[libcamera-devel] [PATCH 3/3] utils: raspberrypi: ctt: Code tidying

[Naushir Patuck]

Hi Ben,

Thank you for this patch!

On Fri, 7 Jul 2023 at 14:41, Ben Benson via libcamera-devel
<libcamera-devel at lists.libcamera.org> wrote:
>
> Altered the way that some lines are laid out, made functions
> more attractive to look at, and tidied up messy areas.
>
> Signed-off-by Ben Benson <ben.benson at raspberrypi.com>
> ---
>  utils/raspberrypi/ctt/ctt_ccm.py | 61 +++++++++++++++-----------------
>  1 file changed, 29 insertions(+), 32 deletions(-)
>
> diff --git a/utils/raspberrypi/ctt/ctt_ccm.py b/utils/raspberrypi/ctt/ctt_ccm.py
> index bd44b4d8..85ca6827 100644
> --- a/utils/raspberrypi/ctt/ctt_ccm.py
> +++ b/utils/raspberrypi/ctt/ctt_ccm.py
> @@ -47,11 +47,8 @@ def degamma(x):
>
>
>  def gamma(x):
> -    # return (x *  * (1 / 2.4)  *  1.055 - 0.055)
> -    e = []
> -    for i in range(len(x)):
> -        e.append(((x[i] / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255)
> -    return e
> +    # Take 3 long array of color values and gamma them
> +    return [((colour / 255) ** (1 / 2.4) * 1.055 - 0.055) * 255 for colour in x]
>
>
>  """
> @@ -96,10 +93,8 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
>      """
>      m_srgb = degamma(m_rgb)  # now in 16 bit color.
>
> -    m_lab = []
> -    for col in m_srgb:
> -        m_lab.append(colors.RGB_to_LAB(col / 256))
> -    # This produces matrix of LAB values for ideal color chart)
> +    m_lab = [colors.RGB_to_LAB(color / 256) for color in m_srgb]
> +    # This produces array of LAB values for ideal color chart)
>
>      """
>      reorder reference values to match how patches are ordered
> @@ -168,7 +163,7 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
>          sumde = 0
>          ccm = do_ccm(r, g, b, m_srgb)
>          # This is the initial guess that our optimisation code works with.
> -
> +        original_ccm = ccm
>          r1 = ccm[0]
>          r2 = ccm[1]
>          g1 = ccm[3]
> @@ -199,12 +194,13 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
>          [r1, r2, g1, g2, b1, b2] = result.x
>          # The new, optimised color correction matrix values
>          optimised_ccm = [r1, r2, (1 - r1 - r2), g1, g2, (1 - g1 - g2), b1, b2, (1 - b1 - b2)]
> +
>          # This is the optimised Color Matrix (preserving greys by summing rows up to 1)
>          Cam.log += str(optimised_ccm)
>          Cam.log += "\n Old Color Correction Matrix Below \n"
>          Cam.log += str(ccm)
>
> -        formatted_ccm = np.array(ccm).reshape((3, 3))
> +        formatted_ccm = np.array(original_ccm).reshape((3, 3))
>
>          '''
>          below is a whole load of code that then applies the latest color
> @@ -213,22 +209,23 @@ def ccm(Cam, cal_cr_list, cal_cb_list):
>          '''
>          optimised_ccm_rgb = []  # Original Color Corrected Matrix RGB / LAB
>          optimised_ccm_lab = []
> -        for w in range(24):
> -            RGB = np.array([r[w], g[w], b[w]])
> -            ccm_applied_rgb = np.dot(formatted_ccm, (RGB / 256))
> -            optimised_ccm_rgb.append(gamma(ccm_applied_rgb))
> -            optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb))
>
>          formatted_optimised_ccm = np.array(ccm).reshape((3, 3))
>          after_gamma_rgb = []
>          after_gamma_lab = []
> -        for w in range(24):
> -            RGB = np.array([r[w], g[w], b[w]])
> +
> +        for red, green, blue in zip(r, g, b):
> +            RGB = np.array([red, green, blue])
> +            ccm_applied_rgb = np.dot(formatted_ccm, (RGB / 256))
> +            optimised_ccm_rgb.append(gamma(ccm_applied_rgb))
> +            optimised_ccm_lab.append(colors.RGB_to_LAB(ccm_applied_rgb))
> +
>              optimised_ccm_applied_rgb = np.dot(formatted_optimised_ccm, RGB / 256)
>              after_gamma_rgb.append(gamma(optimised_ccm_applied_rgb))
>              after_gamma_lab.append(colors.RGB_to_LAB(optimised_ccm_applied_rgb))
> +
>          '''
> -        Gamma After RGB / LAB
> +        Gamma After RGB / LAB - not used in calculations, only used for visualisation
>          We now want to spit out some data that shows
>          how the optimisation has improved the color matricies
>          '''
> @@ -303,8 +300,8 @@ def guess(x0, r, g, b, m_lab):       # provides a method of numerical feedback f
>  def transform_and_evaluate(ccm, r, g, b, m_lab):  # Transforms colors to LAB and applies the correction matrix
>      # create list of matrix changed colors
>      realrgb = []
> -    for i in range(len(r)):
> -        RGB = np.array([r[i], g[i], b[i]])
> +    for red, green, blue in zip(r, g, b):
> +        RGB = np.array([red, green, blue])
>          rgb_post_ccm = np.dot(ccm, RGB)  # This is RGB values after the color correction matrix has been applied
>          realrgb.append(colors.RGB_to_LAB(rgb_post_ccm))
>      # now compare that with m_lab and return numeric result, averaged for each patch
> @@ -315,12 +312,12 @@ def sumde(listA, listB):
>      global typenum, test_patches
>      sumde = 0
>      maxde = 0
> -    patchde = []
> -    for i in range(len(listA)):
> -        if maxde < (deltae(listA[i], listB[i])):
> -            maxde = deltae(listA[i], listB[i])
> -        patchde.append(deltae(listA[i], listB[i]))
> -        sumde += deltae(listA[i], listB[i])
> +    patchde = []  # Create array of the delta E values for each patch. useful for optimisation of certain patches
> +    for listA_item, listB_item in zip(listA, listB):
> +        if maxde < (deltae(listA_item, listB_item)):
> +            maxde = deltae(listA_item, listB_item)
> +        patchde.append(deltae(listA_item, listB_item))
> +        sumde += deltae(listA_item, listB_item)
>      '''
>      The different options specified at the start allow for
>      the maximum to be returned, average or specific patches
> @@ -330,9 +327,8 @@ def sumde(listA, listB):
>      if typenum == 1:
>          return maxde
>      if typenum == 2:
> -        output = 0
> -        for y in range(len(test_patches)):
> -            output += patchde[test_patches[y]]   # grabs the specific patches (no need for averaging here)
> +        output = sum([patchde[test_patch] for test_patch in test_patches])
> +        # Selects only certain patches and returns the output for them
>          return output
>
>
> @@ -341,8 +337,9 @@ calculates the ccm for an individual image.
>  ccms are calculate in rgb space, and are fit by hand. Although it is a 3x3
>  matrix, each row must add up to 1 in order to conserve greyness, simplifying
>  calculation.
> -Should you want to fit them in another space (e.g. LAB) we wish you the best of
> -luck and send us the code when you are done! :-)
> +The initial CCM is calculated in RGB, and then optimised in LAB color space
> +This simplifies the initial calculation but then gets us the accuracy of
> +using LAB color space.
>  """

Should this comment move to patch 1/3?

Naush


>
>
> --
> 2.34.1
>