完成第一个版本的内参标定

calibrate_intri.py

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+import os
+import os.path as osp
+import glob
+import cv2 as cv
+import numpy as np
+import json
+import argparse
+
+def calibrate_camera(imgFolder, chessboardSize, squareSize):
+    # 设置输出目录
+    outputFolder = osp.join(imgFolder, "output")
+    if not osp.exists(outputFolder):
+        os.makedirs(outputFolder)
+
+    # 图片路径
+    imgPaths = []
+    for extension in ["jpg", "png", "jpeg"]:
+        imgPaths += glob.glob(osp.join(imgFolder, "*.{}".format(extension)))
+    if len(imgPaths) == 0:
+        print("No images found!")
+        return
+
+    # 存储世界坐标和像素坐标
+    # 计算出棋盘格中每个网格角点的坐标，之后当成世界坐标
+    board_w, board_h = chessboardSize
+    board_grid = np.zeros((board_w * board_h, 3), np.float32)
+    board_grid[:, :2] = np.mgrid[0:board_w, 0:board_h].T.reshape(-1, 2) * squareSize
+
+    pointsWorld = []
+    pointsPixel = []
+
+    # 遍历图片
+    for imgPath in imgPaths:
+        img = cv.imread(imgPath)
+        gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+
+        # 查找角点
+        ret, corners = cv.findChessboardCorners(gray, (board_w, board_h), None)
+        if ret:
+            cv.cornerSubPix(gray, corners, (11, 11), (-1, -1), (cv.TERM_CRITERIA_EPS + cv.TERM_CRITERIA_MAX_ITER, 30, 0.001))
+            pointsWorld.append(board_grid)
+            pointsPixel.append(corners)
+
+            cv.drawChessboardCorners(img, (board_w, board_h), corners, ret)
+            cv.imwrite(osp.join(outputFolder, osp.basename(imgPath)), img)
+
+    # 标定相机
+    ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(pointsWorld, pointsPixel, gray.shape[::-1], None, None)
+    print("Intrinsic matrix:\n", mtx.astype(np.float32))
+    print("Distortion coefficients:\n", dist.astype(np.float32))
+
+    # 计算重投影误差
+    nimg = len(pointsWorld)
+    img_error = np.zeros(nimg)
+    for i in range(nimg):
+        imgpoints2, _ = cv.projectPoints(pointsWorld[i], rvecs[i], tvecs[i], mtx, dist)
+        error = cv.norm(pointsPixel[i], imgpoints2, cv.NORM_L2) / len(imgpoints2)
+        img_error[i] = error
+
+    good_img = np.where(img_error < 0.5)[0]
+    mean_error = np.mean(img_error[good_img])
+    print("Reprojection error: ", mean_error)
+
+    # 挑选出重投影误差小于1.0的图片，重新标定相机
+    if len(good_img) == 0:
+        print("No images with error < 0.5")
+    elif len(good_img) == nimg:
+        print("All images have error < 0.5")
+        pass
+    else :
+        pointsWorld2 = [pointsWorld[i] for i in good_img]
+        pointsPixel2 = [pointsPixel[i] for i in good_img]
+        ret, mtx, dist, rvecs, tvecs = cv.calibrateCamera(pointsWorld2, pointsPixel2, gray.shape[::-1], None, None)
+        print("Intrinsic matrix:\n", mtx.astype(np.float32))
+        print("Distortion coefficients:\n", dist.astype(np.float32))
+
+    return mtx, dist
+
+
+def write_json_data(mtx, dist, outputFolder):
+    data = {
+        "intrinsic_matrix": mtx.tolist(),
+        "distortion_coefficients": dist.tolist()
+    }
+    with open(osp.join(outputFolder, "calibration.json"), "w") as f:
+        json.dump(data, f, indent=4)
+
+if __name__ == "__main__":
+    mtx, dist = calibrate_camera("./test/intri_imgs", (11, 8), 60)
+    write_json_data(mtx, dist, "./test/intri_imgs/output")
+