update files

app.py

@@ -0,0 +1,130 @@
+import os
+
+import mmcv
+from mmengine import Config
+
+os.system("pip install 'mmengine>=0.6.0'")
+os.system("pip install 'mmcv>=2.0.0rc4,<2.1.0'")
+os.system("pip install mmsegmentation")
+
+import gradio as gr
+import fnmatch
+import cv2
+import numpy as np
+import torch
+from mmseg.apis import init_model, inference_model, show_result_pyplot
+from mmseg.apis import MMSegInferencer
+
+import PIL
+from mim import download
+import warnings
+
+warnings.filterwarnings("ignore")
+
+mmseg_models_list = MMSegInferencer.list_models('mmseg')
+
+path = "./checkpoint"
+if not os.path.exists(path):
+    os.makedirs(path)
+
+
+def clear_folder(folder_path):
+    import shutil
+    for filename in os.listdir(folder_path):
+        file_path = os.path.join(folder_path, filename)
+        try:
+            if os.path.isfile(file_path) or os.path.islink(file_path):
+                os.unlink(file_path)
+            elif os.path.isdir(file_path):
+                shutil.rmtree(file_path)
+        except Exception as e:
+            print(f"Failed to delete {file_path}. Reason: {e}")
+    print(f"Clear {folder_path} successfully.")
+
+
+def save_image(img, img_path):
+    # Convert PIL image to OpenCV image
+    img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+    # Save OpenCV image
+    cv2.imwrite(img_path, img)
+
+
+def download_test_image():
+    # Images
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/59380685/266264420-21575a83-4057-41cf-8a4a-b3ea6f332d79.jpg',
+        'bus.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/59380685/266264536-82afdf58-6b9a-4568-b9df-551ee72cb6d9.jpg',
+        'dogs.jpg')
+    torch.hub.download_url_to_file(
+        'https://user-images.githubusercontent.com/59380685/266264600-9d0c26ca-8ba6-45f2-b53b-4dc98460c43e.jpg',
+        'zidane.jpg')
+
+
+def download_cfg_checkpoint_model_name(model_name):
+    clear_folder("./checkpoint")
+    download(package='mmsegmentation',
+             configs=[model_name],
+             dest_root='./checkpoint')
+
+
+
+# 定义推理函数
+def predict(img, model_name):
+    # 保存输入图片
+    img_path = 'input_image.png'
+    save_image(img, img_path)
+    download_cfg_checkpoint_model_name(model_name)
+
+    config_path = [f for f in os.listdir(path) if fnmatch.fnmatch(f, "*.py")][0]
+    config_path = path + "/" + config_path
+
+    checkpoint_path = [f for f in os.listdir(path) if fnmatch.fnmatch(f, "*.pth")][0]
+    checkpoint_path = path + "/" + checkpoint_path
+
+    # 从配置文件和权重文件构建模型
+    device = torch.cuda.current_device() if torch.cuda.is_available() else 'cpu'
+
+    if device == 'cpu':
+        config_path = Config.fromfile(config_path)
+        # Remove pretrain model download for testing
+        config_path.model.pretrained = None
+        # Replace SyncBN with BN to inference on CPU
+        norm_cfg = dict(type='BN', requires_grad=True)
+        config_path.model.backbone.norm_cfg = norm_cfg
+        config_path.model.decode_head.norm_cfg = norm_cfg
+        config_path.model.auxiliary_head.norm_cfg = norm_cfg
+
+    model = init_model(config_path, checkpoint_path, device=device)
+
+    # 推理给定图像
+    result = inference_model(model, img_path)
+
+    # 保存可视化结果
+    vis_image = show_result_pyplot(model, img_path, result, show=False)
+    vis_image_path = 'output_image.png'
+    cv2.imwrite(vis_image_path, vis_image)
+    output_img = PIL.Image.open(vis_image_path)
+    # 返回输出图片
+    return output_img
+
+download_test_image()
+# 定义输入和输出界面
+inputs_img = gr.inputs.Image(type='pil', label="Input Image")
+model_list = gr.inputs.Dropdown(choices=[m for m in mmseg_models_list], label='Model')
+outputs_img = gr.outputs.Image(type='pil', label="Output Image")
+
+# 启动界面
+title = "MMSegmentation segmentation web demo"
+description = "<div align='center'><img src='https://raw.githubusercontent.com/open-mmlab/mmsegmentation/main/resources/mmseg-logo.png' width='450''/><div>" \
+              "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmsegmentation'>MMSegmentation</a> MMSegmentation 是一个基于 PyTorch 的语义分割开源工具箱。它是 OpenMMLab 项目的一部分。" \
+              "OpenMMLab Semantic Segmentation Toolbox and Benchmark..</p>"
+article = "<p style='text-align: center'><a href='https://github.com/open-mmlab/mmsegmentation'>MMSegmentation</a></p>" \
+          "<p style='text-align: center'><a href='https://github.com/isLinXu'>gradio build by gatilin</a></a></p>"
+examples = [["bus.jpg", "deeplabv3_r101-d8_4xb2-40k_cityscapes-512x1024"],
+            ["dogs.jpg", "pspnet_r50-d8_4xb2-40k_cityscapes-512x1024"],
+            ["zidane.jpg", "fcn_r101-d8_4xb4-80k_ade20k-512x512"]
+            ]
+gr.Interface(fn=predict, inputs=[inputs_img, model_list], outputs=outputs_img, examples=examples,
+             title=title, description=description, article=article).launch()

requirements.txt

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+wget~=3.2
+opencv-python~=4.6.0.66
+numpy~=1.23.0
+torch~=1.13.1
+torchvision~=0.14.1
+pillow~=9.4.0
+gradio~=3.42.0
+ultralytics~=8.0.169
+pyyaml~=6.0
+wandb~=0.13.11
+tqdm~=4.65.0
+matplotlib~=3.7.1
+pandas~=2.0.0
+seaborn~=0.12.2
+requests~=2.31.0
+psutil~=5.9.4
+thop~=0.1.1-2209072238
+timm~=0.9.2
+super-gradients~=3.2.0
+openmim