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)
