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178 lines (147 loc) · 9.69 KB
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import os
import albumentations as A
import CONST
from typing import Dict
from data.USKpts import USKpts
from data.USKptsNPZ import USKptsNPZ
from data.USKpts_EchoNet import USKpts_EchoNet
from transforms import load_transform
from utils.utils_files import copy_train_data
########################
# Loaders:
########################
class datas(object):
"""
A simple class to hold the train/val/test dataset objects.
"""
def __init__(self, loader_func: USKpts, dataset_config: Dict, input_transform: A.core.composition.Compose,
train_filenames_list: str, val_filenames_list: str, test_filenames_list: str):
self.loader_func = loader_func
self.input_transform = input_transform
self.dataset_config = dataset_config
self.dataset_config["kpts_info"] = self.create_kpts_info(num_kpts=dataset_config["num_kpts"], closed_contour=dataset_config["closed_contour"])
assert os.path.exists(dataset_config["img_folder"]), "image repository does not exist."
self.trainset = self.load_train(train_filenames_list)
self.valset = self.load_test(val_filenames_list)
self.testset = self.load_test(test_filenames_list)
def create_kpts_info(self, num_kpts: int, closed_contour: bool) -> Dict:
kpts_info = {'names':[], 'connections':[], 'colors':[]}
kpts_info['names'] = {}
for kpt_indx in range(num_kpts):
kpts_info['names']["kp{}".format(kpt_indx+1)] = kpt_indx
kpts_info['connections'] = [[i, i+1] for i in range(len(kpts_info['names'])-1)]
kpts_info['colors'] = [[0, 0, 255], [255, 85, 0], [255, 170, 0], [255, 255, 0],
[170, 255, 0], [85, 255, 0], [0, 255, 0], [0, 255, 85],
[170, 55, 0], [85, 55, 0], [0, 55, 0], [0, 55, 85]
] # Note: Limited to 12 classes.
kpts_info['closed_contour'] = closed_contour
return kpts_info
def load_train(self, train_filenames_list: str) -> USKpts:
trainset = None
if train_filenames_list is not None:
trainset = self.loader_func(dataset_config=self.dataset_config, filenames_list=train_filenames_list, transform=self.input_transform)
return trainset
def load_test(self, test_filenames_list: str) -> USKpts:
testset = None
if test_filenames_list is not None:
testset = self.loader_func(dataset_config=self.dataset_config, filenames_list=test_filenames_list, transform=None)
return testset
# ============================
# main load dataset module:
# ============================
def load_dataset(ds_name: str, input_transform: A.core.composition.Compose = None, input_size: int = 256, num_frames: int = 1) -> datas:
us_data_folder = CONST.US_MultiviewData
# Copy data to host, if needed
if hasattr(CONST, 'US_MultiviewData_MASTER'):
copy_train_data(master_root_path=CONST.US_MultiviewData_MASTER, host_root_path=us_data_folder, folder_path_from_root="preprocessed/40/")
if ds_name == 'apical': # 427 + 107 = 534 examples
img_dirname = os.path.join(us_data_folder, "apical/frames/") #"/shared-data5/MultiView/apical/movies/" #"/shared-data5/MultiView/apical/frames/"
anno_dirname = os.path.join(us_data_folder, "apical/annotations/") #os.path.join(us_data_folder, "apical/annotations_movies/" #os.path.join(us_data_folder, "apical/annotations/"
loader_func = USKptsNPZ
train_filenames_list = 'files/filenames/apical_train_filenames.txt' #'files/apical_test_filenames.txt' #'files/apical_train_filenames.txt'
val_filenames_list = 'files/filenames/apical_val_filenames.txt' #'files/apical_test_filenames.txt' #'files/apical_val_filenames.txt'
test_filenames_list = 'files/filenames/apical_test_filenames.txt' #'files/apical_test_filenames.txt' #'files/apical_test_filenames.txt'
frame_selection_mode = None
nb_classes, closed_contour = 12, False
elif ds_name == 'echonet40': # 19800 examples
img_dirname = os.path.join(us_data_folder, "preprocessed/40/frames/")
anno_dirname = os.path.join(us_data_folder, "preprocessed/40/annotations/")
loader_func = USKptsNPZ
train_filenames_list = 'files/filenames/40/echonet_train_filenames.txt'
val_filenames_list = 'files/filenames/40/echonet_val_filenames.txt'
test_filenames_list = 'files/filenames/40/echonet_test_filenames.txt'
frame_selection_mode = None
nb_classes, closed_contour = 40, False
elif ds_name == 'echonet_cycle': # 10000 examples # files can be created using preprocess_echonet.py
loader_func = USKpts_EchoNet
img_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/frames/")
anno_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/annotations/")
train_filenames_list = 'files/filenames/echonet_cycle_train_filenames.txt'
val_filenames_list = 'files/filenames/echonet_cycle_val_filenames.txt'
test_filenames_list = 'files/filenames/echonet_cycle_test_filenames.txt'
frame_selection_mode = 'edToEs'
nb_classes, closed_contour = 40, False
elif ds_name == 'echonet_random': # 10000 examples # files can be created using preprocess_echonet.py
loader_func = USKpts_EchoNet
img_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/frames/")
anno_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/annotations/")
train_filenames_list = 'files/filenames/echonet_cycle_train_filenames.txt'
val_filenames_list = 'files/filenames/echonet_cycle_val_filenames.txt'
test_filenames_list = 'files/filenames/echonet_cycle_test_filenames.txt'
frame_selection_mode = 'random'
nb_classes, closed_contour = 40, False
elif ds_name == 'debug': # 10000 examples # files can be created using preprocess_echonet.py
loader_func = USKpts_EchoNet
img_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/frames/")
anno_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/annotations/")
train_filenames_list = 'files/filenames/echonet_cycle_valsmall_filenames.txt'
val_filenames_list = 'files/filenames/echonet_cycle_valsmall_filenames.txt'
test_filenames_list = 'files/filenames/echonet_cycle_valsmall_filenames.txt'
frame_selection_mode = 'random'#'edToEs'
nb_classes, closed_contour = 40, False
elif ds_name == 'debug_edtosd': # 10000 examples # files can be created using preprocess_echonet.py
loader_func = USKpts_EchoNet
img_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/frames/")
anno_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/annotations/")
train_filenames_list = 'files/filenames/echonet_cycle_valsmall_filenames.txt'
val_filenames_list = 'files/filenames/echonet_cycle_valsmall_filenames.txt'
test_filenames_list = 'files/filenames/echonet_cycle_valsmall_filenames.txt'
frame_selection_mode = 'edToEs'#'edToEs'
nb_classes, closed_contour = 40, False
elif ds_name == 'sliding_window': # 10000 examples # files can be created using preprocess_echonet.py
loader_func = USKpts_EchoNet
img_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/frames/")
anno_dirname = os.path.join(us_data_folder, "preprocessed/40/cycle/annotations/")
train_filenames_list = 'files/filenames/echonet_cycle_test_filenames.txt'
val_filenames_list = 'files/filenames/echonet_cycle_test_filenames.txt'
test_filenames_list = 'files/filenames/echonet_cycle_test_filenames.txt'
frame_selection_mode = 'all'#'edToEs'
nb_classes, closed_contour = 40, False
else:
raise NotImplementedError("Can't use dataset {}.".format(ds_name))
dataset_config = {"img_folder": img_dirname, "anno_folder": anno_dirname, "transform": input_transform, "input_size": input_size,
"num_kpts": nb_classes, "closed_contour": closed_contour, "num_frames": num_frames, "frame_selection_mode": frame_selection_mode}
ds = datas(loader_func=loader_func, dataset_config=dataset_config, input_transform=input_transform,
train_filenames_list=train_filenames_list, val_filenames_list=val_filenames_list, test_filenames_list=test_filenames_list)
if ds.trainset is not None and ds.testset is not None:
print("loading dataset : {}.. number of train examples is {}, number of val examples is {}, number of test examples is {}."
.format(ds_name, len(ds.trainset), len(ds.valset), len(ds.testset)))
else:
print('loading empty dataset.')
return ds
if __name__ == '__main__':
ds_name = "sliding_window"#"debug"#"echonet_random"#"echonet_random"#"echonet_cycle"
input_size = 112 #112#256#128 #708 # 224
num_frames = 16 #4, 24
augmentation_type = "strong_echo_cycle" #"strongkeep" #"twochkeep" #"strongkeep"
# ds_name = "echonet40" #"2ch5dist", "2ch5ext", "2ch5_debug"
# augmentation_type = "strongkeep_echo" #"strongkeep" #"twochkeep" #"strongkeep"
#input_transform = None
input_transform = load_transform(augmentation_type=augmentation_type, augmentation_probability=1.0, input_size=input_size, num_frames=num_frames)
ds = load_dataset(ds_name=ds_name, input_transform=input_transform, input_size=input_size, num_frames=num_frames)
g = ds.trainset#ds.valset#ds.trainset
for k in range(10, 30, 1): #len(g)):
#for k in range(len(g)):
dat = g.get_img_and_kpts(index=k)
g.plot_item(k, do_augmentation=False, print_folder=os.path.join("./visu/", ds_name))
g.plot_item(k, do_augmentation=True, print_folder=os.path.join("./visu/", ds_name))