RGB到YIQ并返回python

Question

我有一项任务需要将RGB转换成YIQ，然后再使用简单的函数（在lib:plt cv2 np中）转换回来

我得到的是“天真”的代码解决方案：

def transformRGB2YIQ(imgRGB: np.ndarray) -> np.ndarray:
    """
    Converts an RGB image to YIQ color space
    :param imgRGB: An Image in RGB
    :return: A YIQ in image color space
    """

    yiq_from_rgb = np.array([[0.299, 0.587, 0.114],
                             [0.59590059, -0.27455667, -0.32134392],
                             [0.21153661, -0.52273617, 0.31119955]])

    YIQ = np.dot(imgRGB.reshape(-1, 3), yiq_from_rgb).reshape(imgRGB.shape)

    return YIQ

    pass


def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:
    """
    Converts an YIQ image to RGB color space
    :param imgYIQ: An Image in YIQ
    :return: A RGB in image color space
    """
    yiq_from_rgb = np.array([[0.299, 0.587, 0.114],
                             [0.59590059, -0.27455667, -0.32134392],
                             [0.21153661, -0.52273617, 0.31119955]])
    rgb_from_yiq = np.linalg.inv(yiq_from_rgb)
    RGB = np.dot(imgYIQ.reshape(-1, 3), rgb_from_yiq).reshape(imgYIQ.shape)

    return RGB
    pass

我尝试使用np.dot并重新塑造img，以便将其乘以矩阵，如下所示：

但是没有运气。。。我得到了错误的答案

还尝试：

def transformRGB2YIQ(imgRGB: np.ndarray) -> np.ndarray:
    """
    Converts an RGB image to YIQ color space
    :param imgRGB: An Image in RGB
    :return: A YIQ in image color space
    """

    YIQ = np.ndarray(imgRGB.shape)

    YIQ[:, :, 0] = 0.299 * imgRGB[:, :, 0] + 0.587 * imgRGB[:, :, 1] + 0.114 * imgRGB[:, :, 2]
    YIQ[:, :, 1] = 0.59590059 * imgRGB[:, :, 0] + (-0.27455667) * imgRGB[:, :, 1] + (-0.32134392) * imgRGB[:, :, 2]
    YIQ[:, :, 2] = 0.21153661 * imgRGB[:, :, 0] + (-0.52273617) * imgRGB[:, :, 1] + 0.31119955 * imgRGB[:, :, 2]

    return YIQ

    pass


def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:
    """
    Converts an YIQ image to RGB color space
    :param imgYIQ: An Image in YIQ
    :return: A RGB in image color space
    """
    yiq_from_rgb = np.array([[0.299, 0.587, 0.114],
                             [0.59590059, -0.27455667, -0.32134392],
                             [0.21153661, -0.52273617, 0.31119955]])
    rgb_from_yiq = np.linalg.inv(yiq_from_rgb)

    RGB = np.ndarray(imgYIQ.shape)
    RGB[:, :, 0] = 1.00000001 * imgYIQ[:, :, 0] + 0.95598634 * imgYIQ[:, :, 1] + 0.6208248 * imgYIQ[:, :, 2]
    RGB[:, :, 1] = 0.99999999 * imgYIQ[:, :, 0] + (-0.27201283) * imgYIQ[:, :, 1] + (-0.64720424) * imgYIQ[:, :, 2]
    RGB[:, :, 2] = 1.00000002 * imgYIQ[:, :, 0] + (-1.10674021) * imgYIQ[:, :, 1] + 1.70423049 * imgYIQ[:, :, 2]

    return RGB
    pass

但是这在我的课堂上不是一个有效的答案，有没有关于如何一步到位的想法

Answer 1

经过多次尝试和错误，我找到了一个解决办法

def transformRGB2YIQ(imgRGB: np.ndarray) -> np.ndarray:
    """
    Converts an RGB image to YIQ color space
    :param imgRGB: An Image in RGB
    :return: A YIQ in image color space
    """
    yiq_from_rgb = np.array([[0.299, 0.587, 0.114],
                             [0.59590059, -0.27455667, -0.32134392],
                             [0.21153661, -0.52273617, 0.31119955]])
    OrigShape=imgRGB.shape
    return np.dot(imgRGB.reshape(-1,3), yiq_from_rgb.transpose()).reshape(OrigShape)

    pass


def transformYIQ2RGB(imgYIQ: np.ndarray) -> np.ndarray:
    """
    Converts an YIQ image to RGB color space
    :param imgYIQ: An Image in YIQ
    :return: A RGB in image color space
    """
    yiq_from_rgb = np.array([[0.299, 0.587, 0.114],
                             [0.59590059, -0.27455667, -0.32134392],
                             [0.21153661, -0.52273617, 0.31119955]])
    OrigShape=imgYIQ.shape
    return np.dot(imgYIQ.reshape(-1,3), np.linalg.inv(yiq_from_rgb).transpose()).reshape(OrigShape)

    pass