track png with git lfs

.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

.gitignore

@@ -0,0 +1 @@
+.DS_Store

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("### 疾病诊断模块")
+            diag_select = gr.Dropdown(choices=DIAG_IMG_NAMES, value=DIAG_IMG_NAMES[0], label="选择样例图片", filterable=True)
+            diag_input_img = gr.Image(label="输入图片", interactive=False)
+            diag_btn = gr.Button("开始诊断")
+            diag_result = gr.Textbox(label="诊断结果")
+
+            diag_select.change(fn=show_diag_input, inputs=diag_select, outputs=diag_input_img)
+            diag_btn.click(fn=diagnose_demo, inputs=diag_select, outputs=diag_result)
 
 demo.launch()