From 8a62a8766c7b2d13381802ad7a86bffcfdc47473 Mon Sep 17 00:00:00 2001 From: hesuicong Date: Fri, 4 Sep 2026 15:03:44 +0800 Subject: [PATCH] =?UTF-8?q?=E5=8E=9F=E5=A7=8B=E5=9B=BE=E8=89=B2=E5=BD=A9?= =?UTF-8?q?=E5=9D=87=E8=A1=A1=E5=A4=84=E7=90=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- libs/MVS/calibrate.py | 120 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100755 libs/MVS/calibrate.py diff --git a/libs/MVS/calibrate.py b/libs/MVS/calibrate.py new file mode 100755 index 0000000..a1bb3e7 --- /dev/null +++ b/libs/MVS/calibrate.py @@ -0,0 +1,120 @@ +""" +calibrate.py —— 在跑 OpenMVS 之前，对输入照片做白平衡 + 曝光统一。 +这是消除"源视图色温差异"最彻底、最安全的办法：在像素进入纹理管线之前就对齐。 +用法: python calibrate.py + +做了两件事（都可选、都保守，不会破坏细节）： +1) Gray-World 白平衡：把每张图的通道均值拉向全局中值（去色温差） +2) 全局曝光归一化：把所有图的亮度中位数对齐到全局中位数（去曝光差） + +注意：OpenMVS 的 EstimateGlobalPhotometricCorrection 强依赖稠密点云， +如果没有点云它会被跳过（你之前日志里 "Photometric: no point cloud, skip"）。 +所以在这里预处理是最稳的兜底。 +""" +import os, sys, glob +import numpy as np +import cv2 + + +def gray_world(img_bgr, ref_bgr=None): + """灰度世界白平衡。 + + 若给定 ref_bgr（全局参考图的 BGR 均值），则把本图通道均值拉向 ref； + 否则用本图三通道的均值作为中性灰参考（单图自校正）。 + 不做任何“保守衰减”——必须完全对齐，否则等于没做。 + """ + b, g, r = [img_bgr[:, :, c].astype(np.float32) for c in range(3)] + mb, mg, mr = b.mean(), g.mean(), r.mean() + if ref_bgr is not None: + rb, rg, rr = ref_bgr + else: + # 单图自校正：以三通道共同均值作为中性灰目标 + m = (mb + mg + mr) / 3.0 + rb, rg, rr = m, m, m + kb = rb / mb if mb > 1e-6 else 1.0 + kg = rg / mg if mg > 1e-6 else 1.0 + kr = rr / mr if mr > 1e-6 else 1.0 + # 限制增益上限，防止极端单图把噪声放大 + def clamp_gain(k): + return min(max(k, 0.3), 3.0) + kb, kg, kr = clamp_gain(kb), clamp_gain(kg), clamp_gain(kr) + out = np.stack([ + np.clip(b * kb, 0, 255), + np.clip(g * kg, 0, 255), + np.clip(r * kr, 0, 255), + ], axis=2).astype(np.uint8) + return out + + +def normalize_exposure(img_bgr, target_median): + """曝光归一化：按亮度中位数线性缩放，对齐到全局目标中位数。 + 只做温和限制（上限 2.0），保证不同视图曝光真正一致。""" + gray = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY).astype(np.float32) + med = np.median(gray) + if med < 1.0: + return img_bgr + scale = target_median / med + scale = min(scale, 2.0) # 防止极暗图放大噪声，但允许充分提亮 + out = np.clip(img_bgr.astype(np.float32) * scale, 0, 255).astype(np.uint8) + return out + + +def main(): + if len(sys.argv) < 3: + print("usage: python calibrate.py ") + sys.exit(1) + in_dir, out_dir = sys.argv[1], sys.argv[2] + os.makedirs(out_dir, exist_ok=True) + + paths = sorted(glob.glob(os.path.join(in_dir, "*.*"))) + paths = [p for p in paths if p.lower().endswith((".jpg", ".jpeg", ".png", ".tif", ".bmp"))] + if not paths: + print("no images found in", in_dir) + sys.exit(1) + + # ---- 第一遍：统计所有图，确定全局参考（目标白平衡 + 目标亮度）---- + means = [] # (mb, mg, mr) + meds = [] + for p in paths: + img = cv2.imread(p) + if img is None: + continue + b, g, r = img[:, :, 0].astype(np.float32), img[:, :, 1].astype(np.float32), img[:, :, 2].astype(np.float32) + means.append((b.mean(), g.mean(), r.mean())) + gray = 0.299 * r + 0.587 * g + 0.114 * b + meds.append(np.median(gray)) + + means = np.array(means) + meds = np.array(meds) + + # 全局参考 = 所有图通道均值的均值（= 数据集整体真实色彩），亮度取中位数 + ref_b = means[:, 0].mean() + ref_g = means[:, 1].mean() + ref_r = means[:, 2].mean() + ref_bgr = (ref_b, ref_g, ref_r) + target_median = float(np.median(meds)) + + print(f"[calibrate] {len(paths)} images") + print(f"[calibrate] global reference white-balance (B,G,R) = ({ref_b:.1f}, {ref_g:.1f}, {ref_r:.1f})") + print(f"[calibrate] global reference luminance median = {target_median:.1f}") + + # ---- 第二遍：所有图统一校正到同一参考 ---- + for i, p in enumerate(paths): + img = cv2.imread(p) + if img is None: + continue + + # 1) 白平衡：把本图拉向全局参考 ref_bgr + out = gray_world(img, ref_bgr=ref_bgr) + + # 2) 曝光归一化：亮度中位数对齐到全局目标 + out = normalize_exposure(out, target_median) + + name = os.path.basename(p) + cv2.imwrite(os.path.join(out_dir, name), out) + + print(f"[calibrate] done -> {out_dir}") + + +if __name__ == "__main__": + main()