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Hello,
I want my script to be able to locate a color target, then take an RGB reading of each of the individual patches all through scripting, So in an image like the following,
1. i could find patch A1 of the colorcheckerSG,
2. take an averaged RGB reading,
3. store that value into an array
4. move onto path A2 and repeat...etc
5. once all patches have been analyzed, output RGB array to text file.
I only need help with step 1. and 4. finding the individual patches.
I am just looking into Hough Line transforms but thought it would be good to ask if there are any examples available or if you could point me in the right direction.
I was hoping this could be done via javascript without having to rely on external libraries or plugins.
Thank You!
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Is that an image opened in Ps that has specified dimension, so we can know width and height of single color square?
If so attach file to dowanload...
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Thanks for replying.
Unfortunately the color chart will be in different locations and size depending on the camera location when the image is created. so i cant predict where the chart will be from image to image.
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So the image is built of not only color panel we see, but also grey background? Is a color of that background always grey? If camera make photo of it I guess that grey may be bit ligther/darker but generally its grey with some base color value, right?
Once it may be centered, other time located in upper left corner, somtimes bigger / smaller but always it has 10*14 squares?
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Let's say for this example that yes, the backdrop is always gray and will probably always be a similar lightness and hue.
The color chart never changes as Its a physical object being photographed. So it will always have 10x14 patches.
The chart could be in the corner or on one edge and maybe even rotated. So any method of analysis to locate the chart would be great.
I know that other programs like Argyll CMS use a seperate predefined recognition chart reference file to define the chart pattern for searching in the image. that method might work if i knew how to compare the recognition chart to the image data.