Return np.transpose(np.linalg.lstsq(m_1, m_2)) > first_order_colour_fit(m_1, m_2) # doctest: +ELLIPSISĪrray(, Reference array the array :math:`m_1` will be colour fitted against. Test array :math:`m_1` to fit onto array :math:`m_2`. *ColorChecker* colour rendition charts together. The purpose of that object is for example the matching of two The resulting colour fitting matrix is computed Performs a first order colour fit from given :math:`m_1` colour array to This will likely get in your way when performing linear regression and linear algebra.Ĭolour performs first order colour fitting as follows, i.e multivariate linear regression: def first_order_colour_fit(m_1, m_2): The values of your ground truth colour rendition chart are non-linearly encoded with the sRGB Opto-Electrical Transfer Function (OETF), i.e. The average value of the first box is (30,10,21) and from the second (103,33,87) which is far away from the expected values (115,82.68) and (194,150,130). I have done this for a small part of my color checker image as before, unfortunately it gives me this output: If I understood correctly, I have to multiply the RGB values of my image with: R2 = R*0.68864569 Which gives me the 3x3 Colour fitting matrix: Then I applied the color fitting function: c = first_order_colour_fit(z, q) Iterating over each pixel is not very efficient.Īs Kel Solaar suggested, I tried to converted the ground truth RGB and the RGB values of the color checker image into a linear space by using the oetf_reverse_sRGB: # First I scale the RGB values from 0 to 1
#BEST COLOR CORRECTION CALIBRATION HOW TO#
The average RGB values of the left rectangle should be and of the right rectangle but the value differ quite a lot ~ and ~.Īny suggestion would be very appreciated, I also would like to know how to improve the prediction. If I try to correct a part of my color checker image, I don't get the expected RGB values: import cv2įrom sklearn.preprocessing import PolynomialFeaturesįrom sklearn.linear_model import LinearRegression Grid = GridSearchCV(PolynomialRegression(), param_grid, cv=7) X = ,]Īfter a GridSearch, a degree of 2 should be optimal # This are the average RGB values after image acquisition of the color checker plate # This are the known values of the color checkers (ground truth) I am doing a GridSearch to find the optimal degree and then to use that function to correct the RGB values of my images. My idea was to fit an optimal polynomial function to the RGB values of the 24 color checker points (average) with the real known corresponding values. I am trying to do a color correction of my acquire images by calibrating the RGB values to an image of a color checker with 24 values of the same image session.
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