Consider even the simplest case of using just the color in your image. you can generate these 5 "layers" (for hue values 0,24,90, 117 and 118):
With this code (in python/OpenCV)
# convert to hsv and get just hue
# get orginal image
orig = cv.LoadImage('cakephp.png')
# show original
hsv = cv.CreateImage(cv.GetSize(orig), 8, 3)
hue = cv.CreateImage(cv.GetSize(orig), 8, 1)
sat = cv.CreateImage(cv.GetSize(orig), 8, 1)
val = cv.CreateImage(cv.GetSize(orig), 8, 1)
cv.CvtColor(orig, hsv, cv.CV_RGB2HSV)
# loop to find how many different hues are present...
query = cv.CreateImage(cv.GetSize(orig), 8, 1)
result = cv.CreateImage(cv.GetSize(orig), 8, 1)
for i in range(0,255):
# if a number of pixels are equal - show where they are
if (cv.CountNonZero(result)>1000): # <-what is signficant?
When you convert from a layer representation to an image you are losing information. For instance, you don't know the values of the pixels of the background layer behind the cake. Additionally, you don't know for sure which part of the image belongs to which layer.
However, it may be possible in some cases to recover or estimate at least partially this information. For instance, you could try to separate an image into "layers" using segmentation algorithms. In your example, a simple segmentation based on color would probably work.
As for recovering lost pixel values in the background, there are so-called inpainting techniques that attempt to estimate missing areas in images based on its surroundings.
Lastly, to recover the position and content of texts in images you can rely on Optical Character Recognition (OCR) methods.
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