张正友相机标定法确定相机内参

汤流婉 · 2025-8-20 13:42:24

本文采用张正友标定法确认相机的内参，代码环境为Python3.12。

流程简介：

1、先用同一套棋盘（内角点尺寸为 5×6、方格边长 25 mm）在相机不同姿态下拍摄多张覆盖视场的平面棋盘图片；
2、对每张图片用 OpenCV 的 findChessboardCorners 检测内角点并用 cornerSubPix 做亚像素精修，按棋盘格的物理坐标（以 0.025 m 为单位）生成对应的三维目标点（所有点都在同一平面上）；
3、然后把这些二维角点与对应的三维平面点送入标定算法（OpenCV 的 calibrateCamera 实现了张正友方法的思想：通过每幅图像估计平面对像平面的单应矩阵、由多个单应求解相机内参的线性近似，再用非线性最小二乘对内参、畸变系数及每幅图的外参进行联合优化以最小化重投影误差）。
4、运行结束后你得到了相机内参矩阵（fx, fy, cx, cy）、畸变系数（k1,k2,p1,p2,k3…）、以及每张图的旋转向量和平移向量（tvec 单位为米，因为你用了 0.025 m 的方格边长）；并用重投影误差（你得到的平均约 0.0899 px，calibrateCamera 返回 RMS ≈ 0.4966）评估标定精度——误差极小，说明标定质量很好。
最后把结果保存为 camera_calibration_results.npz（并生成 corners_all.csv、可视化图 _corners.jpg / _reproj.jpg），便于后续去畸变、位姿估计（PnP）或把参数导出为 YAML/JSON 在其他程序中复用。

相机拍摄技巧：
用A4纸打印2-4张棋盘 chessboard_10x7_A4.pdf 采用阿里网盘保存的，可自行下载。

1、准备阶段：用需要测量的摄像机拍摄照片，多个角度、方向拍摄。我拍了400多张才有这几张有效。

2、做检测前创建一个Python项目，在Python代码根目录创建一个calib_images目录。这个目录就是用来存放你做检测的图片

3、调用下面这段Python，来测试你相机的内参。

点击查看代码

import cv2
import numpy as np
import glob
import os
import csv
from datetime import datetime
def calibrate_camera(calib_dir="calib_images",
square_size_mm=25.0,
min_good_images=5,
candidate_cols_range=(4, 10),
candidate_rows_range=(3, 8),
save_csv="corners_all.csv"):
"""
针对用户场景（同一相机、同一棋盘、square_size 已知）做的标定脚本。
- 先尝试在第一张图片上自动检测棋盘尺寸 (CHECKERBOARD)，找到后固定该尺寸用于所有图片。
- 打印并保存每张图片的角点像素坐标，保存合并 CSV 供检查。
"""
square_size = float(square_size_mm) / 1000.0 # mm -> m
if not os.path.exists(calib_dir):
print(f"错误：未找到目录 '{calib_dir}'，请确认路径。")
return
# 收集图片
image_formats = ["*.jpg", "*.jpeg", "*.png", "*.bmp"]
images = []
for fmt in image_formats:
images.extend(sorted(glob.glob(os.path.join(calib_dir, fmt))))
if not images:
print(f"错误：目录 '{calib_dir}' 中未找到任何图片。")
return
print(f"找到 {len(images)} 张图片（{calib_dir}）。开始检测 — {datetime.now().isoformat()}\n")
# 候选 pattern 列表（内角点数）
candidate_patterns = [(c, r) for c in range(candidate_cols_range[0], candidate_cols_range[1] + 1)
for r in range(candidate_rows_range[0], candidate_rows_range[1] + 1)]
# 先用第一张图自动识别棋盘尺寸（更稳妥）
first_img = cv2.imread(images[0])
first_gray = cv2.cvtColor(first_img, cv2.COLOR_BGR2GRAY)
found_pattern = None
flags = cv2.CALIB_CB_ADAPTIVE_THRESH | cv2.CALIB_CB_NORMALIZE_IMAGE | cv2.CALIB_CB_FAST_CHECK
print("尝试在第一张图片上检测棋盘尺寸 (自动识别)...")
for pat in candidate_patterns:
ret, _ = cv2.findChessboardCorners(first_gray, pat, flags)
if ret:
found_pattern = pat
print(f"在第一张图片检测到棋盘内角点尺寸: {found_pattern}，将其作为全局 CHECKERBOARD。\n")
break
if found_pattern is None:
print("警告：第一张图片未能自动识别棋盘尺寸。将遍历候选尺寸寻找第一个能识别的尺寸...")
for pat in candidate_patterns:
ret, _ = cv2.findChessboardCorners(first_gray, pat, flags)
if ret:
found_pattern = pat
print(f"找到尺寸: {found_pattern}\n")
break
if found_pattern is None:
print("注意：未在第一张图片找到可用尺寸。脚本将对每张图片分别尝试候选尺寸（回退策略）。\n")
# 准备存放点与 CSV 输出
objpoints = []
imgpoints = []
used_image_names = []
detected_patterns = []
csv_rows = []
csv_header = ["image", "pattern_cols", "pattern_rows", "corner_index", "x", "y"]
# 检测所有图片
for idx, fname in enumerate(images, start=1):
img = cv2.imread(fname)
if img is None:
print(f"[{idx}/{len(images)}] 警告：无法读取图片 {os.path.basename(fname)}，跳过")
continue
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
success = False
tried_patterns = []
patterns_to_try = [found_pattern] if found_pattern is not None else candidate_patterns
# 若 found_pattern 不为 None，优先尝试它；若失败再回退到所有 candidate_patterns
for pat in patterns_to_try:
if pat is None:
continue
tried_patterns.append(pat)
ret, corners = cv2.findChessboardCorners(gray, pat, flags)
if ret:
# 亚像素精修
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
cols, rows = pat
objp = np.zeros((rows * cols, 3), np.float32)
objp[:, :2] = np.mgrid[0:cols, 0:rows].T.reshape(-1, 2)
objp *= square_size
objpoints.append(objp)
imgpoints.append(corners2)
used_image_names.append(os.path.basename(fname))
detected_patterns.append(pat)
# 保存带角点图
vis = img.copy()
cv2.drawChessboardCorners(vis, pat, corners2, ret)
result_path = os.path.splitext(fname)[0] + "_corners.jpg"
cv2.imwrite(result_path, vis)
# 打印并记录坐标
corners_xy = corners2.reshape(-1, 2)
print(f"[{idx}/{len(images)}] {os.path.basename(fname)}: 检测成功, pattern={pat}, corners={len(corners_xy)}")
per_line = 8
coord_strs = [f"({x:.3f}, {y:.3f})" for x, y in corners_xy]
for i in range(0, len(coord_strs), per_line):
print(" " + " ".join(coord_strs[i:i+per_line]))
print(f" 结果图已保存: {os.path.basename(result_path)}\n")
# 写入 CSV 行
for i_pt, (x, y) in enumerate(corners_xy):
csv_rows.append([os.path.basename(fname), cols, rows, i_pt, f"{x:.6f}", f"{y:.6f}"])
success = True
break # 找到 pattern 则跳出 pattern 尝试
if not success:
# 如果之前只尝试了 found_pattern，而没有成功，回退尝试所有 candidate_patterns
if found_pattern is not None:
for pat in candidate_patterns:
if pat in tried_patterns:
continue
ret, corners = cv2.findChessboardCorners(gray, pat, flags)
if ret:
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
corners2 = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), criteria)
cols, rows = pat
objp = np.zeros((rows * cols, 3), np.float32)
objp[:, :2] = np.mgrid[0:cols, 0:rows].T.reshape(-1, 2)
objp *= square_size
objpoints.append(objp)
imgpoints.append(corners2)
used_image_names.append(os.path.basename(fname))
detected_patterns.append(pat)
vis = img.copy()
cv2.drawChessboardCorners(vis, pat, corners2, ret)
result_path = os.path.splitext(fname)[0] + "_corners.jpg"
cv2.imwrite(result_path, vis)
corners_xy = corners2.reshape(-1, 2)
print(f"[{idx}/{len(images)}] {os.path.basename(fname)}: 检测成功 (回退), pattern={pat}, corners={len(corners_xy)}")
coord_strs = [f"({x:.3f}, {y:.3f})" for x, y in corners_xy]
for i in range(0, len(coord_strs), 8):
print(" " + " ".join(coord_strs[i:i+8]))
print(f" 结果图已保存: {os.path.basename(result_path)}\n")
for i_pt, (x, y) in enumerate(corners_xy):
csv_rows.append([os.path.basename(fname), cols, rows, i_pt, f"{x:.6f}", f"{y:.6f}"])
success = True
break
if not success:
print(f"[{idx}/{len(images)}] {os.path.basename(fname)}: 未检测到角点，已跳过\n")
# 保存 CSV（所有角点）
if csv_rows:
with open(save_csv, "w", newline='', encoding='utf-8') as f:
writer = csv.writer(f)
writer.writerow(csv_header)
writer.writerows(csv_rows)
print(f"所有角点坐标已合并保存为: {save_csv}\n")
else:
print("未检测到任何角点，CSV 未生成。")
good_n = len(objpoints)
print(f"成功检测到 {good_n} 张有效图片（用于标定）。需要至少 {min_good_images} 张。")
if good_n < min_good_images:
print("有效图片不足，无法进行可靠标定。请检查图片质量或拍摄更多不同视角的棋盘照片。")
return
# image_size 使用第一张图片（因为你确认所有图片相同分辨率）
sample_img = cv2.imread(images[0])
image_size = (sample_img.shape[1], sample_img.shape[0]) # (width, height)
# 标定
print("开始标定（calibrateCamera）...")
rms, camera_matrix, dist_coeffs, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, image_size, None, None)
print("\n===== 标定结果 =====")
print(f"calibrateCamera 返回 RMS: {rms:.6f}")
print("相机内参 (camera_matrix):")
print(camera_matrix)
print("\n畸变系数 (dist_coeffs):")
print(dist_coeffs.ravel())
# 平均重投影误差
total_error = 0.0
for i in range(len(objpoints)):
imgpoints2, _ = cv2.projectPoints(objpoints[i], rvecs[i], tvecs[i], camera_matrix, dist_coeffs)
error = cv2.norm(imgpoints[i], imgpoints2, cv2.NORM_L2) / len(imgpoints2)
total_error += error
mean_error = total_error / len(objpoints)
print(f"\n平均重投影误差: {mean_error:.6f} 像素")
# 详细打印内参与畸变
fx = camera_matrix[0, 0]; fy = camera_matrix[1, 1]; cx = camera_matrix[0, 2]; cy = camera_matrix[1, 2]
print("\n相机内参 (详细):")
print(f" fx = {fx:.6f}")
print(f" fy = {fy:.6f}")
print(f" cx = {cx:.6f}")
print(f" cy = {cy:.6f}")
coeffs = dist_coeffs.ravel()
# 可能只有 4 或 5 个畸变参数
coeffs_str = ", ".join([f"{v:.8f}" for v in coeffs])
print(f"\n畸变系数: {coeffs_str}")
# 保存结果
np.savez("camera_calibration_results.npz",
camera_matrix=camera_matrix,
dist_coeffs=dist_coeffs,
rvecs=rvecs,
tvecs=tvecs,
mean_reprojection_error=mean_error,
used_image_names=np.array(used_image_names),
detected_patterns=np.array(detected_patterns),
square_size_m=square_size)
print("\n标定数据已保存: camera_calibration_results.npz")
print("完成。")
if __name__ == "__main__":
calibrate_camera()

复制代码

4、最后会把输出内容：

1). 结果打印出来；

2). 生成每个图片内角点标注的图片文件图片名_corners.jpg

3). 输出一个 camera_calibration_results.npz 文件;

4). 以及 corners_all.csv文件。

5、如有些看不懂，则用下面这段代码转换下。即可分为机器与人都能看懂的文件。代码如下：

点击查看代码

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
print_calib_readable.py
从 camera_calibration_results.npz 读取标定结果，
打印易读报告，并同时生成:
- calibration_readable.txt (人类可读)
- calibration_summary.json (机器可读)
- per_image_reprojection.csv (若 corners_all.csv 存在且可计算逐图误差)
修复 ValueError 的关键点：不把 numpy array 当作布尔值直接判断，而是检查 data.files 中是否包含字段。
"""
import numpy as np
import cv2
import os
import csv
import json
from math import sqrt
NPZ_FILE = "camera_calibration_results.npz"
CORNERS_CSV = "corners_all.csv"
READABLE_TXT = "calibration_readable.txt"
SUMMARY_JSON = "calibration_summary.json"
PER_IMAGE_CSV = "per_image_reprojection.csv"
def load_npz(fn):
if not os.path.exists(fn):
raise FileNotFoundError(f"未找到文件: {fn}")
return np.load(fn, allow_pickle=True)
def to_str_list(arr):
"""把 numpy array/iterable 转为 str 列表，兼容 bytes"""
if arr is None:
return None
out = []
try:
for v in arr:
if isinstance(v, bytes):
out.append(v.decode('utf-8', errors='ignore'))
else:
out.append(str(v))
except Exception:
# fallback: single value
try:
if isinstance(arr, bytes):
return [arr.decode('utf-8', errors='ignore')]
return [str(arr)]
except Exception:
return None
return out
def read_corners_csv(csv_path):
"""读取 corners_all.csv (image, pattern_cols, pattern_rows, corner_index, x, y)"""
if not os.path.exists(csv_path):
return None
d = {}
with open(csv_path, newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
for row in reader:
name = row['image']
x = float(row['x']); y = float(row['y'])
idx = int(row['corner_index'])
d.setdefault(name, []).append((idx, (x, y)))
# sort by index and convert to np.array
for name in list(d.keys()):
arr = [p for i,p in sorted(d[name], key=lambda it: it[0])]
d[name] = np.array(arr, dtype=np.float32)
return d
def format_mat(mat):
return "\n".join([" [" + " ".join(f"{v:12.6f}" for v in row) + "]" for row in mat])
def safe_get(data, *keys):
"""从 data (npz) 中按优先级取值，返回 None 或 value"""
for k in keys:
if k in data.files:
return data[k]
return None
def ensure_list_of_vectors(arr):
"""保证 rvecs/tvecs 转为 python list，元素为 np.array"""
if arr is None:
return None
try:
return [np.array(v) for v in arr]
except Exception:
# 如果是单个 array 扩展为 list
try:
return [np.array(arr)]
except Exception:
return None
def main():
try:
data = load_npz(NPZ_FILE)
except Exception as e:
print("加载 NPZ 失败：", e)
return
# 安全地获取字段（不直接用 data.get(...) 或在 if 中用数组判断）
camera_matrix = safe_get(data, "camera_matrix", "mtx", "camera_mat", "camera_matx")
dist_coeffs = safe_get(data, "dist_coeffs", "dist_coeff", "dist", "dist_coeff")
rvecs = safe_get(data, "rvecs")
tvecs = safe_get(data, "tvecs")
mean_err = safe_get(data, "mean_reprojection_error", "mean_error")
used_images_raw = safe_get(data, "used_image_names")
detected_patterns_raw = safe_get(data, "detected_patterns")
square_size_m_val = safe_get(data, "square_size_m")
rms_val = safe_get(data, "rms", "calibrate_rms") # optional
# normalize some fields
used_images = to_str_list(used_images_raw) if used_images_raw is not None else None
# detected_patterns may be array-like of tuples or strings; normalize to list of (cols,rows) tuples
detected_patterns = None
if detected_patterns_raw is not None:
try:
# try to convert to list of tuples
tmp = []
for p in detected_patterns_raw:
# p could be array([c,r]) or b'(c,r)'
if isinstance(p, (np.ndarray, list, tuple)):
tmp.append((int(p[0]), int(p[1])))
else:
# try parse
s = str(p)
s = s.strip("()[] ")
parts = [int(x) for x in s.replace(",", " ").split() if x.strip().isdigit()]
if len(parts) >= 2:
tmp.append((parts[0], parts[1]))
detected_patterns = tmp
except Exception:
detected_patterns = None
# convert rvecs/tvecs to python lists
rvecs_list = ensure_list_of_vectors(rvecs)
tvecs_list = ensure_list_of_vectors(tvecs)
# start composing readable text and summary dict
lines = []
lines.append("===== Camera Calibration (Human-Readable) =====\n")
if camera_matrix is None:
lines.append("错误：NPZ 文件中未找到相机内参 (camera_matrix)。\n")
# write file and exit
with open(READABLE_TXT, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
print("已生成 (部分) 可读文件：", READABLE_TXT)
return
# print camera matrix
lines.append("相机内参 (camera_matrix) — 3x3 矩阵 (单位: 像素):")
lines.append(format_mat(camera_matrix))
fx = float(camera_matrix[0,0]); fy = float(camera_matrix[1,1]); cx = float(camera_matrix[0,2]); cy = float(camera_matrix[1,2])
skew = float(camera_matrix[0,1]) if camera_matrix.shape[1] > 1 else 0.0
lines.append("\n标准参数名称与数值:")
lines.append(f" fx (焦距 x方向) = {fx:.6f} px")
lines.append(f" fy (焦距 y方向) = {fy:.6f} px")
lines.append(f" cx (主点 x) = {cx:.6f} px")
lines.append(f" cy (主点 y) = {cy:.6f} px")
lines.append(f" skew (相机倾斜/skew) = {skew:.6f} (通常接近 0)\n")
# distortion
if dist_coeffs is None:
lines.append("畸变系数未找到 (dist_coeffs)。\n")
dist_list = []
else:
d = np.array(dist_coeffs).ravel()
lines.append("畸变系数 (distortion coefficients):")
labels = ["k1","k2","p1","p2","k3","k4","k5","k6"]
for i,val in enumerate(d):
lab = labels[i] if i < len(labels) else f"d{i+1}"
lines.append(f" {lab:3s} = {float(val):.8f}")
lines.append("（顺序通常为 k1, k2, p1, p2, k3 ）\n")
dist_list = [float(x) for x in d.tolist()]
# RMS and mean reprojection
if rms_val is not None:
try:
rms_f = float(rms_val)
lines.append(f"calibrateCamera 返回 RMS (函数返回值) = {rms_f:.6f}")
except Exception:
pass
if mean_err is not None:
try:
mean_err_f = float(mean_err)
lines.append(f"mean_reprojection_error (按图片平均计算) = {mean_err_f:.6f} 像素")
except Exception:
pass
lines.append("（推荐关注按图片平均的 mean_reprojection_error，更直观）\n")
if square_size_m_val is not None:
try:
ss = float(square_size_m_val)
lines.append(f"棋盘方格实际边长 (square_size_m) = {ss:.6f} m")
except Exception:
pass
# patterns & images
if detected_patterns is not None:
unique = []
for p in detected_patterns:
if p not in unique:
unique.append(p)
if len(unique) == 1:
lines.append(f"使用的棋盘内角点尺寸 (pattern cols,rows) = {unique[0]} (一致，所有图片使用同一尺寸)")
common_pattern = unique[0]
else:
lines.append("每张图片检测到的棋盘内角点尺寸 (per-image):")
for i, p in enumerate(detected_patterns):
name = used_images[i] if used_images and i < len(used_images) else f"img#{i}"
lines.append(f" {name:20s} -> {p}")
common_pattern = None
else:
common_pattern = None
if used_images is not None:
lines.append(f"\n用于标定的图片数量 = {len(used_images)}")
lines.append("图片列表（用于标定，按序）:")
for nm in used_images:
lines.append(" - " + nm)
lines.append("")
# write readable txt now (we will append per-image detail later)
with open(READABLE_TXT, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
print("已生成可读文本报告：", READABLE_TXT)
# 准备 summary json
summary = {
"fx": fx, "fy": fy, "cx": cx, "cy": cy, "skew": skew,
"distortion": {f"k{i+1}": (dist_list[i] if i < len(dist_list) else None) for i in range(6)},
"distortion_raw": dist_list,
"rms": float(rms_val) if rms_val is not None else None,
"mean_reprojection_error": float(mean_err) if mean_err is not None else None,
"square_size_m": float(square_size_m_val) if square_size_m_val is not None else None,
"pattern_common": common_pattern,
"images_used": used_images if used_images is not None else []
}
# 如果存在 corners_all.csv，则计算逐图重投影误差并写入 CSV 与 JSON
corners_dict = read_corners_csv(CORNERS_CSV)
per_image_stats = []
if corners_dict is None:
print(f"未找到 {CORNERS_CSV}（可选）。若存在该文件，脚本会计算逐图重投影误差及 rvec/tvec。")
else:
# ensure rvecs/tvecs exist
if rvecs_list is None or tvecs_list is None:
print("注意：NPZ 中缺少 rvecs/tvecs，无法计算逐图重投影误差。")
else:
# iterate using used_images order if available
names_order = used_images if used_images is not None else sorted(list(corners_dict.keys()))
# prepare CSV writer
with open(PER_IMAGE_CSV, "w", newline='', encoding='utf-8') as fcsv:
writer = csv.writer(fcsv)
writer.writerow(["image", "pattern_cols", "pattern_rows", "corner_count", "rmse_px",
"tvec_x_m", "tvec_y_m", "tvec_z_m", "rvec_x", "rvec_y", "rvec_z"])
for i, name in enumerate(names_order):
base = os.path.basename(name)
if base not in corners_dict:
# try direct name (maybe used_images stores base names already)
if name in corners_dict:
base = name
else:
print(f"{base}: 在 {CORNERS_CSV} 中未找到对应角点，跳过逐图误差计算。")
continue
img_corners = corners_dict[base]
if i >= len(rvecs_list) or i >= len(tvecs_list):
print(f"{base}: rvecs/tvecs 不足，跳过。")
continue
rvec = rvecs_list[i].reshape(3,1)
tvec = tvecs_list[i].reshape(3,1)
# construct objp based on detected_patterns if available else skip
if detected_patterns is None or i >= len(detected_patterns):
print(f"{base}: 无法构造 objpoints（未保存 detected_patterns），跳过该图重投影计算。")
continue
pat = detected_patterns[i]
cols, rows = pat
objp = np.zeros((rows*cols, 3), np.float32)
objp[:,:2] = np.mgrid[0:cols, 0:rows].T.reshape(-1,2)
if square_size_m_val is not None:
objp[:,:2] *= float(square_size_m_val)
# project
proj, _ = cv2.projectPoints(objp, rvec, tvec, camera_matrix, dist_coeffs)
proj = proj.reshape(-1,2)
if proj.shape != img_corners.shape:
print(f"{base}: 投影点数量 {proj.shape[0]} 与 CSV 中角点数量 {img_corners.shape[0]} 不匹配，跳过。")
continue
err = np.sqrt(np.mean(np.sum((img_corners - proj)**2, axis=1)))
R, _ = cv2.Rodrigues(rvec)
writer.writerow([base, cols, rows, int(img_corners.shape[0]), float(err),
float(tvec[0,0]), float(tvec[1,0]), float(tvec[2,0]),
float(rvec[0,0]), float(rvec[1,0]), float(rvec[2,0])])
per_image_stats.append({
"image": base,
"pattern": [int(cols), int(rows)],
"corner_count": int(img_corners.shape[0]),
"rmse_px": float(err),
"tvec_m": [float(tvec[0,0]), float(tvec[1,0]), float(tvec[2,0])],
"rvec": [float(rvec[0,0]), float(rvec[1,0]), float(rvec[2,0])]
})
print("已生成逐图重投影 CSV：", PER_IMAGE_CSV)
summary["per_image"] = per_image_stats
# 写入 summary json
with open(SUMMARY_JSON, "w", encoding='utf-8') as fj:
json.dump(summary, fj, indent=2, ensure_ascii=False)
print("已生成机器可读摘要：", SUMMARY_JSON)
# append per-image human-readable section to readable txt
with open(READABLE_TXT, "a", encoding='utf-8') as f:
f.write("\n\n===== Per-image Reprojection Summary =====\n\n")
if not per_image_stats:
f.write("无可用的逐图重投影结果（可能缺少 corners_all.csv 或 rvecs/tvecs/detected_patterns）。\n")
else:
for s in per_image_stats:
f.write(f"Image: {s['image']}\n")
f.write(f" pattern (cols,rows) = {s['pattern']} ; corner_count = {s['corner_count']}\n")
f.write(f" reprojection RMSE = {s['rmse_px']:.6f} px\n")
f.write(f" tvec (m) = [{s['tvec_m'][0]:.6f}, {s['tvec_m'][1]:.6f}, {s['tvec_m'][2]:.6f}]\n")
f.write(f" rvec = [{s['rvec'][0]:.6f}, {s['rvec'][1]:.6f}, {s['rvec'][2]:.6f}]\n\n")
print("已把逐图结果追加到可读报告：", READABLE_TXT)
print("\n全部完成。")
if __name__ == "__main__":
main()

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