Update app.py

app.py

@@ -1,384 +1,384 @@
-import torch
-import gradio as gr
-from PIL import Image
-import cv2
-from AV.models.network import PGNet
-from AV.Tools.AVclassifiation import AVclassifiation
-from AV.Tools.utils_test import paint_border_overlap, extract_ordered_overlap_big, Normalize, sigmoid, recompone_overlap, \
-    kill_border
-from AV.config import config_test_general as cfg
-import torch.autograd as autograd
-import numpy as np
-import os
-from datetime import datetime
-
-def creatMask(Image, threshold=5):
-    ##This program try to creat the mask for the filed-of-view
-    ##Input original image (RGB or green channel), threshold (user set parameter, default 10)
-    ##Output: the filed-of-view mask
-
-    if len(Image.shape) == 3:  ##RGB image
-        gray = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
-        Mask0 = gray >= threshold
-
-    else:  # for green channel image
-        Mask0 = Image >= threshold
-
-    # ######get the largest blob, this takes 0.18s
-    cvVersion = int(cv2.__version__.split('.')[0])
-
-    Mask0 = np.uint8(Mask0)
-
-    contours, hierarchy = cv2.findContours(Mask0, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-    areas = [cv2.contourArea(c) for c in contours]
-    max_index = np.argmax(areas)
-    Mask = np.zeros(Image.shape[:2], dtype=np.uint8)
-    cv2.drawContours(Mask, contours, max_index, 1, -1)
-
-    ResultImg = Image.copy()
-    if len(Image.shape) == 3:
-        ResultImg[Mask == 0] = (255, 255, 255)
-    else:
-        ResultImg[Mask == 0] = 255
-    Mask[Mask > 0] = 255
-    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
-    Mask = cv2.morphologyEx(Mask, cv2.MORPH_OPEN, kernel, iterations=3)
-    return ResultImg, Mask
-
-
-def shift_rgb(img, *args):
-    result_img = np.empty_like(img)
-    shifts = args
-    max_value = 255
-    # print(shifts)
-    for i, shift in enumerate(shifts):
-        lut = np.arange(0, max_value + 1).astype("float32")
-        lut += shift
-
-        lut = np.clip(lut, 0, max_value).astype(img.dtype)
-        if len(img.shape) == 2:
-            print(f'=========grey image=======')
-            result_img = cv2.LUT(img, lut)
-        else:
-            result_img[..., i] = cv2.LUT(img[..., i], lut)
-
-    return result_img
-
-
-def CAM(x, img_path, rate=0.8, ind=0):
-    """
-    :param dataset_path: 计算整个训练数据集的平均RGB通道值
-    :param image:  array， 单张图片的array 形式
-    :return: array形式的cam后的结果
-    """
-    # 每次使用新数据集时都需要重新计算前面的RBG平均值
-    # RGB-->Rshift-->CLAHE
-
-    x = np.uint8(x)
-    _, Mask0 = creatMask(x, threshold=10)
-    Mask = np.zeros((x.shape[0], x.shape[1]), np.float32)
-    Mask[Mask0 > 0] = 1
-
-    resize = False
-    R_mea_num, G_mea_num, B_mea_num = [], [], []
-
-    dataset_path = img_path
-    image = np.array(Image.open(dataset_path))
-    R_mea_num.append(np.mean(image[:, :, 0]))
-    G_mea_num.append(np.mean(image[:, :, 1]))
-    B_mea_num.append(np.mean(image[:, :, 2]))
-
-    mea2stand = int((np.mean(R_mea_num) - np.mean(x[:, :, 0])) * rate)
-    mea2standg = int((np.mean(G_mea_num) - np.mean(x[:, :, 1])) * rate)
-    mea2standb = int((np.mean(B_mea_num) - np.mean(x[:, :, 2])) * rate)
-
-    y = shift_rgb(x, mea2stand, mea2standg, mea2standb)
-
-    y[Mask == 0, :] = 0
-
-    return y
-
-
-def modelEvalution_out_big(net, use_cuda=False, dataset='', is_kill_border=True, input_ch=3,
-                           config=None, output_dir='', evaluate_metrics=False):
-    # path for images to save
-    n_classes = 3
-    Net = PGNet(use_global_semantic=config.use_global_semantic, input_ch=input_ch,
-                num_classes=n_classes, use_cuda=use_cuda, pretrained=False, centerness=config.use_centerness,
-                centerness_map_size=config.centerness_map_size)
-    msg = Net.load_state_dict(net, strict=False)
-
-    if use_cuda:
-        Net.cuda()
-    Net.eval()
-
-    image_basename = dataset
-
-    # if not os.path.exists(output_dir):
-    #     os.makedirs(output_dir)
-
-    step = 1
-    # every step of between star and end for loop until len(image_basename)
-
-    # for start_end in start_end_list:
-    image0 = cv2.imread(image_basename)
-    test_image_height = image0.shape[0]
-    test_image_width = image0.shape[1]
-
-    if config.use_resize:
-
-        if min(test_image_height, test_image_width) <= 256:
-            scaling = 512 / min(test_image_height, test_image_width)
-            new_width = int(test_image_width * scaling)
-            new_height = int(test_image_height * scaling)
-            test_image_width, test_image_height = new_width, new_height
-
-        # 大尺寸处理：确保最长边≤1536
-        elif max(test_image_height, test_image_width) >= 2048:
-            scaling = 2048 / max(test_image_height, test_image_width)
-            new_width = int(test_image_width * scaling)
-            new_height = int(test_image_height * scaling)
-            test_image_width, test_image_height = new_width, new_height
-
-    ArteryPredAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
-    VeinPredAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
-    VesselPredAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
-    ProMap = np.zeros((1, 3, test_image_height, test_image_width), np.float32)
-    MaskAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
-    ArteryPred, VeinPred, VesselPred, Mask, LabelArtery, LabelVein, LabelVessel = GetResult_out_big(Net, 0,
-                                                                                                    use_cuda=use_cuda,
-                                                                                                    dataset=image_basename,
-                                                                                                    is_kill_border=is_kill_border,
-                                                                                                    config=config,
-                                                                                                    resize_w_h=(
-                                                                                                        test_image_width,
-                                                                                                        test_image_height)
-                                                                                                    )
-    ArteryPredAll[0 % step, :, :, :] = ArteryPred
-    VeinPredAll[0 % step, :, :, :] = VeinPred
-    VesselPredAll[0 % step, :, :, :] = VesselPred
-
-    MaskAll[0 % step, :, :, :] = Mask
-
-    image_color = AVclassifiation(output_dir, ArteryPredAll, VeinPredAll, VesselPredAll, 1, image_basename)
-
-    return image_color
-
-
-def GetResult_out_big(Net, k, use_cuda=False, dataset='', is_kill_border=False, config=None,
-                      resize_w_h=None):
-    ImgName = dataset
-    Img0 = cv2.imread(ImgName)
-
-    _, Mask0 = creatMask(Img0, threshold=-1)
-    Mask = np.zeros((Img0.shape[0], Img0.shape[1]), np.float32)
-    Mask[Mask0 > 0] = 1
-
-    if config.use_resize:
-        Img0 = cv2.resize(Img0, resize_w_h)
-        Mask = cv2.resize(Mask, resize_w_h, interpolation=cv2.INTER_NEAREST)
-
-    Img = Img0
-    height, width = Img.shape[:2]
-    n_classes = 3
-    patch_height = config.patch_size
-    patch_width = config.patch_size
-    stride_height = config.stride_height
-    stride_width = config.stride_width
-
-    Img = cv2.cvtColor(Img, cv2.COLOR_BGR2RGB)
-    if cfg.dataset == 'all':
-        # # # 将图像转换为 LAB 颜色空间
-        lab = cv2.cvtColor(Img, cv2.COLOR_RGB2LAB)
-
-        # 拆分 LAB 通道
-        l, a, b = cv2.split(lab)
-
-        # 创建 CLAHE 对象并应用到 L 通道
-        clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8, 8))
-        l_clahe = clahe.apply(l)
-
-        # 将 CLAHE 处理后的 L 通道与原始的 A 和 B 通道合并
-        lab_clahe = cv2.merge((l_clahe, a, b))
-
-        # 将图像转换回 BGR 颜色空间
-        Img = cv2.cvtColor(lab_clahe, cv2.COLOR_LAB2RGB)
-
-        if cfg.use_CAM:
-            Img = CAM(Img, dataset)
-
-    Img = np.float32(Img / 255.)
-    Img_enlarged = paint_border_overlap(Img, patch_height, patch_width, stride_height, stride_width)
-    patch_size = config.patch_size
-    batch_size = 2
-    patches_imgs, global_images = extract_ordered_overlap_big(Img_enlarged, patch_height, patch_width,
-                                                              stride_height,
-                                                              stride_width)
-
-    patches_imgs = np.transpose(patches_imgs, (0, 3, 1, 2))
-    patches_imgs = Normalize(patches_imgs)
-    global_images = np.transpose(global_images, (0, 3, 1, 2))
-    global_images = Normalize(global_images)
-    patchNum = patches_imgs.shape[0]
-    max_iter = int(np.ceil(patchNum / float(batch_size)))
-
-    pred_patches = np.zeros((patchNum, n_classes, patch_size, patch_size), np.float32)
-
-    for i in range(max_iter):
-        begin_index = i * batch_size
-        end_index = (i + 1) * batch_size
-
-        patches_temp1 = patches_imgs[begin_index:end_index, :, :, :]
-
-        patches_input_temp1 = torch.FloatTensor(patches_temp1)
-        global_input_temp1 = patches_input_temp1
-        if config.use_global_semantic:
-            global_temp1 = global_images[begin_index:end_index, :, :, :]
-            global_input_temp1 = torch.FloatTensor(global_temp1)
-        if use_cuda:
-            patches_input_temp1 = autograd.Variable(patches_input_temp1.cuda())
-            if config.use_global_semantic:
-                global_input_temp1 = autograd.Variable(global_input_temp1.cuda())
-        else:
-            patches_input_temp1 = autograd.Variable(patches_input_temp1)
-            if config.use_global_semantic:
-                global_input_temp1 = autograd.Variable(global_input_temp1)
-
-        output_temp, _1, = Net(patches_input_temp1, global_input_temp1)
-
-        pred_patches_temp = np.float32(output_temp.data.cpu().numpy())
-
-        pred_patches_temp_sigmoid = sigmoid(pred_patches_temp)
-
-        pred_patches[begin_index:end_index, :, :, :] = pred_patches_temp_sigmoid[:, :, :patch_size, :patch_size]
-
-        del patches_input_temp1
-        del pred_patches_temp
-        del patches_temp1
-        del output_temp
-        del pred_patches_temp_sigmoid
-
-    new_height, new_width = Img_enlarged.shape[0], Img_enlarged.shape[1]
-
-    pred_img = recompone_overlap(pred_patches, new_height, new_width, stride_height, stride_width)  # predictions
-    pred_img = pred_img[:, 0:height, 0:width]
-
-    if is_kill_border:
-        pred_img = kill_border(pred_img, Mask)
-
-    ArteryPred = np.float32(pred_img[0, :, :])
-    VeinPred = np.float32(pred_img[2, :, :])
-    VesselPred = np.float32(pred_img[1, :, :])
-
-    ArteryPred = ArteryPred[np.newaxis, :, :]
-    VeinPred = VeinPred[np.newaxis, :, :]
-    VesselPred = VesselPred[np.newaxis, :, :]
-    Mask = Mask[np.newaxis, :, :]
-
-    return ArteryPred, VeinPred, VesselPred, Mask, ArteryPred, VeinPred, VesselPred,
-
-
-def out_test(cfg, output_dir='', evaluate_metrics=False, img_name='out_test'):
-    device = torch.device("cuda" if cfg.use_cuda else "cpu")
-    model_root = cfg.model_path_pretrained_G
-    model_path = os.path.join(model_root, 'G_' + str(cfg.model_step_pretrained_G) + '.pkl')
-    net = torch.load(model_path, map_location=device)
-
-    image_color = modelEvalution_out_big(net,
-                                         use_cuda=cfg.use_cuda,
-                                         dataset=img_name,
-                                         input_ch=cfg.input_nc,
-                                         config=cfg,
-                                         output_dir=output_dir, evaluate_metrics=evaluate_metrics)
-
-    return image_color
-
-
-def segment_by_out_test(image,model_name):
-    print("✅ 传到后端的模型名：", model_name)
-
-    cfg.set_dataset(model_name)
-    if image is None:
-        raise gr.Error("请上传一张图像。")
-    os.makedirs("./examples", exist_ok=True)
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    temp_path = f"./examples/tmp_upload_{timestamp}.png"
-    image.save(temp_path)
-
-    image_color = out_test(cfg, output_dir='', evaluate_metrics=False, img_name=temp_path)
-    return Image.fromarray(image_color)
-
-def gradio_interface():
-    model_info_md = """
-    ### 📘 模型说明
-
-    | 模型 | 数据集 | patch size |running time |
-    |------|--------|------------|--------|
-    | DRIVE | 小分辨率血管图像 | 256 |30s以内|
-    | HRF | 高分辨率图像（健康、青光眼等） | 2min以内|
-    | LES | 视盘中心图像适配 | 256 |2min以内|
-    | UKBB | UKBB图像 | 256 |2min以内 |
-    | 通用模型（512） | 超清图像，适配性强 | 512 |2min以内|
-    """
-    model_choices = [
-        ("1: DRIVE专用模型", "DRIVE"),
-        ("2: HRF专用模型", "hrf"),
-        ("3: LES专用模型","LES"),
-        ("4: UKBB专用模型", "ukbb"),
-        ("5: 通用模型", "all"),
-    ]
-
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        gr.Markdown("# 👁️ 眼底图像动静脉血管分割")
-        gr.Markdown("上传眼底图像，选择一个模型开始处理，结果将自动生成。")
-
-        with gr.Row():
-            image_input = gr.Image(type="pil", label="📤 上传图像",height=300)
-
-        with gr.Row():
-            with gr.Column():
-                model_select = gr.Radio(
-                    choices=model_choices,
-                    label="🎯 选择模型",
-                    value="DRIVE",
-                    interactive = True
-                )
-                submit_btn = gr.Button("🚀 开始分割")
-            with gr.Column():
-                output_image = gr.Image(label="🖼️ 分割结果")
-
-        gr.Markdown("### 📁 示例图像（点击自动加载）")
-        gr.Examples(
-            examples=[
-                ["examples/DRIVE.tif", "DRIVE"],
-                ["examples/LES.png", "LES"],
-                ["examples/hrf.png", "hrf"],
-                ["examples/ukbb.png", "ukbb"],
-                ["examples/all.jpg", "all"]
-            ],
-            inputs=[image_input, model_select],
-            label="示例图像",
-            examples_per_page=5
-        )
-        with gr.Accordion("📖 模型说明（点击展开）", open=False):
-            gr.Markdown(model_info_md)
-
-        # 功能连接
-        submit_btn.click(
-            fn=segment_by_out_test,
-            inputs=[image_input, model_select],
-            outputs=[output_image]
-        )
-        gr.Markdown("📚 **专用模型**: RIP-AV: Joint Representative Instance Pre-training with Context Aware Network for Retinal Artery/Vein Segmentation")
-        gr.Markdown("📚 **通用模型**: An Efficient and Interpretable Foundation Model for Retinal Image Analysis in Disease Diagnosis.")
-    demo.queue()
-    demo.launch()
-
-
-if __name__ == '__main__':
-    # cfg.set_dataset('all')
-    # image_color = out_test(cfg = cfg, evaluate_metrics=False, img_name=r'.\AV\data\AV-DRIVE\test\images\01_test.tif')
-    # Image.fromarray(image_color).save('image_color.png')
-    #print(cfg.patch_size)
-    gradio_interface()
+import torch
+import gradio as gr
+from PIL import Image
+import cv2
+from AV.models.network import PGNet
+from AV.Tools.AVclassifiation import AVclassifiation
+from AV.Tools.utils_test import paint_border_overlap, extract_ordered_overlap_big, Normalize, sigmoid, recompone_overlap, \
+    kill_border
+from AV.config import config_test_general as cfg
+import torch.autograd as autograd
+import numpy as np
+import os
+from datetime import datetime
+
+def creatMask(Image, threshold=5):
+    ##This program try to creat the mask for the filed-of-view
+    ##Input original image (RGB or green channel), threshold (user set parameter, default 10)
+    ##Output: the filed-of-view mask
+
+    if len(Image.shape) == 3:  ##RGB image
+        gray = cv2.cvtColor(Image, cv2.COLOR_BGR2GRAY)
+        Mask0 = gray >= threshold
+
+    else:  # for green channel image
+        Mask0 = Image >= threshold
+
+    # ######get the largest blob, this takes 0.18s
+    cvVersion = int(cv2.__version__.split('.')[0])
+
+    Mask0 = np.uint8(Mask0)
+
+    contours, hierarchy = cv2.findContours(Mask0, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+    areas = [cv2.contourArea(c) for c in contours]
+    max_index = np.argmax(areas)
+    Mask = np.zeros(Image.shape[:2], dtype=np.uint8)
+    cv2.drawContours(Mask, contours, max_index, 1, -1)
+
+    ResultImg = Image.copy()
+    if len(Image.shape) == 3:
+        ResultImg[Mask == 0] = (255, 255, 255)
+    else:
+        ResultImg[Mask == 0] = 255
+    Mask[Mask > 0] = 255
+    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
+    Mask = cv2.morphologyEx(Mask, cv2.MORPH_OPEN, kernel, iterations=3)
+    return ResultImg, Mask
+
+
+def shift_rgb(img, *args):
+    result_img = np.empty_like(img)
+    shifts = args
+    max_value = 255
+    # print(shifts)
+    for i, shift in enumerate(shifts):
+        lut = np.arange(0, max_value + 1).astype("float32")
+        lut += shift
+
+        lut = np.clip(lut, 0, max_value).astype(img.dtype)
+        if len(img.shape) == 2:
+            print(f'=========grey image=======')
+            result_img = cv2.LUT(img, lut)
+        else:
+            result_img[..., i] = cv2.LUT(img[..., i], lut)
+
+    return result_img
+
+
+def CAM(x, img_path, rate=0.8, ind=0):
+    """
+    :param dataset_path: 计算整个训练数据集的平均RGB通道值
+    :param image:  array， 单张图片的array 形式
+    :return: array形式的cam后的结果
+    """
+    # 每次使用新数据集时都需要重新计算前面的RBG平均值
+    # RGB-->Rshift-->CLAHE
+
+    x = np.uint8(x)
+    _, Mask0 = creatMask(x, threshold=10)
+    Mask = np.zeros((x.shape[0], x.shape[1]), np.float32)
+    Mask[Mask0 > 0] = 1
+
+    resize = False
+    R_mea_num, G_mea_num, B_mea_num = [], [], []
+
+    dataset_path = img_path
+    image = np.array(Image.open(dataset_path))
+    R_mea_num.append(np.mean(image[:, :, 0]))
+    G_mea_num.append(np.mean(image[:, :, 1]))
+    B_mea_num.append(np.mean(image[:, :, 2]))
+
+    mea2stand = int((np.mean(R_mea_num) - np.mean(x[:, :, 0])) * rate)
+    mea2standg = int((np.mean(G_mea_num) - np.mean(x[:, :, 1])) * rate)
+    mea2standb = int((np.mean(B_mea_num) - np.mean(x[:, :, 2])) * rate)
+
+    y = shift_rgb(x, mea2stand, mea2standg, mea2standb)
+
+    y[Mask == 0, :] = 0
+
+    return y
+
+
+def modelEvalution_out_big(net, use_cuda=False, dataset='', is_kill_border=True, input_ch=3,
+                           config=None, output_dir='', evaluate_metrics=False):
+    # path for images to save
+    n_classes = 3
+    Net = PGNet(use_global_semantic=config.use_global_semantic, input_ch=input_ch,
+                num_classes=n_classes, use_cuda=use_cuda, pretrained=False, centerness=config.use_centerness,
+                centerness_map_size=config.centerness_map_size)
+    msg = Net.load_state_dict(net, strict=False)
+
+    if use_cuda:
+        Net.cuda()
+    Net.eval()
+
+    image_basename = dataset
+
+    # if not os.path.exists(output_dir):
+    #     os.makedirs(output_dir)
+
+    step = 1
+    # every step of between star and end for loop until len(image_basename)
+
+    # for start_end in start_end_list:
+    image0 = cv2.imread(image_basename)
+    test_image_height = image0.shape[0]
+    test_image_width = image0.shape[1]
+
+    if config.use_resize:
+
+        if min(test_image_height, test_image_width) <= 256:
+            scaling = 512 / min(test_image_height, test_image_width)
+            new_width = int(test_image_width * scaling)
+            new_height = int(test_image_height * scaling)
+            test_image_width, test_image_height = new_width, new_height
+
+        # 大尺寸处理：确保最长边≤1536
+        elif max(test_image_height, test_image_width) >= 2048:
+            scaling = 2048 / max(test_image_height, test_image_width)
+            new_width = int(test_image_width * scaling)
+            new_height = int(test_image_height * scaling)
+            test_image_width, test_image_height = new_width, new_height
+
+    ArteryPredAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
+    VeinPredAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
+    VesselPredAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
+    ProMap = np.zeros((1, 3, test_image_height, test_image_width), np.float32)
+    MaskAll = np.zeros((1, 1, test_image_height, test_image_width), np.float32)
+    ArteryPred, VeinPred, VesselPred, Mask, LabelArtery, LabelVein, LabelVessel = GetResult_out_big(Net, 0,
+                                                                                                    use_cuda=use_cuda,
+                                                                                                    dataset=image_basename,
+                                                                                                    is_kill_border=is_kill_border,
+                                                                                                    config=config,
+                                                                                                    resize_w_h=(
+                                                                                                        test_image_width,
+                                                                                                        test_image_height)
+                                                                                                    )
+    ArteryPredAll[0 % step, :, :, :] = ArteryPred
+    VeinPredAll[0 % step, :, :, :] = VeinPred
+    VesselPredAll[0 % step, :, :, :] = VesselPred
+
+    MaskAll[0 % step, :, :, :] = Mask
+
+    image_color = AVclassifiation(output_dir, ArteryPredAll, VeinPredAll, VesselPredAll, 1, image_basename)
+
+    return image_color
+
+
+def GetResult_out_big(Net, k, use_cuda=False, dataset='', is_kill_border=False, config=None,
+                      resize_w_h=None):
+    ImgName = dataset
+    Img0 = cv2.imread(ImgName)
+
+    _, Mask0 = creatMask(Img0, threshold=-1)
+    Mask = np.zeros((Img0.shape[0], Img0.shape[1]), np.float32)
+    Mask[Mask0 > 0] = 1
+
+    if config.use_resize:
+        Img0 = cv2.resize(Img0, resize_w_h)
+        Mask = cv2.resize(Mask, resize_w_h, interpolation=cv2.INTER_NEAREST)
+
+    Img = Img0
+    height, width = Img.shape[:2]
+    n_classes = 3
+    patch_height = config.patch_size
+    patch_width = config.patch_size
+    stride_height = config.stride_height
+    stride_width = config.stride_width
+
+    Img = cv2.cvtColor(Img, cv2.COLOR_BGR2RGB)
+    if cfg.dataset == 'all':
+        # # # 将图像转换为 LAB 颜色空间
+        lab = cv2.cvtColor(Img, cv2.COLOR_RGB2LAB)
+
+        # 拆分 LAB 通道
+        l, a, b = cv2.split(lab)
+
+        # 创建 CLAHE 对象并应用到 L 通道
+        clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(8, 8))
+        l_clahe = clahe.apply(l)
+
+        # 将 CLAHE 处理后的 L 通道与原始的 A 和 B 通道合并
+        lab_clahe = cv2.merge((l_clahe, a, b))
+
+        # 将图像转换回 BGR 颜色空间
+        Img = cv2.cvtColor(lab_clahe, cv2.COLOR_LAB2RGB)
+
+        if cfg.use_CAM:
+            Img = CAM(Img, dataset)
+
+    Img = np.float32(Img / 255.)
+    Img_enlarged = paint_border_overlap(Img, patch_height, patch_width, stride_height, stride_width)
+    patch_size = config.patch_size
+    batch_size = 2
+    patches_imgs, global_images = extract_ordered_overlap_big(Img_enlarged, patch_height, patch_width,
+                                                              stride_height,
+                                                              stride_width)
+
+    patches_imgs = np.transpose(patches_imgs, (0, 3, 1, 2))
+    patches_imgs = Normalize(patches_imgs)
+    global_images = np.transpose(global_images, (0, 3, 1, 2))
+    global_images = Normalize(global_images)
+    patchNum = patches_imgs.shape[0]
+    max_iter = int(np.ceil(patchNum / float(batch_size)))
+
+    pred_patches = np.zeros((patchNum, n_classes, patch_size, patch_size), np.float32)
+
+    for i in range(max_iter):
+        begin_index = i * batch_size
+        end_index = (i + 1) * batch_size
+
+        patches_temp1 = patches_imgs[begin_index:end_index, :, :, :]
+
+        patches_input_temp1 = torch.FloatTensor(patches_temp1)
+        global_input_temp1 = patches_input_temp1
+        if config.use_global_semantic:
+            global_temp1 = global_images[begin_index:end_index, :, :, :]
+            global_input_temp1 = torch.FloatTensor(global_temp1)
+        if use_cuda:
+            patches_input_temp1 = autograd.Variable(patches_input_temp1.cuda())
+            if config.use_global_semantic:
+                global_input_temp1 = autograd.Variable(global_input_temp1.cuda())
+        else:
+            patches_input_temp1 = autograd.Variable(patches_input_temp1)
+            if config.use_global_semantic:
+                global_input_temp1 = autograd.Variable(global_input_temp1)
+
+        output_temp, _1, = Net(patches_input_temp1, global_input_temp1)
+
+        pred_patches_temp = np.float32(output_temp.data.cpu().numpy())
+
+        pred_patches_temp_sigmoid = sigmoid(pred_patches_temp)
+
+        pred_patches[begin_index:end_index, :, :, :] = pred_patches_temp_sigmoid[:, :, :patch_size, :patch_size]
+
+        del patches_input_temp1
+        del pred_patches_temp
+        del patches_temp1
+        del output_temp
+        del pred_patches_temp_sigmoid
+
+    new_height, new_width = Img_enlarged.shape[0], Img_enlarged.shape[1]
+
+    pred_img = recompone_overlap(pred_patches, new_height, new_width, stride_height, stride_width)  # predictions
+    pred_img = pred_img[:, 0:height, 0:width]
+
+    if is_kill_border:
+        pred_img = kill_border(pred_img, Mask)
+
+    ArteryPred = np.float32(pred_img[0, :, :])
+    VeinPred = np.float32(pred_img[2, :, :])
+    VesselPred = np.float32(pred_img[1, :, :])
+
+    ArteryPred = ArteryPred[np.newaxis, :, :]
+    VeinPred = VeinPred[np.newaxis, :, :]
+    VesselPred = VesselPred[np.newaxis, :, :]
+    Mask = Mask[np.newaxis, :, :]
+
+    return ArteryPred, VeinPred, VesselPred, Mask, ArteryPred, VeinPred, VesselPred,
+
+
+def out_test(cfg, output_dir='', evaluate_metrics=False, img_name='out_test'):
+    device = torch.device("cuda" if cfg.use_cuda else "cpu")
+    model_root = cfg.model_path_pretrained_G
+    model_path = os.path.join(model_root, 'G_' + str(cfg.model_step_pretrained_G) + '.pkl')
+    net = torch.load(model_path, map_location=device)
+
+    image_color = modelEvalution_out_big(net,
+                                         use_cuda=cfg.use_cuda,
+                                         dataset=img_name,
+                                         input_ch=cfg.input_nc,
+                                         config=cfg,
+                                         output_dir=output_dir, evaluate_metrics=evaluate_metrics)
+
+    return image_color
+
+
+def segment_by_out_test(image,model_name):
+    print("✅ 传到后端的模型名：", model_name)
+
+    cfg.set_dataset(model_name)
+    if image is None:
+        raise gr.Error("请上传一张图像(upload a fundus image)。")
+    os.makedirs("./examples", exist_ok=True)
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    temp_path = f"./examples/tmp_upload_{timestamp}.png"
+    image.save(temp_path)
+
+    image_color = out_test(cfg, output_dir='', evaluate_metrics=False, img_name=temp_path)
+    return Image.fromarray(image_color)
+
+def gradio_interface():
+    model_info_md = """
+    ### 📘 模型说明
+
+    | 模型(model name) | 数据集(dataset) | patch size |running time |
+    |------|--------|------------|--------|
+    | DRIVE | 小分辨率血管图像 | 256 |30s以内|
+    | HRF | 高分辨率图像（健康、青光眼等）| 256 | 2min以内|
+    | LES | 视盘中心图像适配 | 256 |2min以内|
+    | UKBB | UKBB图像 | 256 |2min以内 |
+    | 通用模型（512） | 超清图像，适配性强 | 512 |2min以内|
+    """
+    model_choices = [
+        ("1: DRIVE专用模型", "DRIVE"),
+        ("2: HRF专用模型", "hrf"),
+        ("3: LES专用模型","LES"),
+        ("4: UKBB专用模型", "ukbb"),
+        ("5: 通用模型(general)", "all"),
+    ]
+
+    with gr.Blocks(theme=gr.themes.Soft()) as demo:
+        gr.Markdown("# 👁️ 眼底图像动静脉血管分割(Retinal image artery and vein segmentation)")
+        gr.Markdown("上传眼底图像，选择一个模型开始处理，结果将自动生成。(Upload the retinal image, select a model to start processing, and the results will be generated automatically.)")
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="📤 上传图像(upload)",height=300)
+
+        with gr.Row():
+            with gr.Column():
+                model_select = gr.Radio(
+                    choices=model_choices,
+                    label="🎯 选择模型",
+                    value="DRIVE",
+                    interactive = True
+                )
+                submit_btn = gr.Button("🚀 开始分割(RUN)")
+            with gr.Column():
+                output_image = gr.Image(label="🖼️ 分割结果(Result)")
+
+        gr.Markdown("### 📁 示例图像examples（点击自动加载）")
+        gr.Examples(
+            examples=[
+                ["examples/DRIVE.tif", "DRIVE"],
+                ["examples/LES.png", "LES"],
+                ["examples/hrf.png", "hrf"],
+                ["examples/ukbb.png", "ukbb"],
+                ["examples/all.jpg", "all"]
+            ],
+            inputs=[image_input, model_select],
+            label="示例图像",
+            examples_per_page=5
+        )
+        with gr.Accordion("📖 模型���明desciption（点击展开）", open=False):
+            gr.Markdown(model_info_md)
+
+        # 功能连接
+        submit_btn.click(
+            fn=segment_by_out_test,
+            inputs=[image_input, model_select],
+            outputs=[output_image]
+        )
+        gr.Markdown("📚 **专用模型引用cite**: RIP-AV: Joint Representative Instance Pre-training with Context Aware Network for Retinal Artery/Vein Segmentation")
+        gr.Markdown("📚 **通用模型引用cite**: An Efficient and Interpretable Foundation Model for Retinal Image Analysis in Disease Diagnosis.")
+    demo.queue()
+    demo.launch()
+
+
+if __name__ == '__main__':
+    # cfg.set_dataset('all')
+    # image_color = out_test(cfg = cfg, evaluate_metrics=False, img_name=r'.\AV\data\AV-DRIVE\test\images\01_test.tif')
+    # Image.fromarray(image_color).save('image_color.png')
+    #print(cfg.patch_size)
+    gradio_interface()