Upload images via Git LFS

.gitattributes

@@ -33,3 +33,6 @@ 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
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,322 @@
+import gradio as gr
+import os
+import random
+import pandas as pd
+import time
+from datetime import datetime
+
+# ================= 配置区域 (Configuration) =================
+
+# 图片所在的文件夹
+IMAGE_DIR = "./user_study_pairs"
+
+# 结果保存文件
+RESULT_FILE = "user_study_results.csv"
+
+# 每个任务抽取的样本数量 (修改为 5)
+SAMPLES_PER_TASK = 5
+
+# 任务列表 (文件名必须以这些开头)
+TASKS = ['haze', 'shadow', 'reflections', 'lens_flares']
+
+# 任务名称映射 (Task Names)
+TASK_NAMES = {
+    'haze':        {'cn': '雾霾',       'en': 'Fog/Haze'},
+    'shadow':      {'cn': '阴影',       'en': 'Shadows'},
+    'reflections': {'cn': '玻璃反光',   'en': 'Reflections'},
+    'lens_flares': {'cn': '镜头光晕',   'en': 'Lens Flares'}
+}
+
+# ================= 核心逻辑 (Core Logic) =================
+
+def get_random_batch():
+    """
+    从文件夹中按任务分层随机抽取样本。
+    Sample images stratified by task.
+    """
+    if not os.path.exists(IMAGE_DIR):
+        return []
+
+    all_files = [f for f in os.listdir(IMAGE_DIR) if f.lower().endswith(('.jpg', '.jpeg', '.png'))]
+    selected_batch = []
+    
+    for task in TASKS:
+        # 筛选出属于当前 task 的图片
+        task_files = [f for f in all_files if f.startswith(task)]
+        
+        # 随机抽取 N 张
+        if len(task_files) >= SAMPLES_PER_TASK:
+            chosen = random.sample(task_files, SAMPLES_PER_TASK)
+        else:
+            chosen = task_files
+            
+        for f in chosen:
+            full_path = os.path.join(IMAGE_DIR, f)
+            selected_batch.append((full_path, f, task))
+            
+    random.shuffle(selected_batch)
+    return selected_batch
+
+def save_result(user_id, results):
+    """
+    保存结果到 CSV
+    Save results to CSV.
+    """
+    data = []
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    for img_name, task, is_success in results:
+        data.append({
+            "User_ID": user_id,
+            "Timestamp": timestamp,
+            "Image_Name": img_name,
+            "Task": task,
+            "Is_Success": is_success
+        })
+    
+    df = pd.DataFrame(data)
+    
+    if not os.path.exists(RESULT_FILE):
+        df.to_csv(RESULT_FILE, index=False, encoding='utf_8_sig')
+    else:
+        df.to_csv(RESULT_FILE, mode='a', header=False, index=False, encoding='utf_8_sig')
+
+def generate_label_html(task_key):
+    """
+    根据任务生成图片上方的标签（两栏或三栏）
+    Generate the label above the image based on the task (2-column or 3-column).
+    """
+    if task_key == 'shadow':
+        # Shadow 任务使用三栏布局 (3-column layout for Shadow)
+        return """
+        <div class='big-label-container'>
+            <div style='flex:1;'>Input (输入原图)</div>
+            <div style='flex:1; border-left: 1px solid #ccc; border-right: 1px solid #ccc;'>Masked Shadow (阴影蒙版)</div>
+            <div style='flex:1;'>Output (修复结果)</div>
+        </div>
+        """
+    else:
+        # 其他任务使用两栏布局 (2-column layout for others)
+        return """
+        <div class='big-label-container'>
+            <div style='flex:1;'>Input (输入原图)</div>
+            <div style='flex:1; border-left: 1px solid #ccc;'>Output (修复结果)</div>
+        </div>
+        """
+
+def generate_question_text(task_key):
+    """
+    生成严格中英双语的问题描述
+    Generate strictly bilingual question descriptions.
+    """
+    name_cn = TASK_NAMES.get(task_key, {}).get('cn', '干扰效应')
+    name_en = TASK_NAMES.get(task_key, {}).get('en', 'Artifacts')
+    
+    # 针对 Shadow 任务的专用 Prompt
+    if task_key == 'shadow':
+        q1_cn = "1. <b>蒙版红色区域 (Masked Red Region)</b> 内的阴影是否去除干净？"
+        q1_en = "1. Is the shadow within the <b>Masked Red Region</b> removed cleanly?"
+    else:
+        q1_cn = f"1. 画面中的 <b>“{name_cn}”</b> 是否去除干净？"
+        q1_en = f"1. Is the <b>“{name_en}”</b> removed cleanly?"
+
+    text = f"""
+    <div class='question-box'>
+        <h3>请仔细评估 / Please Evaluate:</h3>
+        <p>{q1_cn}<br>
+           <span class='en-text'>{q1_en}</span></p>
+        <hr style='margin: 10px 0; opacity: 0.3;'>
+        <p>2. 原本的背景内容是否 <b>保持完整</b> 且未被篡改？<br>
+           <span class='en-text'>2. Is the original background <b>preserved</b> without distortion?</span></p>
+    </div>
+    """
+    return text
+
+# ================= 界面构建 (UI Layout) =================
+
+with gr.Blocks(title="Visual Perception Study") as demo:
+    
+    # CSS 样式：使用 Flexbox 确保标签与图片列对齐
+    gr.HTML("""
+    <style>
+        footer {visibility: hidden}
+        .big-label-container {
+            display: flex;
+            text-align: center;
+            font-weight: bold;
+            font-size: 1.1em;
+            color: #2c3e50;
+            background-color: #e8f4f8;
+            padding: 8px 0;
+            border-radius: 8px;
+            margin-bottom: 5px;
+            width: 100%;
+        }
+        .question-box {
+            background-color: #f9f9f9; 
+            padding: 15px; 
+            border-radius: 10px; 
+            border: 1px solid #ddd; 
+            margin-top: 10px;
+        }
+        .en-text {
+            color: #666; 
+            font-style: italic;
+            font-size: 0.95em;
+        }
+    </style>
+    """)
+    
+    # --- 状态变量 ---
+    state_batch = gr.State([])
+    state_index = gr.State(0)
+    state_results = gr.State([])
+    state_user_id = gr.State("")
+
+    # --- 页面 1: 欢迎页 ---
+    with gr.Group(visible=True) as welcome_page:
+        gr.Markdown("# 📋 图像修复主观质量评估 / Perceptual Image Restoration Study")
+        
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("""
+                ### 👋 欢迎 / Welcome
+                本研究旨在评估图像修复算法在真实场景下的表现。
+                This study evaluates the performance of image restoration algorithms in real-world scenarios.
+                
+                ### 🎯 评估标准 / Criteria for Success
+                请判断修复结果是否 **同时满足** 以下两个条件：
+                Please judge whether the restoration meets **BOTH** criteria:
+                
+                * **1. 去除彻底 (Removal Effectiveness)**
+                   中文: 干扰视觉的效应（如雾、影、反光）已基本消失。
+                   English: The visual artifacts (haze, shadow, reflections) are effectively removed.
+                
+                * **2. 保真度 (Content Fidelity)**
+                   中文: 原图的背景物体、结构、颜色未被扭曲或篡改。
+                   English: The original background structures, colors, and details are preserved without distortion.
+                """)
+                
+                gr.Markdown(f"""
+                ---
+                * **题目数量 / Questions**: {len(TASKS) * SAMPLES_PER_TASK}
+                * **匿名性 / Anonymity**: 结果仅用于学术统计 (Results are for academic use only).
+                """)
+        
+        start_btn = gr.Button("开始测试 / Start Survey", variant="primary", size="lg")
+
+    # --- 页面 2: 测试页 ---
+    with gr.Group(visible=False) as quiz_page:
+        
+        # 顶部进度
+        progress_md = gr.Markdown("### ⏳ Progress: 1 / 20")
+        
+        # 图片显示区域
+        with gr.Column():
+            # 动态标签占位符 (Dynamic Label Placeholder)
+            label_html = gr.HTML(value="")
+            
+            image_display = gr.Image(
+                label="", 
+                interactive=False, 
+                show_label=False,
+                container=True
+            )
+        
+        # 问题区域
+        question_html = gr.HTML(value="Loading...")
+        
+        # 按钮区域
+        with gr.Row():
+            btn_fail = gr.Button("❌ 失败 / Failure\n(没去干净 或 改了背景 / Incomplete or Distorted)", size="lg", variant="stop")
+            btn_pass = gr.Button("✅ 成功 / Success\n(去除干净 且 背景一致 / Clean & Preserved)", size="lg", variant="primary")
+
+    # --- 页面 3: 结束页 ---
+    with gr.Group(visible=False) as end_page:
+        gr.Markdown("""
+        # 🎉 感谢您的参与！ / Thank You!
+        
+        您的评估数据已成功提交。您可以直接关闭此页面。
+        Your evaluation data has been submitted. You may close this page now.
+        """)
+        
+    # ================= 交互逻辑 =================
+
+    def start_survey():
+        batch = get_random_batch()
+        if not batch:
+            return {
+                welcome_page: gr.update(visible=True),
+                gr.State(""): "Error: No images found"
+            }
+            
+        u_id = str(int(time.time()))
+        
+        first_path, _, first_task = batch[0]
+        q_text = generate_question_text(first_task)
+        l_html = generate_label_html(first_task)
+        
+        return {
+            welcome_page: gr.update(visible=False),
+            quiz_page: gr.update(visible=True),
+            end_page: gr.update(visible=False),
+            state_batch: batch,
+            state_index: 0,
+            state_results: [],
+            state_user_id: u_id,
+            image_display: first_path,
+            progress_md: f"### ⏳ Progress: 1 / {len(batch)}",
+            question_html: q_text,
+            label_html: l_html
+        }
+
+    def process_answer(is_success, index, batch, results, u_id):
+        # 记录答案
+        _, current_name, current_task = batch[index]
+        results.append((current_name, current_task, is_success))
+        
+        next_index = index + 1
+        
+        if next_index >= len(batch):
+            # 完成
+            save_result(u_id, results)
+            return {
+                quiz_page: gr.update(visible=False),
+                end_page: gr.update(visible=True)
+            }
+        else:
+            # 下一题
+            next_path, _, next_task = batch[next_index]
+            q_text = generate_question_text(next_task)
+            l_html = generate_label_html(next_task)
+            
+            return {
+                state_index: next_index,
+                state_results: results,
+                image_display: next_path,
+                progress_md: f"### ⏳ Progress: {next_index + 1} / {len(batch)}",
+                question_html: q_text,
+                label_html: l_html
+            }
+
+    # 绑定事件
+    start_btn.click(
+        start_survey, 
+        inputs=[], 
+        outputs=[welcome_page, quiz_page, end_page, state_batch, state_index, state_results, state_user_id, image_display, progress_md, question_html, label_html]
+    )
+
+    btn_pass.click(
+        process_answer, 
+        inputs=[gr.Number(1, visible=False), state_index, state_batch, state_results, state_user_id], 
+        outputs=[quiz_page, end_page, state_index, state_results, image_display, progress_md, question_html, label_html]
+    )
+
+    btn_fail.click(
+        process_answer, 
+        inputs=[gr.Number(0, visible=False), state_index, state_batch, state_results, state_user_id], 
+        outputs=[quiz_page, end_page, state_index, state_results, image_display, progress_md, question_html, label_html]
+    )
+
+if __name__ == "__main__":
+    demo.launch(share=True)

requirements.txt

@@ -0,0 +1 @@
+pandas