Python+OpenCV：姿态估计（Pose Estimation）

我不是女神ヾ 2022-12-24 08:54 162阅读 0赞

## Python+OpenCV：姿态估计（Pose Estimation） ##

####################################################################################################
    # 姿态估计(Pose Estimation)
    def lmc_cv_pose_estimation(method):
        """
            函数功能: 姿态估计(Pose Estimation)。
        """
    
        # Load previously saved data
        with np.load('Calibration parameters.npz') as X:
            camera_matrix, dist_coeffs, rvecs, tvecs = [X[i] for i in ('mtx', 'dist', 'rvecs', 'tvecs')]
    
        # 终止条件(termination criteria)
        criteria = (lmc_cv.TERM_CRITERIA_EPS + lmc_cv.TERM_CRITERIA_MAX_ITER, 30, 0.001)
    
        # prepare object points, like (0,0,0), (1,0,0), (2,0,0) ....,(6,5,0)
        objp = np.zeros((6 * 7, 3), np.float32)
        objp[:, :2] = np.mgrid[0:7, 0:6].T.reshape(-1, 2)
    
        # draw a cube or a three-dimensional coordinate axis
        axis = []
        if 0 == method:
            axis = np.float32([[3, 0, 0], [0, 3, 0], [0, 0, -3]]).reshape(-1, 3)
    
        if 1 == method:
            axis = np.float32([[0, 0, 0], [0, 3, 0], [3, 3, 0], [3, 0, 0],
                               [0, 0, -3], [0, 3, -3], [3, 3, -3], [3, 0, -3]])
    
        # 读取所有匹配的图像
        stacking_images = []
        corners_image = []
        gray_image = []
        images = glob.glob('D:/99-Research/TestData/cv/stereo/case1/left*.png')
        for image_name in images:
            image = lmc_cv.imread(image_name, lmc_cv.IMREAD_UNCHANGED)
            image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2RGB)
            corners_image = image.copy()
            gray_image = lmc_cv.cvtColor(image, lmc_cv.COLOR_BGR2GRAY)
    
            # Find the chess board corners
            ret, corners = lmc_cv.findChessboardCorners(gray_image, (7, 6), None)
    
            # If found, add object points, image points (after refining them)
            if ret:
                # 亚像素角点
                corners2 = lmc_cv.cornerSubPix(gray_image, corners, (11, 11), (-1, -1), criteria)
    
                # Find the rotation and translation vectors.
                ret, rvecs, tvecs = lmc_cv.solvePnP(objp, corners2, camera_matrix, dist_coeffs)
    
                # project 3D points to image plane
                imgpts, jac = lmc_cv.projectPoints(axis, rvecs, tvecs, camera_matrix, dist_coeffs)
    
                # 绘制三维坐标轴
                corners_image = lmc_cv_draw_3d_coordinate_axis(method, corners_image, corners2, imgpts)
    
                # stacking images side-by-side
                stacking_image = np.hstack((image, corners_image))
                stacking_images.append(stacking_image)
    
        # 显示图像
        for i in range(len(stacking_images)):
            pyplot.figure('Pose Estimation %d' % (i + 1), figsize=(16, 9))
            # pyplot.subplot(1, 1, 1)
            pyplot.imshow(stacking_images[i], 'gray')
            pyplot.title('Pose Estimation')
            pyplot.xticks([])
            pyplot.yticks([])
            pyplot.savefig('%02d.png' % (i + 1))
        pyplot.show()

####################################################################################################
    # 绘制3D轴
    def lmc_cv_draw_3d_coordinate_axis(method, image, corners, imgpts):
        if 0 == method:
            corners = np.int32(corners)
            imgpts = np.int32(imgpts)
            corner = tuple(corners[0].ravel())
            image = lmc_cv.line(image, corner, tuple(imgpts[0].ravel()), (255, 0, 0), 5)
            image = lmc_cv.line(image, corner, tuple(imgpts[1].ravel()), (0, 255, 0), 5)
            image = lmc_cv.line(image, corner, tuple(imgpts[2].ravel()), (0, 0, 255), 5)
    
        if 1 == method:
            imgpts = np.int32(imgpts).reshape(-1, 2)
            # draw ground floor in green
            image = lmc_cv.drawContours(image, [imgpts[:4]], -1, (0, 255, 0), -3)
            # draw pillars in blue color
            for i, j in zip(range(4), range(4, 8)):
                image = lmc_cv.line(image, tuple(imgpts[i]), tuple(imgpts[j]), 255, 3)
            # draw top layer in red color
            image = lmc_cv.drawContours(image, [imgpts[4:]], -1, (0, 0, 255), 3)
    
        return image

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