def apply_depth_inference_on_folder(folder_images_path, output_path):
"""
This method is used to apply depth inference on a given folder (pretty much self explanatory name, right?)
:param folder_images_path: where our images are located
:param output_path: where the results of our depth inference will be saved
:param checkpoint_path: the checkpoint used to perform the inference
:return:
"""
params = generate_default_monodepth_parameters()
output_path = file_helper.guarantee_path_preconditions(output_path)
left, model, sess = init_tensorflow(CHECKPOINT_PATH, params)
# all_images = file_helper.get_frames_from_folder(folder_images_path)
missing_frames = get_not_processed_frames(folder_images_path, output_path)
with concurrent.futures.ThreadPoolExecutor(max_workers=100) as executor:
fs = [
executor.submit(apply_inference, folder_images_path, image, left,
model, output_path, sess)
for image in missing_frames
]
concurrent.futures.wait(fs)
K.clear_session()
return file_helper.get_frames_from_folder(output_path)
def extract_from_folder(self, frames_path, output_path):
frames = os.listdir(frames_path)
missing_frames = len(get_not_processed_frames(frames_path,
output_path))
if missing_frames > self.THRESHOLD_MISSING_FRAMES:
with ProcessPoolExecutor() as exec:
for frame_name in frames:
path_frame = os.path.join(frames_path, frame_name)
if not os.path.exists(os.path.join(output_path,
frame_name)):
exec.submit(file_utils.file_helper.copy_file,
path_frame, output_path)
def extract_from_folder(self, frames_path, output_path):
missing_frames = get_not_processed_frames(frames_path, output_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
noise_path = '/codes/bresan/remote/spoopy/spoopy/refactored/preprocessing/property/noise_extraction_code/bin/noise_extractor'
for frame in missing_frames:
output_img = join(output_path, frame + "_RF2.bmp")
if not os.path.isfile(output_img):
with ProcessPoolExecutor(max_workers=20) as exec:
output_img = join(output_path, frame)
exec.submit(self.extract_noise, frame, frames_path, noise_path, output_path)
print('Extracting frames with noise')
def extract_from_folder(self, frames_path, output_path):
missing_frames = len(get_not_processed_frames(frames_path,
output_path))
if missing_frames > self.THRESHOLD_MISSING_FRAMES:
extract_rbd_saliency_folder(frames_path, output_path)
def extract_from_folder(self, frames_path, output_path):
missing_frames = get_not_processed_frames(frames_path, output_path)
if len(missing_frames) > self.THRESHOLD_MISSING_FRAMES:
monodepth_simple.apply_depth_inference_on_folder(
frames_path, output_path)