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():
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)
