import os import numpy as np import cv2 as cv import glob import os.path as osp import json from tqdm import tqdm # 先想清楚文件夹结构 # extri文件夹：存放棋盘格照片，命名规则是cam1.jpg, cam2.jpg, cam3.jpg, ... # 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 def write_json(data, output_path): with open(output_path, "w") as f: 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"]: 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)