1121完成了外参和棋盘格检测的版本，整理了文件夹结构
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fuly
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.gitignore
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.gitignore
vendored
@ -4,3 +4,4 @@ __pycache__/
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*.pyc
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*.pyo
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test/
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data/
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31
calib_tools.py
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31
calib_tools.py
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import os
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import numpy as np
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import cv2 as cv
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import glob
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import os.path as osp
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import json
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from tqdm import tqdm
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def write_json(data, output_path):
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with open(output_path, "w") as f:
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json.dump(data, f, indent=None, separators=(',', ':'))
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def read_json(input):
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with open(input, "r") as f:
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data = json.load(f)
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return data
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def read_img_paths(imgFolder):
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imgPaths = []
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for extension in ["jpg", "png", "jpeg", "bmp"]:
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imgPaths += glob.glob(osp.join(imgFolder, "*.{}".format(extension)))
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return imgPaths
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def create_output_folder(baseFolder, outputFolder):
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folder = osp.join(baseFolder, outputFolder)
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if not osp.exists(folder):
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os.makedirs(folder)
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return folder
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137
calibrate_extri.py
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137
calibrate_extri.py
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import os
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from glob import glob
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from os.path import join
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import numpy as np
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import cv2 as cv
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import json
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def read_json(input):
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with open(input, "r") as f:
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data = json.load(f)
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return data
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def solvePnP(k3d, k2d, K, dist, flag, tryextri=False):
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k2d = np.ascontiguousarray(k2d[:, :2]) # 保留前两列
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# try different initial values:
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if tryextri: # 尝试不同的初始化外参
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def closure(rvec, tvec):
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ret, rvec, tvec = cv.solvePnP(k3d, k2d, K, dist, rvec, tvec, True, flags=flag)
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points2d_repro, xxx = cv.projectPoints(k3d, rvec, tvec, K, dist)
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kpts_repro = points2d_repro.squeeze()
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err = np.linalg.norm(points2d_repro.squeeze() - k2d, axis=1).mean()
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return err, rvec, tvec, kpts_repro
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# create a series of extrinsic parameters looking at the origin
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height_guess = 2.7 # 相机的初始高度猜测
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radius_guess = 4. # 相机的初始水平距离猜测，圆的半径，需要根据自己的实际情况调整
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infos = []
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for theta in np.linspace(0, 2 * np.pi, 180):
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st = np.sin(theta)
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ct = np.cos(theta)
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center = np.array([radius_guess * ct, radius_guess * st, height_guess]).reshape(3, 1)
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R = np.array([
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[-st, ct, 0],
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[0, 0, -1],
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[-ct, -st, 0]
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])
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tvec = - R @ center
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rvec = cv.Rodrigues(R)[0]
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err, rvec, tvec, kpts_repro = closure(rvec, tvec)
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infos.append({
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'err': err,
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'repro': kpts_repro,
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'rvec': rvec,
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'tvec': tvec
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})
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infos.sort(key=lambda x: x['err'])
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err, rvec, tvec, kpts_repro = infos[0]['err'], infos[0]['rvec'], infos[0]['tvec'], infos[0]['repro']
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else:
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# 直接求解的初值是零向量
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ret, rvec, tvec = cv.solvePnP(k3d, k2d, K, dist, flags=flag)
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points2d_repro, xxx = cv.projectPoints(k3d, rvec, tvec, K, dist)
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kpts_repro = points2d_repro.squeeze()
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err = np.linalg.norm(points2d_repro.squeeze() - k2d, axis=1).mean()
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# print(err)
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return err, rvec, tvec, kpts_repro
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# 对单个相机进行外参标定
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def _calibrate_extri(k3d, k2d, K, dist, flag, tryfocal=False):
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extri = {}
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methods = [cv.SOLVEPNP_ITERATIVE]
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# 检查关键点数据的数量是否匹配
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if k3d.shape[0] != k2d.shape[0]:
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print('k3d {} doesnot match k2d {}'.format(k3d.shape, k2d.shape))
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length = min(k3d.shape[0], k2d.shape[0])
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k3d = k3d[:length]
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k2d = k2d[:length]
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valididx = k2d[:, 2] > 0 # k2d第三列是置信度，检查是否大于0
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if valididx.sum() < 4: # 筛选出有效的2D和3D关键点，数量大于4
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rvec = np.zeros((1, 3)) # 初始话旋转和平移为0并标记为失败
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tvec = np.zeros((3, 1))
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extri['Rvec'] = rvec
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extri['R'] = cv.Rodrigues(rvec)[0]
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extri['T'] = tvec
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print('[ERROR] Failed to initialize the extrinsic parameters')
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return extri
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k3d = k3d[valididx]
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k2d = k2d[valididx]
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# 优化相机焦距
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# 如果启用焦距优化
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if tryfocal:
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infos = []
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for focal in range(500, 5000, 10): # 遍历焦距范围
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# 设置焦距值
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K[0, 0] = focal # 更新 K 的 fx
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K[1, 1] = focal # 更新 K 的 fy
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for method in methods:
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# 调用 solvePnP
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err, rvec, tvec, kpts_repro = solvePnP(k3d, k2d, K, dist, method)
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# 保存结果
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infos.append({
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'focal': focal,
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'err': err,
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'rvec': rvec,
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'tvec': tvec,
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'repro': kpts_repro
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})
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# 根据重投影误差选择最佳焦距
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infos.sort(key=lambda x: x['err'])
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best_result = infos[0]
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focal = best_result['focal']
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err, rvec, tvec, kpts_repro = best_result['err'], best_result['rvec'], best_result['tvec'], best_result['repro']
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# 更新内参中的焦距
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K[0, 0] = focal
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K[1, 1] = focal
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print(f'[INFO] Optimal focal length found: {focal}, reprojection error: {err:.3f}')
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else:
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# 如果不优化焦距，直接调用 solvePnP
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err, rvec, tvec, kpts_repro = solvePnP(k3d, k2d, K, dist, flag)
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# 保存外参结果
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extri['Rvec'] = rvec
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extri['R'] = cv.Rodrigues(rvec)[0]
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extri['T'] = tvec
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center = - extri['R'].T @ tvec
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print(f'[INFO] Camera center: {center.squeeze()}, reprojection error: {err:.3f}')
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return extri
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def calibrate_extri(kpts_path, intri_path, flag, tryfocal=False, tryextri=False):
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extri = {}
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intri_data = read_json(intri_path)
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kpts_data = read_json(kpts_path)
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# 获取内参
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camnames = list(intri_data.keys())
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for cam in camnames:
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print(f'[INFO] Processing camera: {cam}')
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K = np.array(intri_data[cam]['K'])
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dist = np.array(intri_data[cam]['dist'])
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k3d = np.array(kpts_data[cam]['keypoints3d'])
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k2d = np.array(kpts_data[cam]['keypoints2d'])
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extri[cam] = _calibrate_extri(k3d, k2d, K, dist, flag, tryfocal=tryfocal)
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return extri
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if __name__ == "__main__":
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pass
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@ -7,13 +7,16 @@ import json
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import datetime
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import argparse
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from tqdm import tqdm
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def format_json_data(mtx, dist, image_shape):
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def format_json_data(mtx, dist, image_shape, error):
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data = {
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"time": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
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"K": mtx.tolist(),
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"dist": dist.tolist(),
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"image_shape": image_shape
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"image_shape": image_shape,
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"error": error
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}
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return data
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@ -28,27 +31,37 @@ def read_json(input):
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data = json.load(f)
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return data
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def read_img_paths(imgFolder):
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imgPaths = []
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for extension in ["jpg", "png", "jpeg", "bmp"]:
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imgPaths += glob.glob(osp.join(imgFolder, "*.{}".format(extension)))
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return imgPaths
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def create_output_folder(baseFolder, outputFolder):
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folder = osp.join(baseFolder, outputFolder)
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if not osp.exists(folder):
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os.makedirs(folder)
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return folder
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def calibrate_camera(baseFolder, chessboardSize, squareSize, visualization):
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base_path = "data"
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intri_img_path = osp.join(base_path, "chessboard", "intri")
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intri_vis_path = osp.join(base_path, "vis", "intri")
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json_output_path = osp.join(base_path, 'output_json')
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distortion_images_path = osp.join(base_path, "distortion_images")
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def calibrate_camera(camera, chessboardSize, squareSize, visualization):
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# 设置输出目录
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if visualization:
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outputFolder = create_output_folder(baseFolder, "calibrateOutputImages")
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outputFolder = create_output_folder(intri_vis_path, osp.basename(camera))
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# 图片路径
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imgPaths = read_img_paths(baseFolder)
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imgPaths = read_img_paths(camera)
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if len(imgPaths) == 0:
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print("No images found!")
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print("No images found!\n")
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return
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# 存储世界坐标和像素坐标
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@ -79,8 +92,8 @@ def calibrate_camera(baseFolder, chessboardSize, squareSize, visualization):
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# 标定相机
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image_shape = gray.shape[::-1]
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ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(pointsWorld, pointsPixel, image_shape, None, None)
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print("Intrinsic matrix:\n", mtx.astype(np.float32))
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print("Distortion coefficients:\n", dist.astype(np.float32))
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# print("Intrinsic matrix:\n", mtx.astype(np.float32))
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# print("Distortion coefficients:\n", dist.astype(np.float32))
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# 计算重投影误差
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nimg = len(pointsWorld)
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@ -93,7 +106,7 @@ def calibrate_camera(baseFolder, chessboardSize, squareSize, visualization):
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# good_img = np.where(img_error < 0.5)[0]
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# mean_error = np.mean(img_error[good_img])
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mean_error = np.mean(img_error)
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print("\nReprojection error: ", mean_error)
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print("Reprojection error: ", mean_error)
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# 挑选出重投影误差小于1.0的图片，重新标定相机
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# if len(good_img) == 0:
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@ -108,28 +121,30 @@ def calibrate_camera(baseFolder, chessboardSize, squareSize, visualization):
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# print("Intrinsic matrix:\n", mtx.astype(np.float32))
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# print("Distortion coefficients:\n", dist.astype(np.float32))
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data = format_json_data(mtx, dist, image_shape)
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# 在文件夹根目录下保存相机内参
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outputJsonPath = osp.join(baseFolder, "intri_calib.json")
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write_json(data, outputJsonPath)
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return mtx, dist, image_shape
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# data = format_json_data(mtx, dist, image_shape)
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# # 在文件夹根目录下保存相机内参
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# outputJsonPath = osp.join(baseFolder, "intri_calib.json")
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# write_json(data, outputJsonPath)
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return mtx, dist, image_shape, mean_error
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# calibrate_cameras函数中，照片按照相机编号进行分类
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def calibrate_cameras(baseFolder, chessboardSize, squareSize, visualization):
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cameras = glob.glob(osp.join(baseFolder, '[0-9]'))
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if len(cameras) == 0:
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# baseFolder: 包含图片和输出数据的文件夹，默认是./data，可以通过--folder参数指定
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def calibrate_cameras(chessboardSize, squareSize, visualization):
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cameras_path = glob.glob(osp.join(intri_img_path, "cam[0-7]"))
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if len(cameras_path) == 0:
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print("No camera folders found!")
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return
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outputJsonPath = osp.join(baseFolder, "intri_calib.json")
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data = {}
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for camera in cameras:
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cameraId = osp.basename(camera)
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print("\nCalibrating camera{}...".format(cameraId))
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mtx, dist, image_shape = calibrate_camera(camera, chessboardSize, squareSize, visualization)
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data[cameraId] = format_json_data(mtx, dist, image_shape)
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write_json(data, outputJsonPath)
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print("\nCalibration data saved to: ", outputJsonPath)
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for camera_path in tqdm(cameras_path, desc="Processing Cameras", ncols=100):
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cameraId = osp.basename(camera_path)
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print("\nCalibrating camera {}... ".format(cameraId))
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mtx, dist, image_shape, error = calibrate_camera(camera_path, chessboardSize, squareSize, visualization)
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data[cameraId] = format_json_data(mtx, dist, image_shape, error)
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write_json(data, osp.join(json_output_path, "intri.json"))
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print("Calibration data saved to: ", osp.join(json_output_path, "intri.json"))
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# 去除图像畸变
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def remove_image_distortion(img, mtx, dist):
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@ -140,14 +155,15 @@ def remove_image_distortion(img, mtx, dist):
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dst = dst[y:y + h, x:x + w]
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return dst
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# 用于去除整个文件夹中的图像畸变，保存到文件夹下的distortion_corrected_images文件夹中
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def remove_images_distortion(baseFolder, mtx, dist):
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imgPaths = read_img_paths(baseFolder)
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def remove_images_distortion(mtx, dist):
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imgPaths = read_img_paths(distortion_images_path)
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if len(imgPaths) == 0:
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print("No images found!")
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return
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outputFolder = create_output_folder(baseFolder, "distortion_corrected_images")
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outputFolder = create_output_folder(distortion_images_path, "output_images")
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for imgPath in imgPaths:
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img = cv.imread(imgPath)
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@ -159,31 +175,25 @@ def remove_images_distortion(baseFolder, mtx, dist):
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="相机内参标定和图像去畸变")
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parser.add_argument("--action", type=str, required=True, choices=["camera", "cameras", "distortion"],
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help=" --action camera: 标定单个相机"
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" --action cameras: 标定多个相机"
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parser.add_argument("--action", type=str, required=True, choices=["cameras", "distortion"],
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help=" --action cameras: 标定多个相机"
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" --action distortion: 去除图像畸变")
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parser.add_argument("--folder", type=str, required=True, help="包含相机文件夹的基础文件夹")
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parser.add_argument("--chessboardSize", type=str, default="11,8", help="棋盘格尺寸，格式为'w,h'")
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parser.add_argument("--squareSize", type=float, default=60.0, help="棋盘格方块尺寸")
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parser.add_argument("--chessboardSize", type=str, default="11,8",
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help="棋盘格角点数 (列数, 行数)，例如 '11,8'")
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parser.add_argument("--squareSize", type=float, default=60.0,
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help="棋盘格方块的实际边长（单位与数据一致，例如 mm 或 m）")
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parser.add_argument("--no-vis", dest="vis", action="store_false", help="禁用标定结果的可视化输出")
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args = parser.parse_args()
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chessboardSize = tuple(map(int, args.chessboardSize.split(",")))
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if args.action == "camera":
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print("Calibrating camera...")
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mtx, dist, image_shape = calibrate_camera(args.folder, chessboardSize, args.squareSize, args.vis)
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outputJsonPath = osp.join(args.folder, "intri_calib.json")
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print("Calibration data saved to: ", outputJsonPath)
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elif args.action == "cameras":
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calibrate_cameras(args.folder, chessboardSize, args.squareSize, args.vis)
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if args.action == "cameras":
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calibrate_cameras(chessboardSize, args.squareSize, args.vis)
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elif args.action == "distortion":
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print("Removing image distortion...")
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data = read_json(osp.join(args.folder, "intri_calib.json"))
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print("Removing image distortion, require input folder")
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data = read_json(osp.join(json_output_path, "intri.json"))
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mtx = np.array(data["K"])
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dist = np.array(data["dist"])
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remove_images_distortion(args.folder, mtx, dist)
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remove_images_distortion(mtx, dist)
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else:
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print("Invalid action!")
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parser.print_help()
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185
detect_chessboard.py
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185
detect_chessboard.py
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import os
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import numpy as np
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import cv2 as cv
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import glob
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import os.path as osp
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import json
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from tqdm import tqdm
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# 先想清楚文件夹结构
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# extri文件夹：存放棋盘格照片，命名规则是cam1.jpg, cam2.jpg, cam3.jpg, ...
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# 另一种模式是只检测2d点，不生成3d点，需要指定文件夹和输出路径
|
||||
|
||||
|
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def write_json(data, output_path):
|
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with open(output_path, "w") as f:
|
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json.dump(data, f)
|
||||
|
||||
|
||||
def read_json(input):
|
||||
with open(input, "r") as f:
|
||||
data = json.load(f)
|
||||
return data
|
||||
|
||||
|
||||
def read_img_paths(imgFolder):
|
||||
imgPaths = []
|
||||
for extension in ["jpg", "png", "jpeg", "bmp"]:
|
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imgPaths += glob.glob(osp.join(imgFolder, "*.{}".format(extension)))
|
||||
return imgPaths
|
||||
|
||||
|
||||
def create_output_folder(baseFolder, outputFolder):
|
||||
folder = osp.join(baseFolder, outputFolder)
|
||||
if not osp.exists(folder):
|
||||
os.makedirs(folder)
|
||||
return folder
|
||||
|
||||
base_path = "data"
|
||||
extri_img_path = osp.join(base_path, "chessboard", "extri")
|
||||
extri_vis_path = osp.join(base_path, "vis", "extri")
|
||||
json_output_path = osp.join(base_path, 'output_json')
|
||||
|
||||
def _findChessboardCorners(img, pattern):
|
||||
"basic function"
|
||||
criteria = (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
|
||||
retval, corners = cv.findChessboardCorners(img, pattern,
|
||||
flags=cv.CALIB_CB_ADAPTIVE_THRESH + cv.CALIB_CB_FAST_CHECK + cv.CALIB_CB_FILTER_QUADS)
|
||||
if not retval:
|
||||
return False, None
|
||||
corners = cv.cornerSubPix(img, corners, (11, 11), (-1, -1), criteria)
|
||||
corners = corners.squeeze()
|
||||
return True, corners
|
||||
|
||||
|
||||
def _findChessboardCornersAdapt(img, pattern):
|
||||
"Adapt mode"
|
||||
img = cv.adaptiveThreshold(img, 255, cv.ADAPTIVE_THRESH_GAUSSIAN_C, \
|
||||
cv.THRESH_BINARY, 21, 2)
|
||||
return _findChessboardCorners(img, pattern)
|
||||
|
||||
# 检测棋盘格角点并且可视化
|
||||
def findChessboardCorners(img_path, pattern, show=False):
|
||||
img = cv.imread(img_path)
|
||||
if img is None:
|
||||
raise FileNotFoundError(f"Image not found at {img_path}")
|
||||
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
|
||||
|
||||
# Find the chess board corners
|
||||
for func in [_findChessboardCorners, _findChessboardCornersAdapt]:
|
||||
ret, corners = func(gray, pattern)
|
||||
if ret: break
|
||||
else:
|
||||
return None
|
||||
# 附加置信度 1.0 并返回
|
||||
kpts2d = np.hstack([corners, np.ones((corners.shape[0], 1))])
|
||||
|
||||
if show:
|
||||
# Draw and display the corners
|
||||
img_with_corners = cv.drawChessboardCorners(img, pattern, corners, ret)
|
||||
# 标出棋盘格的原点
|
||||
origin = tuple(corners[0].astype(int)) # 原点的像素坐标
|
||||
cv.circle(img_with_corners, origin, 10, (0, 0, 255), -1) # 绘制原点
|
||||
cv.putText(img_with_corners, "Origin", (origin[0] + 10, origin[1] - 10),
|
||||
cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
|
||||
|
||||
# 标出最后一个点
|
||||
last_point = tuple(corners[-1].astype(int)) # 角点数组的最后一个点
|
||||
cv.circle(img_with_corners, last_point, 10, (0, 255, 0), -1) # 绿色圆点
|
||||
cv.putText(img_with_corners, "Last", (last_point[0] + 15, last_point[1] - 15),
|
||||
cv.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2) # 添加文字 "Last"
|
||||
|
||||
# 显示图像
|
||||
cv.imwrite(osp.join(extri_vis_path, osp.basename(img_path)), img_with_corners)
|
||||
return kpts2d
|
||||
|
||||
|
||||
# 根据棋盘格生成三维坐标，棋盘格坐标系原点在左上角（同时也是全局坐标原点）
|
||||
# 设定标定板z轴朝上，yx表示棋盘在yx平面上
|
||||
# easymocap
|
||||
# 注意，采用11x8的棋盘格，长边是y轴11，短边是x轴8，可以用opencv试一下
|
||||
def getChessboard3d(pattern, gridSize, axis='yx'):
|
||||
# 注意：这里为了让标定板z轴朝上，设定了短边是x，长边是y
|
||||
template = np.mgrid[0:pattern[0], 0:pattern[1]].T.reshape(-1, 2) # 棋盘格的坐标
|
||||
object_points = np.zeros((pattern[1] * pattern[0], 3), np.float32) # 3d坐标，默认向上的坐标轴为0
|
||||
# 长边是x,短边是z
|
||||
if axis == 'xz':
|
||||
object_points[:, 0] = template[:, 0]
|
||||
object_points[:, 2] = template[:, 1]
|
||||
elif axis == 'yx':
|
||||
object_points[:, 0] = template[:, 1]
|
||||
object_points[:, 1] = template[:, 0]
|
||||
else:
|
||||
raise NotImplementedError
|
||||
object_points = object_points * gridSize
|
||||
return object_points
|
||||
|
||||
|
||||
# 检测文件夹下的所有棋盘格图片，生成3d点和2d点，存入json文件
|
||||
# 图片应该按照cam0.jpg, cam1.jpg, cam2.jpg, ...的命名方式，要和内参文件夹对应
|
||||
def detect_chessboard(pattern, gridSize):
|
||||
imgPaths = read_img_paths(extri_img_path)
|
||||
if len(imgPaths) == 0:
|
||||
print("No images found!")
|
||||
return
|
||||
|
||||
data = {}
|
||||
for imgPath in tqdm(imgPaths):
|
||||
camname = osp.basename(imgPath).split(".")[0]
|
||||
keypoints2d = findChessboardCorners(imgPath, pattern, show=True)
|
||||
if keypoints2d is not None:
|
||||
keypoints3d = getChessboard3d(pattern, gridSize)
|
||||
data[camname] = {
|
||||
"keypoints2d": keypoints2d.tolist(),
|
||||
"keypoints3d": keypoints3d.tolist(),
|
||||
"pattern": pattern,
|
||||
"gridSize": gridSize
|
||||
}
|
||||
json_path = osp.join(json_output_path, "chessboard_keypoints.json")
|
||||
write_json(data, json_path)
|
||||
print(f"Saved keypoints to {json_path}")
|
||||
|
||||
|
||||
# 只检测2d点存入json文件
|
||||
def detect_chessboard_2d(imgFolder, pattern, outJsonPath):
|
||||
pass
|
||||
|
||||
|
||||
def test_findChessboardCorners(img_path, pattern, saveDir):
|
||||
imgpaths = read_img_paths(img_path)
|
||||
for imgpath in imgpaths:
|
||||
img = cv.imread(imgpath)
|
||||
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
|
||||
ret, corners = cv.findChessboardCorners(gray, pattern)
|
||||
if ret:
|
||||
# 在棋盘格上绘制角点
|
||||
img_with_corners = cv.drawChessboardCorners(img, pattern, corners, ret)
|
||||
|
||||
# 标出原点
|
||||
origin = tuple(corners[0][0]) # 角点数组的第一个点作为原点
|
||||
cv.circle(img_with_corners, (int(origin[0]), int(origin[1])), 10, (0, 0, 255), -1) # 红色圆点
|
||||
cv.putText(img_with_corners, "Origin", (int(origin[0]) + 15, int(origin[1]) - 15),
|
||||
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) # 添加文字 "Origin"
|
||||
|
||||
# 标出最后一个点
|
||||
last_point = tuple(corners[-1][0]) # 角点数组的最后一个点
|
||||
cv.circle(img_with_corners, (int(last_point[0]), int(last_point[1])), 10, (0, 255, 0), -1) # 绿色圆点
|
||||
cv.putText(img_with_corners, "Last", (int(last_point[0]) + 15, int(last_point[1]) - 15),
|
||||
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1) # 添加文字 "Last"
|
||||
|
||||
# 保存带角点的图像
|
||||
cv.imwrite(osp.join(saveDir, osp.basename(imgpath)), img_with_corners)
|
||||
else:
|
||||
print(f"Failed to detect chessboard corners in {imgpath}")
|
||||
print(f"Saved images to {saveDir}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
# test1
|
||||
img_path = "data/chessboard/extri"
|
||||
pattern = (11, 8)
|
||||
# saveDir = "data/test1"
|
||||
# os.makedirs(saveDir, exist_ok=True)
|
||||
# test_findChessboardCorners(img_path, pattern, saveDir)
|
||||
detect_chessboard(pattern, 60)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user