def exec_model(model):
"""test model on user-provided data, instead of the preset DeepLesion dataset"""
import_tag_data()
model.eval()
device = torch.device(cfg.MODEL.DEVICE)
#while True:
#info = "Please input the path of a nifti CT volume >> "
#while True:
#path = input(info)
# ------- Zhoubing 100 datasets -------
# for num in range(12):
# if num + 1 < 10:
# img_num = 'img000' + str(num + 1)
# #data_dir = '/nfs/masi/leeh43/MULAN_universal_lesion_analysis/results'
# #img_dir = '_nfs_masi_leeh43_zhoubing100_img_' + img_num + '.nii.gz/'
# #result = os.path.join(data_dir, img_dir + 'results.txt' )
# main_dir = '/nfs/masi/leeh43/zhoubing100/img/'
# img_dir = os.path.join(main_dir, img_num + '.nii.gz')
#
# if num + 1 >= 10 and num + 1 < 100:
# img_num = 'img00' + str(num + 1)
# main_dir = '/nfs/masi/leeh43/zhoubing100/img/'
# img_dir = os.path.join(main_dir, img_num + '.nii.gz')
#
# if num + 1 == 100:
# img_num = 'img0' + str(num + 1)
# main_dir = '/nfs/masi/leeh43/zhoubing100/img/'
# img_dir = os.path.join(main_dir, img_num + '.nii.gz')
# if not os.path.exists(img_dir):
# print('file does not exist!')
# continue
# #try:
# ------- ImageVU B Datasets -------
data_dir = os.path.join('/nfs/masi/tangy5/ImageVU_B_bpr_pipeline/INPUTS/cropped/images')
count = 0
for item in os.listdir(data_dir):
img_dir = os.path.join(data_dir, item)
print('reading image ...')
nifti_data = nib.load(img_dir)
count = count + 1
print('Number of Datasets: %d' % count)
print('Load Image: %s' % img_dir)
#break
#except:
#print('load nifti file error!')
while True:
win_sel = '1' #input('Window to show, 1:soft tissue, 2:lung, 3: bone >> ')
if win_sel not in ['1', '2', '3']:
continue
win_show = [[-175, 275], [-1500, 500], [-500, 1300]]
win_show = win_show[int(win_sel)-1]
break
vol, spacing, slice_intv = load_preprocess_nifti(nifti_data)
slice_num_per_run = max(1, int(float(cfg.TEST.TEST_SLICE_INTV_MM)/slice_intv+.5))
num_total_slice = vol.shape[2]
total_time = 0
imageVU_dir = 'ImageVU_B_result'
output_dir = os.path.join(cfg.RESULTS_DIR,imageVU_dir,img_dir.replace(os.sep, '_'))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
slices_to_process = range(int(slice_num_per_run/2), num_total_slice, slice_num_per_run)
msgs_all = []
print('predicting ...')
for slice_idx in tqdm(slices_to_process):
log_file_s = os.path.join(output_dir, 'slice_' + str(slice_idx) + '_resize_shape.csv')
log_file_c = os.path.join(output_dir, 'slice_' + str(slice_idx) + '_contour_location.csv')
log_file_r = os.path.join(output_dir, 'slice_' + str(slice_idx) + '_recist_location.csv')
log_file_mask = os.path.join(output_dir, 'slice_' + str(slice_idx) + '_mask_c.csv')
mask_list = []
ims, im_np, im_scale, crop, mask_list = get_ims(slice_idx, vol, spacing, slice_intv, mask_list)
im_list = to_image_list(ims, cfg.DATALOADER.SIZE_DIVISIBILITY).to(device)
start_time = time()
with torch.no_grad():
result = model(im_list)
result = [o.to("cpu") for o in result]
df_resize = pd.DataFrame()
df_contours = pd.DataFrame()
df_recists = pd.DataFrame()
df_mask = pd.DataFrame()
shape_0, shape_1 = [], []
cour_list1, cour_list2 = [], []
recist_list1, recist_list2 = [], []
info = {'spacing': spacing, 'im_scale': im_scale}
post_process_results(result[0], info)
total_time += time() - start_time
output_fn = os.path.join(output_dir, '%d.png'%(slice_idx+1))
real_slice_num = slice_idx + 1
#contour_list.append('Slice_'+str(real_slice_num))
#recist_list.append(('Slice_'+str(real_slice_num)))
shape_0.append(im_np.shape[0])
shape_1.append(im_np.shape[1])
overlay, msgs = gen_output(im_np, result[0], info, win_show, cour_list1, cour_list2, recist_list1, recist_list2)
df_resize['Shape_0'] = shape_0
df_resize['Shape_1'] = shape_1
df_contours['list1'] = cour_list1
df_contours['list2'] = cour_list2
df_recists['list1'] = recist_list1
df_mask['c'] = mask_list
df_resize.to_csv(log_file_s, index=False)
df_contours.to_csv(log_file_c, index = False)
df_recists.to_csv(log_file_r, index = False)
df_mask.to_csv(log_file_mask, index = False)
cv2.imwrite(output_fn, overlay)
msgs_all.append('slice %d\r\n' % (slice_idx+1))
for msg in msgs:
msgs_all.append(msg+'\r\n')
msgs_all.append('\r\n')
#np.savetxt(log_file_c, cour_list1, cour_list2, delimiter=',', fmt='%s')
with open(os.path.join(output_dir, 'results.txt'), 'w') as f:
f.writelines(msgs_all)
print('result images and text saved to', output_dir)
print('processing time: %d ms per slice' % int(1000.*total_time/len(slices_to_process)))
def exec_model(model):
"""test model on user-provided data, instead of the preset DeepLesion dataset"""
import_tag_data()
model.eval()
device = torch.device(cfg.MODEL.DEVICE)
while True:
info = "Please input the path of a nifti CT volume >> "
while True:
path = input(info)
if not os.path.exists(path):
print('file does not exist!')
continue
try:
print('reading image ...')
nifti_data = nib.load(path)
break
except:
print('load nifti file error!')
while True:
win_sel = input(
'Window to show, 1:soft tissue, 2:lung, 3: bone >> ')
if win_sel not in ['1', '2', '3']:
continue
win_show = [[-175, 275], [-1500, 500], [-500, 1300]]
win_show = win_show[int(win_sel) - 1]
break
vol, spacing, slice_intv = load_preprocess_nifti(nifti_data)
slice_num_per_run = max(
1, int(float(cfg.TEST.TEST_SLICE_INTV_MM) / slice_intv + .5))
num_total_slice = vol.shape[2]
total_time = 0
output_dir = os.path.join(cfg.RESULTS_DIR, path.replace(os.sep, '_'))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
slices_to_process = range(int(slice_num_per_run / 2), num_total_slice,
slice_num_per_run)
msgs_all = []
print('predicting ...')
for slice_idx in tqdm(slices_to_process):
ims, im_np, im_scale, crop = get_ims(slice_idx, vol, spacing,
slice_intv)
im_list = to_image_list(
ims, cfg.DATALOADER.SIZE_DIVISIBILITY).to(device)
start_time = time()
with torch.no_grad():
result = model(im_list)
result = [o.to("cpu") for o in result]
info = {'spacing': spacing, 'im_scale': im_scale}
post_process_results(result[0], info)
total_time += time() - start_time
output_fn = os.path.join(output_dir, '%d.png' % (slice_idx + 1))
overlay, msgs = gen_output(im_np, result[0], info, win_show)
cv2.imwrite(output_fn, overlay)
msgs_all.append('slice %d\r\n' % (slice_idx + 1))
for msg in msgs:
msgs_all.append(msg + '\r\n')
msgs_all.append('\r\n')
with open(os.path.join(output_dir, 'results.txt'), 'w') as f:
f.writelines(msgs_all)
print('result images and text saved to', output_dir)
print('processing time: %d ms per slice' %
int(1000. * total_time / len(slices_to_process)))
def batch_exec_model(model):
"""test model on user-provided folder of data, instead of the preset DeepLesion dataset"""
import_tag_data()
model.eval()
device = torch.device(cfg.MODEL.DEVICE)
info = "Please input the path which contains all nifti CT volumes to predict in batch >> "
while True:
path = input(info)
if not os.path.exists(path):
print('folder does not exist!')
continue
else:
break
nifti_paths = []
for dirName, subdirList, fileList in os.walk(path):
print('found directory: %s' % dirName)
for fname in fileList:
if fname.endswith('.nii.gz') or fname.endswith('.nii'):
nifti_paths.append(os.path.join(path, dirName, fname))
print('%d nifti files found' % len(nifti_paths))
total_time = 0
results = {}
total_slices = 0
for file_idx, nifti_path in enumerate(nifti_paths):
print('(%d/%d) %s' % (file_idx + 1, len(nifti_paths), nifti_path))
print('reading image ...')
try:
nifti_data = nib.load(nifti_path)
except:
print('load nifti file error!')
continue
vol, spacing, slice_intv = load_preprocess_nifti(nifti_data)
slice_num_per_run = max(
1, int(float(cfg.TEST.TEST_SLICE_INTV_MM) / slice_intv + .5))
num_total_slice = vol.shape[2]
results[nifti_path] = {}
slices_to_process = range(int(slice_num_per_run / 2), num_total_slice,
slice_num_per_run)
total_slices += len(slices_to_process)
for slice_idx in tqdm(slices_to_process):
ims, im_np, im_scale, crop = get_ims(slice_idx, vol, spacing,
slice_intv)
im_list = to_image_list(
ims, cfg.DATALOADER.SIZE_DIVISIBILITY).to(device)
start_time = time()
with torch.no_grad():
result = model(im_list)
result = [o.to("cpu") for o in result]
info = {'spacing': spacing, 'im_scale': im_scale, 'crop': crop}
post_process_results(result[0], info)
result = sort_results_for_batch(result[0], info)
results[nifti_path][slice_idx] = result
total_time += time() - start_time
# # sanity check
# im = vol[:, :, slice_idx].astype(float) - 32768
# im = windowing(im, [-175, 275]).astype('uint8')
# im = cv2.cvtColor(im, cv2.COLOR_GRAY2RGB)
# overlay, msgs = draw_results(im, np.array(result['boxes']), scores=np.array(result['scores']),
# tag_scores=np.array(result['tag_scores']), tag_predictions=np.array(result['tag_scores'])>.5,
# contours=np.array(result['contour_mm']))
# plt.imshow(overlay)
# print(msgs)
# plt.show()
output_dir = os.path.join(cfg.RESULTS_DIR)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_fn = os.path.join(output_dir, '%s.pth' % os.path.basename(path))
torch.save(results, output_fn)
print('result images and text saved to', output_fn)
print('processing time: %d ms per slice' %
int(1000. * total_time / total_slices))