整合去雾网页工具

※程序-后处理汇总/2_Adjust_V3_SV直方图调整+SV自动调整_效果一般.py

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+import numpy as np
+from PIL import Image
+from skimage import color, exposure
+from scipy.optimize import minimize
+import os
+import time
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+# ================= 配置区域 =================
+
+# 1. 输入与输出文件夹
+SRC_DIR = "去雾图像-北航合作-Result_Baidu"       # 待处理图片文件夹
+REF_DIR = "去雾图像-北航合作"                    # 参考图(GT)文件夹
+OUT_DIR = "去雾图像-北航合作-Result_Baidu_Own_V3"   # 结果输出文件夹
+
+# 2. 功能开关
+ENABLE_HIST_MATCH = True   # 【开关】 True: 开启直方图匹配; False: 关闭
+MAX_WORKERS = 4            # 【并行】 并行处理的进程数 (建议设为 CPU 核心数，如 4, 8, 16)
+
+# ===========================================
+
+def process_single_image(file_info):
+    """
+    单个图片处理函数 (用于并行调用)
+    file_info: (filename, src_dir, ref_dir, out_dir, enable_hist)
+    """
+    filename, src_folder, ref_folder, output_folder, use_hist = file_info
+    
+    source_path = os.path.join(src_folder, filename)
+    
+    # 1. 寻找对应的参考图
+    # 逻辑：去除文件名后缀 "_result" (例如 "image01_result.png" -> "image01.png")
+    name_no_ext, ext = os.path.splitext(filename)
+    if name_no_ext.endswith("_result"):
+        ref_name_no_ext = name_no_ext[:-7] # 去掉 "_result"
+    else:
+        ref_name_no_ext = name_no_ext
+        
+    ref_filename = ref_name_no_ext + ext
+    ref_path = os.path.join(ref_folder, ref_filename)
+
+    if not os.path.exists(ref_path):
+        return f"[跳过] 找不到参考图: {filename}"
+
+    try:
+        # 2. 读取图片并归一化 (0-1 float)
+        img_src_pil = Image.open(source_path).convert('RGB')
+        img_src = np.array(img_src_pil) / 255.0
+        
+        img_ref_pil = Image.open(ref_path).convert('RGB')
+        if img_src_pil.size != img_ref_pil.size:
+            img_ref_pil = img_ref_pil.resize(img_src_pil.size, Image.BILINEAR)
+        img_ref = np.array(img_ref_pil) / 255.0
+
+        # 3. RGB -> HSV
+        hsv_src = color.rgb2hsv(img_src)
+        hsv_ref = color.rgb2hsv(img_ref)
+
+        # === 新增功能: 直方图匹配 (Histogram Matching) ===
+        if use_hist:
+            # 分离通道
+            s_h, s_s, s_v = hsv_src[:,:,0], hsv_src[:,:,1], hsv_src[:,:,2]
+            r_h, r_s, r_v = hsv_ref[:,:,0], hsv_ref[:,:,1], hsv_ref[:,:,2]
+            
+            # 对 S 和 V 通道进行直方图匹配
+            # 这会将 src 的分布形状强行调整为 ref 的分布形状
+            matched_s = exposure.match_histograms(s_s, r_s)
+            matched_v = exposure.match_histograms(s_v, r_v)
+            
+            # 更新 hsv_src，后续的 minimize 将在此基础上进一步微调系数
+            hsv_src = np.stack([s_h, matched_s, matched_v], axis=-1)
+
+        # 4. 优化 S/V 乘数因子
+        # 即使做了直方图匹配，我们依然计算一个最佳的整体缩放系数，以确保整体误差最小
+        def loss_function(params):
+            ks, kv = params
+            adj_s = np.clip(hsv_src[:,:,1] * ks, 0, 1)
+            adj_v = np.clip(hsv_src[:,:,2] * kv, 0, 1)
+            loss_s = np.mean((adj_s - hsv_ref[:,:,1])**2)
+            loss_v = np.mean((adj_v - hsv_ref[:,:,2])**2)
+            return loss_s + loss_v
+
+        # 初始猜测 [1.0, 1.0]
+        res = minimize(loss_function, [1.0, 1.0], method='Nelder-Mead', tol=1e-4)
+        best_s, best_v = res.x
+        
+        s_percent = int(best_s * 100)
+        v_percent = int(best_v * 100)
+
+        # 5. 应用最终参数
+        hsv_final = hsv_src.copy()
+        hsv_final[:, :, 1] = np.clip(hsv_final[:, :, 1] * best_s, 0, 1)
+        hsv_final[:, :, 2] = np.clip(hsv_final[:, :, 2] * best_v, 0, 1)
+        
+        # 6. 保存结果
+        img_result_rgb = color.hsv2rgb(hsv_final)
+        img_save = Image.fromarray((img_result_rgb * 255).astype(np.uint8))
+        
+        # 命名增加标识，如果开启了直方图匹配，可以在文件名加个标记(可选)，
+        # 这里保持您要求的格式: 原文件名_S_XX_V_XX.png
+        new_filename = f"{name_no_ext}_S_{s_percent}_V_{v_percent}{ext}"
+        save_path = os.path.join(output_folder, new_filename)
+        
+        img_save.save(save_path)
+        
+        match_tag = "[HistMatch]" if use_hist else "[Raw]"
+        return f"{match_tag} 完成: {new_filename} (S={s_percent}%, V={v_percent}%)"
+
+    except Exception as e:
+        return f"[错误] {filename}: {str(e)}"
+
+def main():
+    # 1. 检查文件夹
+    if not os.path.exists(SRC_DIR) or not os.path.exists(REF_DIR):
+        print("错误: 输入或参考文件夹不存在。")
+        return
+    
+    if not os.path.exists(OUT_DIR):
+        os.makedirs(OUT_DIR)
+
+    # 2. 获取文件列表
+    valid_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tif')
+    file_list = [f for f in os.listdir(SRC_DIR) if f.lower().endswith(valid_extensions)]
+    total_files = len(file_list)
+    
+    if total_files == 0:
+        print("源文件夹为空。")
+        return
+
+    print(f"=== 开始处理 ===")
+    print(f"模式: {'直方图匹配 + 参数优化' if ENABLE_HIST_MATCH else '仅参数优化'}")
+    print(f"并行: {MAX_WORKERS} 线程")
+    print(f"数量: {total_files} 张图片")
+    print("-" * 30)
+
+    # 3. 准备任务参数
+    tasks = []
+    for f in file_list:
+        # 打包参数传给 worker
+        tasks.append((f, SRC_DIR, REF_DIR, OUT_DIR, ENABLE_HIST_MATCH))
+
+    # 4. 并行执行
+    start_time = time.time()
+    
+    with ProcessPoolExecutor(max_workers=MAX_WORKERS) as executor:
+        # 提交所有任务
+        futures = [executor.submit(process_single_image, task) for task in tasks]
+        
+        # 获取结果 (as_completed 会在任务完成时立即返回)
+        for i, future in enumerate(as_completed(futures)):
+            result = future.result()
+            print(f"[{i+1}/{total_files}] {result}")
+
+    end_time = time.time()
+    print("-" * 30)
+    print(f"全部完成! 耗时: {end_time - start_time:.2f} 秒")
+    print(f"结果保存在: {OUT_DIR}")
+
+if __name__ == "__main__":
+    # Windows 下使用多进程必须放在 if __name__ == "__main__": 之下
+    main()