def __pixels_to_image3d_md(self, datasets):
"""
:param datasets:
:return:
"""
SeriesInstanceUID = datasets[0].SeriesInstanceUID
dir = Config.get("db", 'dicom_save_dir') + str(SeriesInstanceUID)
vol, tags = cio.read_dicom_series(dir)
print vol
shutil.rmtree(dir)
return vol
def __pixels_to_image3d_md(self, datasets):
"""
:param datasets:
:return:
"""
SeriesInstanceUID = datasets[0].SeriesInstanceUID
dir = Config.get("db", 'dicom_save_dir') + str(SeriesInstanceUID)
"""
dir = r'/home/uii/PKU_Pro_Final/cmove_data/1.2.840.113564.143313591524.3512.636758772708415433.34'
"""
vol, tags = cio.read_dicom_series(dir)
print vol
shutil.rmtree(dir)
return vol
def __pixels_to_image3d(self, datasets):
"""
transform the origin pixels value to numpy and reshape to three dimensions
:param datasets: dicom files
:return:image3D volume
"""
# self.pixels = np.array(self.pixels)
# self.pixels = np.frombuffer(self.pixels, dtype=self.__get_pixels_data_type(datasets[0]), count=-1, offset=0)
# module = self.__check_modality(datasets)
# self.pixels = self.pixels * module['RescaleSlope'] + module['RescaleIntercept']
# self.pixels = np.reshape(self.pixels,
# (len(self.instance_pixels_position), datasets[0].Rows, datasets[0].Columns))
# im = md.Image3d()
#
# # set the origin of Image3D Volume
# im.from_numpy(self.pixels, np.short)
# origin = self.instance_pixels_position[0][2]
# im.set_origin(origin)
#
# # set the pixel spacing of Image3D Volume
# x_spacing = datasets[0].PixelSpacing[0]
# y_spacing = datasets[0].PixelSpacing[1]
# z_spacing = lina.norm(np.array(self.instance_pixels_position[-1][2])
# - np.array(self.instance_pixels_position[0][2]))/(len(self.instance_pixels_position)-1)
# im.set_spacing([x_spacing, y_spacing, z_spacing])
#
# # set the axes of Image3D Volume
# orientation = datasets[0].ImageOrientationPatient
# axis_x = np.array([orientation[0], orientation[1], orientation[2]], np.float32)
# axis_y = np.array([orientation[3], orientation[4], orientation[5]], np.float32)
# axis_z = np.array(self.instance_pixels_position[1][2])-np.array(self.instance_pixels_position[0][2], np.float32)
# axis_z = axis_z/(lina.norm(axis_z))
# axes = [axis_x, axis_y, axis_z]
# axes = np.reshape(axes, (3, 3))
# im.set_axes(axes)
SeriesInstanceUID = datasets[0].SeriesInstanceUID
dir = Config.get("db", 'dicom_save_dir') + str(SeriesInstanceUID)
# dir = r'/home/lyw/rxl5/pyproject/PKU/dcms/1_test'
vol, tags = cio.read_dicom_series(dir)
print vol
shutil.rmtree(dir)
return vol
def build(self, series_path):
"""
dicom to volume and upload volume to DB
:return:volume file path
"""
vol, tags = read_dicom_series(series_path)
seriesuid = tags['0020|000e']
volume_name = seriesuid + '.nii.gz'
# 保存文件到本地
if os.path.exists(file_path_ferry.volumePath):
pass
else:
os.mkdir(file_path_ferry.volumePath)
volfilepath = os.path.join(file_path_ferry.volumePath, volume_name)
if not write_image(vol, volfilepath):
pass
return volfilepath, seriesuid
img_file = os.path.join(self.img_folder,
item['image_uid']) + '.jpg'
im.save(img_file)
output_buffer = StringIO()
im.save(output_buffer, 'JPEG')
binary_data = output_buffer.getvalue()
base64_data = base64.b64encode(binary_data)
base64img_dic[item['image_uid']] = base64_data
return base64img_dic
if __name__ == '__main__':
slice_info = [{
'instance_num': 634,
'best_instance_num': 634,
'voxel_slice_num': 75,
'image_uid': '1.2.840.113704.1.111.1888.1508053946.10640',
'rectangle_patient': (12, 25, 34, 65),
'rectangle_pixel': (15, 24, 65, 87),
'probability': 1,
'rib_class': 1,
'rib_location': 1,
'min_voxel': (1, 2, 3),
'max_voxel': (4, 5, 6)
}]
images, tag = cio.read_dicom_series(
r'/home/lyw/rxl5/1.2.840.113704.1.111.12164.1508053864.7/')
cutter = ImgCutter(slice_info, images)
a = cutter.handle()