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app.py

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+from typing import List
+from glob import glob
+import numpy as np
+from PIL import Image
+from mmseg.models.segmentors.encoder_decoder import EncoderDecoder
+import gradio as gr
+import torch
+import os
+from models.cdnetv1 import CDnetV1
+from models.cdnetv2 import CDnetV2
+from models.dbnet import DBNet
+from models.hrcloudnet import HRCloudNet
+from models.kappamask import KappaMask
+from models.mcdnet import MCDNet
+from models.scnn import SCNN
+from models.unetmobv2 import UNetMobV2
+
+
+class CloudAdapterGradio:
+    def __init__(self, device="cpu", example_inputs=None, num_classes=2, palette=None, other_model_weight_path=None):
+        self.device = device
+        self.example_inputs = example_inputs
+        self.img_size = 256 if num_classes == 2 else 512
+        self.palette = palette
+        self.legend = self.html_legend(num_classes=num_classes)
+
+        self.other_models = {
+            "cdnetv1": CDnetV1(num_classes=num_classes).to(self.device),
+            "cdnetv2": CDnetV2(num_classes=num_classes).to(self.device),
+            "hrcloudnet": HRCloudNet(num_classes=num_classes).to(self.device),
+            "mcdnet": MCDNet(in_channels=3, num_classes=num_classes).to(self.device),
+            "scnn": SCNN(num_classes=num_classes).to(self.device),
+            "dbnet": DBNet(img_size=self.img_size, in_channels=3, num_classes=num_classes).to(
+                self.device
+            ),
+            "unetmobv2": UNetMobV2(num_classes=num_classes).to(self.device),
+            "kappamask": KappaMask(num_classes=num_classes, in_channels=3).to(self.device)
+        }
+        self.name_mapping = {
+            "KappaMask": "kappamask",
+            "CDNetv1": "cdnetv1",
+            "CDNetv2": "cdnetv2",
+            "HRCloudNet": "hrcloudnet",
+            "MCDNet": "mcdnet",
+            "SCNN": "scnn",
+            "DBNet": "dbnet",
+            "UNetMobv2": "unetmobv2",
+            "Cloud-Adapter": "cloud-adapter",
+        }
+
+        self.load_weights(other_model_weight_path)
+
+        self.create_ui()
+
+    def load_weights(self, checkpoint_path: str):
+        for model_name, model in self.other_models.items():
+            weight_path = os.path.join(checkpoint_path, model_name+".bin")
+            weight_path = glob(weight_path)[0]
+            weight = torch.load(weight_path, map_location=self.device)
+            model.load_state_dict(weight)
+            model.eval()
+            print(f"Loaded {model_name} weights from {weight_path}")
+
+    def html_legend(self, num_classes=2):
+        if num_classes == 2:
+            return """
+        <div style="margin-top: 10px; text-align: left; display: flex; align-items: center; gap: 20px;justify-content: center;">
+            <div style="display: flex; align-items: center;">
+                <div style="width: 20px; height: 20px; background-color: rgb(79, 253, 199); margin-right: 10px; "></div>
+                <span>Clear</span>
+            </div>
+            <div style="display: flex; align-items: center;">
+                <div style="width: 20px; height: 20px; background-color: rgb(77, 2, 115); margin-right: 10px; "></div>
+                <span>Cloud</span>
+            </div>
+        </div>
+        """
+        return """
+        <div style="margin-top: 10px; text-align: left; display: flex; align-items: center; gap: 20px;justify-content: center;">
+            <div style="display: flex; align-items: center;">
+                <div style="width: 20px; height: 20px; background-color: rgb(79, 253, 199); margin-right: 10px; "></div>
+                <span>Clear Sky</span>
+            </div>
+            <div style="display: flex; align-items: center;">
+                <div style="width: 20px; height: 20px; background-color: rgb(77, 2, 115); margin-right: 10px; "></div>
+                <span>Thick Cloud</span>
+            </div>
+            <div style="display: flex; align-items: center;">
+                <div style="width: 20px; height: 20px; background-color: rgb(251, 255, 41); margin-right: 10px; "></div>
+                <span>Thin Cloud</span>
+            </div>
+            <div style="display: flex; align-items: center;">
+                <div style="width: 20px; height: 20px; background-color: rgb(221, 53, 223); margin-right: 10px; "></div>
+                <span>Cloud Shadow</span>
+            </div>
+        </div>
+"""
+
+    def create_ui(self):
+        with gr.Row():
+            # 左侧：输入图片和按钮
+            with gr.Column(scale=1):  # 左侧列
+                in_image = gr.Image(
+                    label='Input Image',
+                    sources='upload',
+                    elem_classes='input_image',
+                    interactive=True,
+                    type="pil",
+                )
+                with gr.Row():
+                    # 增加一个下拉菜单
+                    model_choice = gr.Dropdown(
+                        choices=[
+                            "DBNet",
+                            "HRCloudNet",
+                            "CDNetv2",
+                            "UNetMobv2",
+                            "CDNetv1",
+                            "MCDNet",
+                            "KappaMask",
+                            "SCNN",
+                        ],
+                        value="DBNet",
+                        label="Model",
+                        elem_classes='model_type',
+                    )
+                    run_button = gr.Button(
+                        'Run',
+                        variant="primary",
+                    )
+                # 示例输入列表
+                gr.Examples(
+                    examples=self.example_inputs,
+                    inputs=in_image,
+                    label="Example Inputs"
+                )
+
+            # 右侧：输出图片
+            with gr.Column(scale=1):  # 右侧列
+                with gr.Column():
+                    # 输出图片
+                    out_image = gr.Image(
+                        label='Output Image',
+                        elem_classes='output_image',
+                        interactive=False
+                    )
+                    # 图例
+                    legend = gr.HTML(
+                        value=self.legend,
+                        elem_classes="output_legend",
+                    )
+
+        # 按钮点击逻辑：触发图像转换
+        run_button.click(
+            self.inference,
+            inputs=[in_image, model_choice],
+            outputs=out_image,
+        )
+
+    @torch.no_grad()
+    def inference(self, image: Image.Image, model_choice: str) -> Image.Image:
+        return self.simple_model_forward(image, self.name_mapping[model_choice])
+
+    @torch.no_grad()
+    def simple_model_forward(self, image: Image.Image, model_choice: str) -> Image.Image:
+        """
+        Simple Model Inference
+        """
+        ori_size = image.size
+        image = image.resize((self.img_size, self.img_size),
+                     resample=Image.Resampling.BILINEAR)
+        image = np.array(image)
+        image = (image - np.min(image)) / (np.max(image)-np.min(image))
+
+        image = torch.from_numpy(image).unsqueeze(0).to(self.device)
+        image = image.permute(0, 3, 1, 2).float()
+
+        logits: torch.Tensor = self.other_models[model_choice].forward(image)
+        pred_mask = torch.argmax(logits, dim=1).squeeze(
+            0).cpu().numpy().astype(np.uint8)
+
+        del image
+        del logits
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        im = Image.fromarray(pred_mask).convert("P")
+        im.putpalette(self.palette)
+        return im.resize(ori_size, resample=Image.Resampling.BILINEAR)
+
+
+def get_palette(dataset_name: str) -> List[int]:
+    if dataset_name in ["cloudsen12_high_l1c", "cloudsen12_high_l2a"]:
+        return [79, 253, 199, 77, 2, 115, 251, 255, 41, 221, 53, 223]
+    if dataset_name == "l8_biome":
+        return [79, 253, 199, 221, 53, 223, 251, 255, 41, 77, 2, 115]
+    if dataset_name in ["gf12ms_whu_gf1", "gf12ms_whu_gf2", "hrc_whu"]:
+        return [79, 253, 199, 77, 2, 115]
+    raise Exception("dataset_name not supported")
+
+
+if __name__ == '__main__':
+    title = 'Cloud Segmentation for Remote Sensing Images'
+    custom_css = """
+h1 {
+    text-align: center;
+    font-size: 24px;
+    font-weight: bold;
+    margin-bottom: 20px;
+}
+"""
+    hrc_whu_examples = glob("example_inputs/hrc_whu/*")
+    gf1_examples = glob("example_inputs/gf1/*")
+    gf2_examples = glob("example_inputs/gf2/*")
+    l1c_examples = glob("example_inputs/l1c/*")
+    l2a_examples = glob("example_inputs/l2a/*")
+    l8_examples = glob("example_inputs/l8/*")
+
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+    with gr.Blocks(analytics_enabled=False, title=title,css=custom_css) as demo:
+        gr.Markdown(f'# {title}')
+        with gr.Tabs():
+            with gr.TabItem('Google Earth'):
+                CloudAdapterGradio(
+                    device=device,
+                    example_inputs=hrc_whu_examples,
+                    num_classes=2,
+                    palette=get_palette("hrc_whu"),
+                    other_model_weight_path="checkpoints/hrc_whu"
+                )
+            with gr.TabItem('Gaofen-1'):
+                CloudAdapterGradio(
+                    device=device,
+                    example_inputs=gf1_examples,
+                    num_classes=2,
+                    palette=get_palette("gf12ms_whu_gf1"),
+                    other_model_weight_path="checkpoints/gf12ms_whu_gf1"
+                )
+            with gr.TabItem('Gaofen-2'):
+                CloudAdapterGradio(
+                    device=device,
+                    example_inputs=gf2_examples,
+                    num_classes=2,
+                    palette=get_palette("gf12ms_whu_gf2"),
+                    other_model_weight_path="checkpoints/gf12ms_whu_gf2"
+                )
+
+            with gr.TabItem('Sentinel-2 (L1C)'):
+                CloudAdapterGradio(
+                    device=device,
+                    example_inputs=l1c_examples,
+                    num_classes=4,
+                    palette=get_palette("cloudsen12_high_l1c"),
+                    other_model_weight_path="checkpoints/cloudsen12_high_l1c"
+                )
+            with gr.TabItem('Sentinel-2 (L2A)'):
+                CloudAdapterGradio(
+                    device=device,
+                    example_inputs=l2a_examples,
+                    num_classes=4,
+                    palette=get_palette("cloudsen12_high_l2a"),
+                    other_model_weight_path="checkpoints/cloudsen12_high_l2a"
+                )
+            with gr.TabItem('Landsat-8'):
+                CloudAdapterGradio(
+                    device=device,
+                    example_inputs=l8_examples,
+                    num_classes=4,
+                    palette=get_palette("l8_biome"),
+                    other_model_weight_path="checkpoints/l8_biome"
+                )
+
+    demo.launch(share=True, debug=True)

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