在OpenCV中使用StereoBM的差异视差图

我已经把一个立体声凸轮装置放在一起，我很难用它来制作一个好的视差图 . 这是一个两个校正图像和我用它们产生的视差图的例子：

如你所见，结果非常糟糕 . 更改StereoBM的设置并没有太大变化 .

The setup

• 两台相机都是同型号，并通过USB连接到我的电脑 .

• 它们固定在坚硬的木板上，因此它们并不完美 . 它们无法移动，因此它们在校准期间和之后的位置是相同的 .

• 我使用OpenCV校准了立体声对，并使用OpenCV的 StereoBM 类来生成视差图 .

• 它's probably not that relevant, but I'用Python编码 .

Problems I could imagine

我'm doing this for the first time, so I'远非专家，但我已经尝试了 StereoBM 的所有设置排列，虽然我得到了不同的结果，但它们都像上面显示的视差图：黑色和白色的补丁 .

正如我所理解的那样，立体校正应该对齐每个图像上的所有点，以便它们通过直线（在我的情况下是水平线）连接，这一事实进一步支持了这一想法 . 如果我检查彼此相邻的两个经过校正的图片，那么事实并非如此，事实并非如此 . 右侧图片上的对应点比左侧高得多 . 不过，我不确定校准或整流是否是问题 .

The code

实际代码包含在对象中 - 如果你're interested in seeing it in its entirety, it'可用on GitHub . 这是一个实际运行的简化示例（当然在我使用超过2张图片校准的实际代码中）：

import cv2
import numpy as np

## Load test images
# TEST_IMAGES is a list of paths to test images
input_l, input_r = [cv2.imread(image, cv2.CV_LOAD_IMAGE_GRAYSCALE)
                    for image in TEST_IMAGES]
image_size = input_l.shape[:2]

## Retrieve chessboard corners
# CHESSBOARD_ROWS and CHESSBOARD_COLUMNS are the number of inside rows and
# columns in the chessboard used for calibration
pattern_size = CHESSBOARD_ROWS, CHESSBOARD_COLUMNS
object_points = np.zeros((np.prod(pattern_size), 3), np.float32)
object_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)
# SQUARE_SIZE is the size of the chessboard squares in cm
object_points *= SQUARE_SIZE
image_points = {}
ret, corners_l = cv2.findChessboardCorners(input_l, pattern_size, True)
cv2.cornerSubPix(input_l, corners_l,
                 (11, 11), (-1, -1),
                 (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
                  30, 0.01))
image_points["left"] = corners_l.reshape(-1, 2)
ret, corners_r = cv2.findChessboardCorners(input_r, pattern_size, True)
cv2.cornerSubPix(input_r, corners_r,
                 (11, 11), (-1, -1),
                 (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
                  30, 0.01))
image_points["right"] = corners_r.reshape(-1, 2)

## Calibrate cameras
(cam_mats, dist_coefs, rect_trans, proj_mats, valid_boxes,
 undistortion_maps, rectification_maps) = {}, {}, {}, {}, {}, {}, {}
criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,
            100, 1e-5)
flags = (cv2.CALIB_FIX_ASPECT_RATIO + cv2.CALIB_ZERO_TANGENT_DIST +
         cv2.CALIB_SAME_FOCAL_LENGTH)
(ret, cam_mats["left"], dist_coefs["left"], cam_mats["right"],
 dist_coefs["right"], rot_mat, trans_vec, e_mat,
 f_mat) = cv2.stereoCalibrate(object_points,
                              image_points["left"], image_points["right"],
                              image_size, criteria=criteria, flags=flags)
(rect_trans["left"], rect_trans["right"],
 proj_mats["left"], proj_mats["right"],
 disp_to_depth_mat, valid_boxes["left"],
 valid_boxes["right"]) = cv2.stereoRectify(cam_mats["left"],
                                           dist_coefs["left"],
                                           cam_mats["right"],
                                           dist_coefs["right"],
                                           image_size,
                                           rot_mat, trans_vec, flags=0)
for side in ("left", "right"):
    (undistortion_maps[side],
     rectification_maps[side]) = cv2.initUndistortRectifyMap(cam_mats[side],
                                                           dist_coefs[side],
                                                           rect_trans[side],
                                                           proj_mats[side],
                                                           image_size,
                                                           cv2.CV_32FC1)

## Produce disparity map
rectified_l = cv2.remap(input_l, undistortion_maps["left"],
                        rectification_maps["left"],
                        cv2.INTER_NEAREST)
rectified_r = cv2.remap(input_r, undistortion_maps["right"],
                        rectification_maps["right"],
                        cv2.INTER_NEAREST)
cv2.imshow("left", rectified_l)
cv2.imshow("right", rectified_r)
block_matcher = cv2.StereoBM(cv2.STEREO_BM_BASIC_PRESET, 0, 5)
disp = block_matcher.compute(rectified_l, rectified_r, disptype=cv2.CV_32F)
cv2.imshow("disparity", disp)

这里出了什么问题？