diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..f6b1f326ca4ab7cf0c8798856f8fe0020ff82d58 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +*.png filter=lfs diff=lfs merge=lfs -text diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..e43b0f988953ae3a84b00331d0ccf5f7d51cb3cf --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +.DS_Store diff --git a/app.py b/app.py index a7791da8398c0fa8e0da72599213dd74bf188bd5..1cf3051f18b40ee9b474b16d5f4e43fe89a15611 100644 --- a/app.py +++ b/app.py @@ -1,57 +1,111 @@ import gradio as gr -import numpy as np -import pandas as pd -from PIL import Image, ImageEnhance - -# 伪造的算法函数，请替换成你自己的模型 -def enhance_image(img): - enhancer = ImageEnhance.Contrast(img) - enhanced = enhancer.enhance(1.5) - return enhanced - -def diagnose(img): - # 假数据 - stats = pd.DataFrame({ - "指标": ["增龄斑", "黄斑变性"], - "结果": ["正常", "异常"], - "概率": [0.85, 0.15] - }) - return img, stats - -def analyze_structure(img): - # 假数据 - stats = pd.DataFrame({ - "结构": ["血管密度", "分形维数"], - "数值": [4.12, 1.53] - }) - return img, stats - -with gr.Blocks(title="AI眼底影像辅助分析系统") as demo: - gr.Markdown("## AI眼底影像辅助分析系统") +from PIL import Image +import os +import random + +# ========== 配置图片路径 ========== +# 图像增强 +ENHANCE_INPUT_DIR = "f1_input" +ENHANCE_OUTPUT_DIR = "f1_output" +ENHANCE_IMG_NAMES = [f"{i}.png" for i in range(1, 613)] # 1.png ~ 612.png + +# 分割 +SEG_INPUT_DIR = "seg_input" +SEG_OUTPUT_DIR = "seg_output" +SEG_IMG_NAMES = [f"{i}.png" for i in range(1, 6)] # 1.png ~ 5.png + +# 诊断 +DIAG_INPUT_DIR = "diag_input" +DIAG_IMG_NAMES = [f"{i}.png" for i in range(1, 6)] # 1.png ~ 5.png + +# Kvasir 类别 +KVASIR_CLASSES = [ + "正常粘膜 (normal-z-line)", + "食管静脉曲张 (esophagitis)", + "食管腺癌 (barretts)", + "息肉 (polyps)", + "溃疡 (ulcerative-colitis)", + "糜烂 (dyed-lifted-polyps)", + "出血 (dyed-resection-margins)", + "炎症 (bbps-0-1)", + "直肠癌 (bbps-2-3)" +] + +# ========== 功能函数 ========== +def open_img(img_path): + if not os.path.exists(img_path): + # 返回一张空白图片 + return Image.new("RGB", (256, 256), (200, 200, 200)) + return Image.open(img_path) + +# 图像增强模块 +def show_enhance_input(img_name): + path = os.path.join(ENHANCE_INPUT_DIR, img_name) + return open_img(path) + +def enhance_demo(img_name): + path = os.path.join(ENHANCE_OUTPUT_DIR, img_name) + return open_img(path) + +# 图像分割模块 +def show_seg_input(img_name): + path = os.path.join(SEG_INPUT_DIR, img_name) + return open_img(path) + +def segment_demo(img_name): + path = os.path.join(SEG_OUTPUT_DIR, img_name) + return open_img(path) + +# 疾病诊断模块 +def show_diag_input(img_name): + path = os.path.join(DIAG_INPUT_DIR, img_name) + return open_img(path) + +def diagnose_demo(img_name): + pred = random.choice(KVASIR_CLASSES) + conf = round(random.uniform(0.85, 0.99), 2) + return f"诊断结果：{pred}\n置信度：{conf}" + +# ========== Gradio 布局 ========== +with gr.Blocks(title="AI内窥镜影像辅助分析系统") as demo: + gr.Markdown("## AI内窥镜影像辅助分析系统") + + # 上方：图像增强模块 with gr.Row(): - # 图像增强模块 with gr.Column(): gr.Markdown("### 图像增强模块") - input_img = gr.Image(label="原始图片", type="pil") - enhanced_img = gr.Image(label="增强图片") + enhance_select = gr.Dropdown(choices=ENHANCE_IMG_NAMES, value=ENHANCE_IMG_NAMES[0], label="选择样例图片", filterable=True) + enhance_input_img = gr.Image(label="原始图片", interactive=False) enhance_btn = gr.Button("增强图片") - enhance_btn.click(enhance_image, inputs=input_img, outputs=enhanced_img) + enhance_output_img = gr.Image(label="增强结果图片", interactive=False) + # 选择图片自动显示原图 + enhance_select.change(fn=show_enhance_input, inputs=enhance_select, outputs=enhance_input_img) + # 按钮显示增强结果 + enhance_btn.click(fn=enhance_demo, inputs=enhance_select, outputs=enhance_output_img) + + # 下方：左右两大模块 with gr.Row(): - # 疾病诊断模块 + # 左：图像分割 with gr.Column(): - gr.Markdown("### 疾病诊断模块") - diag_img = gr.Image(label="诊断结果图片") - diag_table = gr.Dataframe(headers=["指标", "结果", "概率"]) - diag_btn = gr.Button("疾病诊断") - diag_btn.click(diagnose, inputs=input_img, outputs=[diag_img, diag_table]) + gr.Markdown("### 图像分割模块") + seg_select = gr.Dropdown(choices=SEG_IMG_NAMES, value=SEG_IMG_NAMES[0], label="选择样例图片", filterable=True) + seg_input_img = gr.Image(label="输入图片", interactive=False) + seg_btn = gr.Button("执行分割") + seg_output_img = gr.Image(label="分割结果图片", interactive=False) - # 结构分析模块 + seg_select.change(fn=show_seg_input, inputs=seg_select, outputs=seg_input_img) + seg_btn.click(fn=segment_demo, inputs=seg_select, outputs=seg_output_img) + + # 右：疾病诊断 with gr.Column(): - gr.Markdown("### 结构分析模块") - struct_img = gr.Image(label="结构分析图片") - struct_table = gr.Dataframe(headers=["结构", "数值"]) - struct_btn = gr.Button("结构分析") - struct_btn.click(analyze_structure, inputs=input_img, outputs=[struct_img, struct_table]) + gr.Markdown("### 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