = placesCNN.placesCNN(pycaffe_path, placesCNN_path, image_files)
# fileops.save_features(clip_dir + features_file, fc7)
# print "Extracted fc7 features...\n"
# with open(fpickle, 'r') as pickle_file:
# myclassifier = cPickle.load(pickle_file)
# test_data = dataset.testset(clip_dir, features_file)
# classifier_label_list = classifier.classifier_predict(myclassifier, test_data)
# print "(SVM) Classified frames...\n"
# [finetune_output, finetune_labels] = finetune.mynet(pycaffe_path, finetune_path, image_files)
# print "(Finetuned net) Classified frames...\n"
# [googlenet_cat, googlenet_labels] = googlenet.googlenet(pycaffe_path, googlenet_path, image_files)
# print "Retrieved imagenet labels...\n"
with open('test/out.txt', 'w') as file:
for idx, p_count in enumerate(person_count):
file.write(str(idx + 1))
# file.write('| FINETUNED_SHOT_CLASS | ' + finetune_output[idx] + '| ' + finetune_labels[idx] + '\n')
# file.write('| SVM_SHOT_CLASS | ' + classifier_label_list[idx] +'\n')
# file.write('| OBJ_CLASS | ' + googlenet_cat[idx] + '| ' + googlenet_label + '\n')
# file.write('| SCENE_LOCATION | ' + scene_type_list[idx] + '| ' + places_labels[idx] + '\n')
# file.write('| SCENE_ATTRIBUTES | ' + scene_attributes_list[idx] + '\n')
file.write('| PERSONS COUNT = ' + p_count + ' | ' +
obj_loc_set[idx] + '\n')
file.write('\n')
else:
clip_path = sys.argv[1] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/', 1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/', 1)[1] ## video.mp4
clip = clip_name.rsplit('.', 1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
[shot_boundaries,
py_times] = shotdetect.shotdetect(clip_dir, new_clip_path)
py_images = fileops.get_pyframeslist(clip_dir, clip_name)
[all_images,
all_timestamps] = fileops.rename_frames(clip_dir, keyframe_times,
keyframes_list, py_times,
py_images)
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, all_images,
new_clip_path)
for image in all_images:
os.remove(image)
else:
image_files = all_images
os.remove(clip_dir + clip_name)
print "Video preprocessing done...\n"
[person_count, obj_loc_set] = yolo.yolo(pycaffe_path, yolo_path,
image_files)
print "Retrieved yolo labels...\n"
## Run a model and get labels for keyframe
[fc7, scene_type_list, places_labels, scene_attributes_list
] = placesCNN.placesCNN(pycaffe_path, placesCNN_path, image_files)
fileops.save_features(clip_dir + features_file, fc7)
print "Extracted fc7 features...\n"
with open(fpickle, 'r') as pickle_file:
myclassifier = cPickle.load(pickle_file)
test_data = dataset.testset(clip_dir, features_file)
classifier_label_list = classifier.classifier_predict(
myclassifier, test_data)
print "(SVM) Classified frames...\n"
os.remove(clip_dir + features_file)
[finetune_output,
finetune_labels] = finetune.mynet(pycaffe_path, finetune_path,
image_files)
print "(Finetuned net) Classified frames...\n"
[googlenet_cat,
googlenet_labels] = googlenet.googlenet(pycaffe_path, googlenet_path,
image_files)
print "Retrieved imagenet labels...\n"
format_output.output_labels(
rel_clip_path + output_filename, output_filename, all_timestamps,
image_files, shot_boundaries, classifier_label_list,
finetune_output, finetune_labels, googlenet_cat, googlenet_labels,
scene_type_list, places_labels, scene_attributes_list,
person_count, obj_loc_set)
shutil.rmtree(clip_dir)
print "Processing complete!\n"
overall_end = time.time()
print "Total time taken: %.2f" % (overall_end - overall_start)
def main():
overall_start = time.time()
exec_time = time.strftime('%Y-%m-%d %H:%M')
# Load model paths
caffe_path = path_params.caffe_path
pycaffe_path = path_params.pycaffe_path
finetune_path = path_params.finetune_path
placesCNN_path = path_params.placesCNN_path
googlenet_path = path_params.googlenet_path
yolo_path = path_params.yolo_path
# Load required file paths
fpickle = path_params.fpickle
features_file = path_params.features_file
# Start video processing
clip_path = sys.argv[1] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/',1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/',1)[1] ## video.mp4
clip = clip_name.rsplit('.',1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
[shot_boundaries, py_times] = shotdetect.shotdetect(clip_dir, new_clip_path)
py_images = fileops.get_pyframeslist(clip_dir, clip_name)
[all_images, all_timestamps] = fileops.rename_frames(clip_dir, keyframe_times, keyframes_list, py_times, py_images)
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, all_images, new_clip_path)
for image in all_images:
os.remove(image)
else:
image_files = all_images
os.remove(clip_dir + clip_name)
print "Video preprocessing done...\n"
[person_count, obj_loc_set] = yolo.yolo(pycaffe_path, yolo_path, image_files)
print "Retrieved yolo labels...\n"
## Run a model and get labels for keyframe
[fc7, scene_type_list, places_labels, scene_attributes_list] = placesCNN.placesCNN(pycaffe_path, placesCNN_path, image_files)
fileops.save_features(clip_dir + features_file, fc7)
print "Extracted fc7 features...\n"
with open(fpickle, 'r') as pickle_file:
myclassifier = cPickle.load(pickle_file)
test_data = dataset.testset(clip_dir, features_file)
classifier_label_list = classifier.classifier_predict(myclassifier, test_data)
print "(SVM) Classified frames...\n"
os.remove(clip_dir + features_file)
[finetune_output, finetune_labels] = finetune.mynet(pycaffe_path, finetune_path, image_files)
print "(Finetuned net) Classified frames...\n"
[googlenet_cat, googlenet_labels] = googlenet.googlenet(pycaffe_path, googlenet_path, image_files)
print "Retrieved imagenet labels...\n"
format_output.output_labels(exec_time, rel_clip_path + output_filename, output_filename, all_timestamps, image_files, shot_boundaries, classifier_label_list,
finetune_output, finetune_labels, googlenet_cat, googlenet_labels, scene_type_list, places_labels, scene_attributes_list, person_count, obj_loc_set)
shutil.rmtree(clip_dir)
print "Processing complete!\n"
sht_to_json.sht_to_json(rel_clip_path + output_filename + '.sht')
print "JSON lines ready!\n"
overall_end = time.time()
print "Total time taken: %.2f" %(overall_end-overall_start)
def main():
if sys.argv[1] == 'labels':
main_dir = './full-clips/test/'
annotations_file = '_new_shot_type_testuser.txt'
dir_list = sorted(os.listdir(main_dir))
label_data = []
for dir_name in dir_list:
if os.path.isdir(main_dir + dir_name):
if os.path.exists(main_dir + dir_name + '/' + dir_name + annotations_file):
with open(main_dir + dir_name + '/' + dir_name + annotations_file) as labels_file:
labels = labels_file.readlines()
labels = [label.split('\t')[0] for label in labels]
label_data += labels
label_data = [label + '\n' for label in label_data]
print len(label_data)
with open('./labels_list.txt', 'w') as file:
file.writelines(label_data)
elif sys.argv[1] == 'commercials': ## py org.py commercials /home/sxg755/dataset/train/all_frames/labels_list.txt ~/dataset/train/all_frames/sorted_keyframes_list.txt
## ~/dataset/train/8class_train_keyframes/
labels_f = sys.argv[2]
keyframes_f = sys.argv[3]
main_dir = sys.argv[4] ## ~/dataset/train/8class_train_keyframes/
# train_label_dir = '/home/sxg755/dataset/train/all_frames/labels_list.txt'
# test_label_dir = '/home/sxg755/dataset/test/all_frames/labels_list.txt'
# train_frames = '/home/sxg755/dataset/train/all_frames/cropped/'
# test_frames = '/home/sxg755/dataset/test/all_frames/cropped/'
# train_dir = '/home/sxg755/dataset/train/'
# test_dir = '/home/sxg755/dataset/test/'
label_dir = labels_f.rsplit('/',1)[0] + '/'
frames_path = label_dir + 'cropped/'
trainset_dir = label_dir.rsplit('/',2)[0] + '/'
if not os.path.exists(main_dir):
os.makedirs(main_dir)
with open(labels_f, 'r') as lf:
label_data = lf.readlines()
label_data = [label.split('\n')[0] for label in label_data]
with open(keyframes_f, 'r') as kf:
all_frames = kf.readlines()
all_frames = [keyframe.split('\n')[0] for keyframe in all_frames]
keyframes_path = [frames_path + keyframe for keyframe in all_frames]
final_list = []
for idx, label in enumerate(label_data):
if label not in ['Commercial','Problem/Unclassified', 'Black', 'Sports']:
if label == 'Background_roll' or label == 'Background roll':
# label = 'Background_roll'
final_list.append(main_dir + all_frames[idx] + ' 0\n')
elif label == 'Graphic':
final_list.append(main_dir + all_frames[idx] + ' 1\n')
elif label == 'Weather' or label == 'Weather/Graphic' or label == 'Weather/Person':
label = 'Weather'
final_list.append(main_dir + all_frames[idx] + ' 1\n')
# elif label == 'Sports':
# final_list.append(main_dir + all_frames[idx] + ' 3\n')
elif label == 'Studio':
# label = 'Newsperson(s)'
final_list.append(main_dir + all_frames[idx] + ' 2\n')
elif label == 'Reporter':
final_list.append(main_dir + all_frames[idx] + ' 2\n')
elif label == 'Hybrid' or label == 'Talking_head/Hybrid':
label = 'Hybrid'
final_list.append(main_dir + all_frames[idx] + ' 2\n')
elif label == 'Talking_head':
final_list.append(main_dir + all_frames[idx] + ' 0\n')
shutil.copy(frames_path + all_frames[idx], main_dir)
with open(main_dir + 'data.txt', 'w') as file:
file.writelines(final_list)
print len(final_list)
else:
clip_path = sys.argv[1] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/',1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/',1)[1] ## video.mp4
clip = clip_name.rsplit('.',1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path ## ../../dir/
temp = clip_dir + 'all_frames/' ## ../../dir/all_frames/
if not os.path.exists(temp):
os.makedirs(temp)
keyframe_times = keyframes.keyframes(temp, clip_path)
keyframes_list = fileops.get_keyframeslist(temp, clip_path)
image_files = cropframes.cropframes(temp, keyframes_list, clip_path)
image_files = [image_file.rsplit('/',1)[1] + '\n' for image_file in image_files]
with open(temp + 'keyframes_list.txt', 'aw') as file:
file.writelines(image_files)
def main():
if sys.argv[1] == 'labels':
main_dir = './full-clips/train/'
annotations_file = '_new_shot_type_testuser.txt'
dir_list = sorted(os.listdir(main_dir))
label_data = []
for dir_name in dir_list:
if os.path.isdir(main_dir + dir_name):
if os.path.exists(main_dir + dir_name + '/' + dir_name + annotations_file):
with open(main_dir + dir_name + '/' + dir_name + annotations_file) as labels_file:
labels = labels_file.readlines()
labels = [label.split('\t')[0] for label in labels]
label_data += labels
print dir_name, len(labels)
label_data = [label + '\n' for label in label_data]
print len(label_data)
with open('./train_labels_list.txt', 'w') as file:
file.writelines(label_data)
elif sys.argv[1] == 'commercials': ## py org.py commercials /home/sxg755/dataset/train/all_frames/new_labels_list.txt /home/sxg755/dataset/train/all_frames/new_sorted_keyframes_list.txt
## ~/dataset/train/8class_train_keyframes/
labels_f = sys.argv[2]
keyframes_f = sys.argv[3]
main_dir = sys.argv[4] ## /home/sxg755/dataset/train/new_5class_train_keyframes/
# train_label_dir = '/home/sxg755/dataset/train/all_frames/labels_list.txt'
# test_label_dir = '/home/sxg755/dataset/test/all_frames/labels_list.txt'
# train_frames = '/home/sxg755/dataset/train/all_frames/cropped/'
# test_frames = '/home/sxg755/dataset/test/all_frames/cropped/'
# train_dir = '/home/sxg755/dataset/train/'
# test_dir = '/home/sxg755/dataset/test/'
label_dir = labels_f.rsplit('/',1)[0] + '/'
frames_path = label_dir + 'cropped/'
trainset_dir = label_dir.rsplit('/',2)[0] + '/'
if not os.path.exists(main_dir):
os.makedirs(main_dir)
with open(labels_f, 'r') as lf:
label_data = lf.readlines()
label_data = [label.split('\n')[0] for label in label_data]
with open(keyframes_f, 'r') as kf:
all_frames = kf.readlines()
all_frames = [keyframe.split('\n')[0] for keyframe in all_frames]
keyframes_path = [frames_path + keyframe for keyframe in all_frames]
with open(main_dir + 'data.txt', 'w') as file: pass
final_list = []
for idx, label in enumerate(label_data):
if label not in ['Commercial','Problem/Unclassified', 'Black']:
if label == 'Background_roll' or label == 'Background roll':
label = 'Background_roll'
final_list.append(main_dir + all_frames[idx] + ' 0\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 0\n')
elif label == 'Graphic':
final_list.append(main_dir + all_frames[idx] + ' 1\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 1\n')
elif label == 'Weather' or label == 'Weather/Graphic' or label == 'Weather/Person':
print 'Weather ', all_frames[idx]
label = 'Weather'
final_list.append(main_dir + all_frames[idx] + ' 2\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 2\n')
elif label == 'Sports':
final_list.append(main_dir + all_frames[idx] + ' 3\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 3\n')
elif label == 'Studio':
print 'Studio ', all_frames[idx]
label = 'Newsperson(s)'
final_list.append(main_dir + all_frames[idx] + ' 4\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 4\n')
elif label == 'Reporter':
label = 'Newsperson(s)'
final_list.append(main_dir + all_frames[idx] + ' 4\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 4\n')
elif label == 'Hybrid' or label == 'Talking_head/Hybrid':
label = 'Newsperson(s)'
final_list.append(main_dir + all_frames[idx] + ' 4\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 4\n')
elif label == 'Talking_head':
final_list.append(main_dir + all_frames[idx] + ' 0\n')
with open(main_dir + 'data.txt', 'aw') as file:
file.write(main_dir + all_frames[idx] + ' 0\n')
shutil.copy(frames_path + all_frames[idx], main_dir)
# with open(main_dir + 'data.txt', 'aw') as file:
# file.writelines(final_list)
print len(final_list)
elif sys.argv[1] == 'count':
# with open('/home/sxg755/dataset/train/all_frames/new_sorted_keyframes_list.txt', 'r') as f:
with open('/home/sxg755/dataset/train/new_5class_train_keyframes/data.txt', 'r') as f:
files = f.readlines()
with open('/home/sxg755/dataset/train/new_5class_train_keyframes/data_new.txt','w') as new_f: pass
# cur = ''
# count = 0
for file in files:
path = file.split(' ')[0]
name = path.rsplit('_',1)[0]
name = name.rsplit('/',1)[1]
if name not in ['2015-06-06_1400_US_MSNBC_Melissa_Harris-Perry','2015-09-09_1600_US_KNBC_Today_Show','2014-07-12_1200_US_CNN_New_Day_Saturday','2014-02-14_2330_US_CNN_Crossfire','2014-08-18_2300_US_FOX-News_On_the_Record_with_Greta_Van_Susteren','2015-01-01_2330_US_KCAL_Inside_Edition','2015-07-07_2300_US_KCAL_KCAL_9_News_at_4PM','2015-10-22_1700_US_HLN_The_Daily_Share','2014-08-18_2000_US_HLN_HLN_News_Now','2014-01-01_2200_US_HLN_HLN_Evening_Express','2015-09-14_2100_US_WEWS_Live_on_5','2014-05-19_2258_US_WKYC_Channel_3_News_at_7','2014-03-25_1753_US_BBC_BBC_Persian','2014-04-17_2330_DE_KCET_Deutsche_Welle_Journal','2016-09-24_1230_US_KCET_Asia_Insight','2014-05-03_1600_UK_KCET_BBC_Newsnight','2015-04-04_0330_QA_AlJazeera_Inside_Story','2014-08-18_2330_US_AlJazeera_Real_Money_with_Ali_Velshi','2015-09-09_2230_QA_AlJazeera_Fault_Lines','2014-10-28_1800_CH_TV5_Le_Journal_de_la_RTS','2015-04-15_2055_FR_France-3_Grand_Soir_3','2014-10-10_2200_FR_TV5_Le_Journal_de_France_2',
'2015-02-28_1800_FR_TV5_64_Minutes_Le_Monde_en_Francais','2014-11-25_1630_FR_KCET_France_24']:
with open('/home/sxg755/dataset/train/new_5class_train_keyframes/data_new.txt','aw') as new_f:
new_f.write(file)
# if name != cur:
# print cur, count
# cur = name
# count = 1
# else:
# count += 1
else:
ntg = sys.argv[1]
# clip_path = sys.argv[1] ## ../../dir/video.mp4
# rel_clip_path = clip_path.rsplit('/',1)[0] + '/' ## ../../dir/
# clip_name = clip_path.rsplit('/',1)[1] ## video.mp4
# clip = clip_name.rsplit('.',1)[0] ## video
# output_filename = clip ## video
# clip_dir = rel_clip_path ## ../../dir/
source = os.listdir('/home/sxg755/trainset/new/')
temp = '/home/sxg755/dataset/train/new_all_frames/' ## ../../dir/all_frames/
if not os.path.exists(temp):
os.makedirs(temp)
for file in source:
if file.endswith('.mp4'):
clip_path = '/home/sxg755/trainset/new/' + file
keyframe_times = keyframes.keyframes(temp, clip_path)
keyframes_list = fileops.get_keyframeslist(temp, clip_path)
image_files = cropframes.cropframes(temp, keyframes_list, clip_path)
image_files = [image_file.rsplit('/',1)[1] + '\n' for image_file in image_files]
with open('/home/sxg755/dataset/train/all_frames/keyframes_list.txt', 'aw') as file:
file.writelines(image_files)
def main():
overall_start = time.time()
# Load model paths
caffe_path = path_params.caffe_path
pycaffe_path = path_params.pycaffe_path
finetune_path = path_params.finetune_path
placesCNN_path = path_params.placesCNN_path
googlenet_path = path_params.googlenet_path
yolo_path = path_params.yolo_path
# Load required file paths
fpickle = path_params.fpickle
features_file = path_params.features_file
if sys.argv[1] == "snaps":
# clip_dir = sys.argv[2]
# source = sorted(os.listdir(clip_dir))
# keyframes_list = []
# for file in source:
# if file.endswith(".jpg"):
# image = clip_dir + os.path.basename(file)
# print file
# keyframes_list.append(image)
# keyframes_list.sort(key = fileops.natural_sorting)
# new_clip_path = clip_dir + 'temp.mp4'
# if features_file == 'cropped_places_fc7.csv':
# image_files = cropframes.cropframes(clip_dir, keyframes_list, new_clip_path)
clip_path = sys.argv[2] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/',1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/',1)[1] ## video.mp4
clip = clip_name.rsplit('.',1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, keyframes_list, new_clip_path)
print "Video preprocessing done...\n"
[person_count, obj_loc_set] = yolo.yolo(pycaffe_path, yolo_path, image_files)
print "Retrieved yolo labels...\n"
# shutil.rmtree(clip_dir)
print "Processing complete!\n"
# ## Run a model and get labels for keyframe
# [fc7, scene_type_list, places_labels, scene_attributes_list] = placesCNN.placesCNN(pycaffe_path, placesCNN_path, image_files)
# fileops.save_features(clip_dir + features_file, fc7)
# print "Extracted fc7 features...\n"
# with open(fpickle, 'r') as pickle_file:
# myclassifier = cPickle.load(pickle_file)
# test_data = dataset.testset(clip_dir, features_file)
# classifier_label_list = classifier.classifier_predict(myclassifier, test_data)
# print "(SVM) Classified frames...\n"
# [finetune_output, finetune_labels] = finetune.mynet(pycaffe_path, finetune_path, image_files)
# print "(Finetuned net) Classified frames...\n"
# [googlenet_cat, googlenet_labels] = googlenet.googlenet(pycaffe_path, googlenet_path, image_files)
# print "Retrieved imagenet labels...\n"
with open('test/out.txt','w') as file:
for idx, p_count in enumerate(person_count):
file.write(str(idx+1))
# file.write('| FINETUNED_SHOT_CLASS | ' + finetune_output[idx] + '| ' + finetune_labels[idx] + '\n')
# file.write('| SVM_SHOT_CLASS | ' + classifier_label_list[idx] +'\n')
# file.write('| OBJ_CLASS | ' + googlenet_cat[idx] + '| ' + googlenet_label + '\n')
# file.write('| SCENE_LOCATION | ' + scene_type_list[idx] + '| ' + places_labels[idx] + '\n')
# file.write('| SCENE_ATTRIBUTES | ' + scene_attributes_list[idx] + '\n')
file.write('| PERSONS COUNT = ' + p_count + ' | ' + obj_loc_set[idx] + '\n')
file.write('\n')
else:
clip_path = sys.argv[1] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/',1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/',1)[1] ## video.mp4
clip = clip_name.rsplit('.',1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
[shot_boundaries, py_times] = shotdetect.shotdetect(clip_dir, new_clip_path)
py_images = fileops.get_pyframeslist(clip_dir, clip_name)
[all_images, all_timestamps] = fileops.rename_frames(clip_dir, keyframe_times, keyframes_list, py_times, py_images)
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, all_images, new_clip_path)
for image in all_images:
os.remove(image)
else:
image_files = all_images
os.remove(clip_dir + clip_name)
print "Video preprocessing done...\n"
[person_count, obj_loc_set] = yolo.yolo(pycaffe_path, yolo_path, image_files)
print "Retrieved yolo labels...\n"
## Run a model and get labels for keyframe
[fc7, scene_type_list, places_labels, scene_attributes_list] = placesCNN.placesCNN(pycaffe_path, placesCNN_path, image_files)
fileops.save_features(clip_dir + features_file, fc7)
print "Extracted fc7 features...\n"
with open(fpickle, 'r') as pickle_file:
myclassifier = cPickle.load(pickle_file)
test_data = dataset.testset(clip_dir, features_file)
classifier_label_list = classifier.classifier_predict(myclassifier, test_data)
print "(SVM) Classified frames...\n"
os.remove(clip_dir + features_file)
[finetune_output, finetune_labels] = finetune.mynet(pycaffe_path, finetune_path, image_files)
print "(Finetuned net) Classified frames...\n"
[googlenet_cat, googlenet_labels] = googlenet.googlenet(pycaffe_path, googlenet_path, image_files)
print "Retrieved imagenet labels...\n"
format_output.output_labels(rel_clip_path + output_filename, output_filename, all_timestamps, image_files, shot_boundaries, classifier_label_list,
finetune_output, finetune_labels, googlenet_cat, googlenet_labels, scene_type_list, places_labels, scene_attributes_list, person_count, obj_loc_set)
shutil.rmtree(clip_dir)
print "Processing complete!\n"
overall_end = time.time()
print "Total time taken: %.2f" %(overall_end-overall_start)
def main():
overall_start = time.time()
exec_time = time.strftime('%Y-%m-%d %H:%M')
# Load model paths
caffe_path = path_params.caffe_path
pycaffe_path = path_params.pycaffe_path
placesCNN_path = path_params.placesCNN_path
tv_adnet_path = path_params.tv_adnet_path
features_file = 'dummy.csv' #path_params.features_file
# Start video processing
clip_path = sys.argv[1] ## ../../dir/video.mp4
rel_clip_path = clip_path.rsplit('/', 1)[0] + '/' ## ../../dir/
clip_name = clip_path.rsplit('/', 1)[1] ## video.mp4
clip = clip_name.rsplit('.', 1)[0] ## video
output_filename = clip ## video
clip_dir = rel_clip_path + clip + '/' ## ../../dir/video/
# print rel_clip_path
# print clip_name
# print clip
# print output_filename
# print clip_dir
if not os.path.exists(clip_dir):
os.makedirs(clip_dir)
else:
shutil.rmtree(clip_dir)
os.makedirs(clip_dir)
shutil.copy(clip_path, clip_dir)
new_clip_path = clip_dir + clip_name ## ../../dir/video/video.mp4
keyframe_times = keyframes.keyframes(clip_dir, new_clip_path)
keyframes_list = fileops.get_keyframeslist(clip_dir, new_clip_path)
[all_images,
all_timestamps] = fileops.rename_frames(clip_dir, keyframe_times,
keyframes_list, [], [])
if features_file == 'cropped_places_fc7.csv':
image_files = cropframes.cropframes(clip_dir, all_images,
new_clip_path)
for image in all_images:
os.remove(image)
else:
image_files = all_images
os.remove(clip_dir + clip_name)
print "Video preprocessing done...\n"
# print keyframe_times
# print keyframes_list
# ## Run a model and get labels for keyframe
# print "Running FC7 feature extraction from PlacesCNN...\n"
# [fc7, scene_type_list, places_labels, scene_attributes_list] = placesCNN.placesCNN(pycaffe_path, placesCNN_path, image_files)
# fileops.save_features(clip_dir + features_file, fc7)
# print "Extracted fc7 features...\n"
## Perform classification using the fine tuned ad model
print "Running tv_adnet for category classification...\n"
[tv_adnet_output,
tv_adnet_labels] = tv_adnet.mynet(pycaffe_path, tv_adnet_path,
image_files)
print "(tv-adnet) Classified frames...\n"
print "The top 5 [labels : probabilites] in order are:-"
print tv_adnet_labels
shutil.rmtree(clip_dir)
overall_end = time.time()
print "Total time taken: %.2f" % (overall_end - overall_start)