def process(sample_file,
sample_path,
sample_save_path,
pic_save_path,
threhold=-150.0):
print("Trying to process %s..." % sample_file)
array = np.load(os.path.join(sample_path, sample_file))
array[array >= threhold] = -1000.0
file_name = sample_file.split(".")[0]
#print(file_name)
np.save(os.path.join(sample_save_path, file_name + ".npy"), array)
plot_arr(array, os.path.join(pic_save_path, file_name + ".png"))
def sample_sph(cfg, threhold=-150.0):
label_df = pd.read_csv(cfg.label_file)
#count = 0
non_case = []
for i in label_df.index:
scan_path = label_df.loc[i, "patient_ID"]
print("Start sample nodule_%d of %s" % (int(i), scan_path))
if os.path.exists(
os.path.join(cfg.extract_path,
scan_path)) and int(i) not in cfg.deviant_index:
scan_array = read_scan(os.path.join(cfg.extract_path, scan_path))
nodule_coords = label_df.loc[i, "coord_XYZ"].split(" ")
nodule_x = int(nodule_coords[0])
nodule_y = int(nodule_coords[1])
nodule_z = int(nodule_coords[2])
edge_z_min = nodule_z - cfg.cube_size // 4
edge_z_max = nodule_z + cfg.cube_size // 4
edge_y_min = nodule_y - cfg.cube_size // 2
edge_y_max = nodule_y + cfg.cube_size // 2
edge_x_min = nodule_x - cfg.cube_size // 2
edge_x_max = nodule_x + cfg.cube_size // 2
print(edge_z_min, edge_z_max, edge_y_min, edge_y_max, edge_x_min,
edge_x_max)
nodule_array = scan_array[edge_z_min:edge_z_max,
edge_y_min:edge_y_max,
edge_x_min:edge_x_max]
nodule_64_array = np.ones((32, 64, 64), dtype=np.float32)
nodule_64_array *= -1000.0
padding_z_min = (nodule_64_array.shape[0] -
nodule_array.shape[0]) // 2
padding_z_max = padding_z_min + nodule_array.shape[0]
padding_y_min = (nodule_64_array.shape[1] -
nodule_array.shape[1]) // 2
padding_y_max = padding_y_min + nodule_array.shape[1]
padding_x_min = (nodule_64_array.shape[2] -
nodule_array.shape[2]) // 2
padding_x_max = padding_x_min + nodule_array.shape[2]
nodule_64_array[padding_z_min:padding_z_max,
padding_y_min:padding_y_max,
padding_x_min:padding_x_max] = nodule_array
nodule_64_array[nodule_64_array > threhold] = -1000.0
save_name = "nodule_" + str(int(i)).zfill(3) + "_" + scan_path
picture_path = os.path.join(cfg.p_v3_path, "sample_pictures/")
output_path = os.path.join(cfg.p_v3_path, "samples/")
if not os.path.exists(picture_path):
os.makedirs(picture_path)
if not os.path.exists(output_path):
os.makedirs(output_path)
print("Save nodule %d of %s" % (int(i), scan_path))
pn.plot_arr(nodule_64_array, picture_path + save_name + ".png")
np.save(output_path + save_name + ".npy", nodule_64_array)
#count += 1
del scan_array, nodule_array, nodule_64_array
else:
non_case.append(scan_path)
return non_case
result_arr = np.load(os.path.join(result_path, result_file))
#print(result_arr.shape)
label_arr = np.zeros(
(result_arr.shape[1], result_arr.shape[2], result_arr.shape[3]),
dtype=np.float32)
result_arr = result_arr[0, ...]
for z in range(label_arr.shape[0]):
for y in range(label_arr.shape[1]):
for x in range(label_arr.shape[2]):
if result_arr[z, y, x, 0] >= result_arr[z, y, x, 1]:
label_arr[z, y, x] = 0
else:
label_arr[z, y, x] = 1
return label_arr
if __name__ == '__main__':
result_path = "E:/lidc_seg/lidc_padding_samples/test/label/"
#label_path = "E:/lidc_seg/lidc_padding_samples/test/label/LIDC-IDRI-0004_label_0.npy"
save_path = "E:/lidc_seg/lidc_padding_samples/test/label_picture/"
result_list = os.listdir(result_path)
for result_file in result_list:
print(result_file)
#result_arr = merge_result(result_file, result_path)
result_arr = np.load(result_path + result_file)
result_name = result_file.split(".")[0]
save_name = save_path + result_name + ".png"
print(save_name)
plot_arr(result_arr, save_name)
#-*-coding:utf-8-*- import numpy as np import os from data_padding import padding_patch, normalization from plot_nodule import plot_arr lidc_path = "D:/DOI/" origin_path = os.path.join(lidc_path, "four_radiologist_samples/") padding_path = os.path.join(lidc_path, "lidc_padding_samples/") origin_sample_path = os.path.join(origin_path, "sample/") origin_label_path = os.path.join(origin_path, "label/") padding_sample_path = os.path.join(padding_path, "sample/") padding_label_path = os.path.join(padding_path, "label/") nodule_file = os.path.join(origin_sample_path, "LIDC-IDRI-0004_sample_0.npy") label_file = os.path.join(origin_label_path, "LIDC-IDRI-0004_label_0.npy") nodule_arr = np.load(nodule_file) label_arr = np.load(label_file) nodule_64_arr, label_64_arr = padding_patch(nodule_arr, label_arr) print(np.max(label_64_arr), np.min(label_64_arr)) print(np.max(label_arr), np.min(label_arr)) plot_arr(label_64_arr)
nodule_array = image_array[edge_z_min:edge_z_max,
edge_y_min:edge_y_max,
edge_x_min:edge_x_max]
nodule_64_array = np.ones((32, 64, 64), dtype=np.float32)
nodule_64_array *= -1000.0
padding_z_min = (nodule_64_array.shape[0] - nodule_array.shape[0]) // 2
padding_z_max = padding_z_min + nodule_array.shape[0]
padding_y_min = (nodule_64_array.shape[1] - nodule_array.shape[1]) // 2
padding_y_max = padding_y_min + nodule_array.shape[1]
padding_x_min = (nodule_64_array.shape[2] - nodule_array.shape[2]) // 2
padding_x_max = padding_x_min + nodule_array.shape[2]
nodule_64_array[padding_z_min:padding_z_max,
padding_y_min:padding_y_max,
padding_x_min:padding_x_max] = nodule_array
save_name = os.path.join(cfg.p_v2_path, "deviant/",
case + "_" + str(count).zfill(2))
print(save_name)
np.save(save_name + ".npy", nodule_64_array)
plot_arr(nodule_64_array, save_name + ".png")
count += 1
if __name__ == '__main__':
seriesUID = "1.3.12.2.1107.5.1.4.65018.30000016050712025118100482153"
cfg = config.Config()
data_path = os.path.join(cfg.origin_path, "0800441455/")
extract_path = os.path.join(cfg.extract_path, "0800441455/")
case = "0800441455"
sample_deviant(case, cfg)