视频防抖预研（三）—— 数据的读取与校准

前言

算法仿真的方法主要有两种，python和MATLAB
由于需要对设备进行移植，opencv更加方便。同时MATLAB是收费软件，公司没有买licence。最终选择Python+OpenCV

新手不建议安装anaconda，经常会出现模块导入问题，我还出现过matplotlib模块缺失的问题，所以建议直接在系统中安装python程序。

实现

我把它分为三个部分
第一个部分，数据的读取与校准
第二个部分，数据的处理
第三个部分，视频的处理

数据的读取和校准

定义一个陀螺仪数据读取与校准的类，将有关陀螺仪数据的读取和校准方法包含进来，有利于梳理代码结构。

数据的读取

定义一个陀螺仪数据读取与校准的类，这个类初始化的输入就是文件的路径

class GyroscopeDataFile(object):
    # the file path to read
    # a dictionary of angular velocities
    # This dictionary will store mappings between the timestamp
    # and the angular velocity at that instant.
    def __init__(self, filepath):
        self.filepath = filepath
        self.omega = {}

    def _getfile_object(self):
        return open(self.filepath)

    # read the file and populate the Omega dictionary.
    def parse(self):
        with self._getfile_object() as fp:
            # We validate that the first line of the csv file matches our
            # expectation. If not, the csv file was probably not compatible
            # and will error out over the next few lines.
            firstline = fp.readline().strip()
            if not firstline == "gyro":
                raise Exception("The first line isn't valid")

            # The strip function removed any additional whitespace
            # (tabs, spaces, newline characters, among others)
            # that might be stored in the file.
            for line in fp.readlines():
                line = line.strip()
                parts = line.split(",")
                # convert strings into numetric type
                timestamp = int(parts[3])
                ox = float(parts[0])
                oy = float(parts[1])
                oz = float(parts[2])
                '''
                if timestamp < 100000000:
                    timestamp = timestamp * 10
                if timestamp < 100000000:
                    timestamp = timestamp * 10
                '''
                print("%s: %s, %s, %s" % (timestamp, ox, oy, oz))
                self.omega[timestamp] = (ox, oy, oz)
            return

    # return a sorted list of timestamps from small to large num
    def get_timestamps(self):
        return sorted(self.omega.keys())

数据的序列化

有些中间生成的数据，调试的时候需要经常使用。如果每次都重新运行程序生成的话，会浪费很多的时间。
这里利用pickle模块进行数据的序列化
python文件的打开尽量用with的方法，防止忘记关闭文件，保证安全

def read_gyro_data(self):
    if not os.path.exists(self.gyrofilename):
        print("start creating "+self.gyrofilename)
        with open(self.gyrofilename, "wb") as fp:
            pickle.dump(self.omega, fp)
            return self.omega
    else:
        with open(self.gyrofilename, "rb") as fp:
            return pickle.load(fp)

镜头参数校准

class LenParametersFromMatlab(object):
    """
    get opencv camera parameters from matlab parameters
    """
    def __init__(self,
        radialDistortion,
        tangentialDistortion,
        intrinsicMatrix,
        focalLength,
        principalPoint
    ):
        """
        radialDistortion:     1 x 2 matrix
        tangentialDistortion: 1 x 2 matrix
        intrinsicMatrix:      3 x 3 matrix
        """
        self.kc1 = radialDistortion[0]
        self.kc2 = radialDistortion[1]
        self.kc3 = tangentialDistortion[0]
        self.kc4 = tangentialDistortion[1]
        self.fc1 = intrinsicMatrix[0][0]
        self.fc2 = intrinsicMatrix[1][1]
        self.cc1 = intrinsicMatrix[2][0]
        self.cc2 = intrinsicMatrix[2][1]
        self.focalLength = focalLength
        self.principalPoint = principalPoint

    def getCameraMatrix(self):
        return np.array([
            [self.fc1, 0, self.cc1],
            [0, self.fc2, self.cc2],
            [0,         0,       1]
        ])

    def getdistCoeffs(self):
        return np.array([self.kc1, self.kc2, self.kc3, self.kc4])