"v1"

.gitignore

@@ -0,0 +1 @@
+pretrain_weights/

app.py

@@ -0,0 +1,82 @@
+import gradio as gr
+from feature_combiner import FeatureCombiner
+from pipeline import Pipeline_demo
+# 数据集选择字典
+DATASETS = {
+    "angiography.CHUAC": "CHUAC dataset",
+    "angiography.DCA1": "DCA1 dataset",
+    "angiography.XCAD": "XCAD dataset",
+    "retina.DRIVE": "DRIVE dataset",
+    "retina.CHASEDB1": "CHASEDB1 dataset",
+    "crack.Crack500": "Crack500 dataset"
+}
+
+JSON_FILES = {
+    "crack":"dataset_curvilinear/crack/crack.json",
+    "angiography":"dataset_curvilinear/angiography/angiography.json",
+    "retina":"dataset_curvilinear/retina/retina.json"
+}
+
+is_cpu= True
+pipeline_demo = Pipeline_demo(is_cpu=is_cpu) 
+
+def generate_prompts(dataset):
+    """
+    根据选择的数据集生成prompts。
+    返回semantic map prompt和image prompt。
+    """
+
+    json_index = dataset.split(".")[0]
+    json_file = JSON_FILES[json_index]
+    fc = FeatureCombiner(json_file, DATASETS[dataset])
+    caption_seg, caption_img = fc.combine()
+    return caption_seg, caption_img
+
+def generate_semantic_map(semantic_map_prompt):
+    """
+    根据semantic map prompt生成semantic map。
+    返回semantic map的图像。
+    """
+    # 示例函数体，你需要填充实际的图像生成逻辑
+    return pipeline_demo.generate_semantic_map(semantic_map_prompt)
+
+
+def generate_image(image_prompt,semantic_map_image):
+    """
+    根据image prompt生成图像。
+    返回生成的图像。
+    """
+    # 示例函数体，你需要填充实际的图像生成逻辑
+    return pipeline_demo.generate_image(image_prompt, semantic_map_image)
+
+with gr.Blocks() as demo:
+    gr.Markdown("# DEMO for Expanding Curvilinear Object Segmentation datasets")
+    with gr.Row():
+        with gr.Column(scale=1):
+            dataset_dropdown = gr.Dropdown(label="Select Dataset", choices=list(DATASETS.keys()),value="crack.Crack500")
+            generate_prompts_button = gr.Button("1. Generate Prompts")
+            gr.Markdown("""
+            #### Process:
+            1. Generate Prompts
+            2. Generate Semantic Map based on the prompt
+            3. Generate Image based on the prompt and Semantic Map  
+            NOTE: The time it takes for the CPU provided by Huggingface to complete the generation is too long.  It is recommended that users modify is_cpu=False in app.py to deploy on their own devices.
+            """)
+        with gr.Column(scale=2):
+            semantic_map_prompt_text = gr.Textbox(label="Semantic Map Prompt", lines=5, placeholder="Semantic map prompt will be shown here...", interactive=True)
+            image_prompt_text = gr.Textbox(label="Image Prompt", lines=5, placeholder="Image prompt will be shown here...", interactive=True)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            generate_semantic_map_button = gr.Button("2. Generate Semantic Map")
+            semantic_map_image = gr.Image(width=512, height=512, label="Semantic Map",interactive=False)
+        with gr.Column(scale=1):
+            generate_image_button = gr.Button("3. Generate Image")
+            generated_image = gr.Image(width=512, height=512, label="Generated Image")
+
+    # 绑定函数
+    generate_prompts_button.click(generate_prompts, inputs=[dataset_dropdown], outputs=[semantic_map_prompt_text, image_prompt_text])
+    generate_semantic_map_button.click(generate_semantic_map, inputs=[semantic_map_prompt_text], outputs=[semantic_map_image])
+    generate_image_button.click(generate_image, inputs=[image_prompt_text,semantic_map_image], outputs=[generated_image])
+
+demo.launch()