How to use the plenopticam.misc.yuv_conv function in plenopticam

def correct_luma_outliers(img, n=2, perc=.2, sta=None):

    # status init
    sta = sta if sta is not None else misc.PlenopticamStatus()
    sta.status_msg('Hot pixel removal', True)

    # luma channel conversion
    luma = misc.yuv_conv(img.copy())[..., 0]

    for i in range(n, luma.shape[0]-n):
        for j in range(n, luma.shape[1]-n):
            win = luma[i-n:i+n+1, j-n:j+n+1]

            # hot pixel detection
            num_hi = len(win[win > luma[i, j]*(1-perc)])

            # dead pixel detection
            num_lo = len(win[win < luma[i, j]*(1+perc)])

            if num_hi < win.size/5 or num_lo < win.size/5:
                # replace outlier by average of all directly adjacent pixels
                img[i, j, :] = (sum(sum(img[i-1:i+2, j-1:j+2, :]))-img[i, j, :])/8.

            # progress update

def michelson_contrast(img_tile):
    """ https://colorusage.arc.nasa.gov/luminance_cont.php """

    lum_tile = misc.yuv_conv(img_tile)[..., 0]

    c_m = (lum_tile.max() - lum_tile.min()) / (lum_tile.max() + lum_tile.min())

    return c_m

def auto_contrast(img_arr, p_lo=0.001, p_hi=0.999, ch=0):
    ''' according to Adi Shavit on https://stackoverflow.com/questions/9744255/instagram-lux-effect/9761841#9761841 '''

    # estimate contrast und brightness parameters (by default: achromatic "luma" channel only)
    val_lim = 2**16-1
    img_yuv = misc.yuv_conv(img_arr)
    h = np.histogram(img_yuv[..., ch], bins=np.arange(val_lim))[0]
    H = np.cumsum(h)/float(np.sum(h))
    try:
        px_lo = find_x_given_y(p_lo, np.arange(val_lim), H)
        px_hi = find_x_given_y(p_hi, np.arange(val_lim), H)
    except:
        px_lo = 0
        px_hi = 1
    A = np.array([[px_lo, 1], [px_hi, 1]])
    b = np.array([0, val_lim])
    contrast, brightness = np.dot(np.linalg.inv(A), b)

    return contrast, brightness

def robust_awb(self, t=0.3, max_iter=1000):
        ''' inspired by Jun-yan Huo et al. and http://web.stanford.edu/~sujason/ColorBalancing/Code/robustAWB.m '''

        img = Normalizer(self.central_view).type_norm(dtype='float16', lim_min=0, lim_max=1.0)
        ref_pixel = img[0, 0, :].copy()

        u = .01  # gain step size
        a = .8  # double step threshold
        b = .001  # convergence threshold

        gains_adj = np.array([1., 1., 1.])

        for i in range(max_iter):
            img_yuv = misc.yuv_conv(img)
            f = (abs(img_yuv[..., 1]) + abs(img_yuv[..., 2])) / img_yuv[..., 0]
            grays = np.zeros(img_yuv.shape)
            grays[f < t] = img_yuv[f < t]
            if np.sum(f < t) == 0:
                self.sta.status_msg('No valid gray pixels found.', self.cfg.params[self.cfg.opt_prnt])
                break

            u_bar = np.mean(grays[..., 1])  # estimate
            v_bar = np.mean(grays[..., 2])  # estimate

            # rgb_est = misc.yuv_conv(np.array([100, u_bar, v_bar]), inverse=True)    # convert average gray from YUV to RGB

            # U > V: blue needs adjustment otherwise red is treated
            err, ch = (u_bar, 2) if abs(u_bar) > abs(v_bar) else (v_bar, 0)

            if abs(err) >= a:

def correct_contrast(img_arr, contrast=1, brightness=0, ch=0):

    # color model conversion
    img_yuv = misc.yuv_conv(img_arr)

    # convert to float
    f = img_yuv[..., ch].astype(np.float32)

    # perform auto contrast (by default: "value" channel only)
    img_yuv[..., ch] = contrast * f + brightness

    # clip to input extrema to remove contrast outliers
    img_yuv[..., ch][img_yuv[..., ch] < img_arr.min()] = img_arr.min()
    img_yuv[..., ch][img_yuv[..., ch] > img_arr.max()] = img_arr.max()

    # color model conversion
    img = misc.yuv_conv(img_yuv, inverse=True)

    return img

''' inspired by Jun-yan Huo et al. and http://web.stanford.edu/~sujason/ColorBalancing/Code/robustAWB.m '''

    img = misc.type_norm(img, dtype='float16', lim_min=0, lim_max=1.0)
    ref_pixel = img[0, 0, :].copy()

    u = .01     # gain step size
    a = .8      # double step threshold
    b = .001    # convergence threshold

    gains_adj = np.array([1., 1., 1.])

    #sRGBtoXYZ = [[0.4124564, 0.3575761, 0.1804375], [0.2126729, 0.7151522, 0.0721750], [0.0193339, 0.1191920, 0.9503041]]
    sRGBtoXYZ = [[0.4124564, 0.2126729, 0.0193339], [0.3575761, 0.7151522, 0.0193339], [0.1804375, 0.0721750, 0.9503041]]

    for i in range(max_iter):
        img_yuv = misc.yuv_conv(img)
        f = (abs(img_yuv[..., 1]) + abs(img_yuv[..., 2])) / img_yuv[..., 0]
        grays = np.zeros(img_yuv.shape)
        grays[f V: blue needs adjustment otherwise red is treated
        err, ch = (u_bar, 2) if abs(u_bar) > abs(v_bar) else (v_bar, 0)

        if abs(err) >= a:

# color model conversion
    img_yuv = misc.yuv_conv(img_arr)

    # convert to float
    f = img_yuv[..., ch].astype(np.float32)

    # perform auto contrast (by default: "value" channel only)
    img_yuv[..., ch] = contrast * f + brightness

    # clip to input extrema to remove contrast outliers
    img_yuv[..., ch][img_yuv[..., ch] < img_arr.min()] = img_arr.min()
    img_yuv[..., ch][img_yuv[..., ch] > img_arr.max()] = img_arr.max()

    # color model conversion
    img = misc.yuv_conv(img_yuv, inverse=True)

    return img