Upload app.py

app.py

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+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
+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')
+
+a = 5
+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():
+    model = classnet
+    data = Image_3D
+
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+    model.eval()
+    all_loss = 0
+    length = 0
+    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)
+        a = 5
+        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))
+        return {'急性期': n, '亚急性期': p}
+
+#
+#
+# def image_classifier(inp):
+#     #return {'cat': 0.3, 'dog': 0.7}
+#     return inp
+#
+# def image_read(inp):
+#     image = sitk.GetArrayFromImage(sitk.ReadImage(inp))
+#     ss = np.sum(image)
+#     return str(ss)
+#
+#
+# def upload_file(files):
+#     file_paths = [file.name for file in files]
+#     return file_paths
+#
+# with gr.Blocks() as demo:
+#     file_output = gr.File()
+#     upload_button = gr.UploadButton("Click to Upload a File",  file_types=["image", "video"], file_count="multiple")
+#     upload_button.upload(upload_file, upload_button, gr.Code(''))
+# demo.launch()
+
+import gradio as gr
+import numpy as np
+import nibabel as nib
+import os
+import tempfile
+# 创建一个函数，接收3D数据并返回预测结果
+def predict_3d(data):
+    # 在这里编写您的3D数据处理和预测逻辑
+    # 对于示例目的，这里只返回输入数据的最大值作为预测结果
+    result = np.max(data)
+    return result
+
+# 创建一个用于读取和展示NIfTI数据的Gradio接口函数
+def interface():
+    # 创建一个自定义输入组件，用于读取NIfTI数据
+    input_component = gr.inputs.File(label="Upload NIfTI file")
+
+    # 创建一个输出组件，用于展示预测结果
+    output_component = gr.outputs.Textbox()
+
+    # 定义预测函数，接收输入数据并调用predict_3d函数进行预测
+    def predict(input_file):
+        # 加载NIfTI数据
+        # temp_dir = tempfile.mkdtemp()
+        # temp_file = os.path.join(temp_dir, "temp_file")
+        # shutil.copyfile(file.name, temp_file)
+        nifti_data = nib.load(input_file.name)
+        # 将NIfTI数据转换为NumPy数组
+        data = np.array(nifti_data.dataobj)
+        # 在这里进行必要的数据预处理，例如缩放、归一化等
+        # 调用predict_3d函数进行预测
+        result = predict_3d(data)
+        # 将预测结果转换为字符串并返回
+        return str(result),str(result)
+
+    # 创建Gradio接口，将输入组件和输出组件传递给Interface函数
+    with gr.Box():
+        gr.Textbox(label="First")
+        gr.Textbox(label="Last")
+    iface_1 = gr.Interface(fn=predict, inputs=gr.inputs.File(label="Upload NIfTI file"), outputs=gr.Box)
+
+    return iface
+
+
+def get_Image_reslice(input_file):
+    '''得到图像 返回随即层'''
+    global Image_3D
+    global Current_name
+    Image_3D = sitk.GetArrayFromImage(sitk.ReadImage(input_file.name))
+    Current_name = input_file.name.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
+
+
+def change_image_slice_x(slice):
+
+    image_slice = Image_3D[:, :, slice-1]
+    return image_slice
+
+def change_image_slice_y(slice):
+    image_slice = Image_3D[:, slice-1, :]
+    return image_slice
+
+def change_image_slice_z(slice):
+    image_slice = Image_3D[slice-1,:,:]
+    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
+
+
+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:
+            inp = gr.inputs.File(label="Upload NIfTI file")
+            btn1 = gr.Button("Upload Data")
+            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)
+                btn1.click(get_Image_reslice, inp, [out1, out2, out3,slider1,slider2,slider3])
+                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)
+
+
+            with gr.Tab("Medical Information"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        btn2 = gr.Button(label="临床信息")
+                        out4 = gr.Textbox(label="患病史")
+                        out6 = gr.Textbox(label="现病史")
+
+                    with gr.Column(scale=1):
+                        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)
+                btn2.click(get_medical_message, inputs=None, outputs=[out4,out6])
+                #demo = gr.Series(get_name, prepend_hello, append_nice)
+
+        demo.launch(share=True)
+app = App()
+    # with gr.Blocks() as demo:
+    #     with gr.Row():
+    #         with gr.Column(scale=1):
+    #             text1 = gr.Textbox()
+    #             text2 = gr.Textbox()
+    #         with gr.Column(scale=4):
+    #             btn1 = gr.Button("Button 1")
+    #             btn2 = gr.Button("Button 2")
+    # demo.launch()
+