From 4322f7fdb9cc94450f6bfcd63cfecf6803941dc1 Mon Sep 17 00:00:00 2001 From: kayorl Date: Mon, 30 Mar 2026 10:00:49 +0800 Subject: [PATCH] =?UTF-8?q?fix(=E9=A2=9C=E8=89=B2=E5=8C=B9=E9=85=8D):=20?= =?UTF-8?q?=E5=8A=A8=E6=80=81=E6=A3=80=E6=B5=8B=E5=B9=B6=E6=8E=92=E9=99=A4?= =?UTF-8?q?=E5=8F=82=E8=80=83=E5=9B=BE=E4=B8=AD=E7=9A=84=E9=BB=91=E8=BE=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 新增黑边检测逻辑，通过计算RGB值之和识别并排除接近0的像素。这解决了黑边影响颜色统计准确性的问题，同时保持原有遮罩逻辑不变。 --- smart_color_match.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/smart_color_match.py b/smart_color_match.py index 98b615c..e31d2bd 100644 --- a/smart_color_match.py +++ b/smart_color_match.py @@ -50,6 +50,12 @@ class SmartColorMatch: if curr_ref_img.shape[:2] != curr_gen_img.shape[:2]: curr_ref_img = cv2.resize(curr_ref_img, (curr_gen_img.shape[1], curr_gen_img.shape[0]), interpolation=cv2.INTER_AREA) + # ========================================================== + # [新增修复]：动态检测并排除参考图的黑边 (黑边通常 RGB 值接近 0) + # 计算 RGB 颜色之和，如果极小 (< 0.05) 则认为是黑边，不参与颜色统计 + # ========================================================== + ref_non_black_mask = np.sum(curr_ref_img, axis=-1) > 0.05 + # 屏蔽遮罩逻辑（保持你原有的逻辑） valid_pixels_bool = None if mask_np_batch is not None: @@ -61,11 +67,15 @@ class SmartColorMatch: ref_lab = cv2.cvtColor(curr_ref_img, cv2.COLOR_RGB2LAB) gen_lab = cv2.cvtColor(curr_gen_img, cv2.COLOR_RGB2LAB) + # 结合黑边遮罩和输入的 ignore_mask if valid_pixels_bool is not None: - ref_valid = ref_lab[valid_pixels_bool] + # 参考图需要同时满足 valid_pixels_bool 并且 不是黑边 + ref_combined_mask = valid_pixels_bool & ref_non_black_mask + ref_valid = ref_lab[ref_combined_mask] gen_valid = gen_lab[valid_pixels_bool] else: - ref_valid = ref_lab.reshape(-1, 3) + # 如果没有输入 ignore_mask，仅排除参考图的黑边 + ref_valid = ref_lab[ref_non_black_mask] gen_valid = gen_lab.reshape(-1, 3) # 兜底：防止 mask 异常导致没有有效像素