def original_view2(srcpath, dstpath):
image_list = {}
for f_dcm in find_all_files(srcpath, suffix='.dcm'):
print 'processing dicom image %s ...' % (f_dcm[-4 - 6:])
name = f_dcm[-4 - 6:-4]
dcm = dicom.read_file(f_dcm)
z = dcm.get('SliceLocation')
image_list[z] =np.array(dcm.pixel_array)
images = []
for i in sorted(image_list.keys(), reverse=True):
images.append(image_list[i])
images = np.array(images)
l, w, h = images.shape
print (l,w,h)
for i in range(w):
tmp = images[:, i, :]
path = os.path.join(dstpath, 'tmp_%s.jpg' % (i,))
outpath =path
scipy.misc.imsave(path, tmp)
a = Image.open(path)
b = a.resize((512,int(133*2.5/0.703125)))
b.save(outpath)
def annotation_cut2(dcm_file_path, save_file_path):
'''
:param dcm_file_path:
:param save_file_path:
:return:
class: without annotation:0, nodule:1, small_nodule:2, non_nodule:3
'''
f = open(os.path.join(dcm_file_path, 'annotation_flatten.pkl'))
info = pickle.load(f)
f.close()
csvfile = file(os.path.join(save_file_path, 'split_with_annotation_info.csv'), 'wb')
writer = csv.writer(csvfile)
dcm_num = 0
tmp = [[-32, -32, 32, 32],
[-48, -32, 16, 32],
[-16, -32, 48, 32],
[-32, -48, 32, 16],
[-32, -16, 32, 48],
[-48, -48, 16, 16],
[-16, -16, 48, 48],
[-48, -16, 16, 48],
[-16, -48, 48, 16]
]
for f_dcm in find_all_files(dcm_file_path, '.dcm'):
print 'processing dicom image %s ...' % (f_dcm[-4 - 6:])
dcm = dicom.read_file(f_dcm)
sop_uid = dcm[0x08, 0x18].value
if info.has_key(sop_uid):
dstpath = os.path.join('./tmp/', 'original%d.jpg' % (dcm_num,))
scipy.misc.imsave(dstpath, dcm.pixel_array)
ct_image = Image.open(dstpath)
num = 0
for point in info[sop_uid]['nodules']:
for i in range(len(tmp)):
box = (int(point['centroid'][0] + tmp[i][0]), int(point['centroid'][1] + tmp[i][1]),
int(point['centroid'][0] + tmp[i][2]), int(point['centroid'][1] + tmp[i][3]))
ct_image.crop(box).save(os.path.join(save_file_path, 'dcm_%d_split_%d_a.jpg' % (dcm_num, num)), 'jpeg')
writer.writerow(['dcm_%d_split_%d_a.jpg' % (dcm_num, num), 1])
num += 1
for point in info[sop_uid]['small_nodules']:
for i in range(len(tmp)):
box = (int(point['centroid'][0] + tmp[i][0]), int(point['centroid'][1] + tmp[i][1]),
int(point['centroid'][0] + tmp[i][2]), int(point['centroid'][1] + tmp[i][3]))
ct_image.crop(box).save(os.path.join(save_file_path, 'dcm_%d_split_%d_a.jpg' % (dcm_num, num)), 'jpeg')
writer.writerow(['dcm_%d_split_%d_a.jpg' % (dcm_num, num), 2])
num += 1
for point in info[sop_uid]['non_nodules']:
for i in range(len(tmp)):
box = (int(point['centroid'][0] + tmp[i][0]), int(point['centroid'][1] + tmp[i][1]),
int(point['centroid'][0] + tmp[i][2]), int(point['centroid'][1] + tmp[i][3]))
ct_image.crop(box).save(os.path.join(save_file_path, 'dcm_%d_split_%d_a.jpg' % (dcm_num, num)), 'jpeg')
writer.writerow(['dcm_%d_split_%d_a.jpg' % (dcm_num, num), 3])
num += 1
dcm_num += 1
def original_view(srcpath, dstpath):
image_list = {}
for f_dcm in find_all_files(srcpath, suffix='.dcm'):
print 'processing dicom image %s ...' % (f_dcm[-4 - 6:])
name = f_dcm[-4 - 6:-4]
dcm = dicom.read_file(f_dcm)
z = dcm.get('SliceLocation')
scipy.misc.imsave(os.path.join(dstpath, 'original_%s.jpg' % (name,)), dcm.pixel_array)
image_list[z] = (Image.open(os.path.join(dstpath, 'original_%s.jpg' % (name,))))
images = []
for i in sorted(image_list.keys(), reverse=True):
images.append(image_list[i])
print len(image_list)
def parenchyma_view(srcpath, dstpath):
for f_dcm in find_all_files(srcpath, suffix='.dcm'):
print 'processing dicom image %s ...' % (f_dcm[-4 - 6:])
name = f_dcm[-4-6:-4]
dcm = dicom.read_file(f_dcm)
image = np.array(dcm.pixel_array)
result = _get_parenchyma_mask(image, 625)
scipy.misc.imsave(os.path.join(dstpath, 'parenchyma_%s.jpg' % (name,)), result)
w, h = result.shape[:2]
img2 = np.zeros([w, h])
for i in range(w):
for j in range(h):
if result[i][j] == 255:
img2[i][j] = image[i][j]
scipy.misc.imsave(os.path.join(dstpath, 'parenchyma2_%s.jpg' % (name,)), img2)
def annotation_view(srcpath, dstpath):
f = open(os.path.join(srcpath, 'annotation_flatten.pkl'))
info = pickle.load(f)
f.close()
j = 0
image_list = {}
for f_dcm in find_all_files(srcpath, suffix='.dcm'):
print 'processing dicom image %s ...' % (f_dcm[-4 - 6:])
name = f_dcm[-4 - 6:-4]
dcm = dicom.read_file(f_dcm)
sop_uid = dcm[0x08, 0x18].value
z = dcm.get('SliceLocation')
if info.has_key(sop_uid):
img = dcm.pixel_array
extent = (0, 512, 0, 512)
plt.imshow(img, cmap=plt.cm.gray, origin='upper', extent=extent)
print j, ' figures:'
for point in info[sop_uid]['nodules']:
x1 = point['field'][0]
y1 = 512 - point['field'][1]
x2 = point['field'][0]
y2 = 512 - point['field'][3]
x3 = point['field'][2]
y3 = 512 - point['field'][3]
x4 = point['field'][2]
y4 = 512 - point['field'][1]
plt.plot([x1, x2, x3, x4, x1], [y1, y2, y3, y4, y1], 'r-', linewidth=2, alpha=1)
for point in info[sop_uid]['small_nodules']:
x = point['centroid'][0]
y = 512 - point['centroid'][1]
print 'small_nodules', x, y
plt.plot(x, y, 'bv', alpha=0.5)
for point in info[sop_uid]['non_nodules']:
x = point['centroid'][0]
y = 512 - point['centroid'][1]
print 'non_nodules:', x, y
plt.plot(x, y, 'gx', alpha=1)
plt.savefig(os.path.join(dstpath, '%s.jpg' % (name,)))
image_list[z] = (Image.open(os.path.join(dstpath, '%s.jpg' % (name,))))
plt.close('all')
j += 1
images = []
for i in sorted(image_list.keys(), reverse=True):
images.append(image_list[i])
def parenchyma_cut(dcm_file_path, save_file_path):
'''
class: without annotation:0, nodule:1, small_nodule:2, non_nodule:3
'''
f = open(os.path.join(dcm_file_path, 'annotation_flatten.pkl'))
info = pickle.load(f)
f.close()
csvfile = file(os.path.join(save_file_path, 'split_parenchyma_info.csv'), 'wb')
writer = csv.writer(csvfile)
dcm_num = 0
for f_dcm in find_all_files(dcm_file_path, '.dcm'):
print 'processing dicom image %s ...' % (f_dcm[-4-6:])
dcm = dicom.read_file(f_dcm)
sop_uid = dcm[0x08, 0x18].value
if info.has_key(sop_uid):
#dir_name = os.path.join(save_file_path, 'dcm_split%d' % (dcm_num,))
#os.mkdir(dir_name)
dir_name = save_file_path
dstpath = os.path.join('./tmp/', 'original%d.jpg' % (dcm_num,))
scipy.misc.imsave(dstpath,dcm.pixel_array)
ct_image = Image.open(dstpath)
num = 0
for i in parenchyma_block(f_dcm):
box = (i[0], i[1], i[0]+i[2], i[1]+i[3])
ct_image.crop(box).save(os.path.join(dir_name, 'dcm_%d_split_%d_p.jpg' % (dcm_num, num)), 'jpeg')
flag = 0
for point in info[sop_uid]['nodules']:
if is_in(point['centroid'], box):
flag = 1
writer.writerow(['dcm_%d_split_%d_p.jpg' % (dcm_num, num), 1])
for point in info[sop_uid]['small_nodules']:
if is_in(point['centroid'], box):
flag = 1
writer.writerow(['dcm_%d_split_%d_p.jpg' % (dcm_num, num), 2])
for point in info[sop_uid]['non_nodules']:
if is_in(point['centroid'], box):
flag = 1
writer.writerow(['dcm_%d_split_%d_p.jpg' % (dcm_num, num), 3])
if flag == 0:
writer.writerow(['dcm_%d_split_%d_p.jpg' % (dcm_num, num), 0])
num += 1
dcm_num += 1
def main():
parser = build_parser()
options = parser.parse_args()
assert os.path.exists(options.srcpath)
if not os.path.exists(options.dstpath):
os.mkdir(options.dstpath)
print 'DATA_PATH: ', options.srcpath
print 'OUT_PATH: ', options.dstpath
flag = True
# multiply test cases
for file in os.listdir(options.srcpath):
path = os.path.join(options.srcpath, file)
if os.path.isdir(path) and find_all_files(path, '.dcm') is not None:
print "multiply test cases"
flag = False
dstpath = os.path.join(options.dstpath, file)
if os.path.exists(dstpath):
pass
#os.removedirs(dstpath)
else:
os.mkdir(dstpath)
parse_dir(path)
if options.mode == 'view':
if options.method == 'original':
original_view(path, dstpath)
elif options.method == 'parenchyma':
parenchyma_view(path, dstpath)
elif options.method == 'annotation':
annotation_view(path, dstpath)
else:
print 'wrong method !!!'
elif options.mode == 'cut':
if options.method == 'parenchyma':
parenchyma_cut(path, dstpath)
elif options.method == 'annotation':
annotation_cut(path, dstpath)
else:
print 'wrong method !!!'
# single test case
if flag and find_all_files(options.srcpath, '.xml') and find_all_files(options.srcpath, '.dcm'):
print "single test case"
parse_dir(options.srcpath)
if options.mode == 'view':
if options.method == 'original':
original_view(options.srcpath, options.dstpath)
elif options.method == 'parenchyma':
parenchyma_view(options.srcpath, options.dstpath)
elif options.method == 'annotation':
annotation_view(options.srcpath, options.dstpath)
else:
print 'wrong method !!!'
elif options.mode == 'cut':
if options.method == 'parenchyma':
parenchyma_cut(options.srcpath, options.dstpath)
elif options.method == 'annotation':
annotation_cut(options.srcpath, options.dstpath)
else:
print 'wrong method !!!'