Hello there and thank you for at the very least, trying to help me.
I need to, firstly, load an image and then loop through all pixels of that image and check the color of each pixel.
I have never tried messing around with images or whatnot.
Any help is greatly appreciated.
Thank you.
Looking at the bigger picture, of counting the dots on a dice, I would look at using ImageMagick - with the C++ binding called Magick++ from here
I would be looking at using "Blob Analysis", or "Connected Component Analysis" to count the dots on a dice.
Using this dice...
If I use ImageMagick at the command line like this:
convert dice.png -colorspace gray -threshold 50% \
-define connected-components:verbose=true \
-define connected-components:area-threshold=10 \
-connected-components 8 -auto-level output.png
Output
Objects (id: bounding-box centroid area mean-color):
0: 380x362+0+0 189.6,180.0 103867 srgba(255,255,255,1)
2: 93x92+248+32 293.8,77.5 6743 srgba(0,0,0,1)
4: 92x93+39+241 84.8,286.7 6741 srgba(0,0,0,1)
5: 93x93+248+241 293.8,286.8 6738 srgba(0,0,0,1)
1: 92x92+39+32 84.8,77.5 6736 srgba(0,0,0,1)
3: 93x93+143+136 189.3,182.1 6735 srgba(0,0,0,1)
You can see it has found 5 dots (the first one is actually the whole, white image), and I can put a red box around each dot like this so you can see what it has found:
convert dice.png -stroke red -fill none -strokewidth 1 -draw "rectangle 248,32 341,124" -draw "rectangle 39,241 131,334" -draw "rectangle 248,241 341,334" -draw "rectangle 39,32 131,124" -draw "rectangle 143,136 236,229" result.png
I think you may use OpenCV image processing library. You have a detailed manual for installing for VS2013 here:
OpenCV installation for Visual Studio 2013
After you've installed it. You will get a lot of functions for image processing, including what you are looking for.
For example:
Mat inputImage = imread(filename, CV_LOAD_IMAGE_COLOR);
then:
Cycle through pixels with opencv
One of the methods you could use, is to interpret the file as binary. If you know how to interpret the header part and know what color depth the image has (the header has such info), then it wont be a longshot to just compare binary or hexadecimal color codes - probably hexadecimal since C++ doesnt have a built in binary variable.
if you dont think you can handle binary and need a library to work with, refer to
How do I read JPEG and PNG pixels in C++ on Linux?
EDIT - or just use any image processing libraries such as http://cimg.eu since the objective appears to be interpretation of dice from an image.
Related
I am trying to automate image conversion using ImageMagick CLI. One of the biggest problems with my image set is with tiny artifacts that should be cut out.
My images are generally consistent, with big objects (c.a. 50% of image space) on a white background. Unfortunately, sometimes tiny artifacts may just look bad and make trimming less efficient.
E.g. something like that:
In reality, the big object is not a solid color, it's just a simplified example. It is not necessarily a circle either, it can be a square, rectangle, or something irregular.
I cannot also use any morphology like opening, closing, or erosion. Filters like gaussian or median also are out of the question. I need to keep the big object untouched since the highest possible quality is required.
An ideal solution would be something similar to Contours known for example from OpenCV, where I could find all the uniform objects and if they don't meet certain rules (e.g. threshold of size greater than 5% of the whole image) - fill them with white color.
Is there any similar mechanism in ImageMagick CLI? I've gone through the docs and haven't found a suitable solution to my problem.
Thanks in advance!
EDIT (ImageMagick version):
Version: ImageMagick 7.1.0-47 Q16-HDRI x86_64 20393 https://imagemagick.org
Copyright: (C) 1999 ImageMagick Studio LLC
License: https://imagemagick.org/script/license.php
Features: Cipher DPC HDRI Modules OpenMP(5.0)
Delegates (built-in): bzlib fontconfig freetype gslib heic jng jp2 jpeg lcms lqr ltdl lzma openexr png ps raw tiff webp xml zlib
Compiler: gcc (4.2)
EDIT (Real-life example):
As requested, here is a real-life example. A picture of a coin on a white background, but with some artifacts:
noise under the coin (slightly on the left)
dot under the coin (slightly on the right)
gray irregular shape in the top right corner
The objects might not be necessarily circles like coins but we may assume that there always will be one object with a strong border (no white spaces on the border, like here) and the rest is noise.
Here is one way to do that im Imagemagick 7. First threshold the image so the background is white and the object(s) is black. That will likely be image dependent. NOTE: that JPG is a lousy format, since solid colors are not really truly solid due to the compression. If you can save your images in some non-lossy compressed or uncompress format, that would be better. Then decide on the largest area you need to remove. Use that with connected components processing so that you have only two regions, one white background and one black object. This will be a mask. If you have several objects that is fine also, but they need to be black. I show the textual output showing the two regions. The mask is just the object with the noise removed. So now use the original input, a white image and the mask to composite the first two images so that where the mask is black, the object is used and where the mask is white, the white image will be used. Note, I create the white image by making a copy (clone) of the input and colorizing it 100% with white. The following is in Unix syntax.
Input:
magick coin.jpg -negate -threshold 2% -negate -type bilevel \
-define connected-components:verbose=true \
-define connected-components:area-threshold=1000 \
-define connected-components:mean-color=true \
-connected-components 4 mask.png
Objects (id: bounding-box centroid area mean-color):
0: 1000x1000+0+0 525.8,555.7 594824 gray(255)
44: 722x720+101+58 460.9,417.0 405176 gray(0)
magick coin.jpg \
\( +clone -fill white -colorize 100 \) \
mask.png \
-compose over -composite \
coin_result.png
Mask
Result:
See https://imagemagick.org/script/connected-components.php
and https://imagemagick.org/Usage/compose/#compose and Composite Operator of Convert (-composite, -geometry) at https://imagemagick.org/Usage/layers/#convert
I am still a beginner in coding. I am currently working on a program in C/C++ that is determining pixel position of a defined mark (which is a black circle with white surroundings) in a photo.
I made a mask from the mark and a vector, which contains mask's every pixel value as it's elements (using Magick++ I summed values for Red, Green and Blue). Vector contains aprox. 10 000 values since the mask is 100x100px. I also used threshold functions for simplifying the image.
Than I made a grid, that is doing the same for the picture, where I want to find the coordinates of the mark. It is basically a loop, that is going throught the image and when the program knows pixel values in the grid it immediately compares them with the mask. Main idea is to find lowest difference between the mask and one of the grid positions.
The problem is however that this procedure of evaluating all grids position takes huge amount of time (e.g. the image has 1920x1080px so more than 2 million vectors containing 10 000 values). I decided to cycle the grid not every pixel but for example every 10th column and row, and than for the best corellation from this procedure I selected area where I used every pixel loop. But, this still takes lot of time.
I would like to ask you, if there is some way of improving this method for better (faster) results or this whole idea is not time efficient and I should use different approach.
Thanks for every advice!
Edit: The program will be used for processing multiple images and on all of them the size will be same. This is the picture after threshold, the mark is the big black dot.
Image
The idea that I find interesting is a pyramidal scheme - or progressive refinement: you find the spot at a lower size image then search only a small rectangle in the larger image.
If you reduce your image by 2 in each dimension then you would reduce the time by 4 plus some search effort in the larger image.
This has some problems: the reduction will affect accuracy I expect. You might miss the spot.
You have to cut the sample (template) by the same so you create a half-size template in this case. As you half half half... the template will get blurred into the surrounding objects so it will not be possible to have a valid template; for half size once I guess the dot has a couple of pixels around it.
As you haven't specified a tool or OS, I will choose ImageMagick which is installed on most Linux distros and is available for OSX and Windows. I am just using it at the command-line here but there are C, C++, Python, Perl, PHP, Ruby, Java and .Net bindings available.
I would use a "Connect Components Analysis" or "Blob Analysis" like this:
convert image.png -negate \
-define connected-components:area-threshold=1200 \
-define connected-components:verbose=true \
-connected-components 8 -auto-level result.png
I have inverted your image with -negate because in morphological operations, the foreground is usually white rather than black. I have excluded blobs smaller than 1200 pixels because your circles seem to have a radius of 22 pixels which makes for an area of 1520 pixels (Pi * 22^2).
That gives this output, which means 7 blobs - one per line - with the bounding box and area of each:
Objects (id: bounding-box centroid area mean-color):
0: 1358x1032+0+0 640.8,517.0 1296947 gray(0)
3: 341x350+1017+287 1206.5,468.9 90143 gray(255)
106: 64x424+848+608 892.2,829.3 6854 gray(255)
95: 38x101+44+565 61.5,619.1 2619 gray(255)
49: 17x145+1341+379 1350.3,446.7 2063 gray(0)
64: 43x43+843+443 864.2,464.1 1451 gray(255)
86: 225x11+358+546 484.7,551.9 1379 gray(255)
Note that, as your circle is 42x42 pixels you will be looking for a blob that is square-ish and close to that size - so I am looking at the second to last line. I can draw that in in red on your original image like this:
convert image.png -fill none -stroke red -draw "rectangle 843,443 886,486" result.png
Also, note that as you are looking for a circle, you would expect the area to be pi * r^2 or around 1500 pixels and you can check that in the penultimate column of the output.
That runs in 0.4 seconds on a reasonable spec iMac. Note that you could divide the image into 4 and run each quarter in parallel to speed things up. So, if you do something like this:
#!/bin/bash
# Split image into 4 (maybe should allow 23 pixels overlap)
convert image.png -crop 1x4# tile-%02d.mpc
# Do Blob Analysis on 4 strips in parallel
for f in tile-*mpc; do
convert $f -negate \
-define connected-components:area-threshold=1200 \
-define connected-components:verbose=true \
-connected-components 8 info: &
done
# Wait for all 4 to finish
wait
That runs in around 0.14 seconds.
I am new to OpenCV and I am looking to fuse two images(Panchromatic and Multispectral) using OpenCV with C++. Note that I have already registered the reference image and now I just need to fuse the reference and the sensed image. I could not find any functions that could help me with this. Did I miss something or is there no direct way to fuse two images?
Please suggest any simple way to proceed with the fusion process.
Since you are trying to fuse together the panchromatic and multispectral images, you would need to :
Convert the input images into a suitable format (YUV works for me,
HSI might too).
Fuse the luminance or intensity values of the two images, leaving the color space untouched.
Combine the fused channel with the color information to produce the final image.
.
cvtColor(ref, tmp1, CV_BGR2GRAY, 0);
cvtColor(trans, tmp2, CV_BGR2GRAY, 0);
cv::Mat yuv;
cvtColor(ref, yuv, CV_BGR2YUV, 3);
vector <Mat> channels_ref;
split(yuv, channels_ref);
double alpha = 0.3;
double beta = 1 - alpha;
addWeighted(tmp1, alpha, tmp2, beta, 0.0, channels_ref[0]);
Mat merge[] = {channels_ref[0], channels_ref[1], channels_ref[2]};
cv::merge(merge, 3, output);
cvtColor(output, output, CV_YUV2BGR);
imshow("Linear Blend", output);
waitKey(0);
I revisited this question after a long time and decided to have a go at it as there was no sample imagery available before. In the meantime, I have generated some - see later.
So, let's say you have a hi-res, panchromatic image with 10m resolution something like this:
and a lo-res, multi-spectral image with 40m resolution of the same area, something like this:
Then, just using ImageMagick at the command-line for now (since it is installed on most Linux distros and is available for OSX and Windows), do what I was alluding to in the comments under your original question...
convert hi-res-panchromatic.tif \
\( lo-res-multispectral.tif -resize 400% -colorspace Lab -separate -delete 0 \) \
-set colorspace Lab -combine result.tif
So, that says... "Load up the hi-res image. Then, to one side, load the lo-res image and upsize it to 400% to account for the 40m resolution versus 10m resolution and convert it to Lab colorspace and separate the channels. Delete the Lightness (L) channel of the lo-res image. Now, returning to the main processing from the aside processing, we will have the hi-res image that we loaded first acting as the L channel along with the ab channels (i.e. colour information) of the lo-res image. Combine them from Lab back into RGB and save".
I see you haven't logged on in a year, so I will delay any OpenCV code-writing until anyone else expresses an interest in the question - but I hope the technique is understandable.
Note
As I don't happen to have any geo-registered panchromatic and multi-spectral imagery of the same place, I cheated somewhat... I took a single image and synthesised a panchromatic version using ImageMagick:
convert orig.tif -colorspace gray hi-res-panchromatic.tif
and I synthesised the lo-res multi-spectral image using:
convert orig.tif -resize 25% lo-res-multispectral.tif
Also, note that I just used Lab mode here to do the blending, because it is simpler, but in the comments I suggested using Principal Components Analysis. I may re-visit this again and implement that too...
I'm trying to merge/stitch 2 images together but found that the default stitcher class in OpenCV could not handle my images.
So I started to write my own..
Unfortunately the images are too large to attach to this message (they are both 12600x9000 pixels in size).. so I'll try to explain as good as possible.
The 2 images are not pictures takes by a camera but are tiff files extracted from a PDF file.
The images themselves were actually CAD drawings, so not much gradients in there and therefore I think the default stitcher class could not handle them.
So far, I managed to extract the features and match them.
Also I used the following well known example to stitch them together:
Mat WarpedImage;
cv::warpPerspective(img_2,WarpedImage,homography,cv::Size(2*img_2.cols,2*img_2.rows));
Mat half(WarpedImage,Rect(0,0,img_1.cols,img_1.rows));
img_1.copyTo(half);
I sort of made it fit.. because my problem is that in my case the 2 images could be aligned vertically or horizontally.
By default, all stitch examples on the internet assume the first image is the left image and the 2nd image is the right image.
So my first question would be:
How can I detect if the image is to the left, right, above or below the first image and create a proper sized new image?
Secondly..
Currently I'm getting the proper image.. however, because I'm not having some decent code to check the ideal width and height of the new image, I have a lot of black/empty space in the new image.
What would be the best C++ code to remove those black area's?
(I'm seeing a lot of Python scripts on the net.. but no C++ examples of this.. and I have 0 Python skills....)
Thank you very much in advance for your help.
Greetings,
Floris.
You can reproject the corners of the second image with perspectiveTransform. With the transformed points you can find the relative position of your image and calculate the new image size that will fit both images. This will also let you deal with the black areas, since you have the boundaries of the two images.
Could you please tell me how to what are ranges for Hue, Saturation and Value indices for intense red?
I try to use this values for color tracking and I couldn't find a specific answer via Google.
you can map any color to OpenCV HSV. Actually opencv use 1800 hue cylinder while ideally it is 360, on the orher hand MS paint use 2400 cyllinder.
So to get OpenCV HSV value, simply open MS paint, open mixer, and read the value of HSV, now to map this value into OpenCV HSV multiply it with 180/240.
the range to value for saturation and value is 00-1800
You are the only one who can answer this question, since we don't know your criteria for "intense red". Collect as many samples as you can, some of which you consider intense red and some which are close but just miss the cut. Convert them all to HSL. Study the pattern.
You might put together a small app that has sliders for the H, S, and L parameters and displays a block of color corresponding to the settings. That will tell you your limits very quickly.