I know how to convert from the default BGR color space into others using the cvtColor function. However, I’m very familiar with CIELAB and other color spaces defined by the International Commission on Illumination. The values provided by OpenCV seem to be scaled into 0-256 ranges and not correlate well with any of these tools.
For instance:
RGB = [0,0,0] converted into LAB yields [0, 128, 128] when in CIELAB it should be [0,0,0]
RGB = [255,255,255] converted into LAB yields [255, 128, 128] when in CIELAB it should be [100,0,0]
RGB = [0,255,0] converted into LAB yields [224.0, 42.0, 211.0] when in CIELAB the A scale should be extremely negative. I’ve also never seen an L scale or B scale anywhere near that high.
While I fully expect getting truly accurate color values of an object from an image of an object to be impossible, is there an established (preferably opensource or standard) way to approximate any CIE colorspace from the values provided by OpenCV? I’m sure I can develop an approximation myself by correlating a large number of known comparisons but I would rather not reinvent the wheel. This website: http://colormine.org/ seems to do this and claims to use open source code but the github seems to have been taken down.
Code used:
test = np.zeros((1,1,3), np.uint8)
test[0,0,0]=0
test[0,0,1]=255
test[0,0,2]=0
print(test)
print(cv2.cvtColor(test, cv2.COLOR_RGB2LAB))