def file_list_to_train_list(file_list_name): train_file = file_io.read_file(file_list_name) train_file = [trancos_data_path + t for t in train_file] train_file = [t + " " + t.replace(image_ext, desmap_ext) + " " \ + t.replace(image_ext, mask_ext) for t in train_file] return train_file
def get_file_dict(): file_dir = "/media/dog/data/WebCamT_60000/train_test_separation/" parkway_train_file = file_dir + "Parkway_Train.txt" parkway_test_file = file_dir + "Parkway_Test.txt" downtown_train_file = file_dir + "Downtown_Train.txt" downtown_test_file = file_dir + "Downtown_Test.txt" parkway_train = set(file_io.read_file(parkway_train_file)) parkway_test = set(file_io.read_file(parkway_test_file)) downtown_train = set(file_io.read_file(downtown_train_file)) downtown_test = set(file_io.read_file(downtown_test_file)) return parkway_train, parkway_test, downtown_train, downtown_test
def read_data(self, data_dir, file_name, data_num): file_list = file_io.read_file(file_name) data_len = min(data_num, len(file_list)) images = np.empty((data_len, 32, 32, 3), np.float32) labels = np.empty((data_len), np.uint8) for i in range(data_len): image_name, label = file_list[i].split(" ") images[i, :, :, :] = cv2.imread(data_dir + image_name) / 255.0 labels[i] = int(label) return images, labels
def single_file_list(file_list_name): img_ext = ".jpg" mat_ext = "dots.png.mat" desmap_ext = ".desmap" mask_ext = "_mask.npy" img_size = (227, 227) file_list = file_io.read_file(file_list_name) file_list = [trancos_data_path + f for f in file_list] for f in file_list: mat_name = f.replace(img_ext, mat_ext) f_np = mat_to_np(mat_name) new_name = mat_name.replace(mat_ext, desmap_ext) f_np.tofile(new_name) mask = np.ones(img_size, np.float32) mask_name = f.replace(img_ext, mask_ext) mask.tofile(mask_name)
def name_to_file_list(file_name): file_list = file_io.read_file(file_name) infer_list = list() mask_list = list() for i, f in enumerate(file_list): f = f.split(" ")[0] infer_name = f.replace(".jpg", ".infer_desmap") if not os.path.exists(infer_name): continue mask_name = "/".join(f.split("/")[:-1]) + "_msk_128.npy" infer_list.append(infer_name) mask_list.append(mask_name) file_list[i] = f return file_list, infer_list, mask_list
def file_list_to_train_list(file_list_name): data_list = file_io.read_file(file_list_name) data_list.sort() data_list = [trancos_data_path + t for t in data_list] data_list = file_list_to_list(data_list, unroll_num) desmap_list = [d.replace(data_ext, desmap_ext) for d in \ data_list] mask_list = [d.replace(data_ext, mask_ext) for d in \ data_list] file_list = [i + " " + d + " " + m for i, d, m in \ zip(data_list, desmap_list, mask_list)] return file_list
return parkway_train, parkway_test, downtown_train, downtown_test def save_file(file_list, file_name): file_io.save_file(file_list, file_list_dir + file_name, True) def check_add_to_list(file_dict, file_list, file_name, full_name): if file_name in file_dict: file_list.append(full_name) return file_list if __name__ == "__main__": train_list = file_io.read_file("../file_list/train_list2.txt") test_list = file_io.read_file("../file_list/test_list2.txt") full_list = train_list + test_list file_list_dir = "../file_list/" parkway_train, parkway_test, downtown_train, downtown_test = get_file_dict( ) parkway_train_list = list() parkway_test_list = list() downtown_train_list = list() downtown_test_list = list() for f in full_list: cam_dir_name = f.split(" ")[0].split("/")[-2]
#! /usr/bin/env python from TensorflowToolbox.utility import file_io import sys if __name__ == "__main__": if len(sys.argv) < 2: exit(1) file_name = sys.argv[1] file_list = file_io.read_file(file_name) file_list.sort() file_io.save_file(file_list, file_name)
from TensorflowToolbox.utility import file_io import sys if __name__ == "__main__": file_1 = sys.argv[1] file_2 = sys.argv[2] file_list1 = file_io.read_file(file_1) file_list2 = file_io.read_file(file_2) for f in file_list1: assert (f not in file_list2) for f in file_list2: assert (f not in file_list1)
from TensorflowToolbox.utility import file_io import shutil file_list = file_io.read_file("ucsd_file_label_num.txt") num_list = list() for i, f in enumerate(file_list): if i != 0 and i % 200 == 0 and i != 2000: i = i - 1 f_pre = file_list[i - 1].split(" ")[0] f_curr = file_list[i].split(" ")[0] shutil.copy2(f_pre, f_curr)