You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
120 lines
4.5 KiB
120 lines
4.5 KiB
""" |
|
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 <image_dir> <out_dir> |
|
|
|
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 <image_dir> <out_dir>") |
|
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()
|
|
|