There is an original high quality label. After it's been printed we scan a sample and want to compare it with original to find errors in printed text for example. Original and scanned images are almost of the same size (but a bit different).
ImageMagic can do it great but not with scanned image (I suppose it compares it bitwise but scanned image contains to much "noise").
Is there an utility that can so such a comparison? Or may be an algorithm (implemented or easy to implement) - like the one that uses Cauchy–Schwarz inequality in signal processing?
Adding sample pics.
Original:-
Scanned:-
Further Thoughts
As I explained in the comments, I think the registration of the original and scanned images is going to be important as your scans are not exactly horizontal nor the same size. To do a crude registration, you could find some points of high-contrast that are hopefully unique in the original image. So, say I wanted one on the top-left (called tl.jpg), one in the top-right (tr.jpg), one in the bottom-left (bl.jpg) and one in the bottom-right (br.jpg). I might choose these:
[]
[]3
I can now find these in the original image and in the scanned image using a sub-image search, for example:
compare -metric RMSE -subimage-search original.jpg tl.jpg a.png b.png
1148.27 (0.0175214) # 168,103
That shows me where the sub-image has been found, and the second (greyish) image shows me a white peak where the image is actually located. It also tells me that the sub image is at coordinates [168,103] in the original image.
compare -metric RMSE -subimage-search scanned.jpg tl.jpg a.png b.png
7343.29 (0.112051) # 173,102
And now I know that same point is at coordinates [173,102] in the scanned image. So I need to transform [173,102] to [168,103].
I then need to do that for the other sub images:
compare -metric RMSE -subimage-search scanned.jpg br.jpg result.png
8058.29 (0.122962) # 577,592
Ok, so we can get 4 points, one near each corner in the original image, and their corresponding locations in the scanned image. Then we need to do an affine transformation - which I may, or may not do in the future. There are notes on how to do it here.
Original Answer
It would help if you were able to supply some sample images to show what sort of problems you are expecting with the labels. However, let's assume you have these:
label.png
unhappy.png
unhappy2.png
I have only put a red border around them so you can see the edges on this white background.
If you use Fred Weinhaus's script similar from his superb website, you can now compute a normalised cross correlation between the original image and the unhappy ones. So, taking the original label and the one with one track of white across it, they come out pretty similar (96%)
./similar label.png unhappy.png
Similarity Metric: 0.960718
If we now try the more unhappy one with two tracks across it, they are less similar (92%):
./similar label.png unhappy2.png
Similarity Metric: 0.921804
Ok, that seems to work. We now need to deal with the shifted and differently sized scan, so I will attempt to trim them to only get the important stuff and blur them to lose any noise and resize to a standardised size for comparison using a little script.
#!/bin/bash
image1=$1
image2=$2
fuzz="10%"
filtration="-median 5x5"
resize="-resize 500x300"
echo DEBUG: Preparing $image1 and $image2...
# Get cropbox from blurred image
cropbox=$(convert "$image1" -fuzz $fuzz $filtration -format %# info:)
# Now crop original unblurred image and resize to standard size
convert "$image1" -crop "$cropbox" $resize +repage im1.png
# Get cropbox from blurred image
cropbox=$(convert "$image2" -fuzz $fuzz $filtration -format %# info:)
# Now crop original unblurred image and resize to standard size
convert "$image2" -crop "$cropbox" $resize +repage im2.png
# Now compare using Fred's script
./similar im1.png im2.png
We can now compare the original label with a new image called unhappy-shifted.png
./prepare label.png unhappy-shifted.png
DEBUG: Preparing label.png and unhappy-shifted.png...
Similarity Metric: 1
And we can see they compare the same despite being shifted. Obviously I cannot see your images, how noisy they are, what sort of background you have, how big they are, what colour they are and so on - so you may need to adjust the preparation where I have just done a median filter. Maybe you need a blur and/or a threshold. Maybe you need to go to greyscale.
Related
I can't understand why those two scripts seem to produce a different result, given that the second one is like the first one but separated into two commands.
First script:
convert lena_std.tif -compress None -resize 160x160 -compress None -resize 32x32 test1.bmp
Second script:
convert lena_std.tif -compress None -resize 160x160 test2.bmp
convert test2.bmp -compress None -resize 32x32 test3.bmp
I use the following command to check the difference between the results:
convert test1.bmp test3.bmp -metric AE -compare diff.bmp
I use Imagemagick on Ubuntu 22.04. My convert -version indicates: Version: ImageMagick 6.9.11-60 Q16 x86_64 2021-01-25.
Because when you scale you interpolate pixels.
Roughly, the code considers the pixel at (x,y) in the result, and computes where it comes from in the source. This is usually not an exact pixel, more like an area, when you scale down, or part of a pixel, when you scale up. So to make up the color of the pixel at (x,y) some math is applied: if you scale down, some averaging of the source area, and if you scale up, something that depends on how close the source is to the edge of the pixel and how different the color of neighboring pixels are.
This math can be very simple (the color of the closest pixel), simple (some linear average), a bit more complex (bi-cubic interpolation) or plain magic (sinc/Lanczos), the more complex forms giving the better results.
So, in one case, you obtain a result directly from the source to the pixel you want, and in the other you obtain the final result from an approximation of what the image would look at the intermediate size.
Another way to see it is that each interpolation has a spatial frequency response (like a filter in acoustics), and in one case you apply a single filter and in the other one you compose two filters.
I am currently trying to smooth a height-map of a 2D world. I have multiple images of different 2D worlds, so it's something I'm not going to do manually but rather create a script.
Sample of a heightmap:
As you can see, colors do not blend. I'm looking to blend every space to the color of their neighbours so the slope of the height map is smooth.
What have I tried?
Applying a blur filter, but it's not enough and gives bad quality results.
Applying small noise filters but it's not even close to what I need.
So far...
Here is what happens if I apply the height-map as it is without interpolating the color with it's neighbours.
The result is flat surfaces, instead of slopes/mountain. Hope to make my goal clear.
I believe that interpolating the heights with their neighbours and adding random noise on the surfaces will result in a good quality height-map.
I appreciate your help.
Bonus
Do you have any idea how would I create a simulated normal map from the result of this smooth height-map?
You could try resizing your image down and then back up again to take advantage of interpolation, e.g. for 5% of original size:
magick U0kEbl.png.jpeg -set option:geom "%G" -resize "5%" -resize '%[geom]!' result.png
Here are results for 3%, 5% and 8% of original size:
I have to stitch number of tiles using GraphicsMagick to create one single image. I am currently using -convert with -mosaic with some overlap to stitch tiles. But the stitched image has border where the overlap is done.
Following is the command I am using:
gm convert -background transparent
-page "+0+0" "E:/Images/Scan 001_TileScan_001_s00_ch00.tif"
-page "+0+948" "E:/Images/Scan 001_TileScan_001_s01_ch00.tif"
-page "+0+1896" "E:/Images/Scan 001_TileScan_001_s02_ch00.tif"
-page "+0+2844" "E:/Images/Scan 001_TileScan_001_s03_ch00.tif"
-mosaic "E:/Output/temp/0.png"
The final image looks like this:
How to stitch and Blend without Border?
I've been part of several projects to make seamless image mosaics. There are a couple of other factors you might like to consider:
Flatfielding. Take a shot of a piece of white card with your lens and lighting setup, then use that to flatten out the image lightness. I don't know if GM has a thing to do this, #fmw42 would know. A flatfield image is specific to a lighting setup, lens aperture setting, focus setting and zoom setting, so you need to lock focus/aperture/zoom after taking one. You'll need to do this correction in linear light.
Lens distortion. Some lenses, especially wide-angle ones, will introduce significant geometric distortion. Take a shot of a piece of graph paper and check that the lines are all parallel. It's possible to use a graph-paper shot to automatically generate a lens model you can use to remove geometric errors, but simply choosing a lens with low distortion is easier.
Scatter. Are you moving the object or the camera? Is the lighting moving too? You can have problems with scatter if you shift the object: bright parts of the object will scatter light into dark areas when they move under a light. You need to model and remove this or you'll see seams in darker areas.
Rotation. You can get small amounts of rotation, depending on how your translation stage works and how carefully you've set the camera up. You can also get the focus changing across the field. You might find you need to correct for this too.
libvips has a package of functions for making seamless image mosaics, including all of the above features. I made an example for you: with these source images (near IR images of painting underdrawing):
Entering:
$ vips mosaic cd1.1.jpg cd1.2.jpg join.jpg horizontal 531 0 100 0
Makes a horizontal join to the file join.jpg. The numbers give a guessed overlap of 100 pixels -- the mosaic program will do a search and find the exact position for you. It then does a feathered join using a raised cosine to make:
Although the images have been flatfielded, you can see a join. This is because the camera sensitivity has changed as the object has moved. The libvips globalbalance operation will automatically take the mosaic apart, calculate a set of weightings for each frame that minimise average join error, and reassemble it.
For this pair I get:
nip2, the libvips GUI, has all this with a GUI interface. There's a chapter in the manual (press F1 to view) about assembling large image mosaics:
https://github.com/jcupitt/nip2/releases
Global balance won't work from the CLI, unfortunately, but it will work from any of the libvips language bindings (C#, Python, Ruby, JavaScript, C, C++, Go, Rust, PHP etc. etc.). For example, in pyvips you can write:
import pyvips
left = pyvips.Image.new_from_file("cd1.1.jpg")
right = pyvips.Image.new_from_file("cd1.2.jpg")
join = left.mosaic(right, "horizontal", 531, 0, 100, 0)
balance = join.globalbalance()
balance.write_to_file("x.jpg")
Here is an example using ImageMagick. But since colors are different, you will only mitigate the sharp edge with a ramped blend. The closer the colors are and the more gradual the blend (i.e. over a larger area), the less it will show.
1) Create red and blue images
convert -size 500x500 xc:red top.png
convert -size 500x500 xc:blue btm.png
2) Create mask that is solid white for most and a gradient where you want to overlap them. Here I have 100 pixels gradient for 100 pixel overlap
convert -size 500x100 gradient: -size 500x400 xc:black -append -negate mask_btm.png
convert mask_btm.png -flip mask_top.png
3) Put masks into the alpha channels of each image
convert top.png mask_top.png -alpha off -compose copy_opacity -composite top2.png
convert btm.png mask_btm.png -alpha off -compose copy_opacity -composite btm2.png
4) Mosaic the two images one above the other with an overlap of 100
convert -page +0+0 top2.png -page +0+400 btm2.png -background none -mosaic result.png
See also my tidbit about shaping the gradient at http://www.fmwconcepts.com/imagemagick/tidbits/image.php#composite1. But I would use a linear gradient for such work (as shown here), because as you overlap linear gradients they sum to a constant white, so the result will be fully opaque where they overlap.
One other thing to consider is trying to match the colors of the images to some common color map. This can be done by a number of methods. For example, histogram matching or mean/std (brightness/contrast) matching. See for example, my scripts: histmatch, matchimage and redist at http://www.fmwconcepts.com/imagemagick/index.php and ImageMagick -remap at https://www.imagemagick.org/Usage/quantize/#remap
How do I blend two images - thermal(80x60) and RGB(640x480) efficiently?
If I scale the thermal to 640x480 it doesn't scale up evenly or doesn't have enough quality to do any processing on it. Any ideas would be really helpful.
RGB image - http://postimg.org/image/66f9hnaj1/
Thermal image - http://postimg.org/image/6g1oxbm5n/
If you scale the resolution of the thermal image up by a factor of 8 and use Bilinear Interpolation you should get a smoother, less-blocky result.
When combining satellite images of different resolution, (I talk about satellite imagery because that is my speciality), you would normally use the highest resolution imagery as the Lightness or L channel to give you apparent resolution and detail in the shapes because the human eye is good at detecting contrast and then use the lower resolution imagery to fill in the Hue and Saturation, or a and b channels to give you the colour graduations you are hoping to see.
So, in concrete terms, I would consider converting the RGB to Lab or HSL colourspace and retaining the L channel. The take the thermal image and up-res it by 8 using bilinear interpolation and use the result as the a, or b or H or S and maybe fill in the remaining channel with the one from the RGB that has the most variance. Then convert the result back to RGB for a false-colour image. It is hard to tell without seeing the images or knowing what you are hoping to find in them. But in general terms, that would be my approach. HTH.
Note: Given that a of Lab colourspace controls the red/green relationship, I would probably try putting the thermal data in that channel so it tends to show more red the "hotter" the thermal channel is.
Updated Answer
Ok, now I can see your images and you have a couple more problems... firstly the images are not aligned, or registered, with each other which is not going to help - try using a tripod ;-) Secondly, your RGB image is very poorly exposed so it is not really going to contribute that much detail - especially in the shadows - to the combined image.
So, firstly, I used ImageMagick at the commandline to up-size the thermal image like this:
convert thermal.png -resize 640x480 thermal.png
Then, I used Photoshop to do a crude alignment/registration. If you want to try this, the easiest way is to put the two images into separate layers of the same document and set the Blending mode of the upper layer to Difference. Then use the Move Tool (shortcut v) to move the upper image around till the screen goes black which means that the details are on top of each other and when subtracted they come to zero, i.e. black. Then crop so the images are aligned and turn off one layer and save, then turn that layer back on and the other layer off and save again.
Now, I used ImageMagick again to separate the two images into Lab layers:
convert bigthermalaligned.png -colorspace Lab -separate thermal.png
convert rgbaligned.png -colorspace Lab -separate rgb.png
which gives me
thermal-0.png => L channel
thermal-1.png => a channel
thermal-2.png => b channel
rgb-0.png => L channel
rgb-1.png => a channel
rgb-2.png => b channel
Now I can take the L channel of the RGB image and the a and b channels of the thermal image and put them together:
convert rgba-0.png thermal-1.png thermal-2.png -normalize -set colorpsace lab -combine result.png
And you get this monstrosity! Obviously you can play around with the channels and colourpsaces and a tripod and proper exposures, but you should be able to see some of the details of the RGB image - especially the curtains on the left, the lights, the camera on the cellphone and the label on the water bottle - have come through into the final image.
Assuming that the images were not captured using a single camera, you need to note that the two cameras may have different parameters. Also, if it's two cameras, they are probably not located in the same world position (offset).
In order to resolve this, you need to get the intrinsic calibration matrix of each of the cameras, and find the offset between them.
Then, you can find a transformation between a pixel in one camera and the other. Unfortunately, if you don't have any depth information about the scene, the most you can do with the calibration matrix is get a ray direction from the camera position to the world.
The easy approach would be to ignore the offset (assuming the scene is not too close to the camera), and just transform the pixel.
p2=K2*(K1^-1 * p1)
Using this you can construct a new image that is a composite of both.
The more difficult approach would be to reconstruct the 3D structure of the scene by finding features that you can match between both images, and then triangulate the point with both rays.
I'm working on a project where we need to match original hi-resolution photos to their scaled down counterparts. For example the original may be 2000px x 2000px, and the scaled down version might be 500px x 500px.
In researching how to do this I've found mention that ImageMagick's compare operation can be used to compare larger and smaller images, but that it behaves as though the smaller image has been cropped from the larger--and as a result it performs a very intensive scan (http://www.imagemagick.org/discourse-server/viewtopic.php?f=2&t=16781#p61937).
Is there an option or flag that I can use to indicate that I only want a match if the smaller image has been scaled (not cropped) from the larger image?
You can temporarily scale the larger image down to the size of the smaller image and then compare the resized version to the thumbnails, as described by Marc Maurice on his blog.
convert bigimage.png -resize 500x500 MIFF:- | \
compare - -metric AE -fuzz '10%' smallimage.png null:
Because the resize algorithm is probably different from the original resize algorithm, this will introduce differences, but if the smaller images are only scaled and not changed otherwise, the similarities should be sufficient to do the matching. You'll have to find a suitable metric and threshold though.
If you don't now the thumbnail sizes or if they differ, you may want to downsize both images to a safe size below the minimum of all thumbnail sizes or you grab the thumbnail sizes with
identify -format "%w,%h" smallimage.png