app.py

import gradio as gr
import torch
import os
import numpy as np
import SimpleITK as sitk
from scipy.ndimage import zoom
from resnet_gn import resnet50
import pickle
#import tempfile

def load_from_pkl(load_path):
    data_input = open(load_path, 'rb')
    read_data = pickle.load(data_input)
    data_input.close()
    return read_data


Image_3D = None
Current_name = None
ALL_message = load_from_pkl(r'./label0601.pkl')

Model_Paht = r'./model_epoch62.pth.tar'
checkpoint = torch.load(Model_Paht, map_location='cpu')

classnet = resnet50(
    num_classes=1,
    sample_size=128,
    sample_duration=8)
classnet.load_state_dict(checkpoint['model_dict'])


def resize3D(img, aimsize, order=3):
    """
    :param img: 3D array
    :param aimsize: list, one or three elements, like [256], or [256,56,56]
    :return:
    """
    _shape = img.shape
    if len(aimsize) == 1:
        aimsize = [aimsize[0] for _ in range(3)]
    if aimsize[0] is None:
        return zoom(img, (1, aimsize[1] / _shape[1], aimsize[2] / _shape[2]), order=order)  # resample for cube_size
    if aimsize[1] is None:
        return zoom(img, (aimsize[0] / _shape[0], 1, aimsize[2] / _shape[2]), order=order)  # resample for cube_size
    if aimsize[2] is None:
        return zoom(img, (aimsize[0] / _shape[0], aimsize[1] / _shape[1], 1), order=order)  # resample for cube_size
    return zoom(img, (aimsize[0] / _shape[0], aimsize[1] / _shape[1], aimsize[2] / _shape[2]),
                order=order)  # resample for cube_size


def inference():
    global Image_small_3D
    model = classnet
    data = Image_small_3D

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.eval()
    all_loss = 0
    length = 0
    try:
        with torch.no_grad():
            data = torch.from_numpy(data)
            image = torch.unsqueeze(data, 0)
            patch_data = torch.unsqueeze(image, 0).to(device).float()  # (N, C_{in}, D_{in}, H_{in}, W_{in})

            # Pre : Prediction Result
            pre_probs = model(patch_data)

            # pre_probs = F.sigmoid(pre_probs)#todo
            pre_flat = pre_probs.view(-1)
            np.round(pre_flat.numpy()[0], decimals=2)
            # (1-pre_flat.numpy()[0]).astype(np.float32)
            # pre_flat.numpy()[0].astype(np.float32)
            # p = float(np.round(pre_flat.numpy()[0], decimals=2))
            # n = float(np.round(1 - p, decimals=2))
            p = np.round(float(pre_flat.numpy()[0]), decimals=2)
            n = np.round(float(1 - p), decimals=2)
            return {'急性期': n, '亚急性期': p}
    except:
        return ' '





def get_Image_reslice(input_file):
    '''得到图像 返回随即层'''
    global Image_3D
    global Current_name
    global Input_File
    if isinstance(input_file, str):
        input_file = input_file
    else:
        input_file = input_file.name
    Input_File = input_file
    print(input_file)
    Image_3D = sitk.GetArrayFromImage(sitk.ReadImage(input_file))
    Current_name = input_file.split(os.sep)[-1].split('.')[0].rsplit('_', 1)[0]
    Image_3D = (np.max(Image_3D) - Image_3D) / (np.max(Image_3D) - np.min(Image_3D))
    random_z = np.random.randint(0, Image_3D.shape[0])
    image_slice_z = Image_3D[random_z, :, :]

    random_y = np.random.randint(0, Image_3D.shape[1])
    image_slice_y = Image_3D[:, random_y, :]

    random_x = np.random.randint(0, Image_3D.shape[2])
    image_slice_x = Image_3D[:, :, random_x]
    # return  zoom(image_slice_z, (10 / image_slice_z.shape[0], 10 / image_slice_z.shape[1]), order=3) , \
    #         zoom(image_slice_y, (10 / image_slice_y.shape[0], 10 / image_slice_y.shape[1]), order=3), \
    #         zoom(image_slice_x, (10 / image_slice_x.shape[0], 10 / image_slice_x.shape[1]), order=3)
    return image_slice_z, \
        image_slice_y, \
        image_slice_x, random_z, random_y, random_x, '影像数据加载成功'


def get_ROI(input_file):
    '''得到图像 返回随即层'''
    global ROI_3D
    if isinstance(input_file, str):
        input_file = input_file
    else:
        input_file = input_file.name

    Image_3D = sitk.GetArrayFromImage(sitk.ReadImage(input_file))
    ROI_3D = Image_3D
    unique_elements = np.unique(ROI_3D)
    a = 5
    if np.where(unique_elements>1)[0]:
        return '这个数据没有经过二值化'
    else:
        return '感兴趣区域加载成功'


def change_image_slice_x(slice):
    image_slice = Image_3D[:, :, slice - 1]
    cut_thre = np.percentile(image_slice, 99.9)  # 直方图99.9%右侧值不要
    image_slice[image_slice >= cut_thre] = cut_thre
    image_slice = (((np.max(image_slice) -image_slice)/(np.max(image_slice) - np.min(image_slice)))*255).astype(np.int16)
    a = 5
    return image_slice


def change_image_slice_y(slice):
    image_slice = Image_3D[:, slice - 1, :]
    cut_thre = np.percentile(image_slice, 99.9)  # 直方图99.9%右侧值不要
    image_slice[image_slice >= cut_thre] = cut_thre
    image_slice = (((np.max(image_slice) - image_slice) / (np.max(image_slice) - np.min(image_slice))) * 255).astype(
        np.int16)

    return image_slice


def change_image_slice_z(slice):
    image_slice = Image_3D[slice - 1, :, :]
    cut_thre = np.percentile(image_slice, 99.9)  # 直方图99.9%右侧值不要
    image_slice[image_slice >= cut_thre] = cut_thre
    image_slice = (((np.max(image_slice) - image_slice) / (np.max(image_slice) - np.min(image_slice))) * 255).astype(np.int16)

    return image_slice
def get_medical_message():
    global Current_name
    if Current_name == None:
        return '请先加载数据', ' '
    else:
        past = ALL_message[Current_name]['past']
        now = ALL_message[Current_name]['now']
        return past, now


def clear_all():
    global Image_3D
    global Current_name
    Current_name = None
    Image_3D = None

    return np.ones((10, 10)), np.ones((10, 10)), np.ones((10, 10)), '', '', ' ',"尚未进行预处理 请先预处理再按“分期结果”按钮","尚未加载影像数据","尚未加载感兴趣区域"

def get_box(mask):
    """
    :param mask:  array,输入金标准图像
    :return:
    """
    # 得到boxx坐标
    # 计算得到bbox，形式为[dim0min, dim0max, dim1min, dim1max, dim2min, dim2max]
    indexx = np.where(mask > 0.)  # 返回坐标，几维就是几组坐标，坐标纵向看
    dim0min, dim0max, dim1min, dim1max, dim2min, dim2max = [np.min(indexx[0]), np.max(indexx[0]),
                                                            np.min(indexx[1]), np.max(indexx[1]),
                                                            np.min(indexx[2]), np.max(indexx[2])]
    bbox = [dim0min, dim0max, dim1min, dim1max, dim2min, dim2max]
    return bbox

def arry_crop_3D(img,mask,ex_pix):
        '''
        得到小图，并外扩
        :param img array 3D
        :param mask array
        :param ex_pix: list [a,b,c] 向两侧各自外扩多少 维度顺序与输入一致
        :param z_waikuo：z轴是否外扩，默认第一维  务必提前确认 ！！
        '''
        if len(ex_pix)==1:
            ex_pix=[ex_pix[0] for _ in range(3)]
        elif len(ex_pix) == 2:
            print('如果z轴不外扩，第一维请输入0')
            sys.exit()
        [dim0min, dim0max, dim1min, dim1max, dim2min, dim2max] = get_box(mask)

        #判断能否外扩
        dim0,dim1,dim2 = img.shape

        dim1_l_index = np.clip(dim1min-ex_pix[1],0 ,dim1) #dim1外扩后左边的坐标，若触碰边界，则尽量外扩至边界
        dim1_r_index = np.clip(dim1max + ex_pix[1], 0, dim1)
        dim2_l_index = np.clip(dim2min - ex_pix[2], 0, dim2)
        dim2_r_index = np.clip(dim2max + ex_pix[2], 0, dim2)

        fina_img = img[:, dim1_l_index:dim1_r_index+1, dim2_l_index:dim2_r_index+1]
        fina_mask = mask[:, dim1_l_index:dim1_r_index+1, dim2_l_index:dim2_r_index+1]

        if ex_pix[0]:
            dim0_l_index = np.clip(dim0min - ex_pix[0], 0, dim0)
            dim0_r_index = np.clip(dim0max + ex_pix[0], 0, dim0)
            fina_img = fina_img[dim0_l_index:dim0_r_index+1, :, :]
            fina_mask = fina_mask[dim0_l_index:dim0_r_index+1, :, :]
        else: #不外扩
            print('dim0 不外扩')
            dim0_l_index = dim0min
            dim0_r_index = dim0max
            fina_img = fina_img[dim0_l_index:dim0_r_index+1, :, :]
            fina_mask = fina_mask[dim0_l_index:dim0_r_index+1, :, :]
        return fina_img, fina_mask

def data_pretreatment():
    global Image_3D
    global ROI_3D
    global Image_small_3D
    global Current_name
    global Input_File
    if Image_3D.all() ==None:
        return '没有数据'
    else:
        roi = ROI_3D
        waikuo = [4, 4, 4]
        fina_img, fina_mask = arry_crop_3D(Image_3D,roi,waikuo)

        cut_thre = np.percentile(fina_img, 99.9)  # 直方图99.9%右侧值不要
        fina_img[fina_img >= cut_thre] = cut_thre

        fina_img = resize3D(fina_img, [128,256,128], order=3)
        fina_img = (np.max(fina_img)-fina_img)/(np.max(fina_img)-np.min(fina_img))
        Image_small_3D = fina_img
        return '预处理结束'
class App:
    def __init__(self):
        self.demo = None
        self.main()

    def main(self):
        # get_name = gr.Interface(lambda name: name, inputs="textbox", outputs="textbox")
        # prepend_hello = gr.Interface(lambda name: f"Hello {name}!", inputs="textbox", outputs="textbox")
        # append_nice = gr.Interface(lambda greeting: f"{greeting} Nice to meet you!",
        #                            inputs="textbox", outputs=gr.Textbox(label="Greeting"))

        # iface_1 = gr.Interface(fn=get_Image_reslice, inputs=gr.inputs.File(label="Upload NIfTI file"), outputs=[,gr.Image(shape=(5, 5)),gr.Image(shape=(5, 5))])

        with gr.Blocks() as demo:
            with gr.Row():
                with gr.Column(scale=1):
                    inp = gr.inputs.File(label="Upload MRI file")
                    inp2 = gr.inputs.File(label="Upload ROI file")
                with gr.Column(scale=1):
                    out8 = gr.Textbox(placeholder="尚未加载影像数据")
                    out9 = gr.Textbox(placeholder="尚未加载感兴趣区域")



            with gr.Row():
                btn1 = gr.Button("Upload MRI")
                btn5 = gr.Button("Upload ROI")
                clear = gr.Button(" Clear All")
            with gr.Tab("Image"):
                with gr.Row():
                    with gr.Column(scale=1):
                        out1 = gr.Image(shape=(10, 10))
                        slider1 = gr.Slider(1, 128, label='z轴层数', step=1, interactive=True)
                    with gr.Column(scale=1):
                        out2 = gr.Image(shape=(10, 10))
                        slider2 = gr.Slider(1, 256, label='y轴层数', step=1, interactive=True)
                    with gr.Column(scale=1):
                        out3 = gr.Image(shape=(10, 10))
                        slider3 = gr.Slider(1, 128, label='x轴层数', step=1, interactive=True)

            with gr.Tab("Medical Information"):
                with gr.Row():
                    with gr.Column(scale=1):
                        btn2 = gr.Button(value="临床信息")
                        out4 = gr.Textbox(label="患病史")
                        out6 = gr.Textbox(label="现病史")

                    with gr.Column(scale=1):
                        btn4 = gr.Button("预处理")
                        out7 = gr.Textbox(placeholder="尚未进行预处理 请先预处理再按“分期结果”按钮", )
                        btn3 = gr.Button("分期结果")
                        out5 = gr.Label(num_top_classes=2, label='分期结果')

                btn3.click(inference, inputs=None, outputs=out5)
                btn4.click(data_pretreatment, inputs=None, outputs=out7)
                btn2.click(get_medical_message, inputs=None, outputs=[out4, out6])
                # demo = gr.Series(get_name, prepend_hello, append_nice)

            btn1.click(get_Image_reslice, inp, [out1, out2, out3, slider1, slider2, slider3,out8])
            btn5.click(get_ROI, inputs=inp2, outputs=out9)
            slider3.change(change_image_slice_x, inputs=slider3, outputs=out3)
            slider2.change(change_image_slice_y, inputs=slider2, outputs=out2)
            slider1.change(change_image_slice_z, inputs=slider1, outputs=out1)
            clear.click(clear_all, None, [out1, out2, out3, out4, out6, out5, out7,out8,out9], queue=True)

            gr.Markdown('''# Examples''')
            gr.Examples(
                examples=[["./155086_A_R_MRI.nii.gz"],
                          ["./4077798_A_L_MRI.nii.gz"]],
                inputs=inp,
                outputs=[out1, out2, out3, slider1, slider2, slider3,out8],
                fn=get_Image_reslice,
                cache_examples=True,
            )
            gr.Examples(
                examples=[["./155086_A_R_ROI.nii.gz"],
                          ["./4077798_A_L_ROI.nii.gz"]],
                inputs=inp2,
                outputs=out9,
                fn=get_ROI,
                cache_examples=True,
            )
        demo.queue(concurrency_count=6)
        demo.launch(share=False)


app = App()