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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()
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