/
get_final_test.py
executable file
·57 lines (54 loc) · 2.9 KB
/
get_final_test.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
import copy
import os
import sys
from collections import OrderedDict
import numpy as np
import SimpleITK as sitk
from batchgenerators.utilities.file_and_folder_operations import *
from pymic.io.image_read_write import (load_image_as_nd_array,
save_array_as_nifty_volume,
save_nd_array_as_image)
from pymic.util.image_process import (
convert_label, crop_ND_volume_with_bounding_box, get_ND_bounding_box,
set_ND_volume_roi_with_bounding_box_range)
from path_config import path_dict
if __name__ == "__main__":
if(len(sys.argv) < 3):
print('Number of arguments should be 3. e.g.')
print(' python get_final_test.py input_dir output_dir')
exit()
input_dir = str(sys.argv[1])
output_dir = str(sys.argv[2])
#first_crop = "/mnt/data1/swzhai/dataset/MyoPS/data_preprocessed/imagesTs/crop_information.json"
#second_crop = "/mnt/data1/swzhai/dataset/MyoPS/nnUNet_raw_data_base/nnUNet_raw_data/Task112_MyoPS/imagesTs/crop_information.json"
first_crop = path_dict['MyoPS_data_dir'] + "/data_preprocessed/imagesTs/crop_information.json"
second_crop = path_dict['nnunet_raw_data_dir'] + "/Task112_MyoPS/imagesTs/crop_information.json"
first_json = load_json((first_crop))
second_json = load_json(second_crop)
# seg_dir = "../MyoPS-main/myops/result_test/nnunet_2d"
# save_dir = "../MyoPS-main/myops/result_test/test_ensemble_original"
img_dir = path_dict['MyoPS_data_dir'] + "/data_raw/imagesTs"
maybe_mkdir_p(output_dir)
for pred_name in second_json.keys():
seg_name = pred_name.split("/")[-1]
print(seg_name)
save_name = seg_name
img_name = seg_name.replace(".nii.gz", "_C0.nii.gz")
seg_array = load_image_as_nd_array(join(input_dir, seg_name))["data_array"].squeeze(0)
img_array = load_image_as_nd_array(join(img_dir, img_name))["data_array"].squeeze(0)
zero_array = np.zeros_like(img_array)
print(seg_array.shape, zero_array.shape)
first_json_bbox = first_json[join(img_dir, img_name)]
second_json_bbox = second_json[pred_name]
print(first_json_bbox)
print(second_json_bbox)
# for D, H, W
crop_bbox_min = [0, 0, 0]
crop_bbox_max = [0, 0, 0]
for i in range(3):
crop_bbox_min[i] = first_json_bbox["crop_bbox_min"][i] + second_json_bbox["crop_bbox_min"][i]
crop_bbox_max[i] = first_json_bbox["crop_bbox_min"][i] + second_json_bbox["crop_bbox_max"][i]
print(crop_bbox_min, crop_bbox_max, seg_array.shape)
output_seg_array = set_ND_volume_roi_with_bounding_box_range(zero_array, crop_bbox_min, crop_bbox_max, seg_array, False)
output_seg_array = convert_label(output_seg_array, [1, 2, 3, 4, 5], [200, 500, 600, 1220, 2221])
save_nd_array_as_image(output_seg_array, join(output_dir, save_name), join(img_dir, img_name))