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※程序-后处理汇总/2_Adjust_V1_HSV中SV直方图调整_很一般.py

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+import numpy as np
+from PIL import Image
+from skimage import exposure
+from matplotlib import colors
+
+def match_image_hsv(source_path, reference_path, output_path):
+    # 1. 读取图片并归一化到 0-1 (matplotlib 的 hsv 转换需要 0-1)
+    src_rgb = np.array(Image.open(source_path)) / 255.0
+    ref_rgb = np.array(Image.open(reference_path)) / 255.0
+
+    # 2. 将图片从 RGB 转换为 HSV
+    src_hsv = colors.rgb_to_hsv(src_rgb)
+    ref_hsv = colors.rgb_to_hsv(ref_rgb)
+
+    # 3. 分离 HSV 通道
+    # src_h: 色调, src_s: 饱和度, src_v: 亮度
+    s_h, s_s, s_v = src_hsv[:,:,0], src_hsv[:,:,1], src_hsv[:,:,2]
+    r_h, r_s, r_v = ref_hsv[:,:,0], ref_hsv[:,:,1], ref_hsv[:,:,2]
+
+    # 4. 对 V (亮度) 和 S (饱和度) 通道进行直方图匹配
+    # 我们使用参考图的 V 和 S 分布来调整源图
+    matched_h = exposure.match_histograms(s_h, r_h)
+    matched_v = exposure.match_histograms(s_v, r_v)
+    matched_s = exposure.match_histograms(s_s, r_s)
+
+    # 5. 合并通道
+    # 使用原始的 H (色调)，加上匹配后的 S 和 V
+    # V1 不调整H版本
+    matched_hsv = np.stack([s_h, matched_s, matched_v], axis=2)
+    # V2 调整H版本
+    # matched_hsv = np.stack([matched_h, matched_s, matched_v], axis=2)
+
+    # 6. 转换回 RGB 并保存
+    # 转换回 RGB 后需要 clip 到 0-1 范围，防止数值溢出
+    matched_rgb = np.clip(colors.hsv_to_rgb(matched_hsv), 0, 1)
+    # 将 0-1 转换回 0-255 的整数并保存
+    result_image = Image.fromarray((matched_rgb * 255).astype(np.uint8))
+    result_image.save(output_path)
+    print(f"HSV 处理完成，图片已保存至: {output_path}")
+
+def match_image_appearance(source_path, reference_path, output_path):
+    # 1. 读取图片
+    # source: 需要调整的图片 (第一张, 偏暗)
+    # reference: 目标风格图片 (第二张, 正常)
+    src = np.array(Image.open(source_path))
+    ref = np.array(Image.open(reference_path))
+
+    # 2. 进行直方图匹配
+    # channel_axis=-1 表示对 RGB 每个通道分别进行匹配
+    matched = exposure.match_histograms(src, ref, channel_axis=-1)
+
+    # 3. 保存结果
+    result_image = Image.fromarray(matched.astype(np.uint8))
+    result_image.save(output_path)
+    print(f"处理完成，图片已保存至: {output_path}")
+
+# 使用示例
+source_file = "./去雾图像-北航合作/2025-07-02_084220_VID002.mp4_20251027_001308.661.png"
+reference_file = "./去雾图像-北航合作-Result_Baidu/2025-07-02_084220_VID002.mp4_20251027_001308.661.png"
+# V1
+# match_image_appearance(source_file, reference_file, "adjusted_image_rgb.png")
+# V2
+match_image_hsv(source_file, reference_file, "adjusted_image_hsv.png")

※程序-后处理汇总/2_Adjust_V2_SV自动调整_※Best.py

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+import numpy as np
+from PIL import Image
+from skimage import color
+from scipy.optimize import minimize
+import os
+import time
+from concurrent.futures import ProcessPoolExecutor, as_completed
+
+# ================= 配置区域 =================
+
+max_workers = 8 # 【并行】 并行处理的进程数 (建议设为 CPU 核心数，如 4, 8, 16)
+# 图片后缀
+prefix = "_AOD-Net" # "_10_0.2_result" # "_result"
+# 输入文件夹 (带 prefix 后缀的图片)
+src_dir = "AOD-Net"
+# 参考文件夹 (GT/Ground Truth，无 prefix 后缀)
+ref_dir = "去雾图像-北航合作-雾图"
+# 输出文件夹
+out_dir = "AOD-Net+后处理"
+
+# ===========================================
+
+# 尝试导入 tqdm，如果没安装则定义一个简单的占位符
+try:
+    from tqdm import tqdm
+except ImportError:
+    def tqdm(iterable, **kwargs):
+        return iterable
+
+def process_single_image(filename, src_folder, ref_folder, output_folder, prefix=prefix):
+    """
+    处理单张图片的函数，用于并行调用
+    """
+    source_path = os.path.join(src_folder, filename)
+    
+    try:
+        # --- 寻找对应的参考图 ---
+        # 逻辑：去除文件名后缀 "prefix"
+        name_no_ext, ext = os.path.splitext(filename)
+        
+        if name_no_ext.endswith(prefix):
+            ref_name_no_ext = name_no_ext[:-len(prefix)] # 去掉最后n个字符 (prefix)
+        else:
+            ref_name_no_ext = name_no_ext 
+            
+        ref_filename = ref_name_no_ext + ext
+        ref_path = os.path.join(ref_folder, ref_filename)
+
+        # 检查参考图是否存在
+        if not os.path.exists(ref_path):
+            return f"[跳过] 找不到参考图: {ref_filename} (对应: {filename})"
+
+        # --- 读取图片并归一化 ---
+        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')
+        
+        # 确保参考图尺寸和源图一致
+        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
+
+        # --- 转换到 HSV ---
+        hsv_src = color.rgb2hsv(img_src)
+        hsv_ref = color.rgb2hsv(img_ref)
+
+        # --- 定义损失函数 ---
+        # 注意：在多进程中，loss_function 必须定义在 worker 内部才能访问到 hsv_src/ref
+        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
+
+        # --- 开始优化 ---
+        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)
+
+        # --- 应用最佳参数 ---
+        hsv_new = hsv_src.copy()
+        hsv_new[:, :, 1] = np.clip(hsv_new[:, :, 1] * best_s, 0, 1)
+        hsv_new[:, :, 2] = np.clip(hsv_new[:, :, 2] * best_v, 0, 1)
+        
+        # --- 转回 RGB 并保存 ---
+        img_result_rgb = color.hsv2rgb(hsv_new)
+        img_save = Image.fromarray((img_result_rgb * 255).astype(np.uint8))
+        
+        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)
+        
+        return f"OK: {filename} -> S={s_percent}%, V={v_percent}%"
+
+    except Exception as e:
+        return f"[错误] 处理文件 {filename} 时出错: {str(e)}"
+
+
+def calculate_and_process_batch_parallel(src_folder, ref_folder, output_folder, max_workers=None):
+    # 1. 确保输出目录存在
+    if not os.path.exists(output_folder):
+        os.makedirs(output_folder)
+        print(f"已创建输出目录: {output_folder}")
+
+    # 2. 获取源文件夹内所有图片文件
+    valid_extensions = ('.png', '.jpg', '.jpeg', '.bmp', '.tif')
+    file_list = [f for f in os.listdir(src_folder) if f.lower().endswith(valid_extensions)]
+    
+    total_files = len(file_list)
+    print(f"共发现 {total_files} 张图片，准备开始并行处理 (进程数: {max_workers if max_workers else '自动'})...\n")
+
+    # 3. 并行处理
+    results = []
+    
+    # ProcessPoolExecutor 自动管理进程池
+    # max_workers=None 意味着使用 CPU 核心数
+    with ProcessPoolExecutor(max_workers=max_workers) as executor:
+        # 提交所有任务
+        future_to_file = {
+            executor.submit(process_single_image, filename, src_folder, ref_folder, output_folder): filename 
+            for filename in file_list
+        }
+        
+        # 使用 tqdm 显示进度条，as_completed 会在任何一个任务完成时yield
+        pbar = tqdm(total=total_files, unit="img")
+        
+        for future in as_completed(future_to_file):
+            result_msg = future.result()
+            pbar.update(1)
+            
+            # 如果是错误或跳过信息，打印出来；如果是OK，只更新进度条不刷屏（可选）
+            if not result_msg.startswith("OK"):
+                tqdm.write(result_msg) # 使用 tqdm.write 防止打断进度条
+            # else:
+            #     tqdm.write(result_msg) # 如果想看每张图的详细结果，取消注释这行
+
+        pbar.close()
+
+    print("\n" + "="*30)
+    print("所有处理已完成。")
+
+# 执行
+if __name__ == "__main__":
+    # Windows 下使用多进程必须放在 if __name__ == "__main__": 之下
+    if os.path.exists(src_dir) and os.path.exists(ref_dir):
+        # max_workers 可以手动指定，例如 max_workers=4。如果不填则默认跑满 CPU。
+        calculate_and_process_batch_parallel(src_dir, ref_dir, out_dir, max_workers = max_workers)
+    else:
+        print("错误: 找不到输入文件夹或参考文件夹，请检查路径。")

※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()