#! /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)
def save_file(file_list, file_name):
file_io.save_file(file_list, file_list_dir + file_name, True)
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
if __name__ == "__main__":
data_ext = ".jpg"
desmap_ext = ".desmap"
mask_ext = "_mask.npy"
unroll_num = 5
file_list_dir = "../file_list/"
save_train_file_name = "trancos_train_list1.txt"
save_test_file_name = "trancos_test_list1.txt"
train_file = file_list_to_train_list("../file_list/trancos_org_trainval.txt")
test_file = file_list_to_train_list("../file_list/trancos_org_test.txt")
file_io.save_file(train_file, file_list_dir + save_train_file_name, True)
file_io.save_file(test_file, file_list_dir + save_test_file_name, True)
import numpy as np
import cv2
from TensorflowToolbox.utility import file_io
desmap_dir = "/media/dog/data/UCSD/gtDensities/"
desmap_list = file_io.get_listfile(desmap_dir, "desmap")
new_list = list()
for f in desmap_list:
num = np.sum(np.fromfile(f, np.float32))
format_num = "%.2f" % num
new_list.append(f + " " + format_num)
new_list.sort()
file_io.save_file(new_list, "ucsd_file_label_num.txt")
if __name__ == "__main__":
unroll_num = 5
for i in range(2):
if i == 0:
file_list_name = "/home/shanghang/video_analysis/file_list/world_expo_train_list1.txt"
save_file_name = "../file_list/world_expo_train_list1.txt"
else:
file_list_name = "/home/shanghang/video_analysis/file_list/world_expo_test_list1.txt"
save_file_name = "../file_list/world_expo_test_list1.txt"
file_list = file_io.read_file(file_list_name)
file_list.sort()
unrolled_list = list()
for i, f in enumerate(file_list):
if i == 0:
start_video = f.split("/")[0][:6]
start_i = i
else:
video = f.split("/")[0][:6]
if start_video != video or i == len(file_list) - 1:
new_list = file_list_to_list(file_list[start_i:i], unroll_num)
unrolled_list += new_list
unrolled_list = [reorder_str(f) for f in unrolled_list]
file_io.save_file(unrolled_list, save_file_name, True)
data_list += curr_data_list
desmap_list += curr_desmap_list
mask_list += curr_mask_list
#partition = 0.7
#train_data_len = int(len(data_list) * partition)
#random.shuffle(data_list)
#train_data = data_list[:train_data_len]
#test_data = data_list[train_data_len:]
#train_list += [d + " " + d.replace(data_ext, label_ext) for d in train_data]
#test_list += [d + " " + d.replace(data_ext, label_ext) for d in test_data]
full_file_list = [i + " "+ d + " " + m for i, d, m in \
zip(data_list, desmap_list, mask_list)]
file_num = len(full_file_list)
train_file_list_name = 'train_list1.txt'
train_len = int(file_num * 0.7)
train_file_list = full_file_list[:train_len]
file_io.save_file(train_file_list, file_list_dir + train_file_list_name,
True)
test_file_list_name = 'test_list1.txt'
test_file_list = full_file_list[train_len:]
file_io.save_file(test_file_list, file_list_dir + test_file_list_name,
True)
file_list = train_list
else:
file_list = test_list
img_list = file_io.get_listfile(img_dir, ".jpg")
scale_str = str(new_dsize[0])
for img_name in img_list:
if not img_name.endswith("_" + scale_str + ".jpg"):
continue
label_name = img_name.replace(img_dir,
desmap_dir).replace(".jpg", ".npy")
mask_name = mask_dir + img_name.split("/")[-1][:6] \
+ "_mask_" + scale_str + ".npy"
if not os.path.exists(label_name):
print(label_name, "is not exist")
exit(1)
if not os.path.exists(mask_name):
print(mask_name, "is not exist")
exit(1)
file_list.append(" ".join([img_name, label_name, mask_name]))
train_file_name = "../file_list/world_expo_train_list1.txt"
test_file_name = "../file_list/world_expo_test_list1.txt"
file_io.save_file(train_list, train_file_name, True)
file_io.save_file(test_list, test_file_name, True)