我嘗試使用最小二乘法對影像進行顏色校正。我不明白為什么它不起作用，這應該是顏色校準的標準方式。
首先，我以 CR3 格式提取上面的影像，將其轉換為 RGB 空間，然后使用 OpenCV boundingRect 和 inRange 函式裁剪出四個色塊，將這四個色塊保存在一個名為 coloursRect 的陣列中。使用 vstack 以便存盤每個像素顏色的陣列從 3d 轉換為 2d。因此，例如， colour0 存盤“紅色補丁”的每個像素的 RGB 值。
colour0 = np.vstack(coloursRect[0])
colour1 = np.vstack(coloursRect[1])
colour2 = np.vstack(coloursRect[2])
colour3 = np.vstack(coloursRect[3])
lstsq_a = np.array(np.vstack((colour0,colour1,colour2,colour3)))
然后我在 RGB 中宣告原始參考顏色。
r_ref = [240,0,22]
y_ref = [252,222,10]
g_ref = [30,187,22]
b_ref = [26,0,165]
ref_patches = [r_ref,y_ref,g_ref, b_ref]
每個參考顏色的數量根據實際影像色塊中的像素數量相乘，例如，r_ref 乘以 colour0 陣列的長度。（我知道這是操縱資料的不好方法，但這在理論上應該可行）
lstsq_b_0to255 = np.array(np.vstack(([ref_patches[0]]*colour0.shape[0],[ref_patches[1]]*colour1.shape[0],[ref_patches[2]]*colour2.shape[0],[ref_patches[3]]*colour3.shape[0])))
計算最小二乘并與影像相乘。
lstsq_x_0to255 = np.linalg.lstsq(lstsq_a, lstsq_b_0to255)[0]
img_shape = img.shape
img_s = img.reshape((-1, 3))
img_corr_s = img_s @ lstsq_x_0to255
img_corr = img_corr_s.reshape(img_shape).astype('uint8')
但是這種顏色校正方法不起作用，并且影像中的顏色不正確。我可以知道是什么問題嗎？
編輯：使用 RGB 而不是 HSV 作為參考顏色
uj5u.com熱心網友回復：
忽略影像 ICC 組態檔在此處未正確解碼的事實，這是給定您的參考 RGB 值并使用
該模塊中可用的主要功能如下：
def least_square_mapping_MoorePenrose(y: ArrayLike, x: ArrayLike) -> NDArray:
"""
Compute the *least-squares* mapping from dependent variable :math:`y` to
independent variable :math:`x` using *Moore-Penrose* inverse.
Parameters
----------
y
Dependent and already known :math:`y` variable.
x
Independent :math:`x` variable(s) values corresponding with :math:`y`
variable.
Returns
-------
:class:`numpy.ndarray`
*Least-squares* mapping.
References
----------
:cite:`Finlayson2015`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> y = prng.random_sample((24, 3))
>>> x = y (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> least_square_mapping_MoorePenrose(y, x) # doctest: ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
y = np.atleast_2d(y)
x = np.atleast_2d(x)
return np.dot(np.transpose(x), np.linalg.pinv(np.transpose(y)))
def matrix_augmented_Cheung2004(
RGB: ArrayLike,
terms: Literal[3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22] = 3,
) -> NDArray:
"""
Perform polynomial expansion of given *RGB* colourspace array using
*Cheung et al. (2004)* method.
Parameters
----------
RGB
*RGB* colourspace array to expand.
terms
Number of terms of the expanded polynomial.
Returns
-------
:class:`numpy.ndarray`
Expanded *RGB* colourspace array.
Notes
-----
- This definition combines the augmented matrices given in
:cite:`Cheung2004` and :cite:`Westland2004`.
References
----------
:cite:`Cheung2004`, :cite:`Westland2004`
Examples
--------
>>> RGB = np.array([0.17224810, 0.09170660, 0.06416938])
>>> matrix_augmented_Cheung2004(RGB, terms=5) # doctest: ELLIPSIS
array([ 0.1722481..., 0.0917066..., 0.0641693..., 0.0010136..., 1...])
"""
RGB = as_float_array(RGB)
R, G, B = tsplit(RGB)
tail = ones(R.shape)
existing_terms = np.array([3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22])
closest_terms = as_int(closest(existing_terms, terms))
if closest_terms != terms:
raise ValueError(
f'"Cheung et al. (2004)" method does not define an augmented '
f"matrix with {terms} terms, closest augmented matrix has "
f"{closest_terms} terms!"
)
if terms == 3:
return RGB
elif terms == 5:
return tstack(
[
R,
G,
B,
R * G * B,
tail,
]
)
elif terms == 7:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
tail,
]
)
elif terms == 8:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R * G * B,
tail,
]
)
elif terms == 10:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
tail,
]
)
elif terms == 11:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
tail,
]
)
elif terms == 14:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
R**3,
G**3,
B**3,
tail,
]
)
elif terms == 16:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
R**2 * G,
G**2 * B,
B**2 * R,
R**3,
G**3,
B**3,
]
)
elif terms == 17:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
R**2 * G,
G**2 * B,
B**2 * R,
R**3,
G**3,
B**3,
tail,
]
)
elif terms == 19:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
R**2 * G,
G**2 * B,
B**2 * R,
R**2 * B,
G**2 * R,
B**2 * G,
R**3,
G**3,
B**3,
]
)
elif terms == 20:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
R**2 * G,
G**2 * B,
B**2 * R,
R**2 * B,
G**2 * R,
B**2 * G,
R**3,
G**3,
B**3,
tail,
]
)
elif terms == 22:
return tstack(
[
R,
G,
B,
R * G,
R * B,
G * B,
R**2,
G**2,
B**2,
R * G * B,
R**2 * G,
G**2 * B,
B**2 * R,
R**2 * B,
G**2 * R,
B**2 * G,
R**3,
G**3,
B**3,
R**2 * G * B,
R * G**2 * B,
R * G * B**2,
]
)
def matrix_colour_correction_Cheung2004(
M_T: ArrayLike,
M_R: ArrayLike,
terms: Literal[3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22] = 3,
) -> NDArray:
"""
Compute a colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Cheung et al. (2004)* method.
Parameters
----------
M_T
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R
Reference array the array :math:`M_T` will be colour fitted against.
terms
Number of terms of the expanded polynomial.
Returns
-------
:class:`numpy.ndarray`
Colour correction matrix.
References
----------
:cite:`Cheung2004`, :cite:`Westland2004`
Examples
--------
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> matrix_colour_correction_Cheung2004(M_T, M_R) # doctest: ELLIPSIS
array([[ 1.0526376..., 0.1378078..., -0.2276339...],
[ 0.0739584..., 1.0293994..., -0.1060115...],
[ 0.0572550..., -0.2052633..., 1.1015194...]])
"""
return least_square_mapping_MoorePenrose(
matrix_augmented_Cheung2004(M_T, terms), M_R
)
def colour_correction_Cheung2004(
RGB: ArrayLike,
M_T: ArrayLike,
M_R: ArrayLike,
terms: Literal[3, 5, 7, 8, 10, 11, 14, 16, 17, 19, 20, 22] = 3,
) -> NDArray:
"""
Perform colour correction of given *RGB* colourspace array using the
colour correction matrix from given :math:`M_T` colour array to
:math:`M_R` colour array using *Cheung et al. (2004)* method.
Parameters
----------
RGB
*RGB* colourspace array to colour correct.
M_T
Test array :math:`M_T` to fit onto array :math:`M_R`.
M_R
Reference array the array :math:`M_T` will be colour fitted against.
terms
Number of terms of the expanded polynomial.
Returns
-------
:class:`numpy.ndarray`
Colour corrected *RGB* colourspace array.
References
----------
:cite:`Cheung2004`, :cite:`Westland2004`
Examples
--------
>>> RGB = np.array([0.17224810, 0.09170660, 0.06416938])
>>> prng = np.random.RandomState(2)
>>> M_T = prng.random_sample((24, 3))
>>> M_R = M_T (prng.random_sample((24, 3)) - 0.5) * 0.5
>>> colour_correction_Cheung2004(RGB, M_T, M_R) # doctest: ELLIPSIS
array([ 0.1793456..., 0.1003392..., 0.0617218...])
"""
RGB = as_float_array(RGB)
shape = RGB.shape
RGB = np.reshape(RGB, (-1, 3))
RGB_e = matrix_augmented_Cheung2004(RGB, terms)
CCM = matrix_colour_correction_Cheung2004(M_T, M_R, terms)
return np.reshape(np.transpose(np.dot(CCM, np.transpose(RGB_e))), shape)
我可能會建議直接使用Color，因為有多種方法會根據訓練集給出不同的結果。話雖如此，鑒于您實際上只有 4 種彩色且沒有消色差，因此我不會期望得到很好的結果。這種校準的最低推薦圖表是具有 24 個色塊的 ColorChecker Classic。
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