When comparing two images, both of the images are the same, except that in one of the images the text is moved by a couple of pixels. Please take a look at the below URL. It is a GIF that shows the difference of both the similar images.
https://giphy.com/gifs/9x50JjoLSPZ7lKRebk
My team initially used compare command which doesn't address this issue. Need suggestions please?
You can remove all the text in Imagemagick and just compare the bars by thresholding the Saturation/Chroma channel and then doing the compare. The text is gray, so it has little if any saturation. The bars are cyan, so they are colored and have a medium to high saturation.
convert giphy.gif -colorspace HCL -channel g -separate +channel -threshold 5% +write tmp.gif miff:- | compare -metric rmse - null:
3164.96 (0.0482942)
So this is 4.8% different.
I save tmp.gif, which you do not need, only to show the result of the processing before the compare.
If your version of Imagemagick is too old and you do not have -colorspace HCL, then try HSL or HSB. C and S are similar and measure saturation/chroma.
Related
I am trying to use ImageMagick 7 to detect if a specific channel in an image is largely pure black and pure white (plus a little antialiasing, and there's a chance the image could be pure black). This is to distinguish from another kind of image that shares a naming convention but has photographic-like image data in the r/g/b channels.
(Basically both image types are specular maps from different engines. The one I'm trying to differentiate here is more modern and has the metallic map in the blue channel; the other is much older and just has the specular colour in the RGB channels and the gloss map in the alpha.)
Currently I'm comparing the channel to a clone of itself that has had a 50% threshold applied, using the AE metric to see if it's largely the same apart from a small amount of antialiasing, and a fuzz of 1% to account for occasional aberration from pure black/white. This command works, but of course at the moment it only returns the number of distorted pixels:
magick ( "file.png" -channel b -separate ) ^
( +clone -channel b -separate -threshold 50% ) ^
-fuzz 1% -metric AE -compare ^
-format "%[distortion]" info:
Because the input image sizes will vary, I want to divide the distortion by the total number of pixels in the image to get the relative amount of the image that's not pure black/white -- under 10% has seemed good so far in my manual testing -- but I can't get the format syntax right. Everything I've tried -- for example "%[fx:%[distortion]/w*h]" -- has given the magick: undefined variable `[distortion]' # error/fx.c/FxGetSymbol/1169 error.
What syntax should I use? (And if there's a better way to do what I'm doing, I always appreciate it!)
I believe the following is what you want in Imagemagick. Basically you save the distortion in -set option: argument and then use it in -fx later.
However, +clone gives you just the b channel, so there should be no need for -channel b -separate in your second line.
magick ( "file.png" -channel b -separate ) ^
( +clone -threshold 50% ) ^
-fuzz 1% -metric AE -compare ^
-set option:distort "%[distortion]" ^
-format "%[fx:distort/(w*h)]" info:
Fred (#fmw42) has already provided an excellent method. There is another method for differentiating pure black and white images from greyscale images with a fuller tonal scale which may interest you. Credit to Anthony Thyssen for the technique described here.
If you use -solarize 50% in ImageMagick it inverts all the highlights, so it effectively folds your histogram in half and all the whites become pure black and all the near-whites become near blacks. The command looks like this:
magick INPUT -solarize 50% OUTPUT
So, if I apply that to a couple of input images - the first one pure black and near white, the second a greyscale - and show the corresponding output image on the right you'll see the effect:
If you now inspect the mean and standard deviation of the two solarised images:
magick {a,b}-sol.jpg -format "%f, mean: %[mean], stdev: %[standard-deviation]\n" info:
a-sol.jpg, mean: 2328.91, stdev: 3175.67
b-sol.jpg, mean: 16319.5, stdev: 9496.04
you can see that the mean and standard deviation of the first (pure black and white) image is low because all the bright whites have folded to near blacks, whereas the mean and standard deviation of the greyscale image are both higher because the tones are more spread out.
I am relatively new to image processing, and I have a basic question: what is a distortion of red/green/blue channel, and the distortion of the entire image ?
taken from the imagemagick home site, in the compare command section:
In addition to the visual interpretation of the difference in an image and its reconstruction, we report a mathematical measure of the difference:
magick compare -verbose -metric mae rose.jpg reconstruct.jpg
difference.png Image: rose.jpg Channel distortion: MAE red: 2282.91
(0.034835) green: 1853.99 (0.0282901) blue: 2008.67 (0.0306503)
all: 1536.39 (0.0234439)
I dont understand the concept, can someone explain it to me ?
It's nothing to do with shape transformation or -distort if that is what is confusing you. All that "distortion" means, in this context, is "the specific error metric you requested". When comparing images there are various "metrics" you can select to measure:
AE
MAE
DSSIM
RMSE
etc.
and so on. The "distortion" is just a generic term meaning "whichever one of those you selected".
Here is a little example, a 100x10 red rectangle on a large black background.
convert -size 100x10 xc:red -bordercolor black -border 100 a.png
Now roll that 1-pixel to the right and re-save as b.png:
convert a.png -roll +1+0 b.png
Now compare absolute error:
compare -metric ae [ab].png null:
20
and you can see that the 10 pixels on the left and 10 pixels on the right of the red bar are "distorted".
Now roll down one pixel instead of across and compare again:
convert a.png -roll +0+1 b.png
compare -metric ae [ab].png null:
200
and the "distortion" is 100 pixels along the top pf the red bar that have become black and 100 pixels below the bottom of the red bar that have become red.
It may make more sense if you use the convert based method of comparing rather than the compare method. Here, you use the more common convert command with 2 images and then use -compare as the operator but you now will see the variable called distortion being used and that it just refers to whatever -metric you selected:
convert a.png b.png -metric AE -compare -format "%[distortion]" info:
200
How how would i generate an image of certain resolution containing black and white noise. I want to generate a number of images with each images noise being different. Prefer if done in console of either linux or windows but coding is ok if really have to.
Cheers
Like this with ImageMagick which is installed on most Linux distros and is available for macOS and Windows:
convert -size 512x512 xc:gray +noise random -colorspace gray noise.jpg
Replace convert with magick if using v7+ of ImageMagick.
If you mean pure black and white without shades of grey, and maybe would like a different size and a PNG format, use:
convert -size 600x400 xc:gray +noise random -colorspace gray -threshold 50% noise.png
If you want a different distribution of noise (gaussian, poisson, binomial) or to attenuate the noise, have a look at my other answer here.
I have image like this from my windstation
I have tried get thoose lines recognized, but lost becuase all filters not recognize lines.
Any ideas what i have use to get it black&white with at least some needed lines?
Typical detection result is something like this:
I need detect edges of digit, which seams not recognized with almost any settings.
This doesn't provide you with a complete guide as to how to solve your image processing question with opencv but it contains some hints and observations that may help you get there. My weapon of choice is ImageMagick, which is installed on most Linux distros and is available for OS X and Windows.
Firstly, I note you have date and time across the top and you haven't cropped correctly at the lower right hand side - these extraneous pixels will affect contrast stretches, so I crop them off.
Secondly, I separate your image in 3 channels - R, G and B and look at them all. The R and B channels are very noisy, so I would probably go with the Green channel. Alternatively, the Lightness channel is pretty reasonable if you go to HSL mode and discard the Hue and Saturation.
convert display.jpg -separate channel.jpg
Red
Green
Blue
Now make a histogram to look at the tonal distribution:
convert display.jpg -crop 500x300+0+80 -colorspace hsl -separate -delete 0,1 -format %c histogram:png:ahistogram.png
Now I can see all your data are down the dark, left-hand end of the histogram, so I do a contrast stretch and a median filter to remove the noise
convert display.jpg -crop 500x300+0+80 -colorspace hsl -separate -delete 0,1 -median 9x9 -normalize -level 0%,40% z.jpg
And a final threshold to get black and white...
convert display.jpg -crop 500x300+0+80 -colorspace hsl -separate -delete 0,1 -median 9x9 -normalize -level 0%,40% -threshold 60% z.jpg
Of course, you can diddle around with the numbers and levels, but there may be a couple of ideas in there that you can develop... in OpenCV or ImageMagick.
Is there a way to move all gray colors of a CMYK image (e.g. a CMYK .tiff) into the black (K) plate with ImageMagick?
(In Adobe Acrobat Pro, this functionality is labeled: "Promote grays to CMYK black")
Here's an image to experiment with:
You can view an example of this process on Wikipedia.
Also not a full answer as such, but hopefully useful towards producing one - by Kurt, myself or others. I looked at the Photoshop method of GCR and am adding the characteristic curves that Adobe seem to use for GCR. There are 5 levels, ranging from "None", through "Light", "Medium", "Heavy" and "Full".
I presume the "Light" curve is showing that no black ink is added into the mix till it would be over 50%, and the "Medium" shows the black would have to be only 25% before any gets added, and the "Heavy" shows that only 12-15% of black is needed before black ink gets added into the mixture.
I also add the following reference to assist any other answerers... see PDF here.
Taking into account that the provided example image is NOT a TIFF (as announced), and does NOT use a CMYK color space (as announced), but is a JPEG using sRGB, here is how you would convert it into a TIFF file using CMYK, where the black channel is used:
convert \
http://i.stack.imgur.com/HFnCz.jpg \
-colorspace cmy \
-colorspace cmyk \
cmyk.tiff
To separate out the different colors again and show them as grayscale images each, use these commands:
convert HFnCz.tiff -colorspace cmyk -channel c -separate channel_c.png
convert HFnCz.tiff -colorspace cmyk -channel m -separate channel_m.png
convert HFnCz.tiff -colorspace cmyk -channel y -separate channel_y.png
convert HFnCz.tiff -colorspace cmyk -channel k -negate -separate channel_k.png
I did output to PNG in order to keep the file size a bit smaller...
Here are the 4 color separations. Top left is C, top right is M, bottom left is Y, bottom right is K:
Update
I made a mistake in my original answer. The -negate command parameter should only be there for the blacK channel.